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首页> 外文期刊>European Journal of Operational Research >An adapted ant colony optimization algorithm for the minimization of the travel distance of pickers in manual warehouses
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An adapted ant colony optimization algorithm for the minimization of the travel distance of pickers in manual warehouses

机译：一种适应性蚁群优化算法，用于最小化手动仓库拾取器的行程距离

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This paper proposes a new metaheuristic routing algorithm for the minimization of the travel distance of pickers in manual warehouses. The algorithm is based on the ant colony optimization (ACO) metaheuristic, which, is combined and integrated with the Floyd-Warshall (FW) algorithm, and is therefore referred to as FW-ACO. To assess the performance of the FW-ACO algorithm, two sets of analyses are carried out. Firstly, the capability of the algorithm to provide effective solutions for the picking problem is analyzed as a function of the settings of the main ACO parameters. Secondly, the performance of the FW-ACO algorithm is compared with that of six algorithms typically used to optimize the travel distance of pickers, including exact algorithms for the solution of the travelling salesman problem (where available), two heuristic routing strategies (i.e. S-shape and largest gap) and two metaheuristic algorithms (i.e. the MIN-MAX ant system and Combined+). The comparison is made considering different warehouse layouts and problem complexities. The outcomes obtained suggest that the FW-ACO is a promising algorithm generally able to provide better results than the heuristic and metaheuristic algorithms, and often able to find an exact solution. The FW-ACO algorithm also shows a very efficient computational time, which makes it suitable for defining the route of pickers in real time. The FW-ACO algorithm is finally implemented in a real case study, where constraints exist on the order in which items should be picked, to show its practical usefulness and quantify the resulting savings. (C) 2017 Elsevier B.V. All rights reserved.
机译：本文提出了一种新的成群质路由算法，用于最小化手动仓库中拾取器的行驶距离。该算法基于蚁群优化（ACO）成群化，其与Floyd-Warshall（FW）算法组合并集成，因此被称为FW-ACO。为了评估FW-ACO算法的性能，执行两组分析。首先，分析了算法为拣选问题提供有效解决方案的能力作为主ACO参数的设置的函数。其次，将FW-ACO算法的性能与六种算法的性能进行了比较，用于优化拾取器的行驶距离，包括用于解决旅行推销员问题的精确算法（可用），两个启发式路由策略（即S - 形状和最大间隙）和两个成胸型算法（即MIN-MAX ANT系统和组合+）。考虑到不同的仓库布局和问题复杂性进行比较。所获得的结果表明，FW-ACO通常能够提供比启发式和成群质算法更好的结果，并且通常能够找到精确的解决方案。 FW-ACO算法还示出了非常有效的计算时间，这使得适用于实时定义拾取器的路线。 FW-ACO算法最终在实际研究中实现，其中限制存在于应挑选项目的顺序中，以显示其实际有用性并量化产生的节省。 （c）2017年Elsevier B.V.保留所有权利。

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