Sequence-dependent Family Setup 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 branch-and-price algorithm for parallel machine campaign planning under sequence dependent family setups and co-production

We investigate a tactical level production planning problem in process industries under costly sequence dependent family setups, which drives the need for manufacturing of product families in campaigns. The motivation of our work is float glass manufacturing, which has some unique properties such as co-production and uninterruptible production. To solve the problem, we develop a branch-and-price algorithm based on a novel formulation of the problem. Our algorithm is capable of providing consistent performance in different problem sizes. We compare our proposed algorithm with previous work on the same problem by running computational experiments.

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.

Deep Reinforcement Learning for Minimizing Tardiness in Parallel Machine Scheduling With Sequence Dependent Family Setups

Parallel machine scheduling with sequence-dependent family setups has attracted much attention from academia and industry due to its practical applications. In a real-world manufacturing system, however, solving the scheduling problem becomes challenging since it is required to address urgent and frequent changes in demand and due-dates of products. To minimize the total tardiness of the scheduling problem, we propose a deep reinforcement learning (RL) based scheduling framework in which trained neural networks (NNs) are able to solve unseen scheduling problems without re-training even when such changes occur. Specifically, we propose state and action representations whose dimensions are independent of production requirements and due-dates of jobs while accommodating family setups. At the same time, an NN architecture with parameter sharing was utilized to improve the training efficiency. Extensive experiments demonstrate that the proposed method outperforms the recent metaheuristics, rule-based, and other RL-based methods in terms of total tardiness. Moreover, the computation time for obtaining a schedule by our framework is shorter than those of the metaheuristics and other RL-based methods.

Heuristic Scheduling of Batch Production Processes Based on Petri Nets and Iterated Greedy Algorithms

Wire rod and bar rolling is an important batch production process in steel production systems. A scheduling problem originated from this process is studied in this work by considering the constraints on sequence-dependent family setup time and release time. For each serial batch to be scheduled, it contains several jobs and the number of late jobs within it varies with its start time. First, we model a rolling process using a Petri net (PN), where a so-called rolling transition describes a rolling operation of a batch. The objective of the concerned problem is to determine a firing sequence of all rolling transitions such that the total number of late jobs is minimal. Next, a mixed-integer linear program is formulated based on the PN model. Due to the NP-hardness of the concerned problem, iterated greedy algorithm (IGA)-based methods by using different neighborhood structures and integrating a variable neighborhood descent method are developed to obtain its near-optimal solutions. To test the accuracy, speed, and stability of the proposed algorithms, we compare their solutions of different-size instances with those of CPLEX (a commercial software) and four heuristic peers. The results indicate that the proposed algorithms outperform their peers and have great potential to be applied to industrial production process scheduling. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work deals with a scheduling problem of a batch production process, i.e., wire rod and bar rolling, which is modeled by a Petri net (PN). Due to the NP-hardness of the concerned problem, four iterated greedy algorithm-based methods are developed to solve it. The proposed methods are validated and tested by comparing their solutions with those of four heuristic peers and the exact ones (when available via CPLEX). Extensive experimental results show that they can fast solve one-week-scale instances with better performance than their peers’, thereby proving the readiness to put them in industrial use. When solving a one-month-scale instance, the proposed methods show much better performance than others.

Single machine campaign planning under sequence dependent family setups and co-production

We investigate tactical level production planning problem in process industries, with float glass manufacturing being the specific application domain. In the presence of high sequence dependent family setup costs, the need for planning production in batches, or campaigns as named in the float glass industry, arises. Moreover, the float glass manufacturing has some unique properties such as partially controllable co-production and uninterruptible production. The motivation of our work is a real life problem encountered at a major float glass manufacturing company in Turkey. We develop two mixed integer programming formulations and investigate some variants to solve the problem. Our formulations are capable of handling different resolutions in input data such as demand forecast expressed in discrete time and setup durations expressed in continuous time. We compare formulations both theoretically and by running computational experiments. Furthermore, we conduct additional experiments to gain insights about characteristics of the generated campaign plans from a business perspective.

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.

An Efficient Bi-objective Genetic Algorithm for the Single Batch-Processing Machine Scheduling Problem with Sequence Dependent Family Setup Time and Non-identical Job Sizes

This paper considers the problem of minimizing make-span and maximum tardiness simultaneously for scheduling jobs under non-identical job sizes, dynamic job arrivals, incompatible job families,and sequence-dependentfamily setup time on the single batch- processor, where split size of jobs is allowed between batches. At first, a new Mixed Integer Linear Programming (MILP) model is proposed for this problem; then, it is solved by -constraint method.Since this problem is NP-hard, a bi-objective genetic algorithm (BOGA) is offered for real-sized problems. The efficiency of the proposed BOGA is evaluated to be comparedwith many test problemsby -constraint method based on performance measures. The results show that the proposed BOGAis found to be more efficient and faster than the -constraint method in generating Pareto fronts in most cases.

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.

Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates

This paper studies the scheduling problem in hybrid flow shop (HFS) environment. The sequence dependent family setup time (SDFST) is concerned with minimization of makespan and total tardiness. Production environments in real world include innumerable cases of uncertainty and stochasticity of events and a suitable scheduling model should consider them. Hence, in this paper, due date is assumed to be uncertain and its data follow a normal distribution. Since the proposed problem is NP-hard, two metaheuristic algorithms are presented based on genetic algorithm, namely: Non-dominated Sorting Genetic Algorithm (NSGAII) and Multi Objective Genetic Algorithm (MOGA). The quantitative and qualitative results of these two algorithms have been compared in different dimensions with multi phase genetic algorithm (MPGA) used in literature review. Experimental results indicate that the NSGAII performs very well when compared against MOGA and MPGA in a considerably shorter time.

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.

A simultaneous and iterative approach for parallel machine scheduling with sequence-dependent family setups

In this paper, we address a parallel machine scheduling problem to minimize the total weighted completion time, where product families are involved. Major setups occur when processing jobs of different families, and sequence dependencies are also taken into account. Considering its high practical relevance, we focus on the special case where all jobs of the same family have identical processing times. In order to avoid redundant setups, batching jobs of the same family can be performed. We first develop a variable neighborhood search algorithm (VNS) to solve the interrelated subproblems in a simultaneous manner. To further reduce computing time, we also propose an iterative scheme which alternates between a specific heuristic to form batches and a VNS scheme to schedule entire batches. Computational experiments are conducted which confirm the benefits of batching. Test results also show that both simultaneous and iterative approach outperform heuristics based on a fixed batch size and list scheduling. Furthermore, the iterative procedure succeeds in balancing solution quality and computing time.