Solving Distributed and Flexible Job shop Scheduling Problem using a Chemical Reaction Optimization metaheuristic

Abstract

The current industrial production environment is characterized by highly competitive markets, where customer requirements and expectations are becoming stronger in terms of quality, cost and time to delivery. The modern production systems are generally composed of machines that must participate in the manufacture of several types of products simultaneously and efficiently. The Flexible Job shop Scheduling Problem (FJSP) is one of the scheduling problems where each operation can be processed on different machine and its processing time depends on the used machine and this in a single factory. But in the recent years, many companies decide to move towards the decentralization of their factories which allows them to gain advantages towards their customers. So, we are now interested in Distributed and Flexible Job shop Scheduling Problem (DFJSP) where there is a set of geographically distributed factories in different locations. Each factory contains m machines on which n jobs must be processed. The Distributed scheduling problems and more specifically the DFJSP are much more complicated than standard problems because they involve not only the problem of assigning jobs to machines but also the problem of distribution of jobs in different factories. So, the DFJSP is harder than the FJSP. The DFJSP is classified, as most of scheduling problems, NP-Hard in complexity theory. In this paper, we propose a Chemical Reaction Optimization metaheuristic to solve the Distributed and Flexible Job shop Scheduling Problem in order to minimize the maximum completion time (makespan) among all factories. To evaluate the performance of our algorithm, a set of experiments are performed on well known benchmark instances in the literature.