Distributed Shop Scheduling Research Articles

A learning-based memetic algorithm for energy-efficient distributed flow-shop scheduling with preventive maintenance

In manufacturing systems, implementing preventive maintenance (PM) is essential for ensuring sustainable production since the inevitable wear and tear of machines can significantly affect production efficiency. In today’s decentralized economy, distributed shop scheduling has emerged within the framework of distributed manufacturing to reduce costs, enhance efficiency, and strengthen competitiveness. Thus, this paper proposes a learning-based memetic algorithm (LMA) for addressing the energy-efficient distributed flow-shop scheduling problem with preventive maintenance (EDFSP-PM) to minimize both makespan and total energy consumption simultaneously. First, a mathematical model is formulated and encoding and decoding methods are developed to map solutions to schedules with consideration of PM operations. Second, two heuristics are employed to generate high-quality solutions and various problem-specific operators are designed for different sub-problems and objectives. Third, a hierarchical learning mechanism is proposed via employing multi-layer Q-learning to select appropriate operators for solutions with diverse characteristics. Fourth, a feedback learning mechanism with solution pool is devised to reintegrate solutions from the pool into the search process to enhance search efficiency. Finally, numerical experiments are conducted to verify the effectiveness of the designed mechanisms. The comparative results demonstrate superior performance of the proposed LMA in terms of convergence and diversity.

Effective constructive heuristics for distributed no-wait flexible flow shop scheduling problem

The distributed shop scheduling has gained a great deal of attention due to its high-efficient collaboration and production. This paper studies a distributed no-wait flexible flow shop scheduling problem (DNWFFSP) with the makespan criterion, where each factory includes an identical flexible flow shop with no-wait constraint that requires each job to be processed without any waiting time between consecutive stages. A mixed-integer linear programming model for the DNWFFSP is presented. A machine selection method based on the first earliest available machine rule is presented. Three greedy factory assignment rules incorporating NEH (Nawaz-Enscore-Ham) heuristic are developed to allocate jobs into factories. Fourteen dispatch rules involving simple sorting and decomposition method are developed to construct a priority sequence of jobs. Thus, several constructive heuristics are obtained by combining dispatch rules and factory assignment rules. Furthermore, a constructive heuristic version of variable neighborhood descend is designed to address the DNWFFSP. One of constructive heuristics is selected to generate an initial solution, and five neighborhood search methods are used as the local search procedure. A comprehensive numerical comparison and statistical analysis are carried out to investigate the performance of the proposed approaches. The distributed NEH2 with smallest largest-medium rule obtains better results than other constructive heuristics for large-size instances.