Distributed Flexible Assembly Job Shop Scheduling with Collaborative Production and Delivery: Strategy Analysis and a Path-Relinking-Based Solution Method

This paper addresses a collaborative scheduling problem in distributed processing and assembly factories with multiple locations. With the objectives of minimizing the makespan, total tardiness and transportation cost, a distributed flexible assembly job shop scheduling problem with transportation is formulated, and a solution method is developed by adapting the greedy randomized adaptive search procedure, the path relinking mechanism and variable neighborhood descent to a decomposition framework. Its problem-specific elements are a three-segment encoding that represents machine flexibility and operation sequence flexibility, a topological decoding rule that guarantees precedence feasibility without repair, a factory-consistency repair that enforces the transportation constraints inside the decoder, and a whole-job factory-reassignment neighborhood that is the only move able to act directly on transportation cost. Experiments on 42 instances derived from a real case, including a factorial design of 27 instances over order size, factory network and cost structure, show that the proposed method attains a significantly higher hypervolume than MOEA/D and a deep-Q-network-based memetic algorithm. The strategy comparison shows that collaboration mainly acts on logistics: relative to independent production, collaborative production with transportation constraints reduces transportation cost by about 12% while changing the makespan by less than 1%, and the saving doubles when inter-factory transfers are expensive but falls to 2% when delivery costs dominate. Forbidding cross-regional movement of semi-finished jobs never degrades schedule quality relative to unrestricted collaboration and improves it on 36 of the 42 instances, whereas for the largest order books independent production yields better makespan and tardiness within the same computing budget.