Sequence-dependent Family Setup Times Research Articles

A multi-objective flexible job-shop cell scheduling problem with sequence-dependent family setup times and intercellular transportation by improved NSGA-II

With the development of Industry 4.0 and requirement of smart factory, cellular manufacturing system (CMS) has been widely concerned in recent years, which may leads to reducing production cost and wip inventory due to its flexibility production with groups. Intercellular transportation consumption, sequence-dependent setup times, and batch issue in CMS are taken into consideration simultaneously in this paper. Afterwards, a multi-objective flexible job-shop cell scheduling problem (FJSCP) optimization model is established to minimize makespan, total energy consumption, and total costs. Additionally, an improved non-dominated sorting genetic algorithm is adopted to solve the problem. Meanwhile, for improving local search ability, hybrid variable neighborhood (HVNS) is adopted in selection, crossover, and mutation operations to further improve algorithm performance. Finally, the validity of proposed algorithm is demonstrated by datasets of benchmark scheduling instances from literature. The statistical result illustrates that improved method has a better or an equivalent performance when compared with some heuristic algorithms with similar types of instances. Besides, it is also compared with one type scalarization method, the proposed algorithm exhibits better performance based on hypervolume analysis under different instances.

Evolutionary algorithms for multi-objective flexible job shop cell scheduling

The multi-objective flexible job shop scheduling in a cellular manufacturing environment is a challenging real-world problem. This recently introduced scheduling problem variant considers exceptional parts, intercellular moves, intercellular transportation times, sequence-dependent family setup times, and recirculation requiring minimization of makespan and total tardiness, simultaneously. A previous study shows that the exact solver based on mixed-integer nonlinear programming model fails to find an optimal solution to each of the ‘medium’ to ‘large’ size instances considering even the simplified version of the problem. In this study, we present evolutionary algorithms for solving that bi-objective problem and apply genetic and memetic algorithms that use three different scalarization methods, including weighted sum, conic, and tchebycheff. The performance of all evolutionary algorithms with various configurations is investigated across forty-three benchmark instances from ‘small’ to ‘large’ size including a large real-world problem instance. The experimental results show that the transgenerational memetic algorithm using weighted sum outperforms the rest producing the best-known results for almost all bi-objective flexible job shop cell scheduling instances, in overall.

A Novel Disruptive Innovation-Like Algorithm for Single-Machine Scheduling with Sequence-Dependent Family Setup Times

This paper proposes a novel disruptive innovation-like algorithm (DILA) for the problem of scheduling a single machine with sequence-dependent family setup times to minimize the total tardiness. The proposed algorithm is derived from Christensen’s (1997) theory of disruptive innovation. Based on this theory, a DILA is proposed, which first generates two initial populations: the mainstream market population and the emerging market population. Then, to improve the quality of solutions in the populations, three phases are created within a generation. Finally, the DILA allows the populations to evolve for several generations until the termination condition is met. Two populations constructed in DILA are to overcome the weakness of premature. In addition, two functions—the member-added function and member-removed function—implemented in DILA are added to increase the diversity. The DILA was tested on a dataset of 1440 observations from the literature. The computational results confirm that DILA is very effective.

Makespan optimization in a no-wait flowline manufacturing cell with sequence-dependent family setup times

The no-wait flowline manufacturing cell scheduling problem with sequence-dependent family setup times and the objective of minimizing makespan is NP-hard in the strong sense even in the two-machine case. This work proposes an efficient matheuristic for solving this problem to optimality in a reasonable computational time. To demonstrate the effectiveness and efficiency of the proposed matheuristic, computational experiments are performed using a well-known benchmark problem set. The computational results reveal that all test instances of the problem are solved to optimality using the proposed matheuristic, and that the computational times of the matheuristic are comparable with those of state-of-the-art meta-heuristics for finding approximate solutions. This work contributes significantly to the research into algorithms for solving this computationally intractable problem.

A Markovian approach for multi-level multi-product multi-period capacitated lot-sizing problem with uncertainty in levels

This paper considers a multi-level, multi-item, multi-period capacitated lot-sizing problem with sequence-dependent family set-up times, set-up carry over and uncertainty in levels due to uncertainty in inspection, rework and scrap. In this study, we, first, determined total processing time for each product of each family. Then, expected number of times associated with visiting each level of each product as well as amount of raw materials are calculated. We developed a mixed integer linear programming model with a numerical example and sensitivity analysis.

Hybrid flow shop batching and scheduling with a bi-criteria objective

This paper addresses the hybrid flow shop batching and scheduling problem where sequence-dependent family setup times are present and the objective is to simultaneously minimize the weighted sum of the total weighted completion time and total weighted tardiness. In particular, it disregards the group technology assumptions by allowing for the possibility of splitting pre-determined groups of jobs into inconsistent batches in order to improve the operational efficiency. A benchmark of small size problems is considered to show the benefits of batching on group scheduling. Since the problem is strongly NP-hard, several algorithms based upon tabu search are developed at three levels, which move back and forth between batching and scheduling phases. Two algorithms incorporate tabu search into the framework of path-relinking to exploit the information on good solutions. These tabu search/path-relinking algorithms comprise several distinguishing features including two relinking procedures to effectively construct paths and the stage-based improvement procedure to consider the move interdependency. The best tabu search algorithm as a local search algorithm is compared to a population-based algorithm, and the superiority of the former over the latter is shown using a statistical experiment. The initial solution finding mechanism is implemented to trigger the search into the solution space. The efficiency and effectiveness of the best algorithm is verified with the help of the results found by CPLEX. The results show that the best algorithm, based on tabu search/path relinking and the stage-based improvement procedure, could find solutions at least as good as CPLEX, but in drastically shorter computational time. In order to reflect the real industry requirements, dynamic machine availability times, dynamic job release times, machine eligibility and machine capability for processing jobs, desired lower bounds on batch sizes, and job skipping are considered.

ILS Heuristics for the Single-Machine Scheduling Problem with Sequence-Dependent Family Setup Times to Minimize Total Tardiness

This paper addresses a single-machine scheduling problem with sequence-dependent family setup times. In this problem the jobs are classified into families according to their similarity characteristics. Setup times are required on each occasion when the machine switches from processing jobs in one family to jobs in another family. The performance measure to be minimized is the total tardiness with respect to the given due dates of the jobs. The problem is classified asNP-hard in the ordinary sense. Since the computational complexity associated with the mathematical formulation of the problem makes it difficult for optimization solvers to deal with large-sized instances in reasonable solution time, efficient heuristic algorithms are needed to obtain near-optimal solutions. In this work we propose three heuristics based on the Iterated Local Search (ILS) metaheuristic. The first heuristic is a basic ILS, the second uses a dynamic perturbation size, and the third uses a Path Relinking (PR) technique as an intensification strategy. We carry out comprehensive computational and statistical experiments in order to analyze the performance of the proposed heuristics. The computational experiments show that the ILS heuristics outperform a genetic algorithm proposed in the literature. The ILS heuristic with dynamic perturbation size and PR intensification has a superior performance compared to other heuristics.

Concurrent scheduling of manufacturing cells considering sequence-dependent family setup times and intercellular transportation times

This paper deals with the problem of scheduling operations in cellular manufacturing systems. A concurrent approach is proposed for job shop cell scheduling while the objective is to minimize the makespan. An integer linear programming model is developed by considering exceptional elements, intercellular moves, intercellular transportation times, and sequence-dependent family setup times. A modification of the model with reduced number of functional constraints is then presented to enhance the efficiency of the model in terms of computational time. In order to efficiently solve real-size problems in a reasonable amount of time, a heuristic approach based on the genetic algorithm (GA) is also developed. The proposed GA is enhanced with problem-specific knowledge of the search space by using a specialized repair strategy, a decoding procedure based on the concept of active schedules and a problem-specific mutation operator which prevents the algorithm from searching redundant solutions. Results reveal that the modified mixed integer linear programming model significantly reduces the computational time and the proposed GA is capable of providing promising solutions to large-scale problems.

A hybrid tabu search for batching and sequencing decisions in a single machine environment

This paper proposes a hybrid tabu search (HTS) to minimise the total weighted tardiness (TWT) for the batching and sequencing of jobs originating from incompatible families in which sequence dependent family setup times exist on single machine. The developed HTS includes distinguished features such as the strict arc based tabu classification along with dynamic tabu tenures, hybrid neighbourhood structures and iterative phases which consist of job and batch sequencing phases. The authors developed a testing methodology to determine the quality of the HTS solution. A mixed integer linear programing (MILP) model was developed to evaluate the optimality of the solution of the HTS for a small-size instance that consists of 640 problems. In addition, three dispatching rule heuristic combinations (EDD–EDD, EDD–BATCS and ATC–BATCS) were developed to test the HTS for large-size instances that deals with 1440 problems. The HTS provided comparable results with the MILP for small-size instances and outperformed the developed dispatching heuristics.

A Non-Permutation Flow Shop Manufacturing Cell Scheduling Problem with Part's Sequence Dependent Family Setup Times

This article presents a new mathematical model for a dynamic flow shop manufacturing cell scheduling problem (DFMCSP) with agreeable job release date for each part family where family setup times are dependent on sequence of parts within families. It means this article considers non-permutation schedules for both sequence of families and sequence of parts within families. The objective is minimizing the Makespan (Cmax). Since, this problem belongs to NP-Hard class. Therefore, reaching an optimal solution in a reasonable computational time by using exact methods is extremely difficult. Thus, this article proposes meta-heuristic methods such as Genetic Algorithms (GA) and Tabu Search (TS). Finally, the computational results compare efficiency of the proposed algorithms under the performance measures.

Minimising makespan in flowshop group scheduling with sequence-dependent family set-up times using inserted idle times

This paper focuses on the permutation flowshop group scheduling problem to minimise makespan, which is typically found in flowline manufacturing cells. In contrast to classical flowshop scheduling, it is characterised by a scheduling task at two levels: on the one hand, jobs within part families and on the other hand, a number of part families have to be sequenced. Integrating sequence-dependent set-up times for every changeover of families, this problem can represent practical cases. By modelling each family as a job with time lags, some specific problem characteristics of the group scheduling problem are pointed out. It is shown that generating job sequences by minimising the sum of inserted machine idle times instead of makespan on the first level of scheduling and the use of the schedule heads on the second level can lead to significant improvements for some test problems. These findings are used for the improvement of existing constructive heuristic algorithms, whose effectiveness is assessed for several test instances with sequence-dependent family set-up times.

Flow shop batching and scheduling with sequence-dependent setup times

This paper addresses the flow shop batching and scheduling problem where sequence-dependent family setup times are present and the objective is to minimize makespan. We consider violating the group technology assumption by dividing product families into batches. In order to reduce setup times, inconsistent batches are formed on different machines, which lead to non-permutation schedules. To the best of our knowledge, this is the first time that the splitting of job families into inconsistent batches has been considered in a flow shop system. A tabu search algorithm is developed which contains several neighbourhood functions, double tabu lists and a multilevel diversification structure. Compared to the state-of-the-art meta-heuristics for this problem, the proposed tabu search algorithm achieves further improvement when the group scheduling assumption is dropped. Also, various experiments conducted on the benchmark problem instances confirm the benefits of batching. Therefore, it will be prudent for the practitioners to consider adopting inconsistent batches and non-permutation schedules to improve their operational efficiency within a reasonable amount of computational effort.

Scheduling a Bi-Objective Hybrid Flow Shop with Sequence-Dependent Family Setup Times Using Metaheuristics

This paper deals with the problem of hybrid flow shop scheduling. In this investigation, we considered group scheduling within the area of sequence-dependent family setup times and two objectives of minimizing makespan and total tardiness are taken into consideration simultaneously. Due to the computational complexity in solving these set of problems with multiple objectives, metaheuristics has a high priority, because these algorithms are capable of solving combinatorial problems in a reasonable time. This study focuses on three multi-objective algorithms, multi-objective genetic algorithm, sub-population genetic algorithm-II and non-dominated sorting genetic algorithm-II, to solve the mentioned problem. In order to investigate the effectiveness and efficiency of applying the noted metaheuristics for such an NP-hard problem, we evaluate non-dominated solution sets obtained via each algorithm through some evaluation metrics.

Solving the serial batching problem in job shop manufacturing systems

This paper addresses the serial batch scheduling problem embedded in a job shop environment to minimize makespan. Sequence dependent family setup times and a job availability assumption are also taken into account. In consideration of batching decisions, we propose a tabu search algorithm which consists of various neighborhood functions, multiple tabu lists and a sophisticated diversification structure. Computational experiments show that our algorithm outperforms a well-known tabu search approach which is developed for solving the traditional job shop problem. These results also confirm the benefits of batching.

Scheduling a bi-criteria flowshop manufacturing cell with sequence-dependent family setup times

Flowshop manufacturing cell scheduling problems (FMCSPs) with sequence-dependent family setup times (SDFSTs) have become a key area in the field of scheduling. Though the need to consider multiple criteria in real-world scheduling is widely recognised, most of the currently available algorithms for FMCSPs with SDFSTs only deal with the optimisation of a single performance criterion. In this study, a two-level multi-start simulated annealing (TLMSA) heuristic was presented for this problem with the objectives of simultaneously minimising makespan and total flow time (or total tardiness). To validate the performance of the proposed approach, the obtained sets of non-dominated solutions were evaluated by seven multi-objective performance indicators. The computational results showed that the proposed TLMSA significantly outperforms the benchmark algorithm in terms of the seven performance indicators. It is believed that it can serve as a new benchmark approach for future research on the two FMCSPs with SDFSTs addressed in this study.

Robust scheduling on a single machine to minimize total flow time

In a real-world manufacturing environment featuring a variety of uncertainties, production schedules for manufacturing systems often cannot be executed exactly as they are developed. In these environments, schedule robustness that guarantees the best worst-case performance is a more appropriate criterion in developing schedules, although most existing studies have developed optimal schedules with respect to a deterministic or stochastic scheduling model. This study concerns robust single machine scheduling with uncertain job processing times and sequence-dependent family setup times explicitly represented by interval data. The objective is to obtain robust sequences of job families and jobs within each family that minimize the absolute deviation of total flow time from the optimal solution under the worst-case scenario. We prove that the robust single machine scheduling problem of interest is NP-hard. This problem is reformulated as a robust constrained shortest path problem and solved by a simulated annealing-based algorithmic framework that embeds a generalized label correcting method. The results of numerical experiments demonstrate that the proposed heuristic is effective and efficient for determining robust schedules. In addition, we explore the impact of degree of uncertainty on the performance measures and examine the tradeoff between robustness and optimality.

Minimizing makespan in a flow-line manufacturing cell with sequence dependent family setup times

The flow-line manufacturing cell scheduling problem (FMCSP) with sequence dependent family setup times (SDFSTs) is a topic of great concern for many industrial applications, but under-represented in the research literature. In this study, a two-level iterated greedy (TLIG) heuristic is proposed to minimize makespan of this strongly NP-hard problem. The performance of the proposed TLIG heuristic is compared against eight existing algorithms on a benchmark problem set used in earlier studies. The experimental results reveal that the proposed TLIG heuristic is very effective and efficient at finding near-optimal solutions for FMCSPs with SDFSTs.

Metaheuristics for scheduling a no-wait flowshop manufacturing cell with sequence-dependent family setups

This paper examines the no-wait flowshop manufacturing cell scheduling problem (FMCSP) with sequence-dependent family setup times. To the best of our knowledge, the present study is among the first to investigate FMSCPs with no-wait consideration, though it is a necessary production constraint in many real-world applications. In view of the strongly NP-hard nature of this problem, three metaheuristic-based algorithms were proposed and empirically evaluated for effectively finding optimal schedules. The experimental results demonstrate that the proposed algorithms are effective and efficient at finding good quality solutions for the FMCSP with makespan criterion.

Integrated production planning with sequence-dependent family setup times

This paper proposes an integrated optimization model of aggregate production planning (APP), family disaggregation planning, and family scheduling problems in hierarchical production planning (HPP) systems considering sequence-dependent family setup times. The model obtains the optimal production plan for each product type and product family in each period, together with the globally optimal production sequence of product families in all planning periods. The proposed model is tested with randomly generated experimental data consistent with what is prevalent in the manufacturing industry and its results are compared with those of the traditional HPP models. Our results show that the integrated model realizes greater cost savings.

Applying multi-start simulated annealing to schedule a flowline manufacturing cell with sequence dependent family setup times

Meta-heuristics that attempt to obtain (near) global optimal solutions of NP-hard combinatorial optimization problems generally require diversification to escape from local optimality. One way to achieve diversification is to utilize the multi-start hill climbing strategy. By combining the respective advantages of the multi-start hill climbing strategy and simulated annealing (SA), an effective multi-start simulated annealing (MSA) heuristic is proposed to minimize the makespan for a flowline manufacturing cell scheduling problem with sequence dependent family setup times. The heuristic performance is evaluated by comparing the results achieved by the proposed heuristic with those achieved by the existing meta-heuristics. The computational results show that following multi-start refinement the proposed MSA heuristic is more effective compared to the state-of the-art meta-heuristics on the same benchmark instances.