Search

The scheduling of disassembly lines is of great importance to achieve optimized productivity. In this paper, we address the Hybrid Disassembly Line Balancing Problem that combines linear disassembly lines and U-shaped disassembly lines, considering multi-skilled workers, and targeting profit and carbon emissions. In contrast to common approaches in reinforcement learning that typically employ weighting strategies to solve multi-objective problems, our approach innovatively incorporates non-dominated ranking directly into the reward function. The exploration of Pareto frontier solutions or better solutions is moderated by comparing performance between solutions and dynamically adjusting rewards based on the occurrence of repeated solutions. The experimental results show that the multi-objective Advantage Actor-Critic algorithm based on Pareto optimization exhibits superior performance in terms of metrics superiority in the comparison of six experimental cases of different scales, with an excellent metrics comparison rate of 70%. In some of the experimental cases in this paper, the solutions produced by the multi-objective Advantage Actor-Critic algorithm show some advantages over other popular algorithms such as the Deep Deterministic Policy Gradient Algorithm, the Soft Actor-Critic Algorithm, and the Non-Dominated Sorting Genetic Algorithm II. This further corroborates the effectiveness of our proposed solution. [ABSTRACT FROM AUTHOR]

With the rapid development of the economy and technology, the rate of product replacement has accelerated, resulting in a large number of products being discarded. Disassembly is an important way to recycle waste products, which is also helpful to reduce manufacturing costs and environmental pollution. The combination of a single-row linear disassembly line and a U-shaped disassembly line presents distinctive advantages within various application scenarios. The Hybrid Disassembly Line Balancing Problem (HDLBP) that considers the requirement of multi-skilled workers is addressed in this paper. A mathematical model is established to maximize the recovery profit according to the characteristics of the proposed problem. To facilitate the search for optimal solution, a new strategy for agents in reinforcement learning to interact with complex and changeable environments in real-time is developed, and deep reinforcement learning is used to complete the distribution of multi-products and disassembly tasks. On this basis, we propose a Soft Actor–Critic (SAC) algorithm to effectively address this problem. Compared with the Deep Deterministic Policy Gradient (DDPG) algorithm, Advantage Actor–Critic (A2C) algorithm, and Proximal Policy Optimization (PPO) algorithm, the results show that the SAC can get the approximate optimal result on small-scale cases. The performance of SAC is also better than DDPG, PPO, and A2C in solving large-scale disassembly cases. [ABSTRACT FROM AUTHOR]