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Disassembly is an essential step in a remanufacturing process via which valuable parts and material of end-of-life (EOL) products can be well reused and resource waste is reduced. Disassembly sequence planning focuses on finding the best disassembly sequence for a given EOL product by considering economic and environmental performance. In a practical disassembly process, one may face a disassembly operation failure risk due to the difficulty of knowing EOL products’ exact information in advance. Despite its importance in impacting disassembly outcomes, the existing work fails to consider it comprehensively. This work proposes a stochastic biobjective DSP problem with the objectives of maximizing disassembly profit and minimizing energy consumption by doing so. A chance-constrained programming model is established, where a chance constraint ensures a fixed confidence level of disassembly failure. To solve it efficiently, a multiobjective multiverse optimization algorithm with stochastic simulation is proposed. Experiments are carried out on four products. Results demonstrate that it outperforms some state-of-the-art algorithms in terms of solution performance. [ABSTRACT FROM AUTHOR]

Hybrid flow shop scheduling problems have gained an increasing attention in recent years because of its wide applications in real-world production systems. Most of the prior studies assume that the processing time of jobs is deterministic and constant. In practice, jobs’ processing time is usually difficult to be exactly known in advance and can be influenced by many factors, e.g., machines’ abrasion and jobs’ feature, thereby leading to their uncertain and variable processing time. In this paper, a dual-objective stochastic hybrid flow shop deteriorating scheduling problem is presented with the goal to minimize makespan and total tardiness. In the formulated problem, the normal processing time of jobs follows a known stochastic distribution, and their actual processing time is a linear function of their start time. In order to solve it effectively, this paper develops a hybrid multiobjective optimization algorithm that maintains two populations executing the global search in the whole solution space and the local search in promising regions, respectively. An information sharing mechanism and resource allocating method are designed to enhance its exploration and exploitation ability. The simulation experiments are carried out on a set of instances, and several classical algorithms are chosen as its peers for comparison. The results demonstrate that the proposed algorithm has a great advantage in dealing with the investigated problem. [ABSTRACT FROM AUTHOR]