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Energy-aware hybrid flow-shop scheduling problem considering ageing workforce: A Multi-Tasking Metaheuristic
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DOI：10.1016/j.swevo.2026.102480.png)
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Abstract
En 中文
In modern manufacturing system, despite the advancement of automation, human workers still remain an indispensable resource. However, the impacts of the ageing workforce are frequently overlooked in task assignment and resource allocation, leading to suboptimal productivity. Meanwhile, modern manufacturing systems are facing increasing pressure to reduce energy consumption. The workforce characteristics directly influence machine utilization and processing efficiency, thereby affecting overall energy consumption. Therefore, this paper investigates the energy-aware hybrid flow-shop scheduling problem considering ageing workforce (EAHFSP-AW), aiming to minimize makespan and total energy consumption simultaneously. First, a mixed-integer linear programming (MILP) model is formulated to characterize the EAHFSP-AW. To solve the problem efficiently, a Multi-Tasking Metaheuristic (MTM) is proposed. A specialized decoding heuristic based on two dispatching rules is developed. Unlike traditional multi-objective metaheuristics that rely on serial or synchronous structure, the MTM adopts a parallel and asynchronous search framework composed of three search tasks with differentiated optimization responsibilities. Two convergence-oriented auxiliary tasks intensify the search for boundary solutions corresponding to makespan and energy consumption, respectively, while a diversity-oriented main task focuses on exploring well-distributed non-dominated solutions. These tasks interact asynchronously through a shared external archive, where the convergence-oriented tasks continuously contribute promising boundary solutions and the diversity-oriented task exploits such information to search a well-converged and well-distributed non-dominated solution set. Finally, comprehensive computational experiments are conducted. Experimental results validate the effectiveness of the MTM’s architectural design and its superior performance in solving the EAHFSP-AW compared to state-of-the-art algorithms.
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