A beam search approach to the container loading problem

Araya, Ignacio; Riff, María Cristina

doi:10.1016/j.cor.2013.09.003

Cited by 54 publications

(20 citation statements)

References 21 publications

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“…Given a container and a set of boxes, the container loading problem aims to determine the load position and place rotation in the container in order to maximize the space utilization with basic geometric constraints [32]. This paper addresses the problem of loading a single container with the following assumptions [28,33,34]: Our model uses a three-dimensional Cartesian coordinate system, as shown in Figure 1.…”

Section: Problem Statementmentioning

confidence: 99%

Optimization of Heterogeneous Container Loading Problem with Adaptive Genetic Algorithm

Xia

et al. 2018

Complexity

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This paper studies an optimized container loading problem with the goal of maximizing the 3D space utilization. Based on the characteristics of the mathematical loading model, we develop a dedicated placement heuristic integrated with a novel dynamic space division method, which enables the design of the adaptive genetic algorithm in order to maximize the loading space utilization. We use both weakly and strongly heterogeneous loading data to test the proposed algorithm. By choosing 15 classic sets of test data given by Loh and Nee as weakly heterogeneous data, the average space utilization of our algorithm reaching 70.62% outperforms those of 13 algorithms from the related literature. Taking a set of test data given by George and Robinson as strongly heterogeneous data, the space utilization in this paper can be improved by 4.42% in comparison with their heuristic algorithm.

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Section: Problem Statementmentioning

confidence: 99%

Optimization of Heterogeneous Container Loading Problem with Adaptive Genetic Algorithm

Xia

et al. 2018

Complexity

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“…RBS uses local search on partial solutions to recover from previously made poor choices. The interested reader is referred to Araya and Riff [2], Baldi et al [3], Birgin et al [4], Blum and Miralles [5], and the references therein for examples of recent beam search implementations.…”

Section: Iterated Beam Searchmentioning

confidence: 99%

An iterated beam search algorithm for the green vehicle routing problem

Yavuz

2017

Networks

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Green, or, alternative-fuel vehicles provide opportunities to improve fleet operators' financial and environmental bottom lines. They also present challenges due to limited driving ranges, limited refueling infrastructure, and lengthy refueling times. This paper addresses a green vehicle routing problem of homogeneous service fleets with stated challenges, and develops an Iterated Beam Search algorithm for its solution. The developed algorithm can employ different lower and upper bounding strategies, and can work as an exact or a heuristics algorithm. A dominance strategy is also developed to improve efficiency of the algorithm. Computational experiments on various-size test instances show that the algorithm's performance is comparable to existing approaches.

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“…Gonçalves and Resende (2012) published a genetic algorithm. Zhu and Lim (2012) suggested a Greedy-Lookahead algorithm and Araya and Riff (2014) proposed a beam search approach.…”

Section: Literature Reviewmentioning

confidence: 99%

A two‐stage packing problem procedure

Moura

Bortfeldt

2016

Int Trans Operational Res

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This paper deals with a two-stage packing problem that has to be solved in the daily distribution process of a Portuguese trading company. In the first stage, boxes including goods are to be packed on pallets, while in the second stage these pallets are loaded into one or more trucks. The boxes have to be transported to different customers, and the main goal is to guarantee a sufficient utilization of the truck loading space. A two-stage packing procedure is proposed to cover both problem stages. First, boxes are loaded onto pallets by means of a well-known container loading algorithm. Then, trucks are filled with loaded pallets by a tree search algorithm. The performance of the two-stage approach was evaluated using a set of instances that are based on actual company data.

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A beam search approach to the container loading problem

Cited by 54 publications

References 21 publications

Optimization of Heterogeneous Container Loading Problem with Adaptive Genetic Algorithm

Optimization of Heterogeneous Container Loading Problem with Adaptive Genetic Algorithm

An iterated beam search algorithm for the green vehicle routing problem

A two‐stage packing problem procedure

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