Sequence-dependent Family Setup Research Articles

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.

Single machine scheduling with sequence-dependent family setups to minimize maximum lateness

Motivated by a real-life scheduling problem in a steel wire factory in China,this paper considers the single machine scheduling problem with sequence-dependent family setup times to minimize maximum lateness. In view of the NP-hard nature of the problem, structural (dominance and neighbourhood)properties of the problem are described and used in the tabu search algorithms to find optimal or near-optimal schedules. These proposed structural properties quickly exclude unpromising and/or non-improving neighbours from further search.Empirical results on the randomly generated and real-life problem instances from a factory in China show that the proposed heuristic algorithms utilizing the structural properties can obtain optimal or near optimal solutions with a reasonable computational effort.

A branch and bound enhanced genetic algorithm for scheduling a flowline manufacturing cell with sequence dependent family setup times

This paper addresses the permutation flowline manufacturing cell with sequence dependent family setup times problem with the objective to minimize the makespan criterion. We develop a cooperative approach including a genetic algorithm and a branch and bound procedure. The latter is probabilistically integrated in the genetic algorithm in order to enhance the current solution. Moreover, the application of the branch and bound algorithm is based upon the decomposition of the problem into subproblems. The performance of the proposed method is tested by numerical experiments on a large number of representative problems.

Due windows group scheduling using an effective hybrid optimization approach

This paper considers the problem of scheduling part families (groups) and jobs within each part family in a hybrid flow shop manufacturing cell with sequence-dependent family setups times where jobs should be completed at times as close as possible to their respective due dates, and hence both earliness and tardiness should be penalized while processing parts (jobs) in each family together. It is assumed that earliness and tardiness penalties will not occur if a job is completed within the due window. The objective is to determine a schedule that minimizes sum of the earliness and tardiness of jobs. To this problem, the hybrid metaheuristic algorithm combined elements from particle swarm optimization; simulated annealing and variable neighborhood search are developed. The aim of using a hybrid metaheuristic is to raise the level of generality so as to be able to apply the same solution method to several problems. Problem sizes ranging in size from small, medium, to large are considered along with three levels of flexibility. The higher the number of stages and the number of parallel machines in each stage, the higher is the flexibility introduced into the problem. A design of experiments approach is employed to calibrate the parameters and operators of the algorithm. We present computational experiments on 126 problems and compare the results with the simulated annealing and genetic algorithms that presented recently. The computational results show that our proposed algorithm is more efficient than the other methods.

Using simulated annealing to schedule a flowshop manufacturing cell with sequence-dependent family setup times

This paper proposes a simulated annealing-based meta-heuristic to minimise makespan in a flowshop manufacturing cell with sequence-dependent family setup times. To escape from local minima, Cauchy function – rather than the Boltzmann function – is used during the annealing process. The effectiveness and efficiency of the proposed simulated annealing-based meta-heuristic is compared against the existing heuristics on a benchmark problem dataset used in earlier studies. These computational results show that the proposed simulated annealing-based meta-heuristic is highly effective as compared to the state-of-the-art meta-heuristics for this problem on the same benchmark instances.

Permutation and non-permutation schedules for the flowline manufacturing cell with sequence dependent family setups

Through the use of an effective simulated annealing algorithm, this paper presents an extensive computational investigation concerning the performance evaluation of non-permutation vs. permutation schedules for the flowline manufacturing cell with sequence dependent setup times. The results show that there are significant improvements made by using non-permutation schedules over permutation schedules regarding completion-time based and due-date based criteria. It is suggested that practitioners could adopt non-permutation schedules to improve their operational efficiency within a reasonable amount of computational effort.

Metaheuristics for scheduling a non-permutation flowline manufacturing cell with sequence dependent family setup times

The broad applications of cellular manufacturing make flowline manufacturing cell scheduling problems with sequence dependent family setup times a core topic in the field of scheduling. Due to computational complexity, almost all published studies focus on using permutation schedules to deal with this problem. To explore the potential effectiveness of treating this argument using non-permutation schedules, three prominent types of metaheuristics—a simulated annealing, a genetic algorithm and a tabu search—are proposed and empirically evaluated. The experimental results demonstrate that in general, the improvement made by non-permutation schedules over permutation schedules for the due-date-based performance criteria were significantly better than that for the completion-time-based criteria. The results of this study will provide practitioners a guideline as to when to adopt a non-permutation schedule, which may exhibit better performance with additional computational efforts.

Meta-heuristics for scheduling a flowline manufacturing cell with sequence dependent family setup times

This paper presents various Tabu Search (TS) based meta-heuristics for scheduling the part families and jobs within each family in a flowline manufacturing cell (also called a flowshop) with sequence dependent family setup times to minimize makespan. Concepts of Elitism and the acceptance of worse moves from simulated annealing (SA) are examined in the proposed meta-heuristics to improve intensification and diversification. The effectiveness and efficiency of the proposed TS based meta-heuristics are compared against the best meta-heuristic and heuristic algorithms reported so far for this problem on several test problems. Computational results show that the best proposed TS based meta-heuristic is quite effective in minimizing makespan in a reasonable amount of CPU time.

Satisfying due-dates in the presence of sequence dependent family setups with a special comedown structure

This paper addresses a static, n-job, single-machine scheduling problem with sequence dependent family setups. The setup matrix follows a special structure where a constant setup is required only if a job from a smaller indexed family is an immediate successor of one from a larger indexed family. The objective is to minimize the maximum lateness (Lmax). A two-step neighborhood search procedure and an implicit enumeration scheme are proposed. Both procedures exploit the problem structure. The enumeration scheme produces optimum solutions to small and medium sized problems in reasonable computational times, yet it fails to perform efficiently in larger instances. Computational results show that the heuristic procedure is highly effective, and is efficient even for extremely large problems.

Minimizing sum of completion times on a single machine with sequence-dependent family setup times

This paper presents a branch-and-bound (B&B) algorithm for minimizing the sum of completion times in a single-machine scheduling setting with sequence-dependent family setup times. The main feature of the B&B algorithm is a new lower bounding scheme that is based on a network formulation of the problem. With extensive computational tests, we demonstrate that the B&B algorithm can solve problems with up to 60 jobs and 12 families, where setup and processing times are uniformly distributed in various combinations of the [1,50] and [1,100] ranges.

Evolutionary algorithms for scheduling a flowshop manufacturing cell with sequence dependent family setups

This paper considers the problem of scheduling part families and jobs within each part family in a flowshop manufacturing cell with sequence dependent family setups times where it is desired to minimize the makespan while processing parts (jobs) in each family together. Two evolutionary algorithms-a Genetic Algorithm and a Memetic Algorithm with local search-are proposed and empirically evaluated as to their effectiveness in finding optimal permutation schedules. The proposed algorithms use a compact representation for the solution and a hierarchically structured population where the number of possible neighborhoods is limited by dividing the population into clusters. In comparison to a Multi-Start procedure, solutions obtained by the proposed evolutionary algorithms were very close to the lower bounds for all problem instances. Moreover, the comparison against the previous best algorithm, a heuristic named CMD, indicated a considerable performance improvement.

Satisfying due-dates in a job shop with sequence-dependent family set-ups

This paper addresses job shop scheduling with sequence dependent family set-ups. Based on a simple, single-machine dynamic scheduling problem, state dependent scheduling rules for the single machine problem are developed and tested using Markov Decision Processes. Then, a generalized scheduling policy for the job shop problem is established based on a characterization of the optimal policy. The policy is combined with a 'forecasting' mechanism to utilize global shop floor information for local dispatching decisions. Computational results show that performance is significantly better than that of existing alternative policies.

Scheduling Jobs on Parallel Machines with Sequence-Dependent Family Set-up Times

This paper proposes an efficient heuristic to minimise the total weighted tardiness of a set of tasks with known processing times, due dates, weights and family types for parallel machines. A three-phase heuristic is presented to minimise total weighted tardiness. In the first phase, jobs are listed by the earliest due date and then divided into small job-sets according to a decision parameter. In the second phase, jobs are grouped by the due date within applicable families using apparent tardiness cost with set-up (ATCS), and the sequence of jobs within families is improved through the use of the tabu search method. In the third phase, jobs are allocated to machines using a threshold value and a look-ahead parameter. The comprehensive simulation results show that the proposed heuristic performs better than the ATCS and rolling horizon procedure at a significantly reduced total weighted tardiness.

A REVIEW OF FLOWSHOP SCHEDULING RESEARCH WITH SETUP TIMES

Flowshop scheduling problems with setup times arise naturally in many practical situations. This paper provides a review of static and deterministic flowshop scheduling research involving machine setup times. The literature is classified into four broad categories, namely sequence independent job setup times, sequence dependent job setup times, sequence independent family setup times, and sequence dependent family setup times. Using the suggested classification scheme, this paper organizes the flowshop scheduling literature involving setup and/or removal times and summarizes the existing research for different flowshop problem types. This review reveals that, while a considerable body of literature on this subject has been created, there still exist several potential areas worthy of further research.