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A hybrid discrete state transition algorithm for combinatorial optimization problems

FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Hu, Enze He, Jianjun Shen, Shuai Cent South Univ Sch Automation Changsha Peoples R China

The discrete state transition algorithm (DSTA) has been wildly applied to deal with combinatorial optimization problems. However, its low convergence accuracy limits its application in large-scale optimization problems. Aiming at the convergence performance and search intensity of the algorithm, a hybrid discrete state transition algorithm (HDSTA) is proposed in this work by introducing tabu search and elite solution set. Firstly, a searching mechanism with the integration of DSTA and tabu search (TS) is established, which allows moving to adjacent solutions at an increased cost to escape from the local optimum. Specifically, a tabu list as adaptive memory is adopted to avoid the loop when deviating from local optima. Secondly, an elite solution set is introduced to integrate the information of the previous optimal solution and the global optimal solution, and the search strategy is modified to expand the range and diversity of candidate solutions. Finally, the proposed HDSTA is verified according to the real data on two well-known optimization problems (staff assignment problem and traveling salesman problem) and the real data of an industrial case. The experimental results show the effectiveness of the proposed algorithm in large-scale optimization problems.

关键词： hybrid discrete state transition algorithm tabu search combinatorial optimization problems elite solution set multi-AGV task assignment problem

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A dynamic integrated scheduling method based on hierarchical planning for heterogeneous AGV fleets in warehouses

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FRONTIERS IN NEUROROBOTICS 2023年 16卷 1053067页

作者： Hu, Enze He, Jianjun Shen, Shuai Cent South Univ Sch Automat Changsha Peoples R China

In modern industrial warehouses, heterogeneous and flexible fleets of automated guided vehicles (AGVs) are widely used to improve transport efficiency. However, as their scale and limit of battery capacity increase, the complexity of dynamic scheduling also increases dramatically. The problem is to assign tasks and determine detailed paths to AGVs to keep the multi-AGV system running efficiently and sustainedly. In this context, a mixed-integer linear programming (MILP) model is formulated. A hierarchical planning method is used, which decomposes the integrated problem into two levels: the upper-level task-assignment problem and the lower-level path-planning problem. A hybrid discrete state transition algorithm (HDSTA) based on an elite solution set and the Tabu List method is proposed to solve the dynamic scheduling problem to minimize the sum of the costs of requests and the tardiness costs of conflicts for the overall system. The efficacy of our method is investigated by computational experiments using real-world data.

关键词： automated guided vehicles dynamic integrated scheduling task assignment path planning hierarchical planning hybrid discrete state transition algorithm

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