Solving a Batching-Scheduling Problem on a Multi-Operational Parallel Machine Using Two Meta Heuristic Algorithms

This paper tackles the problem of batching scheduling on a multi-operational parallel machine with precedence constraint. Based on the literature, batching accelerates the production process and decreases the transportation costs. Therefore, it is mentioned as a very important subject in scheduling. On the other hand, most of researches consider the processing time as a predefined and constant parameter that is not reasonable in real industries.In this paper a mathematical model is presented in order to minimize the makespan in which the processing times of jobs are considered as a function of their batch size. The proposed model is known to be NP-hard, therefore two Mehta-heuristic algorithms including genetic algorithm (GA) and simulated annealing (SA) are offered so as to find near optimal solutions in reasonable run time. In order to check the verification of proposed model, the problem is solved optimally for small scales and the results are compared with the solutions of GA and SA. Computational study demonstrates that the proposed methods can found the solutions with very low gap in a suitable run time.