Pre-Marshalling Problem: Heuristic solution method and instances generator

Expósito‐Izquierdo, Christopher; Melián-Batista, Belén; Moreno-Vega, J. Marcos

doi:10.1016/j.eswa.2012.01.187

Cited by 95 publications

(65 citation statements)

References 10 publications

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“…The concept of fixed containers is the same as the concept of well-located containers shown in Ref. [2].…”

Section: Definitions Of Cpmp and Related Itemsmentioning

confidence: 99%

Search space reduction with multiset for effectively solving the container pre-marshalling problem

Koike

2017

AIP Conference Proceedings

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Abstract. The task of finding the minimum sequence of container movements that transforms an initial bay into a bay that does not require redundant relocations during an actual loading operation is referred to as the container pre-marshalling problem (CPMP). A majority of existing approaches focus only on improving computational efficiency through heuristics, which may not guarantee the optimality of obtained solutions. This paper discusses an efficient search method for obtaining optimal solutions of the CPMP. Unlike most existing approaches, which introduce heuristics for efficiency, this paper pursues an optimization method that preserves optimality without heuristics. Further, through experimental results, we demonstrate how the proposed approach surpasses heuristic approaches in both optimality and efficiency in large-scale problem instances.

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“…The concept of fixed containers is the same as the concept of well-located containers shown in Ref. [2].…”

Section: Definitions Of Cpmp and Related Itemsmentioning

confidence: 99%

Search space reduction with multiset for effectively solving the container pre-marshalling problem

Koike

2017

AIP Conference Proceedings

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“…As only a few instances can be solved with the model, a simple two-phase heuristic is devised for real-life instances. In (Exposito-Izquierdo et al, 2012) some simple but efficient algorithms and an instance generator are presented, whereas (Forster and Bortfeldt, 2012) presents a heuristic tree search procedure. A neighborhood search heuristic for the same problem is presented in (Lee and Chao, 2009).…”

Section: Literature Reviewmentioning

confidence: 99%

A new approach to the Container Positioning Problem

et al. 2015

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In this paper the Container Positioning Problem is revisited. This problem arises at busy container terminals and requires one to minimize the use of block cranes in handling the containers that must wait at the terminal until their next means of transportation. We propose a new Mixed Integer Programming model that not only improves on earlier attempts at this problem, but also better reflects reality. In particular, the proposed model adopts a preference to reshuffle containers in line with a just-in-time concept, as it is assumed that data is more accurate the closer to a container's scheduled departure the time is. Other important improvements include a reduction in the model size, and the ability of the model to consider containers initially at the terminal. In addition, we describe several classes of valid inequalities for this new formulation and present a rolling horizon based heuristic for solving larger instances of the problem. We show that this new formulation drastically outperforms previous attempts at the problem through a direct comparison on instances available in the literature. Furthermore, we also show that the rolling horizon based heuristic can further reduce the solution time on the larger of these instances as well as find acceptable solutions to much bigger, artificially generated, instances.

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“…Another idea is provided by [11], who describe an A* algorithm which can be considered as a blueprint for the more advanced approach described in this paper. The approach by [11] does not include any symmetry breaking or branching rules, and only uses a simple lower bound. Some comments on logical observations leading to a lower bound are provided in [51].…”

Section: Articlementioning

confidence: 99%

“…The first, using integer programming, is proposed in [29] and the second is a basic A* algorithm from [11]. Optimal approaches, unless they can be executed within reasonable times, are often used to quantify the performance of heuristic methods and policies.…”

Section: Rmgc Vehiclementioning

confidence: 99%

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Solving the pre-marshalling problem to optimality with A* and IDA*

2016

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We present a novel solution approach to the container pre-marshalling problem using the A* and IDA* algorithms combined with several novel branching and symmetry breaking rules that significantly increases the number of pre-marshalling instances that can be solved to optimality. A* and IDA* are graph search algorithms that use heuristics combined with a complete graph search to find optimal solutions to problems. The container pre-marshalling problem is a key problem for container terminals seeking to reduce delays of inter-modal container transports. The goal of the container pre-marshalling problem is to find the minimal sequence of container movements to shuffle containers in a set of stacks such that the resulting stacks are arranged according to the time each container must leave the stacks. We evaluate our approach on three well-known datasets of pre-marshalling problem instances, solving over 500 previously unsolved instances to optimality, which is nearly twice as many instances as the current state-of-the-art method solves. e

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Pre-Marshalling Problem: Heuristic solution method and instances generator

Cited by 95 publications

References 10 publications

Search space reduction with multiset for effectively solving the container pre-marshalling problem

Search space reduction with multiset for effectively solving the container pre-marshalling problem

A new approach to the Container Positioning Problem

Solving the pre-marshalling problem to optimality with A* and IDA*

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