Product recovery has received greater attention in recent years mainly due to increased environmental awareness of consumers and stricter environmental regulations imposed by governments. In product recovery, disassembly of the product into its constituent parts is the most significant activity and generally performed on a disassembly line. During disassembly, a complete or partial disassembly of the product may be preferred. In complete disassembly, all parts must be disassembled, while partial disassembly allows to disassemble a subset of parts (e.g., the ones with relatively high revenues). This study deals with a partial disassembly line balancing and sequencing (PDLBS) problem considering revenues of parts to be disassembled, general workstation cost, additional cost of workstation(s) with hazardous parts, and cost of direction changes. For the PDLBS problem, a generic mixed integer programming (MIP) model, with the aim of maximizing total profit, is developed. To strengthen the MIP formulation, two sets of valid inequalities are proposed. The computational results show that the MIP model with valid inequalities is able to provide optimal solutions for the PDLBS problems with up to 30 tasks. To obtain near-optimal solutions for large-sized problems, a MIP-based solution approach is proposed. The proposed approach decomposes the entire MIP model into selection and assignment (SA) and sequencing (SEQ) models. The SA model is an exact relaxation of the MIP model (with valid inequalities) obtained by removing all the sequencing variables and constraints. Hence, SA model also produces an efficient upper bound for the PDLBS problem. The SEQ model, accordingly, aims to find an optimal sequence of tasks subject to the fixed selection and assignment of tasks provided by the SA model. The computational results show that the proposed MIP-based solution approach provides efficient solutions with small optimality gaps for large-sized problems.

近年来，产品回收受到更多关注，主要是由于消费者环保意识的增强和政府实施的更严格的环境法规。在产品回收中，将产品分解成其组成部分是最重要的活动，通常在分解线上进行。在拆卸过程中，最好对产品进行完全或部分拆卸。在完全拆卸中，必须拆卸所有零件，而部分拆卸允许拆卸零件的子集（例如，收入相对较高的零件）。本研究涉及部分拆卸线平衡和排序 (PDLBS) 问题，其中考虑了要拆卸部件的收入、一般工作站成本、带有危险部件的工作站的额外成本以及方向改变的成本。对于 PDLBS 问题，开发了一个通用混合整数规划 (MIP) 模型，其目的是使总利润最大化。为了加强 MIP 公式，提出了两组有效的不等式。计算结果表明，具有有效不等式的 MIP 模型能够为多达 30 个任务的 PDLBS 问题提供最佳解决方案。为了获得大型问题的近似最优解，提出了一种基于 MIP 的解法。所提出的方法将整个 MIP 模型分解为选择和分配 (SA) 和排序 (SEQ) 模型。SA 模型是 MIP 模型（具有有效的不等式）的精确松弛，通过删除所有排序变量和约束获得。因此，SA 模型也为 PDLBS 问题生成了一个有效的上限。相应地，SEQ 模型，旨在根据 SA 模型提供的任务的固定选择和分配找到最佳任务序列。计算结果表明，所提出的基于 MIP 的解决方案方法为大型问题提供了具有较小最优差距的有效解决方案。