Optimized Distributed Job Shop Scheduling Using Balanced Job Allocation and Modified Ant Colony Optimization

Abstract

Many challenges are being faced by the manufacturing industry: ensuring profitable growth, reducing costs, increasing productivity, and giving quick responses to customers. To become more productive, reduce transportation costs, and reduce bottleneck on a single factory, industrial companies are shifting from single to distributed systems. Scheduling problems like distributed job shop, distributed flow shop, and distributed process planning are becoming a popular field to study. We try to solve the distributed job shop scheduling problem (DJSP) where the allocation of jobs to different factories needs to be done and additionally, the determination of good operation schedules for each factory. The goal of DJSP is to minimize the makespan over all the factories. To solve this problem, we first use a method of allocating jobs to factories to evenly distribute the workloads among all the factories. Later, we use a bio-inspired algorithm on each factory after the allocations, namely ant colony optimization to get a solution that is close to the most optimal solution. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.