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An effective multi-objective evolutionary algorithm for solving the AGV scheduling problem with pickup and delivery
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DOI：10.1016/j.knosys.2021.106881.png)
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Abstract
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This paper investigates a new automatic guided vehicle scheduling problem with pickup and delivery from the goods handling process in a matrix manufacturing workshop with multi-variety and small-batch production. The problem aims to determine a solution that maximizes customer satisfaction while minimizing distribution cost. For this purpose, a multi-objective mixed-integer linear programming model is first formulated. Then an effective multi-objective evolutionary algorithm is developed for solving the problem. In the algorithm, a constructive heuristic is presented and incorporated into the population initialization. A multi-objective local search based on an ideal-point is used to enforce the exploitation capability. A novel two-point crossover operator is designed to make full use of valuable information collected in the non-dominated solutions. A restart strategy is proposed to avoid the algorithm trapping into a local optimum. At last, a series of comparative experiments are implemented based on a number of real-world instances from an electronic equipment manufacturing enterprise. The results show that the proposed algorithm has a significantly better performance than the existing multi-objective algorithms for solving the problem under consideration. (c) 2021 Elsevier B.V. All rights reserved.
Keywords:
Matrix manufacturing workshop
Automated guided vehicles
Scheduling
Pickup and delivery
Multi-objective evolutionary algorithm
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