Constraint-based simulated annealing (CBSA) approach to solve the disassembly scheduling problem
Constraint-based simulated annealing (CBSA) approach to solve the disassembly scheduling problem
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DOI：
10.1007/s00170-011-3670-2
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发表时间：
2012-06
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影响因子：
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通讯作者：
Prakash Prakash-Prakash;D. Ceglarek;M. Tiwari
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作者：
Prakash Prakash-Prakash;D. Ceglarek;M. Tiwari
全球化，再加上环境要求，已经率先对产品寿命终止提出了新的要求，这是产品生命周期管理的最后阶段，特别是对于产品再制造和回收，其中涉及产品拆卸以检索所需的部件和零件。最优拆卸计划的选择是再制造和回收行业面临的一个重大挑战，因为它直接影响到制造单元的库存，并影响最终产品的成本。提出了一种基于约束的模拟退火（CBSA）算法，用于确定具有总装产品结构的产品的订货和拆卸计划，以最小化库存水平，考虑到部件的共性。CBSA算法采用基于约束的遗传算子结合模拟退火（SA）方法，使算法更具搜索探索性（保证最优或接近最优解），并有效地收敛到最优解（耗时更少）。与标准模拟退火算法和遗传算法相比，该算法具有更高的避免局部最优解的可能性。这是通过探索点的群体，而不是解决方案空间中的单个点来实现的。最后，以具有通用性的零件拆卸调度问题为例，验证了该方法的有效性。
Globalization, coupled with environmental requirements, has spearheaded new levels of requirements for product end-of-life, the last phase of product lifecycle management especially for product remanufacturing and recycling which involves product disassembly to retrieve the desired parts and subassemblies. Selection of optimal disassembly schedule is a major challenge for remanufacturing and recycling industries as it directly affects the inventory of the manufacturing unit and influences the final product cost. This paper proposes a constraint-based simulated annealing (CBSA) algorithm methodology to determine the ordering and disassembly schedule to minimize inventory level for products with general assembly product structure, i.e., taking into consideration part commonalities. The proposed CBSA algorithm uses the constraint-based genetic operators integrated with the simulated annealing (SA) approach that makes the algorithm more search exploratory (guarantee the optimal or near-optimal solution) and converge efficiently to the optimal solutions (less time-consuming). The proposed algorithm has higher likelihood of avoiding local optima as compared with standard SA and genetic algorithms. This is achieved by exploring a population of points, rather than a single point in the solution space. The proposed methodology is validated using a numerical case study for disassembly scheduling problem with part commonality.