Remanufacturing contributes to achieving economical, environmental, and social sustainability, and one of its main steps is disassembly aiming to acquire a set of recyclable and reusable components from end-of-life products. This research considers a multi-objective multi-product disassembly sequence planning problem under uncertain circumstances to realize a trade-off among economic, environmental, and social sustainability. Firstly, a multi-objective chance-constrained programming model is formulized to achieve maximal disassembly profit and minimal noise pollution while satisfying energy consumption requirements and obeying various complex product structures. Secondly, a multi-objective group teaching optimization algorithm combining a stochastic simulation approach is particularly devised to handle the problem. In the designed approach, problem-specific encoding and decoding methods are employed to represent and produce feasible solutions. The stochastic simulation approach is utilized to assess the feasibility and performance of the obtained solutions under uncertain environments. Rank and crowding distance approaches are introduced to realize ability grouping, namely, dividing the population into two groups. Precedence preserving crossover and mutation operators are separately utilized on the two groups to achieve population evolution, and an adaptive local search method is developed to enhance exploitation. Thirdly, comparison experiments on some real-world test problems with different scales are carried out. Through dissecting the experimental results with three performance metrics, it can be observed that the devised approach outperforms its competitors by 9.39%–10.00%, 11.37%–59.86%, and 2.36%–7.73% regarding performance, respectively. The experimental results demonstrate the efficiency and excellence of the devised approach in providing high-quality disassembly schemes for managers and engineers.

Remanufacturing is regarded as a sustainable manufacturing paradigm of energy conservation and environment protection. To improve the efficiency of the remanufacturing process, this work investigates an integrated scheduling problem for disassembly and reprocessing in a remanufacturing process, where product structures and uncertainty are taken into account. First, a stochastic programming model is developed to minimize the maximum completion time (makespan). Second, a Q-learning based hybrid meta-heuristic (Q-HMH) is specially devised. In each iteration, a Q-learning method is employed to adaptively choose a premium algorithm from four candidate ones, including genetic algorithm (GA), artificial bee colony (ABC), shuffled frog-leaping algorithm (SFLA), and simulated annealing (SA) methods. At last, simulation experiments are carried out by using sixteen instances with different scales, and three state-of-the-art algorithms in literature and an exact solver CPLEX are chosen for comparisons. By analyzing the results with the average relative percentage deviation (RPD) metric, we find that Q-HMH outperforms its rivals by 9.79%−26.76%. The results and comparisons verify the excellent competitiveness of Q-HMH for solving the concerned problems.

At present, home health care (HHC) has been accepted as an effective method for handling the healthcare problems of the elderly. The HHC scheduling and routing problem (HHCSRP) attracts wide concentration from academia and industrial communities. This work proposes an HHCSRP considering several care centers, where a group of customers (i.e., patients and the elderly) require being assigned to care centers. Then, various kinds of services are provided by caregivers for customers in different regions. By considering the skill matching, customers’ appointment time, and caregivers’ workload balancing, this article formulates an optimization model with multiple objectives to achieve minimal service cost and minimal delay cost. To handle it, we then introduce a brain storm optimization method with particular multi-objective search mechanisms (MOBSO) via combining with the features of the investigated HHCSRP. Moreover, we perform experiments to test the effectiveness of the designed method. Via comparing the MOBSO with two excellent optimizers, the results confirm that the developed method has significant superiority in addressing the considered HHCSRP.

Currently, manufacturing enterprises face increasingly fierce market competition due to the various demands of customers and the rapid development of economic globalization. Hence, they have to extend their production mode into distributed environments and establish multiple factories in various geographical locations. Nowadays, distributed manufacturing systems have been widely adopted in industrial production processes. In recent years, many studies have been done on the modeling and optimization of distributed scheduling problems. This work provides a literature review on distributed scheduling problems in intelligent manufacturing systems. By summarizing and evaluating existing studies on distributed scheduling problems, we analyze the achievements and current research status in this field and discuss ongoing studies. Insights regarding prior works are discussed to uncover future research directions, particularly swarm intelligence and evolutionary algorithms, which are used for managing distributed scheduling problems in manufacturing systems. This work focuses on journal papers discovered using Google Scholar. After reviewing the papers, in this work, we discuss the research trends of distributed scheduling problems and point out some directions for future studies.