Flow Shop Scheduling Using Self Organising Migration Algorithm

This paper presents the application of self organinsing migration algorithm (SOMA) to the scheduling problem of flow shop. Flow shop is regarded as the most widely utilized shop management system. Two different benchmark problems are attempted with good results obatined both in comparison with the optimal and other published heuristics. INTRODUCTION Advanced manufacturing systems often rely on metaheuristics to solve time constrained scheduling problems. This is largly due to the intractable problems commonly presented in such systems. Flow shop scheduling (FSS) can be considered as one of the common manufacturing problems that is regularly realized using optimization techniques (Onwubolu, 2002). The evolution of optimization techniques has been mainly attributed to the increase in complexity of problems encountered. Two branches of heuristics exist: constructive and improvement (Onwubolu and Mutingi 1999). Constructive methods are usually problem dependent (Cambell et al. 1970, Nawaz et al. 1983). Improvement methods are those involving populationbased heuristics, which usually follow a naturally occurring paradigm. Some of these are genetic algorithms (GA), tabu search (TS), neural networks (NN), simulated annealing (SA) and particle swamp optimization (PSO) among others. Self organinsing migration algorithm (SOMA), was presented in Zelinka (2002, 2006), as a novel tool for real optimization problem. This basically implies that SOMA can effectively solve real domain problems involving continuous values. However, a separate branch of optimization problem exits; namely NP hard problems, of which flow shop is an example, which still presents considerable challenge. The aim of this paper is to introduce the first permuatative SOMA heuristic which is then applied to the flow shop scheduling problem. The paper is divided in the following sections: the next section introduces flow shop scheduling, the following section to that presents SOMA and its permutative refinements. This is followed by the results and analysis part and finally the work is concluded. FLOW SHOP SCHEDULING In many manufacturing and assembly facilities a number of operations have to be done on every job. Often, these operations have to be done on all jobs in the same order, which implies that the jobs have to follow the same route. The machines are assumed to be set up and the environment is referred to as flow shop (Pinedo 1995). The flow shop can be formatted generally by the sequencing on n jobs on m machines under the precedence condition. The general constraints that are assessed for a flow shop system is the time required to finish all jobs or makespan, minimizing of average flow time, and the maximizing the number of tardy jobs. The minimization of completion time for a flow shop schedule is equivalent to minimizing the objective function ! " = Cm, j j=1 n # (1)