Yanghua Pan , Shanshan Li , Ting Qu , Liqiang Ding , Naiqi Wu , George Q. Huang
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Dynamic integrated optimization of batching and routing in narrow-aisle order picking systems with congestion consideration
The e-commerce industry has driven logistics centers to process massive and time sensitive orders more efficiently and effectively. However, the high land and construction costs have forced the warehouse to adopt a narrow channel layout to increase its capacity, which in turn has caused congestion and further reduced its operational efficiency. Therefore, how to carry out optimization of order batching and picking paths in narrow channels to avoid channel congestion and pursue a lowest total cost has become the problem that this article aims to solve. This article develops a Markov decision process model to address the online order multi-period picking planning optimization challenge, and proposes the Genetic Algorithm-Ratliff Rosenthal-Time Weighted Similarity (GA-RR-MTWS) algorithm which innovatively integrates congestion considerations into the optimization process. A myopic cost function approximation strategy is introduced aiming at minimizing the total cost of a whole working day. Comparative experimental analysis demonstrates the modified cost function GA-RR-MTWS's superior performance in reducing total picking cost and congestion, particularly in complex, multi-aisle environments with multiple pickers. The method's ability to manage congestion and optimize routing significantly improves overall warehouse efficiency.
期刊介绍:
Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.