Scheduling a single vehicle in the just-in-time part supply for a mixed-model assembly line

This paper focuses on the scheduling of a single vehicle, which delivers parts from a storage centre to workstations in a mixed-model assembly line. In order to avoid part shortage and to cut down total inventory holding and travelling costs, the destination workstation, the part quantity and the de...

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Bibliographic Details
Published in:Computers & operations research 2013-11, Vol.40 (11), p.2599-2610
Main Authors: Rao, Yun-Qing, Wang, Meng-Chang, Wang, Kun-Peng, Wu, Tou-Ming
Format: Article
Language:English
Subjects:
Analysis of variance
Applied sciences
Assembly
Assembly lines
Automotive components
Deliveries
Delivery scheduling
Design of experiments
Exact sciences and technology
Experimental design
GASA
Genetic algorithms
Inventory control, production control. Distribution
Just in time
Mathematical models
Mathematics
Mixed-model assembly line
Operational research and scientific management
Operational research. Management science
Part supply
Probability and statistics
Scheduling
Scheduling, sequencing
Sciences and techniques of general use
Statistics
Studies
Transportation problem (Operations research)
Variance analysis
Vehicle scheduling
Work stations
Workstations
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Summary:This paper focuses on the scheduling of a single vehicle, which delivers parts from a storage centre to workstations in a mixed-model assembly line. In order to avoid part shortage and to cut down total inventory holding and travelling costs, the destination workstation, the part quantity and the departure time of each delivery have to be specified properly according to predetermined assembly sequences. In this paper, an optimisation model is established for the configuration that only one destination workstation is involved within each delivery. Four specific properties of the problem are deduced, then a backward-backtracking approach and a hybrid GASA (genetic algorithm and simulated annealing) approach are developed based on these properties. Both two approaches are applied to several groups of instances with real-world data, and results show that the GASA approach is efficient even in large instances. Furthermore, the existence of feasible solutions (EOFS) is analysed via instances with different problem settings, which are obtained by an orthodox experimental design (ODE). An analysis of variance (ANOVA) shows that the buffer capacity is the most significant factor influencing the EOFS. Besides this, both the assembly sequence length and distances to workstations also have noticeable impacts.
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2013.05.007