Dongjun Cai, Xi Xiang, Loo Hay Lee, Ek Peng Chew, Kok Choon Tan
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引用次数: 0
Abstract
AbstractA robotic compact storage and retrieval system (RCSRS) is a grid-based system that has been popularly implemented by online retailers around the world recently. This article addresses a robot dispatching problem for RCSRS, aiming to reduce robot empty travelling time and workstation idle time. The robot dispatching problem is to assign tasks to robots when the task sequences in workstations are given. Additionally, the robots follow a fast dual-command transaction process in this problem. Hence, a mixed integer programming model catering for the dual-command transaction process is formulated to optimize system performance. Furthermore, an adaptive neighbourhood search with an ε-greedy algorithm is proposed to solve the problem. The experimental results substantiate the superiority of the authors' proposed algorithm compared to various algorithms used in practice and discussed in the relevant literature. Moreover, optimal system configurations for the instances generated are suggested.Keywords: Compact storage systemrobot dispatchingdual-commandadaptive neighbourhood search AcknowledgementsDongjun Cai thanks his industrial supervisor, Dr Ng Ging Meng, who provided supportive suggestions and the resources needed to carry out the work. They also express immense gratitude to their research supervisor and mentor, the late Professor Loo Hay Lee, for providing patient guidance, advice and encouragement throughout this study.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article. No new data were created or analysed in this study.Additional informationFundingXi Xiang is supported by the National Natural Science Foundation of China [Grant No. 72301029]. Dongjun Cai is supported by the funding of the Centre of Excellence in Modelling and Simulation for Next Generation Ports [Grant No. 3030012222341].
期刊介绍:
Engineering Optimization is an interdisciplinary engineering journal which serves the large technical community concerned with quantitative computational methods of optimization, and their application to engineering planning, design, manufacture and operational processes. The policy of the journal treats optimization as any formalized numerical process for improvement. Algorithms for numerical optimization are therefore mainstream for the journal, but equally welcome are papers which use the methods of operations research, decision support, statistical decision theory, systems theory, logical inference, knowledge-based systems, artificial intelligence, information theory and processing, and all methods which can be used in the quantitative modelling of the decision-making process.
Innovation in optimization is an essential attribute of all papers but engineering applicability is equally vital. Engineering Optimization aims to cover all disciplines within the engineering community though its main focus is in the areas of environmental, civil, mechanical, aerospace and manufacturing engineering. Papers on both research aspects and practical industrial implementations are welcomed.