Integrated production and delivery with single machine and multiple vehicles

•We consider multi-objective scheduling with a single machine and multiple vehicles.•The goal is to minimize vehicle delivery and total customer waiting time.•We propose a PD-NSGA-II algorithm for this NP-hard problem.•The performance of the algorithm is tested through random data.•It is shown that...

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Veröffentlicht in:Expert systems with applications 2016-09, Vol.57, p.12-20
Hauptverfasser: Li, Kai, Zhou, Chao, Leung, Joseph Y-T., Ma, Ying
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container_title Expert systems with applications
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creator Li, Kai
Zhou, Chao
Leung, Joseph Y-T.
Ma, Ying
description •We consider multi-objective scheduling with a single machine and multiple vehicles.•The goal is to minimize vehicle delivery and total customer waiting time.•We propose a PD-NSGA-II algorithm for this NP-hard problem.•The performance of the algorithm is tested through random data.•It is shown that the algorithm can offer high-quality solutions in reasonable time. This paper considers a class of multi-objective production–distribution scheduling problem with a single machine and multiple vehicles. The objective is to minimize the vehicle delivery cost and the total customer waiting time. It is assumed that the manufacturer’s production department has a single machine to process orders. The distribution department has multiple vehicles to deliver multiple orders to multiple customers after the orders have been processed. Since each delivery involves multiple customers, it involves a vehicle routing problem. Most previous research work attempts at tackling this problem focus on single-objective optimization system. This paper builds a multi-objective mathematical model for the problem. Through deep analysis, this paper proposes that for each non-dominated solution in the Pareto solution set, the orders in the same delivery batch are processed contiguously and their processing order is immaterial. Thus we can view the orders in the same delivery batch as a block. The blocks should be processed in ascending order of the values of their average workload. All the analysis results are embedded into a non-dominated genetic algorithm with the elite strategy (PD-NSGA-II). The performance of the algorithm is tested through random data. It is shown that the proposed algorithm can offer high-quality solutions in reasonable time.
doi_str_mv 10.1016/j.eswa.2016.02.033
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source Elsevier ScienceDirect Journals
subjects Algorithms
Blocking
Construction
Delivery scheduling
Expert systems
Mathematical models
Pareto optimization
PD-NSGA-II algorithm
Production–distribution scheduling
Strategy
Vehicle routing
Vehicles
title Integrated production and delivery with single machine and multiple vehicles
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