Solution Strategies for a Multiport Container Ship Stowage Problem

Parreño-Torres, Consuelo; Álvarez-Valdés, Ramón; Parreño, Francisco

doi:10.1155/2019/9029267

Cited by 19 publications

(11 citation statements)

References 25 publications

(48 reference statements)

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“…For such cases, several heuristics, meta-heuristics, evolutionary algorithms, and variants of linear programming approaches that are capable of generating efficient solutions within relatively short CPU times have been successfully applied by many researchers (e.g. [33], [29], [13], [30], [31]), as also summarized in Table 1. Similarly, we propose a two-stage heuristic solution methodology which includes an IP formulation in stage 1 and SH algorithm in stage 2.…”

Section: Two-stage Heuristic Methodologymentioning

confidence: 99%

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A Mathematical Model and Two-Stage Heuristic for the Container Stowage Planning Problem With Stability Parameters

2020

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Owing to the significant increase in the volume of world trade, mega-container vessels are being used to meet transportation demands. As the size of the vessels increases, the loading sequence of containers onto the vessels presents an important challenge for planners. In this study, we consider the container stowage planning problem with stability constraints (e.g. shear force, bending moment, trim) and develop a mixed integer linear programming (MILP) formulation which generates load plans by minimizing total cost associated with the over-stows and trimming moments. Our study adopts a holistic perspective which encompasses several real-world features such as different container specifications, a round-robin tour of multiple ports, technical limitations related to stack weight, stress, and ballast tanks. We also propose a two-stage heuristic solution methodology that employs an integer programming (IP) formulation and then a swapping heuristic (SH) algorithm. This approach first acquires a lower bound on the total over-stow cost with the IP model, thereby creating an initial bay plan. Then, it applies the SH algorithm to this initial bay plan to minimize cost resulting from trimming moments. The efficiency of the MILP formulation and heuristic algorithm is investigated through numerical examples. The results have shown that the heuristic has greatly improved the solution times as well as the size of the solvable problems compared to the MILP formulation. In particular, the two-stage heuristic can solve all size problem instances within an average optimality gap of 0-25% in less than 8 minutes, whereas the MILP can only achieve an approximate optimality gap of 55-80% in 2 hours. INDEX TERMS Container stowage plan, mixed integer linear programming, over-stow, stability.

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Section: Two-stage Heuristic Methodologymentioning

confidence: 99%

“…The model employs a given master bay plan and implements the GRASP algorithm to determine an efficient slot plan. Similarly, [29] studied the multi-port CSP problem. They proposed an IP model and a GRASP algorithm that is capable of generating stowage plans with minimum over-stow for large scale problems.…”

Section: Related Workmentioning

confidence: 99%

A Mathematical Model and Two-Stage Heuristic for the Container Stowage Planning Problem With Stability Parameters

2020

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“…Work [5] gives a Boolean model of integer programming for the task on determining the position of containers aboard a ship along its route in order to minimize the number of unproductive movements. However, when drawing up a mathematical model, it was assumed that all containers were the same size while the conditions of stability and the rules for placing dangerous cargoes were not considered.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Authors of [5] tested the effectiveness of integer programming models to solve the bay plan problem.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Construction of a mathematical model and a method for arranging hazardous cargoes on a containership

Kamieniev

Kamienieva

Tsymbal

2019

EEJET

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“…In the maritime transport of containers, the ships usually have a circular route, spending a long time at ports [3]. The multi-port stowage planning problem (MPSP) consists of determining how to stow a given set of containers of different types into a set of available locations on a container ship [4,5]. The containers may have different port destinations.…”

Section: Introductionmentioning

confidence: 99%

Solving the Integrated Multi-Port Stowage Planning and Container Relocation Problems with a Genetic Algorithm and Simulation

2022

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The greater flow of containers in global supply chains requires ever-increasing productivity at port terminals. The research found in the literature has focused on optimizing specific parts of port operations but has ignored important features, such as the stack-wise organization of containers in a container ship and port yard and the effects of interconnection among the operations in both places. The objective of this paper is to show the importance of designing an integrated plan of the container relocation problem at the port yard with the stowage planning problem for loading and unloading a ship through ports. Both individual problems are NP-Complete, and exact approaches for each problem are only able to find optimal or feasible solutions for small instances. We describe a simulation-optimization methodology that combines simulation, a genetic algorithm, and a new solution representation based on rules. The test results show that the solution from the integrated plan is mutually beneficial for port yards and ship owners.

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Solution Strategies for a Multiport Container Ship Stowage Problem

Cited by 19 publications

References 25 publications

A Mathematical Model and Two-Stage Heuristic for the Container Stowage Planning Problem With Stability Parameters

A Mathematical Model and Two-Stage Heuristic for the Container Stowage Planning Problem With Stability Parameters

Construction of a mathematical model and a method for arranging hazardous cargoes on a containership

Solving the Integrated Multi-Port Stowage Planning and Container Relocation Problems with a Genetic Algorithm and Simulation

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