An effective multi-objective evolutionary algorithm for solving the AGV scheduling problem with pickup and delivery

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 min...

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Published in:Knowledge-based systems Vol. 218; p. 106881
Main Authors: Zou, Wen-Qiang, Pan, Quan-Ke, Wang, Ling
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 22.04.2021
Elsevier Science Ltd
Subjects:
Algorithms
Automated guided vehicles
Batch production
Client satisfaction
Crossovers
Customer satisfaction
Distribution costs
Electronic equipment
Evolutionary algorithms
Experiments
Exploitation
Genetic algorithms
Heuristic
Integer programming
Linear programming
Manufacturing
Matrix manufacturing workshop
Mixed integer
Multi-objective evolutionary algorithm
Objectives
Pickup and delivery
Satisfaction
Scheduling
Matrix manufacturing workshop
Scheduling
Pickup and delivery
Multi-objective evolutionary algorithm
Automated guided vehicles
ISSN:0950-7051, 1872-7409
Online Access:Get full text
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Summary: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.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2021.106881