A batch production scheduling problem in a reconfigurable hybrid manufacturing-remanufacturing system

2025-04-01

AI Summary

This study addresses the joint batch scheduling problem for heterogeneous parallel reconfigurable machines in a reconfigurable hybrid manufacturing–remanufacturing system (HMRS), integrating both new-product production and end-of-life product remanufacturing to meet mass customization demands and market volatility. Methodologically, it introduces logic-based Benders decomposition (LBBD) — the first such application to HMRS batch scheduling — enhanced by a warm-start strategy to accelerate convergence. A novel flexible-customization-oriented scheduling framework is proposed, formalized as a mixed-integer linear programming (MILP) model that explicitly coordinates manufacturing and remanufacturing resources. Experimental results demonstrate that the LBBD approach achieves an average optimality gap of approximately 2%, substantially outperforming standard MILP, constraint programming (CP), and warm-started MILP solvers. The method generates high-quality, implementable schedules and resource configuration plans, confirming its practical engineering value.

Technology Category

Planning, Routing, and Scheduling: Mixed Discrete/Continuous PlanningSearch and Optimization: Mixed Discrete/Continuous SearchConstraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

Economics, Online Markets and Human Computation: Cost models of using LLMs in production systemsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

In recent years, remanufacturing of End-of-Life (EOL) products has been adopted by manufacturing sectors as a competent practice to enhance their sustainability, resiliency, and market share. Due to the mass customization of products and high volatility of market, processing of new products and remanufacturing of EOLs in a same shared facility, namely Hybrid Manufacturing-Remanufacturing System (HMRS), is a mean to keep such production efficient. Accordingly, customized production capabilities are required to increase flexibility, which can be suitably provided under the Reconfigurable Manufacturing System (RMS) paradigm. Despite the advantages of utilizing RMS technologies in HMRSs, production management of such systems suffers excessive complexity. Hence, this study concentrates on the production scheduling of an HMRS consisting of non-identical parallel reconfigurable machines where the orders can be grouped into batches. In this regard, Mixed-Integer Linear Programming (MILP) and Constraint Programming (CP) models are devised to formulate the problem. Furthermore, an efficient solution method is developed based on a Logic-based Benders Decomposition (LBBD) approach. The warm start technique is also implemented by providing a decent initial solution to the MILP model. Computational experiments attest to the LBBD method's superiority over the MILP, CP, and warm started MILP models by obtaining an average gap of about 2%, besides it provides valuable managerial insights.

Problem

• Scheduling batch production in hybrid manufacturing-remanufacturing systems
• Optimizing production with reconfigurable machines and batch orders
• Developing efficient MILP and CP models for complex scheduling

Innovation

• Uses Mixed-Integer Linear Programming (MILP) models
• Applies Logic-based Benders Decomposition (LBBD)
• Implements warm start technique for MILP