Order Driven Disassemby Worker Scheduling for Multi-Factory Remanufacturing Optimization

Abstract

Multi-factory remanufacturing systems often operate under practical constraints, including heterogeneous task
requirements and limited availability of skilled labor, where
factory and workstation operations cannot be assumed to be
continuously active. In such environments, worker availability
and order demand jointly determine factory activation, workstation utilization, and task allocation, leading to tightly coupled
decisions across multiple resource levels. Effectively coordinating
these interdependent factors is critical for improving operational
efficiency in distributed remanufacturing networks. This study
investigates a multi-factory remanufacturing problem that jointly
optimizes order allocation, worker assignment, and factory activation decisions under labor constraints. A mixed-integer linear
programming model is developed to capture the interactions
among factories, workstations, and workers while considering
disassembly task structures derived from order requirements.
To solve the resulting complex optimization problem, the Alpha
Evolution algorithm is employed and compared with several
representative metaheuristic approaches, including the Improved
Beluga Whale Reproductive Optimization, Coati Population Algorithm, Fruit Fly Optimization Algorithm, Dingo Optimization
Algorithm, and Modified Dung Beetle Mating Optimization. Experimental results demonstrate that coordinated resource activation can significantly enhance labor utilization and overall system
performance. The proposed Alpha Evolution algorithm approach
achieves competitive or superior performance in solution quality
and stability across multiple test scenarios.