Abstract
In this paper we address the problem of simultaneous scheduling of machines and vehicles in flexible manufacturing systems. The studied problem is a job shop where the jobs have to be transported between the machines by automatic guided vehicles. In addition to the processing of jobs, we consider the transportation aspect as an integral part of the optimization process. To deal with this problem, we propose a new solution representation based on vehicles rather than machines. Each solution can thus be evaluated using a discrete event approach. An efficient neighbouring system is then described and implemented into three different metaheuristics: iterated local search, simulated annealing and their hybridisation. Computational results are presented for a benchmark of 40 literature instances. New upper bounds are found for 11 of them, showing the effectiveness of the presented approach.
Highlights
In this paper we address the problem of simultaneous scheduling of machines and vehicles in flexible manufacturing systems
A Flexible Manufacturing System (FMS) can be described as follows: FMS consists of several machine tools along with part and tool handling devices such as robots, arranged so that it can handle any family of parts for which it has been designed and developed
We present first the construction heuristic we have developed, and the metaheuristics according to the Blum and Roli (2003) formulation
Summary
A Flexible Manufacturing System (FMS) can be described as follows: FMS consists of several machine tools along with part and tool handling devices such as robots, arranged so that it can handle any family of parts for which it has been designed and developed. This paper is devoted to the study of the classical job shop problem where transportation times are considered. It is classified as a JR | tkl ,tk′l | Cmax problem according to the α | β | γ –notation introduced by Graham et al (1979). Several authors note the necessity to carefully plan the interactions between process and transportation activities (Raman et al (1986), Ulusoy et al
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