Constructive and improvement flow shop scheduling heuristics: An extensive evaluation

A number of optimization techniques are available to solve the scheduling problems. Most of the scheduling problems are combinatorial optimization problems which are too difficult to be solved optimally, and hence heuristics are used to obtain 'good' solutions in reasonable time. Scheduling deals with the allocation of resources. Scheduling is a decision-making process that has, as a goal, the optimization of two or more objectives. The specific goal of this paper is to perform a comparative evaluation of the exisiting constructive heuristic algorithms and to propose an improvement heuristic algorithm. The improvement heuristic algorithm proposed is a genetic algorithm. The benchmark problems are used to evaluate these algorithms. The genetic algorithm (GA) is a potentially powerful tool for solving complex optimization problems. In this paper, the heuristic procedures considered for evaluation are Palmer's algorithm, Gupta's algorithm, C D S algorithm, Dannenbring's R A algorithm, N E H algorithm and the proposed genetic algorithm. The algorithms are coded in G language. The performance measure considered for evaluation is the makespan.