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Recently, finding solutions to assembly flowshop scheduling problems is a topic of extensive discussion in research communities. While existing research assumes that job processing times are constant numbers, in several practical situations, due to several external factors like machine breakdowns, working environment changes, worker performance instabilities, and tool quality variations and unavailability, job processing times may vary. In this study, therefore, we address a two-stage assembly flowshop scheduling problem with two scenario-dependent jobs processing times to minimise the maximum makepsan among both scenarios (called robust makespan) In view of the NP-hard nature, we first derive a dominance property and a lower bound to propose a branch-and-bound algorithm to find a permutation schedule with minimum makespan. Following that, we use Johnson's rule to propose eight polynomial heuristics for finding near-optimal solutions. Furthermore, we propose four cloud theory-based simulated annealing (CSA) hyper-heuristic algorithms incorporating seven low level heuristics to solve a robust two-stage assembly flowshop problem with scenario-dependent processing times. Finally, we empirically evaluate the effectiveness of all the proposed algorithms in minimising the robust makespan. [ABSTRACT FROM AUTHOR]

This paper delves into the scheduling of the two-machine flow-shop problem with step-learning, a scenario in which job processing times decrease if they commence after their learning dates. The objective is to optimize resource allocation and task sequencing to ensure efficient time utilization and timely completion of all jobs, also known as the makespan. The identified problem is established as NP-hard due to its reduction to a single machine for a common learning date. To address this complexity, this paper introduces an initial integer programming model, followed by the development of a branch-and-bound algorithm augmented with two lemmas and a lower bound to attain an exact optimal solution. Additionally, this paper proposes four straightforward heuristics inspired by the Johnson rule, along with their enhanced counterparts. Furthermore, a population-based genetic algorithm is formulated to offer approximate solutions. The performance of all proposed methods is rigorously evaluated through numerical experimental studies. [ABSTRACT FROM AUTHOR]

The two-stage assembly scheduling problem has attracted increasing research attention. In many such problems, job processing times are commonly assumed to be fixed. However, this assumption does not hold in many real production situations. In fact, processing times usually decrease steadily when the same task is performed repeatedly. Therefore, in this study, we investigated a two-stage assembly position-based learning scheduling problem with two machines in the first stage and an assembly machine in the second stage. The objective was to complete all jobs as soon as possible (or to minimise the makespan, implying that the system can perform better and efficient task planning with limited resources). Because this problem is NP-hard, we derived some dominance relations and a lower bound for the branch-and-bound method for finding the optimal solution. We also propose three heuristics, three versions of the simulated annealing (SA) algorithm, and three versions of cloud theory-based simulated annealing algorithm for determining near-optimal solutions. Finally, we report the performance levels of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper studies a single-machine multitasking scheduling problem together with two-agent consideration. The objective is to look for an optimal schedule to minimize the total tardiness of one agent subject to the total completion time of another agent has an upper bound. For this problem, a branch-and-bound method equipped with several dominant properties and a lower bound is exploited to search optimal solutions for small size jobs. Three metaheuristics, cloud simulated annealing algorithm, genetic algorithm, and simulated annealing algorithm, each with three improvement ways, are proposed to find the near-optimal solutions for large size jobs. The computational studies, experiments, are provided to evaluate the capabilities for the proposed algorithms. Finally, statistical analysis methods are applied to compare the performances of these algorithms. [ABSTRACT FROM AUTHOR]

This study examines a two-stage three-machine flow-shop assembly scheduling model in which job processing time is considered as a mixed function of a controlled truncation parameter with a sum-of-processing-times-based learning effect. However, the truncation function is very limited in the two-stage flow-shop assembly scheduling settings. To overcome this limitation, this study investigates a two-stage three-machine flow-shop assembly problem with a truncation learning function where the makespan criterion (completion of the last job) is minimized. Given that the proposed model is NP hard, dominance rules, lemmas and a lower bound are derived and applied to the branch-and-bound method. A dynamic differential evolution algorithm, a hybrid greedy iterated algorithm and a genetic algorithm are also proposed for searching approximate solutions. Results obtained from test experiments validate the performance of all the proposed algorithms. [ABSTRACT FROM AUTHOR]

When the workers carry out the operation of many tasks, they will be fatigued. Meanwhile, the experience performance will also be degraded over time owe to mal-position or mal-alignment of jobs, abrasion of tools, etc. The maintenance activities will be required to maintenance acceptable production rates. In this study, some machine scheduling problems with deteriorating jobs and rate-modifying activities are investigated simultaneously. We assume the rate-modifying activity duration depends on the running time between the two rate-modifying activities. The objective functions are to minimize the makespan, the total completion time and the combination of the earliness, the tardiness, and the common due date. Polynomial-time algorithms are provided to optimally solve the three problems for the single-machine case. Finally, we also consider the parallelmachine problem to minimize only the total completion time by converting it into the corresponding assignment problem. [ABSTRACT FROM AUTHOR]

This paper studies linear non-increasing processing times and the common/slack due window assignment problems on a single machine, where the actual processing time of a job is a linear non-increasing function of its starting time. The aim is to minimize the sum of the earliness cost, tardiness cost, due window location and due window size. Some optimality results are discussed for the common/slack due window assignment problems and two O(nlogn) time algorithms are presented to solve the two problems. Finally, two examples are provided to illustrate the correctness of the corresponding algorithms. [ABSTRACT FROM PUBLISHER]

Job precedence can be found in some real-life situations. For the application in the scheduling of patients from multiple waiting lines or different physicians, patients in the same waiting line for scarce resources such as organs, or with the same physician often need to be treated on the first-come, first-served basis to avoid ethical or legal issues, and precedence constraints can restrict their treatment sequence. In view of this observation, this paper considers a two-machine flowshop scheduling problem with precedence constraint on two jobs with the goal to find a sequence that minimizes the total tardiness criterion. In searching solutions to this problem, we build a branch-and-bound method incorporating several dominances and a lower bound to find an optimal solution. In addition, we also develop a genetic and larger-order-value method to find a near-optimal solution. Finally, we conduct the computational experiments to evaluate the performances of all the proposed algorithms. [ABSTRACT FROM AUTHOR]

• We investigate a robust version single-machine problem with two-scenario-based dependent processing times. • Some dominance rules and lower bounds are used in a branch-and-bound algorithm for exact solutions. • Five heuristics and a simulated annealing hyper-heuristic with seven low level heuristics are proposed to solve this problem. Many practical productions are full of significant uncertainties. For example, the working environment may change, machines may breakdown, workers may become unstable, etc. In such an environment, job processing times should not be fixed numbers. In light of this situation, we investigate a single-machine problem with two-scenario-based processing times, where the goal is to minimize the maximum total completion times over two scenarios. When the uncertainty of the job processing times is confronted, the robust version of this problem is NP-hard, even for very restricted cases. To solve this problem, we derive some dominance rules and a lower bound for developing branch-and-bound algorithms to find optimal solutions. As for determining approximate solutions, we propose five heuristics, adopting combined two-scenario-based dependent processing times, to produce initial solutions and then improve each with a pairwise interchange. Further, we propose a simulated annealing hyper-heuristic incorporating the proposed seven low level heuristics to solve this problem as well. Finally, the performances of all proposed algorithms are tested and reported. [ABSTRACT FROM AUTHOR]