Dynamic lotsizing and scheduling on multiple lines to meet the customer due dates is significant in multi-line production environments. Therefore, this study investigates dynamic lotsizing and scheduling problems in multiple flexible machining lines considering mixed products. In addition, uncertainty in demand and machine failure is considered. A mathematical model is proposed for the considered problem with an aim to maximize the probability of completion of product models from different customer orders. A constructive heuristic method (CHLP) is proposed to solve the current problem. The proposed heuristic involves the steps to distribute different customer order demands among multiple lines and schedule them considering balancing of makespan between the lines. The performance of CHLP is measured with famous heuristics from the literature, based on the test problem instances. Results indicate that CHLP gives better results in terms of quality of results as compared to other famous literature heuristics.

A mixed-flow human–machine collaborative disassembly line balancing problem is introduced, considering the various recycling methods for waste products and the relationship between the attributes of each product part and the corresponding disassembly operator. The problem aims to optimize the number of workstations, balance the idle time, and minimize the disassembly cost. To address this, an Improved Artificial Fish Swarming Algorithm (IAFSA) was designed based on the combination of the problem characteristics, and the IAFSA algorithm was applied to a mixed-flow television (TV) disassembly example and compared with two different algorithms. The solution shows that the proposed algorithm optimizes the proposed algorithm by 14.3%, 52.3%, and 9.8%, respectively, on the three objectives. Finally, the performance of the three algorithms is compared using Non-dominant rate (NR) and Generation distance (GD) metrics.

Increasing productivity and efficiency in factories with make-to-order (MTO) production systems have attracted growing interest of academia and industry. In MTO companies, efficient order release and schedule are essential for succeeding in today’s marketplace. However, dynamic demand of customers and constrained resources make it difficult to achieve, as well as limiting the profits. Thus, to overcome the problem of order releasing and multi-item scheduling considering the capacity constrained resources investigated, a heuristic approach is proposed based on the drum-buffer-rope (DBR) method. The proposed heuristic is tested on different types of problems based on due date tightness and demand of products. The performance of the proposed heuristic is compared with other famous heuristic methods in literature. End results indicate that the proposed heuristic based on the DBR method outperforms against the other competitors, and it gives more significant results when optimal buffer size is adopted.

This study proposes an improved Fruit Fly Optimization Algorithm integrated with Simulated Annealing (SA-FOA) for hybrid flow shop scheduling problems with dual objectives of minimizing makespan and total tardiness. The algorithm adopts a three-stage integration strategy to generate high-quality initial populations, surpassing random initialization. During olfactory search, insertion-based neighborhood operations expand search scope, while visual search incorporates simulated annealing acceptance criteria to escape local optima. Validation employs three scalable instances, comparing SA-FOA against basic FOA and classical scheduling rules. Experimental results demonstrate significant superiority in Inverted Generational Distance (IGD), Non-dominant rate (NR), and Convergence Matrix (C-matrix metrics), highlighting enhanced convergence, distribution, and diversity. Notably, performance advantages amplify with problem scale growth.

For assembly lines of circuit printing board (PCBs), this study investigated an integrated scheduling problem of the component allocation problem (CAP) and component placement sequence problem (CPSP) with the fixed interval preventive maintenance to minimize the maximum completion time (${\mathrm{C}}_{\max }$), the total energy consumption (TEC), and the total maintenance time (TMT) simultaneously. An improved spider monkey optimization (ISMO) algorithm is proposed with selecting the local leader (LL) and the global leader (GL) using a parallel lattice coordinate system. We compared the proposed ISMO algorithm with the classical optimizations, including SMO, NSGA-Ⅲ, DE, and PSO, by the production data of an enterprise; the results showed that the ISMO algorithm can obtain pareto solutions with better convergence and diversity.

Aiming at the impact of machine failure on scheduling schemes in actual production, this paper proposes a multi-fidelity optimization approach considering the preventive maintenance of the machine (MOAPMM). The genetic algorithm (GA) is used as a low-fidelity model to generate a number of feasible solutions; the feasible solutions are sorted, grouped, and selected as high-quality solutions according to the EOCBA method; the high-fidelity model considering machine preventive maintenance (PM) is constructed by using the FlexSim^@ software; and the high-quality solutions are simulated to obtain the optimal scheduling scheme. Experimental results show that our proposed method outperforms approaches that do not consider machine PM in terms of completion time for the flexible job shop scheduling problem with machine failure.