Generating stowage plans in the steel industry - Modelling steel coils loading procedures while incorporating pre-haulage by rail transport

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2016.TIL.8068

DELFT UNIVERSITY OF TECHNOLOGY

Generating stowage plans

in the steel industry

‘Modelling steel coils’ loading procedures while

incorporating pre-haulage by rail transport’

H.J.A. Schoenmaker

MSc thesis Transport, Infrastructure & Logistics 9/22/2016 Supervisors: Prof.dr.ir. G. Lodewijks Dr. J.H.R. van Duin Ir. M.B. Duinkerken N. Krenning E. Lute

[This report addresses a research concerning a logistical problem at Tata Steel, a steel production company. Steel coils are considered as end-products that are being shipped towards costumers overseas. A deterministic algorithm is created to show the benefits of coil-specific stowage planning. When incorporating pre-haulage by rail transport the efficiency of the loading process can be improved. The proposed model increases standardisation and improves planning time and wagon utilisation.]

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2016.TIL.8068

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2016.TIL.8068

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PREFACE

After obtaining a bachelor degree in Mechanical Engineering, further education was sought within the Delft University of Technology in the field of transport and logistics. This thesis report is the final step towards graduation as a Master of Science with a completed MSc Transport, Infrastructure & Logistics program including the specialisation of Engineering.

The research presented in this report is initiated based on an assignment at a large multinational steel production company. The assignment involves improvement of stowage- and cluster plans concerning the outbound vessels that are loaded with steel coils. The problem addressed in this report includes multidisciplinary issues subjected to both mechanical and logistical facets.

Since this is a graduation research project, the practical issues will be approached in a scientific way. Knowledge obtained in both bachelor and master programs is reflected in this research. Supervision is provided by professor, and head of the ‘Transport Engineering and Logistics’ department, G. Lodewijks. He is at the same time the chairman of the assessment committee. M.B. Duinkerken originates from the same department and acts as a mentor throughout the whole research focussing on the mechanical engineering perspective. J.H.R. van Duin acts as the second mentor guiding the research from a logistics perspective and addressing the multi-actor system.

At the steel production company two managers are actively involved with the project and act as the supervisors on the spot. Both company managers guide the project based on the objectives of their departments respectively. At the planning and logistics department N. Krenning is the manager responsible for internal planning activities and E. Lute is responsible for harbour and warehousing activities.

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LIST OF TABLES

Table 1: Average significant improvements in percentages of model output with respect to the real system derived from the Wilcoxon signed rank sum test and the Hodges-Lehmann median test (Appendix D: Statistical analyses) ... 13

Table 2: Main actors and interests ... 28

Table 3: Stacking rules for hopper type cargo holds ... 39

Table 4: Stacking rules for box type cargo holds ... 39

Table 5: Vessel characteristics ... 40

Table 6: Product data ... 40

Table 7: Stowage preconditions ... 40

Table 8: System parameters (rail transport capacities) ... 41

Table 9: Model applicability for job activities ... 45

Table 10: Data overview ... 50

Table 11: Verification requirements for the input data ... 54

Table 12: Validation results ... 56

Table 13: Shipment types ... 58

Table 14: Past data KPI’s per type A shipment ... 58

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Table 16: Past data KPI’s per type C shipment ... 58

Table 17: Model outputs per type A shipment... 59

Table 18: Model outputs per type B shipment ... 59

Table 19: Model outputs per type C shipment ... 60

Table 20: Conclusions Wilcoxon signed rank sum tests (ref. Appendix D: Statistical analyses) ... 63

Table 21: Hodges-Lehmann median tests’ outcomes for load factors and wagons including an A & B shipment type separation ... 64

Table 22: Comparison table of wagon slot- and load factors and the amount of train wagons for shipment types A & B. ... 65

Table 23: Planning times comparison of shipment types A and B ... 66

Table 24: Planning times comparison of shipment type C ... 67

Table 25: The expected impacts and benefits associated with functionalities of the model ... 69

Table 26: Expected costs and benefits of the implementation a coil-specific stowage plan generation model (values from Appendix F) ... 70

Table 27: Verification input set A ... 81

Table 28: Verification output A – stowage plan ... 82

Table 29: Verification output set A – output values ... 82

Table 30: Verification input set B ... 83

Table 31: Verification output B – stowage plan ... 84

Table 32: Verification output B – output values ... 84

Table 33: Input set C for verifying the correct filling of train wagons ... 85

Table 34: Verification output C – stowage plan ... 85

Table 35: Verification output C - stowage plan incl. storage areas ... 86

Table 36: Verification output C - output values ... 87

Table 37: Verification product data sample 1 ... 88

Table 38: Verification product data sample 2 ... 90

Table 39: Hold dimensions and type per vessel type A, B and C ... 91

Table 40: Vessel load factors comparison ... 92

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Table 42: Train wagon load factors comparison ... 93

Table 43: Number of train wagons comparison ... 93

Table 44: Comparison table of the planning times and the model run times ... 94

Table 45: Shipment types A ... 95

Table 46: Shipment types B ... 96

Table 47: Shipment types C ... 97

Table 48: Wilcoxon critical values (Keller G., 2000, pp. 595)... 102

Table 49: Costs savings by efficient pre-haulage by rail transport ... 130

Table 50: Extra available rail capacity in tonnes per year ... 131

Table 51: Costs savings associated with reduced planning times for transatlantic shipments ... 131

Table 52: Costs savings associated with reduced planning times for coastal shipments ... 131

Table 53: Overtime costs stowage planning ... 132

Table 54: Costs of damaged coils in cargo holds of larger shipments ... 132

Table 55: Implementation costs in first three years ... 133

LIST OF FIGURES

Figure 1: Visual representation of the steel coil supply to the harbour ... 16

Figure 2: Methodology ... 18

Figure 3: Simplified version of the modelling process (Sargent, 2011) ... 19

Figure 4: Loading ETTS’s (= ‘eye to the sky’ or vertical oriented steel coil) into a hopper-type hold. ... 24

Figure 5: IDEF0 Plant Activities ... 25

Figure 6: IDEF0 On-Site Logistics ... 26

Figure 7: IDEF0 Stevedoring & Warehousing ... 27

Figure 8: IDEF0 Coordination ... 28

Figure 9: Information flowchart (left) and associated actors (right block column) from ship selection to the actual loading of the vessel ... 30

Figure 10: Material flow from production to shipment ... 31

Figure 11: Pyramidal stowage of horizontal coils ... 32

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Figure 13: Locking coil configuration ... 32

Figure 14: Pyramidal stowage of vertical coils ... 33

Figure 15: Line stowage and honeycomb stowage ... 33

Figure 16: Model representation including model boundaries and function and function control blocks (Veeke, Ottjes, & Lodewijks, 2008) ... 35

Figure 17: Graphical user interface of the model implementation ... 47

Figure 18: Occupancy rates train wagon types - March 2016 (Bogel, 2016) ... 49

Figure 19: Bulk vessel with six hopper-type cargo holds (Pinterest, 2016) ... 50

Figure 20: Experimental plan ... 57

Figure 21: Graphical representation of wagon slot factors of the past shipments and the model output ... 61

Figure 22: Graphical representation of wagon load factors of the past shipments and the model output ... 61

Figure 23: Wagon slot factor means per shipment type including standard errors ... 62

Figure 24: Wagon load factor means per shipment type including standard errors ... 62

Figure 23: Schematic representation of a floating coil ... 68

Figure 26: SPSS output Wilcoxon related samples – Vessel load factors differences ... 98

Figure 27: SPSS output Wilcoxon related samples – Wagon slot factors differences ... 99

Figure 28: SPSS output Wilcoxon related samples – Wagon load factors differences ... 99

Figure 29: SPSS output Wilcoxon related samples – Required train wagons differences ... 100

Figure 30: SPSS output Wilcoxon related samples – Wagon slot factors differences (shipment types A & B only) ... 100

Figure 31: SPSS output Wilcoxon related samples – Wagon load factors differences (shipment types A & B only) ... 101

Figure 32: SPSS output Wilcoxon related samples – Required train wagons differences (shipment types A & B only) ... 101

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EXECUTIVE SUMMARY

This report documents the author’s graduation thesis in MSc Transport, Infrastructure & Technology at the Delft University of Technology. The research tackles an operational problem at a large steel production company from a logistical and mechanical point of view. The main objective is to improve steel coil stowage plans to increase the efficiency of the loading process.

The problem that initiates this research comes from the observation that the supply sequence of the coils by rail transport towards the harbour does not match the preferred demand of ordered coils. With a correct supply sequence the coils can ideally be directly loaded into cargo holds of the outbound vessels. The coil sequence demand at the quay side is not precisely clear since the stowage plans are constructed based on aggregated information about the cargo load. This report develops a solution that increases the efficiency of the loading process by automatically generating detailed stowage plans while simultaneously assigning the cargo to train wagons to improve the pre-haulage by rail transportation. This approach to stowage planning improves the efficiency of the train wagon utilisation and drastically decreases the time required to create detailed stowage plans. The detailed stowage planning method also reduces the amount of required train wagons, thereby increasing the rail transport capacity. By creating detailed stowage plans and incorporated coil-specific dimensions and weights, less coil damage is expected at stowage operations.

The problem is tackled by studying related research, analysing the current system and constructing a model. A deterministic algorithm is set up to show improvements of the proposed approach compared to the current system by performing computational experiments. The conducted literature study resulted in an observed research gap that indicates that related research lacks the following: ‘Modelling multi-destination steel coil stowage plans while incorporating operational constraints and pre-haulage by rail transport.’ Besides the academic literature study the current system of stowage planning and operations is analysed by the use of company documents and work instructions and by visiting the harbour at the steel production plant, interviewing employees and experiencing the shop floor activities and operations. Both the research gap and the findings during the current system analysis lead to the concept of building a computer model to generate coil-specific stowage plans. By using more data available, regarding the cargo and vessels associated with the shipments, detailed stowage plans could be generated. These detailed stowage plans can clear up the misalignments between steel coil supply and demand at the quayside during the loading and stevedoring activities. A conceptual model is designed to determine all required functions for the model to be capable to generate such detailed stowage plans. As many operational constraints as possible should be integrated within the model. A generalised approach allows different cargo compositions and different vessel types to be processed by the model. The model’s algorithm is set up in a deterministic way to generate just one solution at each run. The model is being specified within the Delphi environment, since the language coheres with the planning software present at the steel production company. Data structures are set up to contain the information about vessel characteristics, product data, stowage preconditions and system parameters. A four dimensional index is created to assign the coils to a hold, a tier, a tier level and a tier position. Coils are allocated based on diameter and weight, coil type and destination while considering the dimensions of the cargo hold and spacing required for the use of lifting equipment.

To indicate the performance of either the current system or the model, key performance indicators (KPI’s) are determined. The KPI’s are set up in such a way that they can measure both the real system and the model performances. The KPI’s consist of vessel load factors, average train wagon load

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factors and planning times. The load factors represent the effective utilisation of the capacities of the vessel or train wagons. Train wagon load factors are either calculated based on the available slots (slot factor) and based on the maximum load of the wagons. The planning time represents either the time it took for an expert planner to construct a stowage plan or the runtime required by the model to generate the detailed stowage plan. The current system’s KPI’s are derived from shipment data and train wagon data associated with outbound shipments.

To evaluate the working of the model, both verification and validation processes are used. The verification process evaluates the correctness of the model in relation to the conceptual model. Different verification steps represent the functionalities designed in the conceptual phase. These steps are verified by using artificial shipment data that represent simplified shipments. The validation process is done by the use of the expert knowledge of the stowage planners. In cooperation with the experts a set of validation criteria is set up. To fully validate the model to let it ideally resemble the real planning operations, all of the criteria must be satisfied. However, the model lacks validity at some points. As a result the model cannot be used in real operational situations at this stage in the model development process. Although the model is not valid on each criterion, it is valid to show the potential benefits associated with the proposed coil-specific stowage planning method.

The model is constructed to show how the stowage planning can be improved to increase the efficiency of the loading process. The experiments are set up to show the performance of the model compared to the performance of the real stowage planning system. Real former shipments are used as cases to be served as input for the experiments. Three types of shipments are selected, differing in vessel type, number of cargo holds, dimensions and capacities. All cargo loads differ regarding to amounts, weights, dimensions, types and destinations of coils. The data regarding the specific shipments is gathered and processed to show the KPI values of the, in total, fourteen real shipments. Model runs are performed based on the cargo loads and vessel types and data regarding the stowage conditions, and again KPI’s are computed. A comparative analysis subsequently shows the differences in performances of the real system and the model. As expected, the average runtime of the model was much faster than the time taken by the human planners to construct the stowage plans. The planning time decreased from 3 to 6 hours towards a runtime of several minutes or a maximum of 1.5 hours. The load factors of the vessels did not show any significant differences. The train wagon slot factors show do significant increases (confidence interval 95%) since the wagons are filled simultaneously when the coils are allocated to positions in the ships’ holds. Transatlantic shipments (types A & B) show more significant results when comparing the average train wagon slot- and load factors. For transatlantic shipments only, a yearly rail network capacity increase of over 120 kilotons is expected. The coastal shipments do show significant decreases in planning time but no improvements are observed considering train wagon utilisation. Table 1 shows the improvements of the model performance with respect to the real system’s performance. Besides the KPI values also the required train wagons per shipment are compared.

The benefits of the proposed stowage planning approach are expected to improve the efficiency of pre-haulage by rail transport, the quality of the delivered coils (less coil damage) and the degree of automation in stowage planning activities. These benefits results in monetary gains by increasing the number of required train trip per shipment, reducing overtime and planning time and decreasing the amount of coil damages at stowage operations. However one should note that implementation costs are required before detailed stowage plan generation model can be operational. The costs are expected to be covered by the benefits during the third year of implementation and substantial profit is prospected. The limitations of the model however should be criticised, the coils are currently not selected or allocated based on weight. Other constraints aren’t considered as well, such as the

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assignment of dedicated cargo holds for specific coils types. Future benefits can increase the value of the stowage planning approach, since the model is not fully developed. The model development process is considered in the implementation costs but should be professionally assessed to obtain a realistic forecast and reduce financial risks. With the automated generation of detailed stowage plans, cargo estimations and vessel selection can be done quickly causing great time savings. Operational deficiencies during loading of the vessel can be handled more efficiently when the stowage planning can be easily updated. When the efficiency of the loading process is improved the turn-around time of the vessel can be shortened in the future resulting in improved port productivity.

To use to potential of the proposed stowage planning model, more elaborate development of the model as advised. Changes in operations are proposed to enhance the capability of the real system to adopt the detailed stowage planning approach. Further research in updateable stowage planning and operational testing is recommended and financial analysis is advised to assess the risks of investment. Table 1: Average significant improvements in percentages of model output with respect to the real system derived from the Wilcoxon signed rank sum test and the Hodges-Lehmann median test (Appendix D: Statistical analyses)

Comparison model performance to real system performance

Average % change (N=14) Transatlantic- and coastal shipments

Vessel load factor 0%

Wagon slot factors 8%

Wagon load factor 7%

Required train wagons -8%

Planning time -85%

Transatlantic shipments

Wagon slot factors 13%

Wagon load factor 13%

Required train wagons -13%

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1. INTRODUCTION

At steel production company Tata Steel in IJmuiden different types of steel products are produced, stored, transported internally and shipped towards customers. At the inbound side, raw materials are being shipped in, processed and transported to the production facilities. This report focusses on the phase after the different production processes, namely the distribution of end-products.

The products mostly consist of steel coils and package materials. To be specific, the end-products considered in this report are steel strip coils of various types and sizes. The coils can be either horizontally or vertically oriented depending on the coil type and material. The material properties differ from untreated steel to high quality, coated and ultra-thin steel conditions. The products are heavy products that can weigh up to 30 metric tonnes or even more per steel coil. Depending on the material properties and quality of the products, products should be protected against environmental conditions. For the production of different end-products, different factories are present which are spread out over the entire company terrain. Products are transported by rail towards one of the warehouses or towards outbound rail tracks or to the harbour for inland waterways or sea bound transportation. From the harbour the products are shipped via inland waters or sea-bound shipping lines.

At the company’s harbour, three quays are used for outbound transport. Two quays are capable of handling larger vessels and one quay, an All Weather Terminal (AWT), is capable of handling relatively smaller vessels (up to lengths of approximately 100 m) at a roofed cargo handling facility. This research will handle the loading of the ships with an aim to improve the ships’ loading procedures, the load space configurations of the cargo holds and supply sequences of coils towards the harbour. Especially when large ships are being loaded involving multiple product types and destinations, complications can arise. The focus of the report lies at the stowage planning of the larger vessels (length > 100 m). This report demonstrates possibilities to improve the stowage planning and the steel coil loading process by approaching the subject from a multidisciplinary perspective.

This research is set up in cooperation with a steel production company and the initiatives for the selection of the research subject constitute from practical issues observed at the transport and logistics activities around the company’s harbour facilities. The issues encountered in practice are scoped and translated into a problem statement grounded to this research. To approach and possibly solve the problem, the research questions and appropriate methodology are constructed.

1.1 SCOPE

To set the scope for this research a selection has to be made about what things to incorporate and what things not to dive into. At first the subject of on-site logistics at a steel production site is addressed. Next only the logistical processes after the production of steel coils is included, involving storage, on-site transport and preparation of outbound transport of end products. The research will be conducted from a multi-disciplinary perspective approaching the problem from a logistical and a mechanical point of view. Operational conditions are encountered; however the problem will be simplified at the modelling phase.

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1.1.1 LOGISTICAL

The research focusses on the logistical activities performed at the outbound harbour of a steel production site. These activities include rail transportation of steel coils towards the harbour and loading them into cargo/bulk vessels. Also the operational planning activities are considered. At the harbour the steel coils are either temporarily stored at the harbour’s quay warehouse or directly loaded into the holds of the ships. Logistical flows at the harbour are taken into account. The research addresses the loading of train wagons at the storage areas and the (un)loading and stowage activities at the harbour. The internal rail transport network between storage locations and the harbour quays falls out of the scope.

1.1.2 MECHANICAL

The products considered in this report are limited to vertical and horizontal oriented steel coils. The steel coils can vary based on size, weight and material. Variations in steel quality or material properties are not directly taken into account in this research. The products are being loaded into different types of ships changing in size, hold geometries, number of holds and stacking constraints. The mechanical aspects of the research relate to constraints based on assumptions about static and dynamic loads, stacking configurations and geometries. The static forces at the interfaces between the tank top and the lower steel coils will not be elaborated on in this study. However assumptions are made to cover the mechanical background (Appendix A: Research assumptions).

For a very simplified visualization of the considered system see Figure 1.

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1.2 PROBLEM STATEMENT

The products at the harbour are supplied based on a cluster plan: a plan that determines the sequence of products supplied to the harbour to enhance the loading process. Clustering involves the use of available product data and vessel and stowage information to obtain priorities in the product supply sequences. The harbour should be internally supplied in such a way that the loading of the ships can be performed in an efficient way. By determining clusters of products to be delivered at the harbour, every rail shipment could be theoretically loaded directly into the ship’s hold at the right position. However information about the ship hold sizes, product details or product availability is not incorporated. In practise, the predefined clusters of products do not always perfectly match the stacking configuration in the hold of the ship. Consequentially the steel products need to be rearranged at the harbour’s quays, or products need to be temporarily stored away. When the delivery sequence of requested products at the harbour (= ‘supply’) does not meet demand due to stacking configuration in practise, rerouting might be necessary and delays will occur. The problem causes extra movements and ‘waste’ activities that negatively affect the efficiency of the loading process.

‘The predefined sequence of supplying the products to the harbour does not always correspond with the preferred sequence for the actual stowage configurations.’

1.3 RESEARCH QUESTIONS

To potentially solve the problem the research should be guided in the right direction to be able to answer the right questions. This research aims to answer the following research questions:

Main research question:

 ‘How can steel coil stowage plans be improved to increase the efficiency of the loading process?’

The main research question is supported by several sub questions. When conducting a research, firstly the former research and literature should be assessed in order to identify a research gap. In order to obtain possible improvements, the current system should be analysed and the performance should be measured. The selection of key performance indicators contributes to the measurement of the current system. By constructing a model and taking into account the KPI’s, changes can be theoretically put to test to map possible improvements and solutions to the main research question. Sub questions:

 ‘What research gap can be defined concerning former research about steel coil stowage plan modelling?’

 ‘What is the current system concerning the stowage planning?’  ‘How can the steel coil stowage planning activities be modelled?’

 ‘What are key performance indicators for the stowage process of steel coils?’

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In order to finalize this research these questions should be answered or at least a solution direction should be provided. The methodology will give a step by step structure to guide the approach towards useful conclusions.

1.4 METHODOLOGY

To guide the research towards answering the research questions, a systematic approach is set up. The questions themselves already indicate a certain approach to potentially solve the problem. To scientifically tackle the practical issues at stake, a literature study is performed to refer to related research. A research gap will be defined to assist in setting the scope for this research and to obtain a contribution to science. The findings originated from the literature study will be used throughout the report for reference. The framework below displays the sequence of steps to be taken to arrive at the final conclusions of this research.

A model will be constructed to represent an adjusted and simplified version of the real system at stake. During and after the creation of the model used in this research, verification and validation of the model is required. To evaluate the working of the model several checks will be executed to assess the correctness of the model with respect to the concept and the real system. This is done in order to assure correct working of the model and alignment with assumptions about the real-life system. The ‘Construct model’ step in the above figure includes the creation of both the conceptual and the computerized model (implemented in a software environment). Whether the computerized model complies with the conceptual model will be controlled by verification. Validation will be performed by comparison of the computerized model to the current system and comparison of the conceptual model to the current system. The validity of the data is needed to ensure correct inputs concerned with the current system.

The validation and verification activities will be performed during the research. The ‘Simplified version of the modelling process’ (Sargent, 2011) is used to keep a structured overview of a scientific approach to constructing a model or simulation model. During the experiments, the model outcomes are compared to real system data analyses. To evaluate both real life and model performance, different performance indicators are set up (KPI’s). After the experiments, the results are analysed and discussed to show possible improvements or limitations by the model compared to the real system.

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19 Figure 3: Simplified version of the modelling process (Sargent, 2011)

The problem concerned by this report should be first be looked up in existing literature studies to possibly obtain already conducted related research. From this related research can be learned and existing literature can influence this research by guiding towards a research gap. To assess the possible academic contributions of the research, a literature study is documented in the next chapter. Recapitulation

The scope views this research from a logistical and mechanical perspective to cover both flows of goods as mechanical loads and configurations for stacking. The problem states a misalignments of demand and supply sequences of the steel coils at the harbour of the steel production company. One main research question concerns possible improvement of the loading process and is supported by sub questions to guide the research. A methodology is set up to structure the research step by step in order to eventually answer all research questions.

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2. RELATED RESEARCH AND LITERATURE

To obtain understanding of previously performed research, related literature is studied. The problem addressed in this report involves the creation of stowage plans and the modelling of cargo loading. Because stowage plans for container ships are more commonly researched than the stowage of steel coils, container stowage will be looked into as well. Therefore these topics are leading in the investigation of related research and literature in this report. The goal of this literature study is to assign a research gap that indicate a solution direction and simultaneously answer the first research sub question.

2.1 MODELLING CARGO LOADING

The literature study of related research starts with the search of methods to model the cargo loading process. The generally applicable methods of stowage or loading can consist of purely mathematical frameworks. The goal of studying this area is to obtain knowledge of how to incorporate different constraints into a logistic cargo loading model.

Many cargo loading problems can be associated with the bin packing problem. Load space allocation of trucks, trains, airplanes or ships can be optimized by tackling this problem. However multiple physical and operational constraints make it difficult to relate a basic bin packing model to reality. In 1996 a report was produced by B. Verheij where a multi destination bin packing problem is addressed. In a structured approach the problem is tackled by starting with a simple two-dimensional bin packing problem. In the end, a packing algorithm is given and experimentally tested showing highly competitive volume allocations taking into account multiple destinations, different box types and order sizes (Verweij, 1996).

Silvano Martello and Daniele Vigo propose an approach to achieve an exact solution to the two-dimensional bin packing problem (2DBPP). This problem can be seen as a cutting stock problem where standard-sized pieces of stock material are cut into a predefined number of smaller pieces with certain dimensions. New lower bounds are proposed to use in a branch-and-bound algorithm for the exact solution of the 2DBPP (Martello & Vigo, 1998). Joining forces with Andrea Lodi, Martello and Vigo researched the three-dimensional bin packing problem and introduced a Tabu search framework exploiting a new constructive heuristic (Lodi, Vigo, & Martello, 2002). In this research the 3DBPP is approached by adopting a layered technique where items are ordered based on height and base area. By using this approach each layer can be solved with a one-dimensional bin packing problem algorithm.

Liu et al. propose a two objective two dimensional bin packing model to evaluate on number of bins and deviations of centre of gravity. In order to find the best solutions of the MOBPP-2D problem, a multiobjective evolutionary particle swarm optimization (MOEPSO) algorithm is used. “Both single objective and multi-objective bin packing problems can be easily handled using MOEPSO, which demonstrated that MOEPSO is a good candidate for solving real world bin packing problem.” (Liu, Tan, Huang, Goh, & Ho, 2008).

The article written by Caresta, Schwarze and Voß addresses a reasonably fast modelling approach, meaning low computational time, to solve block relocation problems (BRP’s) (Caserta, Schwarze, & Voß, 2012). The approach is based on a mathematical model for optimal solutions for general BRP’s in cases where instances are small. The approach is extended with realistic assumptions derived from literature to be able to solve medium-sized instances. The heuristic is tested by comparison with a

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state-of-the-art commercial solver and literature references. The method is a basic model that could be extended to three dimensions. One could also incorporate the use of handling equipment.

Bruns, Knust and Shakhlevich address practicalities of different storage loading problems based on their complexity (Bruns, Knust, & Shakhlevich, 2016). Assumed is that storage areas consist of fixed stacks with limitations in stacking heights. Stacking constraints, based on weights etc., are incorporated as well. The paper proposes algorithms applied at special situations. However the algorithms might be useful for general real world problems.

2.2 CONTAINER STOWAGE

Container stowage is a subject addressed largely in literature. Principles involving multi-port stowage and stacking sequences can be learned from in these cases. Obviously stacking configurations and physical or structural constraints will be different compared to steel coils stowage.

The article by Imai et al. includes a more specific approach of assigning containers to their exact location when simultaneously stowage planning and the loading sequence are determined. Stability of the ship is taken into account based on angle of list, trim and the metacentric height (GM) (Imai, Sasaki, Nishimura, & Papadrimitriou, 2006). Stacking configurations and container positions are used in evaluating the expected number of rehandles. Besides the genetic algorithm, an objective function is used to obtain the resulting values. Different computational cases vary in ship size, container volume, stack arrangements, initial ship conditions and ship hold arrangements.

In 2007 a heuristic method is presented by Sciomachen and Tanfani to solve a master bay plan problem based on its relation with the three dimensional bin-packing problem (Sciomachen & Tanfani, 2007). The items are containers and the bin consists of the ship’s holds. The approach tries to find stowage plans to maximize quay productivity. In the same year a methodology, consisting of objective functions, is presented by Wilson and Roach for the automation of stowage planning and solving container allocation problems. Workable solutions (reasonable computational times) are offered by a heuristic driven computerised methodology that models how human planners solve the container to slot allocation problem (Wilson & Roach, 2007).

In 2010, Aye et al. describe a visualization and simulation tool for stowage of containers in a container vessel. The visualization of the stowage plan and the highlight and navigation options can be used to analyse the allocation sequence and help constructing better allocation algorithms (Aye, Low, Ying, Jing, Fan, & Min, 2010). Future work will incorporate more constraints and manual adjustment possibilities. The approach focusses on the ship’s stability and the time spent at port.

The Multi-Port Master Bay Plan Problem (MP-MBPP) is addressed by Ambrosino et al. and a mixed integer programming (MIP) heuristic is proposed. Two point of views are adopted, that of ship coordinator and that of terminal planner. Operational and structural constraints of both the ships and the terminals are considered. The model allows feasible solutions for stowage plans for containerships with capacities up to 18000 TEU (Ambrosino, Paolucci, & Sciomachen, 2015).

Araujo et al. aim to minimize the number of movements for loading and unloading while taking into account the stability of the ship. A multi-objective method based on clustering search is presented and compared to a mono-objective method and to the Pareto fronts. The Pareto Clustering Search (PCS) produces better results, that is; obtaining a good solution produced in a reasonable timespan, than the Pareto Simulated Annealing (PSA) metaheuristic (Araujo, Chaves, De Salles Neto, & De Azevedo, 2016). The multi-objective optimization provides some advantages in flexibility.

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2.3 STEEL COILS STOWAGE

Found in a lesser extend is the amount of literature associated with the loading of steel coils into the holds of a ship or bulk vessel. Logically the loading of steel coils of different sizes includes more structural constraints than the loading methods of identically shaped containers. Stacking configurations should be considered and the ships’ tank top loads can become critical.

Already in 2001, Umeda et al. looks into the allocation of steel rolls into ships’ holds. Taking into account the ship’s stability on both left-to-right and back-to-forth directions, the allocation problem is modelled by specifying the coils positions in vector notations (Umeda, Kitamura, Konishi, Kanamura, & Takami, 2001). The allocation planning is approached as a combinatorial optimization problem. The model takes into account crane productivity. The evaluation function used involves an operational know-how term that benefits the results concerning the amount of unloaded coils. Optimization is done by applying the simulated annealing method.

When comparing the ship’s loading space to a warehouse, the article of Zapfel and Wasner can be useful. The article addresses a real warehouse sequencing problem concerning a two level steel coil storage facility. The research includes positioning of coils, routing, retrieval and restorage orders (Zapfel & Wasner, 2006). The authors developed a mathematical non-linear mixed integer optimization model. The proposed solution involves a local search algorithm.

According to Yang and Tang when establishing a model of ship consolidation planning, the outgoing sequence of strip coils can be determined based on ship capacity, destination and due date of the coils (Yang & Tang, 2009). The shuffling of the coils is being minimized. Scatter search is proposed to improve initial solutions that originate from heuristics based on operational know-how. The improved SS algorithm is effective for solving CPRSP according to computational results.

The paper by Tang et al. addresses an approach to handle the Ship Stowage Planning Problem (SSPP) by aiming to minimize the ship’s imbalance, the number of shuffles and the dispersion of coils for the same destination (Tang, Liu, Yang, Li, & Li, 2015). A TS algorithm is used in this paper, as those algorithms proved to be effective local search techniques able to avoid being trapped in a local optimum. The modified TS algorithm shows great improvement compared to manual solutions, it shows optimal solutions for small problems and good solutions for medium and large scale scenarios. The article by Li & Tian proposes a two-layer multi-objective variable neighbourhood search (TLMOVNS) algorithm. The research focusses on an integrated optimization problem of consolidation planning and transportation scheduling of finished products from multiple storage locations towards the dock yard (Li & Tian, 2015). Stowage planning is not explicitly researched in this case; however the on-site logistics in alignment with the stowage is addressed.

2.4 FINDINGS & RESEARCH GAP

For general modelling purposes involving cargo loading procedures, different heuristic and mathematical algorithms have been largely researched. Basic methods to cope with loading/stacking constraints can be derived from literature. Solution generation and optimization methods are used to obtain useful results.

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Models considering container stowage involve mostly operational constraints evaluated by the number of shuffles required or by the dispersion of the containers. Besides these logistic measures, also the stability of the ship is taken into account as either a constraint or performance measure. Some scientific literature addresses the positioning of steel coils in stacking areas. Even ship stowage of steel coils with different dimensions is handled and modelled. Models can include stacking constraints of steel coils with different dimensions based on standard stacking configurations. Most model techniques include search algorithms to optimize selection of generated solutions.

In relation to the previously defined problem definition, literature studies can contribute to the approach of this research. Based on the studied literature a research gap can be defined. The related research found in this literature study lacks the following:

‘Modelling multi-destination steel coil stowage plans while incorporating operational constraints and pre-haulage by rail transport.’

The supply of the steel coils to the harbour should be done efficiently and according to the stowage plan. When taking into account the pre-haulage of the steel coils, the loading process at the on-site harbour can possibly benefit. Before starting the modelling process, first the current system should be analysed in order to obtain a reference situation.

Recapitalisation

The associated literature and related research is tackled in a predefined order scoping down to the subject of steel coil stowage planning. General cargo loading models and container stowage is considered and learned from in the process of determining the research gap. The research gap is defined indicating the absence of multi-destination steel coil stowage plan generation that simultaneously takes into account the coil supply by rail transport.

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3. CURRENT SYSTEM DESCRIPTION

The current system of supplying the harbour, storing and loading of outbound cargo is looked into to obtain understanding of the situation. The system addressed in this research consists of the transport and logistics activities that take place after production and initial storage of the end-products. The focus of the system description contains the stowage planning activities and stowage operations at the harbour department of the company. First the system is described by representing the organisational structure, pointing out the different actors and visualising the decision flow. After describing the system, the system’s operations are addressed focussing on material flows and mechanical constraints at stowage.

3.1 SYSTEM

After production of steel coils, initial storage takes place until the coils are being transported again by rail or truck. Around 60% of the outbound transport is being done by either inland or sea waters. Most end-products, being stored initially in the yards and warehouses, must eventually be transported towards the on-site harbour.

When the outbound vessels are arranged, the harbour planning makes sure that the cargo ships can be scheduled and received at the proper quay side. At this point the ship’s cargo is largely known but can still be adjusted or increased. The ship’s cargo, consisting of end-products stored at different warehouses or yards, is subsequently ordered based on the product types and the ship’s preferred stowage plan. The stowage plan determines the sequence of the holds to be loaded together with an estimation about the tonnages per hold. Based on the stowage plan and the overall characteristics of the cargo (product types and average dimensions and weights) clusters are defined which indicate the order of the products to be supplied to the harbour. The products should grouped in such a way that subsequently products are delivered that correspond in weight and dimensions. A certain percentage of the ship’s cargo (often about 50%), is already stored in the warehouse at the harbour to be able to allow on the spot adjustments in stacking configuration and to reduce reliance of the supply of material by rail.

At the moment the steel rolls are delivered at the harbour and the vessel is ready for loading, the workings shifts and crane operators start loading the vessel’s holds according to the stowage plan and loading instructions. Depending on the product types, certain stacking configurations are used with corresponding lashing methods.

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3.1.1 IDEF0 REPRESENTATION

In order to obtain a detailed system description, an Integration Definition Function Modelling technique IDEF0 is used. IDEF0 is developed to obtain structural graphical representations of systems or enterprises (National Institute of Standards and Technology, 1993). The IDEF0 representation of the current system visualizes the activities and the work flows from the upper level activities towards the system considered in this report. The representation is scoped down from the upper level in four steps (the next four figures).

 Plant Activities  On-Site Logistics

 Stevedoring & Warehousing  Coordination

The activities at the entire steel production plant include the actual production and the logistics at both inbound and outbound. For the plant to be able to be active raw materials need to be available and orders need to be placed. At the end of all plant activities a loaded vessel should be ready for shipment.

Figure 5: IDEF0 Plant Activities

At the On-Site Logistics department, three divisions take care of the different transport and logistics activities. The On-Site Planning team takes care of the scheduling and planning of the harbour, rail transport and storage activities. The Rail Transport and Stevedoring & Warehousing departments take care of the operations regarding the supply of the cargo to the outbound vessels at the harbour. The Stevedoring & Warehousing department ensures the loading of the cargo onto the trains that are

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transported to the assigned quay at the harbour by the Rail Transport department. Subsequently the cargo is loaded into the outbound vessel and stowed according the stowage plan.

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27 Figure 7: IDEF0 Stevedoring & Warehousing

At the coordination of the stevedoring and warehousing activities the feasibility of the proposed cargo vessels is assessed based on the ship’s characteristics and the assigned cargo. When a ship is approved the cluster and stowage planning activities can start. The stowage plan contains instructions about the allocation of the cargo to the ship’s cargo holds. A cluster plan is constructed to translate the preferred loading and stevedoring sequence towards clusters of cargo to be supplied to the quayside by rail transport. Often the outbound vessels have predefined conditions for the stowage of the coils. Those stowage conditions can be determined by either the ship management and the captain or the shipping company. The stowage plan has to be approved by the vessel management before the loading and stevedoring activities can start.

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28 Figure 8: IDEF0 Coordination

3.1.2 ACTORS

From the IDEF0 representations, the involved actors can be derived from the higher to the lower scale of the processes. The scope of the system addresses the internal logistics together with the outbound shipping and the vessel management. At the stowage activities physical and mechanical constraints are present. Multiple actors are involved and different interests come at stake in these matters. Interests at stake originate from planning aspects, transport costs and structural constraints to operational activities and priorities of shipments. When loading of the ship’s cargo holds is being prepared in advance different actors should be taken into account. The main actors considered in this report and their associated interests are listed below:

Table 2: Main actors and interests

Main actors Interests

Shipping company Manage shipment contracts and transportation

Vessel management Manage ship operations

Rail Transport Provide on-site transportation of steel coils

Stevedoring & Warehousing (Un)Loading and stowage of on-site and outbound transport

Outbound Logistics Arrange capable outbound transportation for low costs

Production Achieve production targets

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3.1.3 DECISION FLOW

In order to accurately analyse the current system, the information flows concerning the ship loading plan should be structured. Awareness about the information flows and decision variables is necessary when constructing a model concerning these activities. The information flows are obtained by contacting employees from different departments within the logistics of the steel company and by experiencing their daily activities. The information can consist of order and product specification, outbound logistics, vessel types, harbour planning, rail & equipment capacity, stowage plans, loading sequences, top stowage products and cluster planning.

Order specification

During the activities at the sales and outbound logistics departments orders will become more specified resulting in more specific product information. One should note that the current activities concerning the loading preparation of the outbound vessels is based on average products details (dimensions, weights etc.).

Initially only estimated quantities and consolidated weights are known when the ship selection is started. When the ship type and product types, exact quantities and destinations are known, the vessel is technically approved (or rejected) based on manual estimations and experience knowledge. At the technical approval process the vessel’s characteristics are taken into account and ship drawings are consulted. The technical approval indicates whether the vessel’s hold characteristics are sufficient to cope with the cargo load. When a vessel is approved, alterations in the cargo composition might still occur. In the case of additional changes in cargo composition approval is again required.

Stowage and cluster planning

At the moment the cargo vessel is approved and scheduled and the cargo load is specified the stowage and cluster planning activities start. The vessel management provides stowage preconditions such as a loading sequence and the hold allocation of the different cargo loads per destination. The expert planners of the stevedoring and warehousing department have to approve the vessel before the stowage planning process can start. The cluster plan specifies the supply sequence of the coils towards the harbour based on clusters of products and aggregated information.

A cluster plan is set up to group the products for delivery to the on-site harbour. The supply sequence of steel coils transported to the harbour is initiated by the clusters. Generally clusters are not larger than the maximum capacity of a batch coils to be transported by either rail or other transport means. The clusters are arranged in order from large to small to ensure that the coils can be stacked on top of each other. However, since the arranging of coils from large to small is based on averages of material dimensions of multiple steel coils deviations can be present. Therefore, in practise the coils are not perfectly sorted by size (see ‘problem statement’).

After the stowage and cluster planning is finished, the vessel management or the captain has to approve the loading sequences. Based on the ship’s stability and tank top loads the plans are approved or rejected. The actual loading, stevedoring and securing of the cargo load can start at this point.

To assess the consequences of certain decisions or influences by the actors, the operations are looked into and material flows and stevedoring activities are described in the next sections.

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30 Loading procedure Ship characteristics O rd er sp ec ifica ti o n Ship selection Technical approval Rejected Approved Product types and quantities Product quantities & destination(s)

Stowage / Cluster plan

Average product dimensions Stowage conditions Captain’s approval Approved Rejected Start loading procedure

Outbound

logistics

Stevedoring &

Warehousing

Vessel

management

Stevedoring &

Warehousing

Figure 9: Information flowchart (left) and associated actors (right block column) from ship selection to the actual loading of the vessel

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3.2 OPERATIONS

To create a realistic image of the current system, operational conditions should be taken into account. During operations regarding the logistical activities at the steel plant large and heavy steel coils need to be transported with limited availability of resources. The loading and stowage activities at the quaysides are subjected to multiple physical constraints and again limited resources are available. To obtain understanding of the operational aspects of the system the on-site material flow is followed and the stowage and securing activities are described.

3.2.1 MATERIAL FLOW

The material flows of the end products that are shipped to the customer follow a certain trajectory on site from production towards outbound shipment. Products can be transported while packed in plastic foil or without packaging. The steel coils consist of two configurations, vertical or horizontal oriented.

The different types of steel coils can originate from the warm- and cold rolling production facilities and are subsequently stored in the yards and warehouses. Most yards and warehouses have cranes to move coils to and from train wagons. Assumed is that immediate storage is required after production since the continuous production operations should not be interrupted. At the moment that the outbound vessel is scheduled and clustered, the on-site transport can be arranged. The steel coils are transported to the harbour’s quays by rail transport (either open or hooded wagons). At the harbour the coils are either loaded in the ship’s holds by quay cranes or temporarily stored at the harbour warehousing facilities. Forklifts are used for transportation to the harbour warehouse, on the quay and within large ship holds. The figure below shows the activities associated with the material flow considered in this report.

Figure 10: Material flow from production to shipment

The steel coils can be stored in 16 warehouses or yards located throughout the plant terrain. The train wagons are loaded with steel coils designated to a certain shipment. The loaded train wagons are consolidated at the shunting yard into complete trains. These trains transport the steel coils towards the right quay crane to start loading and stowage or the trains transport steel coils towards the temporary storage at the quayside warehouse.

3.2.2 STOWAGE AND SECURING

Stowage of the material can be is currently performed used two different methods: pyramidal stowage and stowage from side to side. The methods correspond to two different hold types

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respectively: Hopper-type holds and Box-type holds. Both methods of stowage require securing methods such as lashing and wooden dunnage and wedges are used to keep the steel coils in place during transport overseas or by inland waters.

The presented schematic figures of steel coil stowage configurations originate from source:

http://www.tatasteel-ls.com/port-information/stowage-and-securing.html

1. Stowage of horizontal steel coils  Pyramidal stowage

Figure 11: Pyramidal stowage of horizontal coils  Side to side stowage

Figure 12: Side to side stowage of horizontal coils

Figure 13: Locking coil configuration

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33 2. Stowage of vertical coils

 Pyramidal stowage

Figure 14: Pyramidal stowage of vertical coils

 Line stowage and honeycomb stowage

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34 Recapitulation

The current system, or real system, concerning stowage planning at a large steel production company is now described with respect to the system’s logistical, organizational, and operational aspects. The system description scopes down to the stowage and cluster planning processes and also considers the different stakeholders, the material flow and stacking methods. The IDEF0 schemes have contributed to the description by visualizing the scope of this research and showing the connections within the organization. The stowage and cluster planning processes are currently using generalized information about orders and aggregated coil data, not using specific details about the produced coils ready for transport. When specific product details (storage location, weights and dimensions of the steel coils) are incorporated in the stowage planning, a more detailed stowage plan could be created. A detailed stowage plan can indicate precisely in what sequence the coils should be supplied to the harbour and to what position in the ship the coils should be stowed. The different actors mentioned will need to share more information and make the information available more timely. Close collaboration with the shipping companies for example will make it easier to obtain the right vessel information at the desired time. To handle coil-specific information a computational model is needed that can handle the complex stowage problems and associated constraints. The next chapter will handle the modelling phase of the research. The creation of a model that can generate coil-specific stowage plans is split up in a conceptual- and a specified-, or computerized-, modelling process.

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4. MODEL

After defining a problem statement, studying related research and analysing the current system, the modelling phase is entered to design and create a model to tackle the problem, bridge the research gap and improve the current system. A model will be constructed to capture the stowage planning process for steel coils. In contradiction to the real system, the model does incorporate detailed information about the steel coil cargo to allocate each coil to a position. In order to fill up the research gap operational constraints must be taken into account and pre-haulage by rail transport is considered. Besides resembling part of the current stowage planning and operations, the model will introduce a coil-specific stowage planning method. The creation of the model is divided into two parts: designing a conceptual model and implementation as a computerized model.

4.1 CONCEPTUAL MODEL

In this section the conceptual model is explained describing its inputs, outputs and functions with respect to the model objectives. At first the model’s boundaries are set and the objectives of the model are defined. The conceptual model incorporates the different constraints derived from the real system. While the model is simplified in some fields, in other fields the model will be more elaborate than the current system (e.g. the use of detailed information about the steel coil cargo). The conceptual model contains all actions and functional requirements for the model to eventually answer the research questions, achieve the objectives and show KPI values. In addition a step by step planning approach is mentioned to let the stowage planning be less dependent on operational deficiencies.

Coil allocation

Control of constraints

KPI’s Stacking configurations Stowage preconditions System parameters Vessel characteristics

Product data Train wagon data

Detailed stowage plan

Figure 16: Model representation including model boundaries and function and function control blocks (Veeke, Ottjes, & Lodewijks, 2008)

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4.1.1 MODEL BOUNDARIES

The model is first defined by its boundaries and the interfaces at the boundaries, as shown in the above figure. In order obtain manageable model boundaries the system is simplified with a number of assumptions (Appendix A: Research assumptions).

In the above figure the model’s inputs, output, constraints (or requirements) and performance measures are displayed. Information about the vessel and the cargo load serves as input. Stowage preconditions, stacking configurations and system parameters control the generation of the stowage plan by setting constraints. Stowage preconditions are often determined by the shipping company and/or vessel management. Stacking configurations determine the predefined rules for stacking the steel coils in the ships’ holds. KPI’s measure how well the objectives are being reached.

The function of allocating coils to positions in the ship’s cargo holds is the main aspect of the model. However the allocation must satisfy multiple operational constraints in order to produce a valid stowage plan. The function control (upper block in the figure) evaluates allocation results with respect to the constraints and initiates further allocation. The evaluated results indicate the performance by the use of key performance indicators (KPI’s). When allocating the coils to the cargo holds, already train wagons are being filled by grouping the cargo based on the storage origins. The detailed stowage plan and the wagon data serve as output of the model.

4.1.2 OBJECTIVES

The model is to be designed to achieve certain objectives. Whether or not, and to what extent, these objectives are reached can be interpreted by retrieving the KPI values. Solutions are generated based on the following objectives:

Main objective:

‘Generate coil-specific stowage plans for both horizontal and vertical oriented coils while incorporating pre-haulage by rail transport.’ The model should use the coil-specific data (exact information about dimensions, weight, storage location and type of the coils) to create a stowage plan that considers the supply to the harbour by train wagons.

Sub objectives:

‘Allocate coils to positions in the ship’s holds.’ Every coil allocated to the vessel should have a specified position indicated by hold, tier and tier position (or slot).

‘Satisfy operational constraints and stowage preconditions.’ Operational constraints or assumptions about stacking methods and involved mechanical loads should be considered. The stowage preconditions agreed on beforehand also serve as limitations within the model. ‘Promote high load factors of trains and train wagons.’ To enhance the occupancy of the trains, train wagons should be fully loaded at the storage locations. The storage location of the coil should be considered while allocating the coils to their positions.

Generating stowage plans in the steel industry - Modelling steel coils loading procedures while incorporating pre-haulage by rail transport