Automated design of heuristics for the container relocation problem using genetic programming

Đurasević, Marko; Đumić, Mateja

doi:10.1016/j.asoc.2022.109696

Cited by 16 publications

(4 citation statements)

References 42 publications

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“…We also intended to extend the considered problem to include additional constraints, such as using a fleet of electric vehicles that is becoming more prominent, and adapt the algorithm to efficiently solve such problems as well. Finally, it should be noted that truck scheduling between ports is not an isolated problem, but rather is closely connected to other problems encountered in container yard terminals [34][35][36] and it is intended to consider more realistic scenarios that consider solving several of such problems jointly [37].…”

Section: Discussionmentioning

confidence: 99%

A Variable Neighborhood Search Method with a Tabu List and Local Search for Optimizing Routing in Trucks in Maritime Ports

Matijević,

Đurasević,

Jakobović

2023

Mathematics

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Logistics problems represent an important class of real-world problems where even small improvements in solution quality can lead to significant decreases in operational costs. However, these problems are usually NP-hard; thus, they are mostly solved using metaheuristic methods. To improve their performance, there is substantial research on crafting new and refined metaheuristics to derive superior solutions. This paper considers a truck routing problem within a naval port, where the objective is to minimize the total distance traveled by all the vehicles to distribute a given set of containers. Due to the large volume of goods that are being transferred through ports, it is imperative to improve the operation times at such ports to improve the throughput. To achieve this goal, a novel variable neighborhood search method that integrates a tabu list, an iterative local search procedure, and parallelization of neighborhood generation is proposed and evaluated. The experimental results demonstrate that the proposed method achieves similar results to the state of the art, but in a smaller amount of time.

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Section: Discussionmentioning

confidence: 99%

A Variable Neighborhood Search Method with a Tabu List and Local Search for Optimizing Routing in Trucks in Maritime Ports

Matijević,

Đurasević,

Jakobović

2023

Mathematics

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“…In order to improve system throughput, in [18], the authors analyze dual-tray vertical lift modules (VLMs), which are developed based on mini-load AS/RS and single-tray VLM, and the model's result gives guidelines for throughput estimation by designers and managers of warehousing systems. In [19], the authors deal with the container relocation problem using genetic programming (GP) and open a new research direction for the study of container relocation rules (RRs).…”

Section: Storage and Retrieval Operationsmentioning

confidence: 99%

A Novel Parts-to-Picker System with Buffer Racks and Access Racks in Flexible Warehousing Systems

He,

Guan,

Hou

et al. 2024

Sustainability

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With the tremendous development of the logistics industry, the global market of automated warehousing has been growing rapidly. Meanwhile, the warehousing industry shows drawbacks, such as low storage capacity and poor efficiency. By comparing and analyzing the shuttle-based storage and retrieval system (SBS/RS), miniload automated storage and retrieval system (AS/RS), and KIVA system, a novel efficient parts-to-picker approach in flexible warehousing systems is proposed. Among them, buffer racks and access racks, associated with the access of automated mobile robots (AMRs) and stackers are used. The results show that compared with other parts-to-picker systems (such as the KIVA system), this system provides a significant increase in storage capacity (more than three times), and the picking efficiency is also very high at various layout scales, where the picking efficiency is no less than the KIVA system when the number of AMRs reaches the max. The novel system is suitable for small-, medium-, and large-scale warehouses in terms of showing high capacity and producing excellent space utilization. More importantly, this system can easily compete with its traditional counterparts by using a layout of high density without much increase in cost. This sustainable improvement realizes the efficient utilization of spatial resources and provides important support for the construction of green supply chains.

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“…GP has been successfully used as a hyperheuristic method [14,49] to automatically generate heuristics for various types of combinatorial optimisation problems, such as scheduling problems [50,51], the travelling salesman problem [52,53], vehicle and arc routing problems [54][55][56], the container relocation problem [57], and the cutting stock problem [15,58]. When used to generate DRs for scheduling problems, GP has the task of finding a suitable PF that can be used by the DR to evaluate scheduling decisions.…”

Section: Algorithmmentioning

confidence: 99%

Assessing the Ability of Genetic Programming for Feature Selection in Constructing Dispatching Rules for Unrelated Machine Environments

Đurasević,

Jakobović,

Picek

et al. 2024

Algorithms

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The automated design of dispatching rules (DRs) with genetic programming (GP) has become an important research direction in recent years. One of the most important decisions in applying GP to generate DRs is determining the features of the scheduling problem to be used during the evolution process. Unfortunately, there are no clear rules or guidelines for the design or selection of such features, and often the features are simply defined without investigating their influence on the performance of the algorithm. However, the performance of GP can depend significantly on the features provided to it, and a poor or inadequate selection of features for a given problem can result in the algorithm performing poorly. In this study, we examine in detail the features that GP should use when developing DRs for unrelated machine scheduling problems. Different types of features are investigated, and the best combination of these features is determined using two selection methods. The obtained results show that the design and selection of appropriate features are crucial for GP, as they improve the results by about 7% when only the simplest terminal nodes are used without selection. In addition, the results show that it is not possible to outperform more sophisticated manually designed DRs when only the simplest problem features are used as terminal nodes. This shows how important it is to design appropriate composite terminal nodes to produce high-quality DRs.

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Automated design of heuristics for the container relocation problem using genetic programming

Cited by 16 publications

References 42 publications

A Variable Neighborhood Search Method with a Tabu List and Local Search for Optimizing Routing in Trucks in Maritime Ports

A Variable Neighborhood Search Method with a Tabu List and Local Search for Optimizing Routing in Trucks in Maritime Ports

A Novel Parts-to-Picker System with Buffer Racks and Access Racks in Flexible Warehousing Systems

Assessing the Ability of Genetic Programming for Feature Selection in Constructing Dispatching Rules for Unrelated Machine Environments

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