Stochastic Scheduling and Quality Inspection in Remanufacturing Systems

Abstract

This research provides an in-depth examination of stochastic scheduling and quality inspection within remanufacturing systems, a critical area in sustainable manufacturing that encompasses disassembly, reprocessing, and reassembly stages. The study begins with an overview of recoverable areas, tracing the material flow from disassembly shops through reprocessing shops to reassembly shops, establishing the foundational framework for subsequent analyses. A comprehensive exploration of scheduling policies and constraints is undertaken, including routing strategies such as random routes, shortest queue, round-robin, and reverse flows; inventory management practices covering work-in-progress, initial, final, and spare parts storage with associated replenishment policies; and resource allocation strategies involving parallel and serial facilities, expert capacities, and retrieving policies. Quality inspection processes are meticulously addressed through the assignment and scheduling of parts and experts, ensuring rigorous quality control throughout the remanufacturing cycle. Performance analysis is conducted using advanced simulation techniques, evaluating a broad spectrum of metrics such as simulation time, waiting time, resource utilization, inventory levels, replenishment frequency, queue lengths, and machine assignment, sequencing, and scheduling bottlenecks. Visual representations, including Gantt charts and utilization graphs, enhance the interpretation of system dynamics and resource efficiency. The research proposes two configuration topologies: one featuring a bank of serial machines with intermediate buffers to optimize flow and minimize waiting times, and another utilizing a pool of parallel machines and a team of experts/technicians to improve flexibility and throughput. Statistical analysis and visualization techniques are employed to support the findings, leveraging computational tools for process modeling. Future research directions focus on the development and application of advanced optimization techniques to enhance scheduling efficiency and the exploration of predictive and management strategies for reverse remanufacturing supply chains, contributing to the advancement of sustainable industrial practices.