Distributed Scheduling Combined With Traveling Salesman Problem: An Iterated Local Search

This paper addresses a new type of distributed assembly permutation flow-shop scheduling problem, where the single distributed assembly line in the conventional version is replaced by multiple assembly factories. Then within this scheduling framework, we consider two transportation problems. One is the transportation of jobs between origin and the assigned processing factory by obeying the capacity constraint of transporting vehicle, which is formulated as a point to point transportation with capacity constraint. Another is the transportation of processed jobs between the processing factory and one or several assembly factories without violating the capacity constraint of vehicles, which is modeled as multi-site traveling salesman problem with load capacity constraints. We consolidate the scheduling and transporting problems into a distributed permutation flow-shop scheduling combined with multiple assemblies and traveling salesman problem. Meanwhile, we propose an iterated adaptive local search heuristic by incorporating meta-Lamarckian learning mechanism and annealing based acceptance criterion. Simulation results show that the proposed approach is capable to yield satisfactory solutions. To the best of our knowledge, this is the first report to model transporting constraints in distributed assembly permutation flow-shop scheduling problem.