The heterogeneous multi-factory production network scheduling with adaptive communication policy and parallel machine

Traditionally centralized manufacturing planning, scheduling, and control mechanisms are being found to be insufficiently flexible to respond to highly dynamic variations in the market requirements. In order to be competitive in today’s rapidly changing business world, organizations have shifted from a centralized to a decentralized structure in many areas of decision making. Distributed scheduling is an approach that enables local decision makers to create schedules that consider local objectives and constraints within the boundaries of the overall system objectives. In this paper, we assumed that production takes place in several factories, which may be geographically distributed in different locations, in order to take advantage from the trend of globalization. In this approach, the factories that are available to process the jobs have different speeds in which each factory has parallel identical machine. The optimization criterion is the minimization of the maximum completion time or makespan among the factories. After proposing mixed integer linear programming model for the problem, we developed a heuristic and genetic algorithm. For the proposed genetic algorithm, at first, to represent the solutions, we suggested a new encoding scheme, and then proposed a local search based on the theorem developed in the paper. Finally, we compare the obtained solutions using the lower bound developed in this paper. The results show the proposed algorithms to be very efficient for different structures of instances.