On the investigation of the large-scale grouping constrained storage location assignment problem

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@PhdThesis{Jing_Xie:thesis,

• author = "Jing Xie",
• title = "On the investigation of the large-scale grouping constrained storage location assignment problem",
• school = "School of Science, College of Science, Engineering and Health, RMIT University",
• year = "2017",
• address = "Australia",
• month = aug,
• keywords = "genetic algorithms, genetic programming",
• URL = "https://researchbank.rmit.edu.au/view/rmit:162142",
• URL = "https://researchbank.rmit.edu.au/eserv/rmit:162142/Xie.pdf",
• size = "235 pages",
• abstract = "The primary focus of this study is a novel optimisation problem, namely Storage Location Assignment Problem with Grouping Constraint (SLAP-GC). The problem stems from real-world applications and is significant in theoretical values and applicability in resource allocation tasks where groupings must be considered. The aim of this problem is to minimize the total operational cost in a warehouse through stock rearrangement. The problem consists of two interdependent subproblems, grouping same product items and assigning items to minimize picking distance. The interactions between these two subproblems make this problem significantly different from previous Storage Location Assignment Problems (SLAP), a well-studied field in logistics. Existing approaches for SLAP are not directly applicable for SLAP-GC. This dissertation lays a foundation for research on grouping constraints and other optimisation problems with similar interactions between subproblems. Firstly this study presents a formal definition of SLAP-GC. Then it offers a formal proof of NP-completeness of SLAP-GC by reducing from a well-known 3-Partition problem to SLAP-GC. This suggests that the real-world instances of SLAP-GC should not be tackled with exact approaches, but with approximation and heuristic approaches. Then, we explored decomposition and modelling techniques for SLAP-GC and developed three types of promising heuristic approaches: a hyperheuristic approach, a metaheuristic approach and a matheuristic approach. Comprehensive experimental studies are conducted on both synthetic benchmark instances and real-world instances to examine their efficiency, efficacy, and scalability. Through the analysis of the experimental results, the suitability of proposed methods is verified on various SLAP-GC scenarios. In addition, we demonstrate in this study that with the proposed decomposition, large-scale SLAP-GC can be handled efficiently by the three proposed heuristic-based approaches.",

}

Genetic Programming entries for Jing Xie

Citations