Using time-dependent route choice
model to analyze the profitability of
multimodal transport in flower
industry
From the perspective of buyers of the FloraHolland
Company
Using time-dependent route choice model to
analyze the profitability of multimodal
transport in flower industry
From the perspective of buyers of the FloraHolland Company
ByZ. Chen
in partial fulfilment of the requirements for the degree of
Master of Science
in Transport, Infrastructure and Logistics
at the Delft University of Technology,
to be defended publicly on Monday December 14th, 2015 at 09:30 AM.
Graduation committee
Chair: Pro. dr. ir. G. Lodewijks TU Delft
First Supervisor: Dr. ir. F. Corman TU Delft
Second Supervisor: Dr. Mo Zhang TU Delft
Third Supervisor: Dr. ir. Adam Pel TU Delft
I
Summary
As the largest flower auction and home of floriculture in the Netherlands, the FloraHolland Company offers its members several sales channels, of which the auction clock is the most well-known. The auction clocks are centrally located in important flower production regions: Aalsmeer, Naaldwijk, Rijnsburg, and Eelde. Aalsmeer clocks have largest annual turnover (about 50%) among them, and only Aalsmeer is regarded as the origin location in this report [1][12][13]. The annual data of the output of cut flower is the sum of the annual output of these auction clocks. Germany is the largest export destination, and Rose is the largest export product of the FloraHolland Company. [1][12][13]
Flowers start out from being harvested, packaged and transported to FloraHolland from growers and delivered to European countries. For perishable products time is money. FloraHolland auction works from Monday to Friday and auctioning begins from 6:00 am every morning and ends before 11:00 am. Products will be delivered within 90 minutes after being purchased. Buyers can bid and purchase via The Internet. To ensure the quality of products, long-distance orders are transported by air and short/medium-distance orders, especially in European countries, are transported by trucks. [6]
The supply chain of flower industry [29] has been researched by previous literature. There are several stakeholders in the transport section: growers [2], the FloraHolland Company, buyers, transport companies, terminal operators, and governments. Growers are the suppliers [31] of the FloraHolland Company. The FloraHolland is the intermediary business connecting all these stakeholders in flower industry in this report. The FloraHolland Company does not include all services for its buyers and transportation service is outsourced to transport companies. Transport companies as the transport operator play the role of carriers, providing transport service. Buyers as the customer of the FloraHolland Company play the role as shippers: choosing their transport operators and trading with them directly. [3] Thus, lower cost of transport is beneficial for the buyer. Because of the rising cost of road transport, the idea of introducing cheaper transport modes, such as rail and waterborne transport, on this section has been proposed by the buyers. [3]
Multimodal transport has been widely used for the freight of cargo with no particular requirements on transport conditions in long-distance. [19] Top 3 export countries (Germany, United Kingdom, and France) are in European [1][12][13], and the total share of them already exceed half of the total amount. The distances from the FloraHolland Company to these countries cannot be regarded as long-distance. [19] A large proportion of all products have strict requirements for transport. Whether multimodal transport can replace unimodal transport is uncertain.
The report is aimed to analyze the profitability of cut flower transport using multimodal transport in short/medium-distance by building a time-dependent route choice model to choose an optimal route between the origin and the destination by comparing the route costs of multimodal and unimodal transport from the perspective of buyers of the FloraHolland Company.
In the scope of the report, the geography of the transport is chose from the Aalsmeer clock auction in the Netherlands to the largest cut flower export country Germany. Then, routes with unimodal and
II
multimodal transport are researched. Road transport is selected as the mode of unimodal transport. Rail, inland waterway (IWW), and shortsea transport are selected as the main modes of multimodal transport. The largest export product of cut flower, rose, is selected. Roses are transported in refrigerated containers. In the time-dependent model, the trucks departure hourly at the origin is from 1:00 am to 24:00 pm.
The research is from buyers’ perspective, and the preference of buyers of spending minimum cost is the key indicator to evaluate whether multimodal transport can be more beneficial than unimodal transport or not in this case. As the shipper, buyers prefer lower transport cost. As the customer of the FloraHolland Company, buyers prefer less quality loss during the transport. A mathematic model is constructed to express these demands of buyers, and these demands of buyers are converted to be expressed by cost.
The cost of unimodal and multimodal transport will be compared and the route with lower cost is preferred. An important input series of the model is the transport mode choice. In unimodal transport, the route cost from road transport is calculated from the model. In multimodal transport, the cost of the routes with main modes of rail and waterborne transport are calculated separately. As the time sensitive product, transport time is a key factor for the results. Time-dependent departure time of 24 hours per day at the origin will be set as another input to the model. Containers at the origin have opportunities to departure at any hour within one day, and the route cost of one route is influenced dramatically by the departure time, especially in multimodal transport. The results of route choice from the origin and the destination at any departure time will be presented and analyzed in the case study. Two cities in Germany, Duisburg and Hamburg, are chosen to be the destinations of the case study. These two destinations are chosen for the reason that all kinds of transport modes in multimodal transport mentioned in the report can be included: rail and inland waterway transport to Duisburg, and rail and shortsea transport to Hamburg.
Because of the lacking support of observations and literature, calibration of the model cannot be done. The validation of the results is completed by the sensitivity analysis. In the sensitivity analysis, all constants with assumed values in the case study are tested. The route choice is not sensitive to the value of time, the constants in the BPR function and the damage ratio as defined in this study. It is sensitive to the penalty of delay, especially for the shipments that departure after 12:00 pm of a day. The price of distance of road transport is much higher than rail or waterborne transport. It is sensitive for routes with unimodal road transport but not for routes with multimodal transport. Depreciation ratio is piecewise sensitive. The assumption of the values of constants in the case study can be regarded as reasonable.
Using the time-dependent route choice model, the costs of each route choice is calculated from the perspective of buyers. The initial results of the case study present that unimodal road transport is always the optimal choice to Duisburg. While, if departure before 12:00 pm, rail transport is the optimal choice to Hamburg; if departure after then, unimodal road transport becomes the optimal route choice. The lines of route costs of unimodal transport are relatively flat, except peaks caused by congestion at
III
daily peak hours. The lines of route costs of multimodal transport are zigzags and increasing with the delay of departure time. The route cost of IWW transport to Duisburg is about 300-500 euros per TEU (from minimum to maximum route cost). The route cost of Rail transport to Duisburg is about 250-420 euros per TEU. The route cost of rail transport to Hamburg is about 320-550 euros per TEU. The route cost of Shortsea transport to Hamburg is about 500-900 euros per TEU. The route costs of unimodal transport to Duisburg and Hamburg are about 200 and 480 euros per TEU separately.
From the analyses of the initial results, the conclusions are following:
1. The route cost of unimodal transport is simply composed of transport cost and depreciation cost, where transport cost is much higher than depreciation cost. In the compositions of route costs of multimodal transport, schedule delay cost and depreciation cost are zigzagging and increasing. Other costs, such as haulage cost, main mode transport cost, transshipment cost at terminal and damage cost, are not obviously influenced by the departure time.
2. Departure time affects the transport time of using different transport modes. The route with shortsea transport takes the longest time about 30 to 50 hours, which is followed by the route with IWW transport as around 23 hours. The routes with rail transport take about 10 to 25 hours. Unimodal transport routes only spend transport time less than 5 hours. The waiting time outside of transshipment terminal can be as much as 24 hours by shortsea transport and 20 hours by rail transport. The penalty of delay of multimodal routes are 170 euros per TEU by IWW to Duisburg, 90 euros per TEU by rail to Duisburg, 400 euros per TEU by shortsea to Hamburg, and 140 euros per TEU by rail to Hamburg. This takes approximate half of the minimal route cost, or one third of the maximal route cost.
3. The route cost of rail transport is mainly influenced by depreciation cost and schedule delay cost, which are increasing with the transport time from the origin to the destination and transport time will be longer when departure later. When these two costs were at minimum values, the route cost of unimodal transport is smaller than rail transport to Duisburg. When these two costs were at minimum values, the route cost of unimodal transport is larger than rail transport to Hamburg. With transport time increasing, the growth of route cost of rail transport caused by increasing schedule delay cost and
IV
depreciation cost exceed the route cost of unimodal transport after 12:00 pm. Without the increasing influence of schedule delay cost and depreciation cost, the difference (mainly caused by main mode transport cost) between route cost of rail transport to Duisburg and Hamburg is only 60 euros per TEU, which means that route cost of multimodal transport is not affected obviously by distance. Road transport is (200 and 480 euros per TEU).
4. Increasing departure frequency of vehicles of main modes can obviously shorten the waiting time for transshipment outside of the terminal and the transport time from the origin to the destination is decreasing at the same time. But, the increasing trend of route costs multimodal transport modes are similar as before, and the optimal route choices are the same as the initial results.
5. The time resolution of the analysis is proved to have no influence on the route choice decisions. 6. The introduction of green tax is also not influenced the result of route choice, although the route costs of unimodal transport increase faster than route costs of multimodal transport. The result is not sensitive to the value of green tax.
7. In scenario analysis, an ideal case beneficial for multimodal transport modes is proposed. The policies of this ideal case come from conclusions from the sensitivity analysis and main findings of the case study. Multimodal transport can be more attractive with the following behaviors by related stakeholders:
Variable Behavior Stakeholders
Transport time on road Prediction in future: Increasing density on
Highway -
Waiting time Increase the frequency of departure of main modes
Transport operator and port authority
Depreciation ratio Decrease decaying rate of fresh cut flowers FloraHolland Company and transport operator Damage ratio Decrease the damaged loss during
transshipment
FloraHolland Company and transport operator PAT (Preferred arrival
time) More flexible preferred arrival time Buyer
Green tax Introduce green tax Government
Multimodal transport is not more beneficial than unimodal transport for buyers from the initial results of the model which represents the current situation. However, multimodal transport can be profitability in flower industry from the perspective of buyers of the FloraHolland Company if the stakeholders formulate policies beneficial to multimodal transport.
V
Table of content
1. Background of flower industry ... 1
1.1. Introduction of the FloraHolland Company ... 1
1.2. The species of most popular cut flowers and their properties ... 2
1.3. Cold chain of rose ... 5
1.4. Stakeholders analysis ... 6
2. Problem statement, research objectives and scope ... 10
2.1. Problem statement ... 10
2.2. Research objective ... 10
2.3. Scope ... 11
2.4. Research approach... 14
3. Literature review ... 15
3.1. Containerized freight transport planning ... 15
3.2. Methodology ... 17 4. Modelling ... 19 4.1. Objective function ... 19 4.2. Cost function ... 21 4.2.1. Transport cost ... 22 4.2.2. Depreciation cost ... 25 4.2.3. Damage cost ... 26
4.2.4. Differences of VOT, depreciation ratio and damage ratio ... 26
4.3. Computational process ... 28
5. Case study ... 32
5.1. Case Introduction ... 32
5.2. Available transport modes ... 35
5.2.1. Road transport ... 35
5.2.2. Rail transport... 35
5.2.3. Shortsea transport ... 36
5.2.4. Inland waterway transport (IWW) ... 37
VI
5.3.1. The relevant values of one container of roses ... 37
5.3.2. Constants in the model ... 38
5.3.3. Road transport ... 39
5.3.4. Rail transport... 40
5.3.5. Shortsea transport ... 41
5.3.6. Inland waterway transport (IWW) ... 41
5.4. Calibration ... 42 5.5. Validation ... 42 5.5.1. Value of time ... 44 5.5.2. BPR function ... 47 5.5.3. Penalty of delay ... 57 5.5.4. Price of distance ... 59 5.5.5. Depreciation ratio ... 61 5.5.6. Damage ratio ... 64 5.6. Main findings ... 65
5.6.1. The time-dependent route choice ... 65
5.6.2. Route specific analysis ... 66
5.6.3. Route cost composition ... 67
5.6.4. Influencing factors of route choice ... 70
5.6.5. Explanation of the optimal route choice of the initial result ... 74
5.6.6. Influence of scheduled departure time of main modes ... 75
5.6.7. Influence of the resolution of the analysis ... 77
5.6.8. Influence of green tax ... 78
5.6.9. An ideal scenario for multimodal transport ... 81
6. Conclusions and recommendations ... 84
6.1. Conclusions ... 84
6.1.1. Conclusions of the research objective ... 84
6.1.2. Conclusions of the main findings of the case study ... 85
6.2. Recommendations ... 87
6.2.1. Recommendations to stakeholders ... 87
6.2.2. Recommendations to other kinds of products ... 88
VII
Appendix ... 90 Reference ... 100
VIII
List of Figures
Figure 1 Diesel-electric refrigerated container ... 4
Figure 2 Cold chain of rose from farm to buyers ... 5
Figure 3 Danish trolleys and buckets ... 6
Figure 4 Power-interest of stakeholders ... 8
Figure 5 Scope ... 11
Figure 6 Supply flow chart ... 12
Figure 7 Demand flow chart ... 13
Figure 8 Research Approach ... 14
Figure 9 Transport cost ... 24
Figure 10 Typical VOT switching value between modes ... 27
Figure 11 Sketch map of different kinds of transport time ... 27
Figure 12 Input and output of the model ... 29
Figure 13 Programming flow... 31
Figure 14 A radial network with the alternatives of mode combinations ... 33
Figure 15 Sensitivity analysis: VOT ... 45
Figure 16 Sensitivity analysis: highway capacity ... 48
Figure 17 Sensitivity analysis: constant “a” in BPR ... 51
Figure 18 Sensitivity analysis: constant “b” in BPR ... 54
Figure 19 Performance of constant “a” and “b” in BPR function ... 56
Figure 20 Sensitivity analysis: penalty of delay... 57
Figure 21 Sensitivity analysis: price of distance ... 60
IX
Figure 23 The trend of B value per TEU ... 67
Figure 24 Compositions of route cost of unimodal transport ... 68
Figure 25 Composition of the route cost of multimodal transport ... 69
Figure 26 Transport time ... 71
Figure 27 Waiting time at terminal ... 72
Figure 28 Composition of Schedule delay cost ... 73
Figure 29 Results of hourly scheduled departure time of main modes ... 75
Figure 30 Compositions of route cost by hourly scheduled departure time of main modes ... 76
Figure 31 Results with half an hourly departure time ... 78
Figure 32 Sensitivity analysis: green tax ... 80
Figure 33 Route cost with green tax ... 81
Figure 34 An ideal scenario ... 83
Figure 35 Rail route from Rotterdam to Duisburg and Hamburg ... 94
Figure 36 Departure frequency of trains between Rotterdam to Duisburg ... 94
Figure 37 Shortsea time schedule ... 95
Figure 38 Inland waterway route from Rotterdam Port to Duisburg Port ... 96
Figure 39 Barge time schedule from Rotterdam to Duisburg ... 96
X
List of Tables
Table 1 Optimum temperature and maximum storage periods for selected flowers ... 3
Table 2 Stakeholders’ interest ... 7
Table 3 Denotations ... 20
Table 4 Criteria box ... 34
Table 5 SWOT analysis of truck transport ... 35
Table 6 SWOT analysis of rail transport ... 36
Table 7 SWOT analysis of short sea transport ... 36
Table 8 SWOT analysis of inland waterway transport ... 37
Table 9 Road transport basic data ... 39
Table 10 Daily traffic flow on Highway ... 40
Table 11 Rail transport basic data... 40
Table 12 Short sea transport basic data ... 41
Table 13 Inland waterway transport basic data ... 41
Table 14 Average value of the route cost ... 43
Table 15 D-value of route cost: VOT ... 46
Table 16 Sensitivity of VOT ... 47
Table 17 D-value of route cost: Highway capacity ... 49
Table 18 Sensitivity of Highway Capacity... 50
Table 19 D-value of route cost: constant “a” ... 52
Table 20 Sensitivity of constant “a” ... 53
Table 21 D-value of route cost: constant “b” ... 55
XI
Table 23 D-value of route cost: penalty of delay ... 58
Table 24 Sensitivity of penalty of delay ... 59
Table 25 D-value of route cost: price of distance ... 60
Table 26 Sensitivity of price of distance ... 61
Table 27 D-value of route cost: depreciation ratio ... 63
Table 28 Sensitivity of depreciation ratio ... 64
Table 29 Sensitivity of damage ratio ... 64
Table 30 Optimal route choice ... 65
Table 31 Feature of lines from different destinations and modes ... 66
Table 32 Emission factors for all modes ... 79
Table 33 Average value of route cost with green tax ... 79
Table 34 Sensitivity of green tax ... 80
Table 35 Positive conditions for applying multimodal transport ... 82
Table 36 Definition of weight classes of cargo ... 90
Table 37 Load and empty weight for volume, average and heavy containers [43] ... 90
Table 38 Definition of transport modes and vehicle types for container transport ... 91
Table 39 Load parameter of container transport ... 92
Table 40 Carbon taxes in five European countries ... 93
Table 41 Truck haulage costs ... 98
Table 42 Rail transport cost ... 98
Table 43 Shortsea transport cost ... 98
1
1. Background of flower industry
1.1. Introduction of the FloraHolland Company
As the largest flower auction and home of floriculture in the Netherlands, the FloraHolland Company offers its members several sales channels, of which the auction clock is the most well-known. The auction clocks are centrally located in important flower production regions: Aalsmeer, Naaldwijk, Rijnsburg, and Eelde. Aalsmeer clocks have largest annual turnover (about 50%) among them, and only Aalsmeer is regarded as the origin location in this report [1][12][13]. The annual data of the output of cut flower is the sum of the annual output of these auction clocks. Germany is the largest export destination, and Rose is the largest export product of the FloraHolland Company. [1][12][13]
Flowers start out from being harvested, packaged and transported to FloraHolland from growers and delivered to European countries. For perishable products time is money. FloraHolland auction works from Monday to Friday and auctioning begins from 6:00 am every morning and ends before 11:00 am. Products will be delivered within 90 minutes after being purchased. Buyers can bid and purchase via The Internet. To ensure the quality of products, long-distance orders are transported by air and short/medium-distance orders, especially in European countries, are transported by trucks. [6]
The supply chain of flower industry [29] has been researched by previous literature. There are several stakeholders in the transport section: growers [2], the FloraHolland Company, buyers, transport companies, terminal operators, and governments. Growers are the suppliers [31] of the FloraHolland Company. The FloraHolland is the intermediary business connecting all these stakeholders in flower industry in this report. The FloraHolland Company does not include all services for its buyers and transportation service is outsourced to transport companies. Transport companies as the transport operator play the role of carriers, providing transport service. Buyers as the customer of the FloraHolland Company play the role as shippers: choosing their transport operators and trading with them directly. [3] Thus, lower cost of transport is beneficial for the buyer. Because of the rising cost of road transport, the idea of introducing cheaper transport modes, such as rail and waterborne transport, on this section has been proposed by the buyers. [3]
Flowers in this report refer to particular to cut flowers, which are time sensitive products with high value and limited lifetime. One of the most obvious shortages of multimodal transport is relatively longer transport time than road transport and the transport time will be limited strictly by the storage time of flowers. Normally, flowers bought within the European area (short-distance and moderate-distance) can be delivered to buyers within one day, but this can hardly be realized by multimodal transport. In multimodal transport, lots of time is spent on transshipment terminals for unloading and loading containers. The route choices of waterborne and rail transport networks are much less than road transport nowadays, which means that the distance of the shortest path of delivery by multimodal is impossible to be shorter than unimodal transport (road transport here). The longer distance is needed to have containers reached to destinations by multimodal transport comparing to unimodal transport. What’s more, waterborne and rail transport have no significant advantage of speed comparing to road
2
transport. Thus, the total time from origin to the destination will be longer by multimodal transport than unimodal (road transport).
The trucks have the problem of congestion on the road, while, rail and waterborne transport modes in multimodal transport are assumed as not in this case. It’s also assumed that anytime containers arriving at terminals, vehicles of main modes (rail and waterborne transport in multimodal transport) have space for them. Thus, rail and waterborne transport modes will always operate on schedule, not only for departure but also for arrival. Because of the relatively small demand for flowers (see Chapter 3.3.1) transport operator has to follow the strict schedule of rail and waterborne transport. The waiting times at terminals for these kinds of transport modes are different because of their different capacities and scheduled departure time. While it is assumed in the report that the operation cost of transshipment at terminals is constant.
Normally, the costs of fully-loaded and empty-returned refrigerated containers will be calculated separately. When applying a price to transport service, transport operators calculate the cost of the two parts together in reality. The quoted price offered to shippers includes empty-returned cost.
When calculating the traffic flow on Highway, containers of flowers that transport to cities are selected as research objects rather than add more TEU on the flow. The influence of the added containers to the total traffic flow will be ignored, such as increased congestion time, longer waiting time at the terminal and so on. It is assumed that all transport modes have enough service capacity for the flowers. It means that the reason for containers to wait at terminal is caused by scheduled departure time of main modes and queuing time for transshipment at the terminal (reason such as train service is too busy to load these containers will not be considered).
1.2. The species of most popular cut flowers and their properties
Table 1 lists 13 flower species (12 fresh cut flower included) which are most popular in customer markets. It can be seen that the storage temperature of flowers should be kept persistently around4℃. To keep a cold temperature, the refrigerated containers need to be applied to the supply chain. The maximum period of storage can be from 1 day to one month. The most time-sensitive flowers that can only store within one week are being required to be delivered to buyers as soon as possible. The buyers have to choose fastest transport modes. For the reason that the storage period of rose is two weeks that moderate, the product that will be studied in this report simplifies as rose, and the data will be extracted. Rose has become the leading product from Kenya to the EU market. Thus, the data should be more acquirable than other species in common sense. In this way, the mode choice can be simplified by exclusively studying rose.
3
Table 1 Optimum temperature and maximum storage periods for selected flowers
Source: [29]
The refrigerated containers can be roughly classified into two kinds of fuel consumption: diesel and electricity. Mostly, refrigerated containers consume electric power in the truck fleet and consume diesel in rail and waterborne transport. Flowers have to change containers during multimodal transport from containers consuming electric power at trucks and consuming diesel at barges (or trains and ships) at terminals, which results in longer transport time and higher transshipment cost. They will get huge damaged both by moving and the temperature variation when flowers are explored out of the protection of refrigerated container during the process of unloading from one container and reloading in another one. If flowers cannot transport from origin to destination by one container, the multimodal transport is not feasible for this case. However, the new developed diesel-electric reefer container can solve this kind of problem. This kind of technology offers great opportunity for multimodal transport of cold chain.
4
Figure 1 Diesel-electric refrigerated container
Source: [39]
Eliminating the problems of flower storage time and container energy source, this topic is doable to attempt multimodal transport in the flower industry. The next section is going to go inside of the cut flower supply chain from grower to buyer.
5
1.3. Cold chain of rose
The technology of fresh logistics is implanted in flower transport to ensure the minimum value reduction during transport. The closed cold chain provides optimally designed cooling systems to decrease effects of temperature abuse. The optimal vase life is realized by well treatments and packaging at the beginning. The technology of quality tracing and tracking is widely used to safeguard the cold chain, such as RFID. [30]
Packaging house Cold storage Kenya flower farm
Nairobi airport Refrigerated
transport
Pre-cooling Schiphol airport Aalsmeer flower
auction Germany markets
Holland flower shops
Import Export
Kenya ？
Figure 2 Cold chain of rose from farm to buyers
During the whole supply chain, such as one in Figure 2, roses are imported from Kenya to the Netherlands and exported to Germany. The Flora Holland Company has contracts with farmers in Kenya. Roses are harvested, cold stored, packaged, processed and then transported by planes from Kenya. Flowers are unloaded at Schiphol airport and transported by trucks to Aalsmeer Flower Company Center for auction and then exported to other European countries, such as Germany in this report.
In reality the flower transport by trucks from Aalsmeer flower auction to Germany buyers. This report will focus on the transport of this section and compare multimodal transport with unimodal transport. CC containers are widely used in flower industry to reduce the damage of moving (Figure 3). Flowers imported from growers are packaged in paper boxes. The quality inspector of the FloraHolland Company will open the package and sort out flowers with several quality levels. The flowers with same species and quality will be repackaged in buckets and then wait for buyers. As soon as buyers purchase the flowers, workers of the FloraHolland Company will collect buckets of flowers from their purchasing list. Then workers put them on the Danish trolleys that have wheels and be easy to be transported in very short distance (especially indoor). When loading the trolleys on the containers, they are positioned in fixed place to eliminate the damage caused by collision inside of the container. Flowers in one container will be regarded as one unit to transport in the report.
6
Figure 3 Danish trolleys and buckets
All trolleys equip with an RFID (Radio Frequency Identification) tag of the UHF (Ultra High Frequency) type. Container Centralen uses the same technology as its CC containers. The RFID tag can be read from a few meters away and offers many options for tracking and tracing. The FloraHolland Company has already widely used RFID for tracking and tracing throughout its premises, and there are several initiatives up and running in the supply chain too. [1]
1.4. Stakeholders analysis
There are mainly six stakeholders play important roles in the supply chain of flower industry: buyer, transport operator, farmer, FloraHolland Company, terminal operator and the government.
Each of them plays a different role in the flower supply chain. They have the unique influence of other stakeholders. Stakeholders have their positive impact and negative impact due to their role, influence, and power. Table 2 illustrates the specific characteristics of stakeholders.
7
Table 2 Stakeholders’ interest
Stakeholders Role Influence and power Objectives Concerns/ Negative impact Buyer Shippers - Owner of the freight
-Decides transport mode, and transport operator - Cheaper transport cost - Short transport time - High quality of the cold chain
- Less choice for transport operator - Non-transparent transport price from transport operator - Relative higher transport cost Transport operator
Carrier Decides the transport price, carries out the transport
Offer competitive transport price and service level
to attract
customers
- Increasing Road transport cost Congestion - Less choice for transport modes on flower industry nowadays
Farmer Growers of flowers
Have impact on the maximum transport time, depreciation ratio
Good quality of flowers returns for the higher benefit.
- Lack of growing technical knowledge - Lack of buyer’s market FloraHolland Company Intermediary business
Collect and offer information between buyers, farmers and transport operators.
- Attract more buyers
- Lower cost of the products
--
Terminal operator Terminal operator
The service level at transshipment
terminal influence the total transport time.
Efficiently transshipment operation and lower price attract more customers - Long waiting time at transshipment terminal - Higher transshipment cost Government Regulation maker
The price of each transport modes can be influenced by government’s policy support.
- Fewer trucks on highway mean less risk of car accidents - Support more sustainable transport modes - Increasing number of trucks on highway - Not enough terminal facility infrastructure - Insufficient support for water and rail transport
8
Among these six stakeholders, the transport cost is borne by buyers. Flower buyers are not only the customers of the FloraHolland Company but shippers [51] in this business model. From the shippers’ perspective, costs, delivery time, and transport condition are the three most important factors in the transport mode choice for the following reasons.
Seller delivery agreement is the most commonly used agreement between the FloraHolland Company and the buyers, which defines that buyers handle delivering the products at their costs. Thus, a low delivery costs is highly preferred by the buyers.
Flowers are time sensitive with high depreciation costs. Well, controlled delivery time is important to maintain the value of the flowers.
Flowers require special transport conditions. Depending on the species, transport is required to be carried out under specific temperature, humidity, ethylene level, crop protection within a limited time.
When zoom in the multimodal transport choice on flower industry, these stakeholders show different power to influence the decision and different interest from the transformation from unimodal to multimodal transport choice (in Figure 4).
High Low High low
Power
Interest
FloraHolland Company Transport Operator Gorvenment Grower Buyer Terminal OperatorFigure 4 Power-interest of stakeholders
The terminal operator has the highest interest but low power for multimodal transport decision. Providing more customized and cheaper transport service can attract more customers. Normally buyers do not choose water or rail transport service in, which means service at the terminal is not necessary. The moment multimodal has been chosen flower industry brings much income. However, the terminal operator is passive to wait for the decision from transport operator and buyers.
9
Transport operator and buyer share similar interest from the decision of multimodal transport, where transport operator is a little bit higher. The lower transport cost can result in higher profit. Transport operator has more service choices, unimodal transport, and various multimodal transports. These maintain the competitiveness advantages over a competitor and help to attract more customers in the long run. By contrast, the buyer can only benefit by lower transport cost. Transport operator, in this case, is the scheme maker, and he can guide buyer choosing his service.
The government can make policies such as increase green tax, support constructing infrastructure and subsidize the green transport modes, and these policies result in lower cost of multimodal transport can guide transport operator and buyers’ choice. The government does not receive direct interest, and it can improve the public welfare with this decision.
The FloraHolland Company, who controls the most information of the whole supply chain from grower to the buyer, doesn’t join any decision of transport directly. While, its suggestion to all other stakeholders results in huge effect and can influence all their decision, including transport decision. The company can benefit from the potential increasing customers with cheaper transport cost and more customized services. Growers cannot involve in the discussion of transport choice of buyers, but it can benefit for the same reason as the FloraHolland Company with increasing demand for products.
10
2. Problem statement, research objectives and scope
2.1. Problem statement
Multimodal transport has been popularized since globalization. Long distance transportations with different geography environments produce many restrictions on unimodal transport (road transport), and water or air transportation are required to add to the voyage. More and more crowded road, increasing congestion time [36] and a higher risk of serious accidents with heavy loaded trucks [45] on Highway stimulate the demand for the development of multimodal transport of not only long-distance transport but short/medium-distance transport. In multimodal transport, the damage may happen during the transshipment and the transport time may be longer than road transport. Thus, the cargo that can store in a long time and has no particular requirements on storage condition is more suitable for multimodal transport and cargo with the strict requirement is mostly transported by truck in short/medium-distance. However, as the container technology has used prevailingly in the transport industry, perishable goods have the possibility to apply multimodal transport in short/medium-distance transport. If multimodal transport of perishable goods is feasible as well, multimodal transport can be carried out in all industries.
As a famous flower imports and exports country, the flower industry makes up a significant component of the Netherlands market. The quantity of daily flower transport is so considerable that the transformation from road transport to multimodal has a great potential to improve the profits by lowering the cost. Fresh flowers are time sensitive, and it should be taken necessary precautions that the packaging can protect the flowers from damage or decay. All these constraints are big challenges for multimodal. To make quantifying comparison with the result of unimodal and multimodal, all positive and negative effects will be expressed by cost. The cost or loss occur during the transport will be paid by flower buyers eventually. This report is going to set up a model to research the problem.
2.2. Research objective
The research objective of this report is answering the research question:
Under what conditions multimodal transport can be more beneficial than unimodal transport for cut flowers delivery in short/medium distance from shipper’s perspective?
Four sub-objectives are defined to guide the study towards to the main objective: 1. Find criteria to describe the preference of flower buyers in the transport section;
2. Calculate route costs from the model with unimodal and multimodal transport routes separately and make an optimal choice;
3. Analyze the results for the profitability of multimodal transport under the current situation; 4. Analyze under what conditions multimodal transport can be more attractive than unimodal
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2.3. Scope
The scope of the report is illustrated in Figure 5. Firstly, the geography of the transport is scoped from the Aalsmeer clock auction in the Netherlands to the largest cut flower export country Germany. Then, routes with unimodal and multimodal transport are researched. Road transport is selected as the mode of unimodal transport. Rail, inland waterway (IWW), and shortsea transport are selected as the main modes of multimodal transport. The largest export product of cut flower, rose, is selected. Roses are transported in refrigerated containers. In the time-dependent model, the trucks departure hourly at the origin is from 1:00 am to 24:00 pm.
Scope
Geography scope: Origin: Aalsmeer Destination: Germany Commodity scope: Cut flower: rose
Transport modes scope:
Unimodal transport: Road
Multimodal transport: Rail, Inland waterway (IWW) and Shortsea
Freight appearance:
Container: Refrigerated container
The horizon:
24 hours: From 1:00am to 24:00pm
The resolution:
1 hour
12 Transshipment (unload&load) Transshipment (unload&load) Containerized terminal at origin End-hualage Main modes:
Rail, IWW and shortsea Pre-hualage
Departure time Transport time
Waiting outside
Queuing inside
The origin The
destination Containerized terminal at destination Queuing inside
Origin
1 Hour Free-flow traffic
Destination
Congestion Congestion
Unimodal transport
Multimodal transport
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From the view of the supplier, containers start to be transported at the origin. The supply flow of unimodal transport and multimodal transport are similar but also different (see Figure 6). The departure time starts to count when trucks start to leave from the origin.
In unimodal transport, the intercity model is conducted. Congestion problems more happen in highways close to big cities. The application of intercity model in unimodal transport is more close to reality. Assuming intercity sections of highways have congestion problems, it’s also assumed in this case that congestion is taken into consideration within 1 hour’s free-flow traffic driving distance near the origin and destinations. Between intercity zones at the origin and the destination, it is free-flow traffic zone. No transshipment happens in unimodal transport.
In multimodal transport, containers have to be transshipped twice: one is at the terminal after haulage, and another one is at the terminal before end-haulage. Containers depart at the origin by pre-haulage. When containers arrive at the transshipment terminal, they may need to wait outside if the arriving time is not the same as the scheduled departure time of main modes. When entering the terminal, containers need to queue before being unloaded and loaded. After loading in the vehicle of the main mode, containers wait to depart until the scheduled departure time from the transshipment terminal. When containers arrive at the transshipment terminal at the destination, containers only need to queue to be unloaded from the train/barge/ship and loaded on trucks. Then, trucks will deliver containers to the destination.
Unimodal/multimodal transport Truck/train/barge/ship
Origin
Destination
Figure 7 Demand flow chart
From the view of buyers (Figure 7), they ordered a certain amount of flowers from the FloraHolland Company. The service of flower transport is purchased to transport operator by buyers. The buyers receive the flowers transported from the origin of the FloraHolland Company. The transport mode choice during this section is mainly decided by buyers for minimum transport cost. The costs of different mode choices during this section will influence the decision of buyers, and this “cost” will be researched in this report.
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2.4. Research approach
The whole process (see Figure 8) of the time-dependent route choice model can be separated into two parts: the first phase is named as “Premodel work and planning” and the second phase is named as “Modelling, Programming, and Analysis”.
Scoping
Data Collection
Base Model Building
Programming
Sensitivity Analysis
Select modeling approach and software Performance criteria
Data collection plan
Data availability Data requirements Data collection Denotation Define variables
Set up mathematic model from shipper’s perspective
Scenario Analysis
Translate mathematic model into Matlab code
Check for coding mistakes Run the initial results
Set up criteria to evaluate the sensitive of constants
Try to change the size of the value of constants in a certain range
Propose policies for multimodal transport
Evaluate the policies Premodel Work
and Planning
Modelling, Programming and
Analysis
Figure 8 Research Approach
Source: [27]
The working product of “Pre-model work and planning” is mainly shown in thesis proposal and the information is collected. The part two of the thesis is mainly about how “Modelling, Programming and Analysis” works and its results.
Collecting information related to the topic and deciding the research scope are the targets of the phase one. The time-dependent route choice model is decided as the approach to studying cut flower transport performance in this scenario and Matlab is chosen as the tool to do the mathematic
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calculation to realize it in consideration of numerous calculations. Then data collected are regarded as input constants of the mathematical model in phase two.
Four steps will be finished progressively in phase two. The first step is building a time-dependent route choice objective function, where it is designed based on the “Scope” and the preference of buyers on the researched transport section. Variables need to be used or calculated listed, and state mathematic model will be written and explained. Matlab is used to translate the static mathematic model into the time-dependent route choice model’s programming code. After checking all bugs, results will be run and collected here. Then, it is setting up revising model with similar theory background. Because of limited reliability of data, it is necessary to find out the most sensitive value, analyzing the influence of the possible error of the data and real results in next step. The last step of scenario analysis summarizes the important factors which can improve the competitiveness of multimodal transport comparing to unimodal transport and policies. The policies will be evaluated in this step.
In the initial result of programming, whether multimodal transport is more beneficial can be figured out. Conducting sensitivity analysis, the model can be validated and most sensitivity constants that can influence the results will be found out. With the conclusions from main findings from the analysis of initial results and sensitivity analysis, an ideal solution to improve the profitability of multimodal transport in this case can be proposed. Then, the research question can be answered.
3. Literature review
Here we briefly review the main issues in optimization approaches in freight transport routing and planning with the static and dynamic models (STA and DTA). Mode-route choice consists of generating choice sets, assigning generalized costs to each alternative based on its characteristics (such as costs, time, penalty factors and so on). Then the best alternative is decided with the equilibrium models such as DUE, SUE, AON and so on. Where, STA integrates the time-dimension and only considers “average” conditions and DTA specifically accounts for temporal variations in transport conditions and route choices. In some cases in DTA, this choice can be updated over time as more information comes available.
3.1. Containerized freight transport planning
Using unimodal and multimodal transport delivery containerized freight face two different kinds of situations. Referring to the case of this report, in the route of unimodal transport, containers will transport from the origin to the destination directly; while, in the route of multimodal transport, transshipment between different kinds of transport modes will be taken into consideration. The route of unimodal transport is considered as one link and the route of multimodal consists by links.
Fosgerau (2009) gave a definition for route choice that the idea of viewing route choice is regarded as a sequence of link choices. In this report, they figured out that route choice modeling is mainly focused on path based models. The set of path alternatives is enormous and smaller choice sets need to be
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generated using some algorithm. This method can be applied to the route of multimodal transport. In the path of pre-haulage, the arrival time at the transshipment terminal of the container is influenced by the congestion condition on the road and path choice of the driver. The arrival time at the terminal is important for the planning to predict the waiting time outside of the terminal for transshipment. Assuming that the transport time and cost of pre-haulage have been optimized with some algorithm, the penalty of waiting time outside of the terminal can bring huge waste and this path choice is not optimal from the view of route choice.
The route choice problem is also defined in Frejinger (2008) and he provided a different method to solve this problem. It starts from models for route choice analysis in a static network setting. For a given origin-destination pair and a given transport mode, drivers are assumed to choose a route at the origin and follow it to the destination without considering changes in traffic conditions. Then, Frejinger (2008) changed perspective and presented literature related to adaptive route choice in stochastic and time-dependent networks [22]. The route is regarded as whole in the static model and the connections between links in the route can also be optimized by the later introduced dynamic model.
In a static model, all vehicles traveling on the link experience the same travel time [28]. The work of static optimal route choice for road transport has been modeled by (Jahn, 2005) (Bottom 2000). Their main goal is to provide information to drivers who do not know the area well. They only compute shortest paths on travel time, geographic distance, or other appropriate measures. Wiegmans and Koning (2013) used a static model for multimodal route choice with a case study of inland waterway cost performance. They developed a model to calculate multimodal freight transport cost for any origin-destination pair in Europe accessible by both the multimodal inland waterway and unimodal road transport. It analyzed the problem that to what extent multimodal inland waterway freight transport is competitive with unimodal road transport regarding transport cost. What it analyzed is similar to the research objective of this report that whether multimodal transport is competitive with unimodal transport regarding transport cost from the perspective of buyers should be analyzed at first. The transport cost model of Wiegmans and Koning (2013) is composed of transport cost of time, transport cost of distance and schedule delay cost. This model will be used in the cost calculation of pre-haulage, main mode and end-haulage links separately, which transport separately by different kinds of transport modes. However, the static model cannot reflect the relationship between departure time and congestion problem because transport time is influenced by the congestion level which is dynamic with departure time. Also, the outside waiting time for transshipment cannot be calculated based on reality here. Then, the dynamic model should be introduced in this case.
In contrast to the static traffic assignment problem, Merchant and Nemhauser (1978) proposed to work with dynamic models. In general, the existing Dynamic Traffic Assignment (DTA) models can be categorized into two approaches: simulation (Mahmassani et al., 1992) and mathematical formulation (Carey, 1987) [8]. WHK Lam and Huang (2003) summarized that the first approach emphasizes microscopic traffic flow characteristics and the second approach is strictly compliant with traffic assignment principles (Wardrop, 1952), such as Wardrop’s first and second principles.
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From the view of traffic flow, Y.Chiu (2011) concluded that volume-to-capacity (v/c) ratio determines the congestion. The capacity (vehicles per hour) of one link is constant. In static models, usually capacity constraints directly affect travel times. In most of the static models, the larger volumes of traffic result in the longer travel times, but the volume is not constrained and hence can exceed the capacity (i.e. v/c ratios can be larger than 1). In most of the dynamic models, usually it’s considered how capacity constraints affect the traffic flows, and hence in turn also affect travel times. If link inflow is higher than link outflow (volume), the density of the link will increase (congestion), and the speed will decrease (fundamental speed–density relationship). Therefore, the link travel time will increase [36]. In this dynamic case, the volume-to-capacity (v/c) ratio is a constant larger than 1 and congestion happens on this link. The latter dynamic models are more realistic. The main difficulty of the analysis approach is the addition of realistic traffic dynamics to the complicated dynamic travel choice formulations, which is also called as a real-time dynamic assignment. Both of the inflow and outflow on the link are dynamic that the volume-to-capacity ratio of the links is dynamic. Thus, the volume-to-capacity (v/c) ratio is also dynamic. Then, the problem of choosing the shortest path becomes more difficult.
From the literature, it is found that more research on the development of DTA models has been carried out using the second approach. WHK Lam and Huang (2003) concluded that these previous related models are mainly formulated by mathematical programming approach (Merchant and Nemhauser, 1978; Janson, 1991), optimal control theory approach (Friesz et al., 1989; Wie et al., 1990; Lam and Huang, 1995), and variational inequality approach (Friesz et al., 1993; Wie et al., 1995; Ran and Boyce, 1996; Chen, 1999) [8]. This approach has been well defined and adhered to a dynamic version of Wardrop’s principles, such as user equilibrium assignment, optimal user assignment, and system optimal assignment.
3.2. Methodology
From previous literature reviews, it is concluded that static models are not able to reflect the relationship between departure time and congestion level, and also unable to calculate the outside waiting time for transshipment in route choice. The transport times of all routes of the O-D pair are the same. While, in route cost of multimodal transport, transport time can be affected dramatically by congestion on pre-haulage and waiting time outside of terminal for transshipment. For the reason that strict departure schedule rules vehicles of the main mode, different choice of departure time can lead to a different transport time. As a time sensitive product, it is predicted that little change on transport time can result in obvious different route cost. Route cost in this report is the criterion for optimal route choice. Thus, the static model is not suitable for this case.
In Dynamic Traffic Assignment, it is composed of two parts: time-dependent and distribution assignment. The time-dependent method can make up for the problem that static model cannot solve above.
Distribution assignment with equilibrium models is useful to find out the shortest path with the dynamic flow on the route. While, a large datasets demand for O-D distribution assignment is regarded as a restricted condition for applying DTA in this report. The traffic transporting flowers between Aalsmeer and cities in Germany is only a small portion of the traffic flow along this network. Therefore it doesn’t
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have a large impact on the traffic conditions and travel times of the O-D pairs. For this reason, it’s not necessary to compute the travel times using an equilibrium assignment. Instead, it is sufficient to use the current travel times as input, and assumed that the travel times will not change significantly due to any changes in the mode/route choice. While, the travel times vary during the day due to congestion may have an impact on the results of route choice, and therefore a time-dependent assignment is used in this case.
The optimal route choice of this report will concern the preference of shippers of the researched supply chain from an econometric point of view. In the model, departure time at the origin is set as a repetitive variable for a chosen route. The cost function describing the route choice problem is composed of predicted preferences of shippers of the studied supply chain. It includes transport cost offered by transport operators, decaying loss and collisional compression loss during transport section. The shortest path between O-D pair is simplified from distribution assignment. Link performance functions, as suggested by the Bureau of Public Roads (BPR) by Kohler and Strehler (2013), are a widely accepted approach to model load-dependent travel times. Early/late arrival penalties include in the transport cost. The departure time of the main mode of multimodal transport at the terminal is determined by both arriving time at the terminal and its scheduled departure time.
A case study applies to the time-dependent route choice model. The product in the case is the rose of cut flower. Destinations are cities of Duisburg and Hamburg in Germany, which have geographical advantage enabling different kinds of transport modes. The distances of paths are measured with Google Map and regarded as the shortest path between each two nodes. The departure time within one day at the origin with resolution 1 hour is the input to the model with a chosen mode in a route. For one O-D pair, the cost of each route will be calculated 24 times. The optimal route for each O-D pair will be chosen with the consideration of the departure time. Sensitivity analysis will test all constants of the case in the model for validation.
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4. Modelling
4.1. Objective function
To achieve this research objective, a criterion needs to be established to simplify the comparison between the unimodal and multimodal transport of flower. The three important factors of flower buyers: cost, time and transport condition will all be transformed into “cost”. The transport cost is the sum of transport cost of time, transport price of distance and schedule delay cost [26]. The transport time affects the degree of decay of flowers, and this will be calculated as the depreciation cost in the formula. The damage cost estimates the costs happen during transport related to its condition.
“Transport time” is defined as the total time starts departing from the origin to the destination. From the perspective of buyers (also shippers), they want to get maximum profit, and the formula should look like this:
Maximum profit of buyers
= Max(Selling price of flower − Auction price of flower − Transport cost − Damage cost)
The “selling price of flower” is paid by the customers of buyers. Buyers pay all other costs, such as the cost of the products, the cost of the transport service and damage loss during transport, before sales. However, the selling price is lower than buyer’s expected price because of the reduction of quality during transport. Longer vase life and better quality flowers can be sold higher price to customers. Depreciation cost should be taken into consideration.
Selling price of flower = Expected price of flower − Depreciation cost
Either increasing the selling price or decreasing the cost of buyers can result in a higher profit of buyers. It is assumed that the “expected price of flower” and “auction price of flower” are decided by flower species, flower market or previous depreciation. Changing transport mode from the place of departure to the destination has little influence to them. While transformation from unimodal to multimodal may change the total value of “transport cost”, “depreciation cost” and “damage cost” and this report will try to find out the possibility of implementing of multimodal in flower industry process from this perspective.
The total costs of these three parts are what flower buyers want to minimize during the transport. Thus, it is named as “Total route cost” in the report. Total route cost can write like this:
Total route cost = Transport cost + Depreciation cost + Damage cost
With the basic formulation, buyers can calculate the part they want to reduce during the transport and buyers are beneficial from less cost on the route. While, the total route cost is various with different transport mode choice and this decision can be divided into unimodal and multimodal transport. In flower industry, the unimodal transport can be only regarded as road transport by trucks and there are mainly three main modes: rail, inland waterway and shortsea in multimodal of short/medium-distance transport. In unimodal transport, the road congestion condition is influential on the total time from the
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place of departure to the destination. In multimodal transport, not only road condition but the time schedule of main modes influences it. Thus, the departure time may be an important variable within each route choice. The influence of departure time will be explained in detail and tested in simulation part.
The cost function can express the objective function:
𝐌𝐢𝐧𝐢𝐦𝐢𝐳𝐞: 𝐓𝐨𝐭𝐚𝐥 𝐫𝐨𝐮𝐭𝐞 𝐜𝐨𝐬𝐭 = 𝐟 (𝐑𝐨𝐮𝐭𝐞 𝟏, 𝐑𝐨𝐮𝐭𝐞 𝟐, … … )
𝐖𝐡𝐞𝐫𝐞: 𝐝𝐞𝐩𝐚𝐫𝐭𝐮𝐫𝐞 𝐭𝐢𝐦𝐞 = [𝟏, 𝟐𝟒]
𝐦𝐨𝐝𝐞 = {𝐑𝐨𝐚𝐝 𝐭𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭, 𝐑𝐚𝐢𝐥 𝐭𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭, 𝐈𝐖𝐖 𝐭𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭, 𝐒𝐡𝐨𝐫𝐭𝐬𝐞𝐚 𝐭𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭}
Total route costs of multimodal transport will be calculated by cost function “f(Route1, Route2,…)” and compared with unimodal transport. If the total route cost of multimodal transport can be smaller than unimodal, multimodal transport is proved as a potential substitution of unimodal transport in the flower industry.
In unimodal transport, one truck will deliver a container from origin to destination and “road transport” can replace the expression of “unimodal transport”. However in multimodal transport, containers cannot realize door to door service by train/barge/ship. Two kinds of modes transport the containers in this case: haulage and main mode. To distinguish the different links of the route of multimodal transport, the word “main mode” is introduced here to express the core mode of the route of multimodal transport. The truck, train, barge, and ship are vehicles of the road, rail, IWW (Inland Waterway) and shortsea transport. The correspondent nouns, such as truck and road transport, are grouped to express the similar problems in a different way in different situations.
Table 3 Denotations
Name Description Unit
x Destination: Duisburg or Hamburg -
y The main mode of each route: road, rail,
IWW and shortsea transport -
t1=i Hourly departure time within one day -
t2 The time when trucks arrive at
transshipment terminal -
t3 Departure time of main modes -
t4 Arrival time at destination terminal -
t5 Departure time of the end-haulage -
t6 Final arrival time -
B Route cost euro/TEU
TC Transport cost euro/TEU
DeC Depreciation cost euro/TEU
DaC Damage cost euro/TEU
TTpre/TTmain/TTend/TTtrans Time spend on pre-haulage, main mode,
end-haulage, and transshipment hours
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WO Waiting time of trucks outside of the
terminal hours
QT Queuing time of trucks inside of the terminal
for transshipment hours
Cpre/Ctrans/Cmain/Cend
Compositions of transport cost: transport cost of pre-haulage, transshipment, main
mode and end-haulage
euro/TEU
SCD Schedule delay cost euro/TEU
𝛂 Early arrival cost euro/hour
𝛃 Late arrival cost euro/hour
𝛄 Waiting time cost euro/hour
PAT Preferred arrival time at the destination -
Ppre/Pmain/Pend Price of distance of transport modes on each
link of the route euro
Lpre/Lmain/Lend Distance of each link of the route kilometer
VOT Value of time euro/hour
re Depreciation ratio euro/hour
ra Damage ratio euro/transshipment
time
ttrans Number of transshipment times -
4.2. Cost function
Congestion problem of unimodal transport or pre/end-haulage of multimodal transport is related to the daily peak time. Waiting time of trucks at rail/IWW/shortsea terminal is related to the departure time of trucks and the scheduled time of these main modes. Choosing departure time may be an important decision to reduce the influence of congestion at peak time or decrease the waiting time caused by fixed time schedule of main modes. Minimum time waste results in less transport cost of time and depreciation.
The route cost per TEU will be calculated for each route, and the objective function can be transformed into the formula:
Objective function: Min 𝐵𝑥(𝑦, 𝑖) = 𝑇𝐶𝑥(𝑦, 𝑖) + DeC𝑥(𝑦, 𝑖) + 𝐷𝑎𝐶𝑥(𝑦, 𝑖) (1)
Where: 𝑖 = [1,24] (1.1)
𝑦 = {𝑅𝑜𝑎𝑑, 𝑅𝑎𝑖𝑙, 𝐼𝑊𝑊, 𝑆ℎ𝑜𝑟𝑡𝑠𝑒𝑎} (1.2)
As the origin is fixed as Aalsmeer clock auction, the notation “x” presents the destination of the route. The route between one pair of OD is determined by the transport mode choice: “y”. “i” is the departure time at the origin within one day from 1:00 am to 24:00 pm. So, the objective function is aimed to find out a route of one pair of OD with minimum route cost. The transport mode and departure time can be known then. The unit of route cost is “euro per TEU”.
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Transport cost of time, transport cost of distance and schedule delay cost make up the total transport cost. Transport cost of time and distance will be calculated three times in multimodal transport: pre-haulage, main mode, and end-haulage.
Transport cost of time is decided by transport time of a route from the origin to the destination and value of time (VOT) of the cargo. Normally, the VOT is an empirical data of each kind of cargo needs to transport, and the value of VOT is collected by interviewing stakeholders. The value of VOT is decided by the product when data is collected. As the cut flower is a time sensitive cargo road or air transport is the primary choice. The road and air transport are more expensive than rail and waterborne transport [36]. Thus, the VOT of the flower is relatively higher.
Transport cost of time
Part of the transport cost rises with the increase of transport time. Costs, such as the cost of the security system (tracking and tracing), crop protection and packaging (including container rent), IT-system, insurance (human and cargo) and so on, will increase with longer transport time. [28] In this report, transport cost of time will be simply calculated in linear formulation with transport time (hour) and VOT (Value of time, euro per TEU per hour). The inputs are departure time and transport mode. The transport time of pre-haulage is calculated based on the hourly traffic flow with BPR function, to take congestion problem into account.
In a highway network, there is a function illustrating the relationship between resistance and volume of traffic. The Bureau of Public Roads (BPR) developed a link (In multimodal transport: one is from the origin to the transshipment at the origin, and another one is from the transshipment at the destination to the destination. In unimodal transport: intercity sections on highway) congestion function, which we will term 𝑇𝑇𝑡𝑟𝑢𝑐𝑘(𝑡𝑛) . [41] 𝑇𝑇𝑝𝑟𝑒/𝑒𝑛𝑑𝑥(𝑦, 𝑖) = 𝐹𝐹𝑇𝑇 𝑝𝑟𝑒 𝑒𝑛𝑑𝑥(1 + 𝑎 ( 𝐹𝑡𝑟𝑢𝑐𝑘(𝑡𝑥(𝑦, 𝑖)) 𝑆𝑡𝑟𝑢𝑐𝑘 ) 𝑏 ) (2) - 𝑇𝑇𝑝𝑟𝑒/𝑒𝑛𝑑(𝑥, 𝑦, 𝑖) : the travel time for a vehicle on a link
- 𝐹𝐹𝑇𝑇𝑝𝑟𝑒/𝑒𝑛𝑑 : free-flow time on link of time. This data comes from the shortest travel time in Google map.
- 𝐹𝑡𝑟𝑢𝑐𝑘(𝑡𝑛(𝑥, 𝑦, 𝑖)) : flow (volume) of traffic on link of the time - 𝑆𝑡𝑟𝑢𝑐𝑘 : mixed capacity of traffic including all kinds of vehicles - (𝑎, 𝑏) : congestion constants
The time spent on the links depends on the level of congestion. This formulation is used to calculate the transport time of pre-haulage and end-haulage links by a truck on the highway. The traffic flow is time-dependent with the departure time. When the values of constant “an” and “b” have been determined, time-dependent traffic flow will result in time-dependent transport time on links with congestion problem.
