Benchmark Flowshop Scheduling Problems Research Articles

A two-phase simulated annealing algorithm to minimise the completion time variance of jobs in flowshops

In this paper, simulated annealing (SA) algorithm is employed to solve the flowshop scheduling problem with the objective of minimising the completion time variance (CTV) of jobs. Four variants of the two-phase SA algorithm (SA-I to SA-IV) are proposed to solve flowshop scheduling problem with the objective of minimising the CTV of jobs without considering the right shifting of completion time of jobs on the last machine. The proposed SA algorithms have been tested on 90 benchmark flowshop scheduling problems. The solutions yielded by the proposed SA variants are compared with the best CTV of jobs reported in the literature, and the proposed SA-III is found to perform well in minimising the chosen performance measure (CTV) particularly in medium and large size problems.

A modified ant-colony optimisation algorithm to minimise the completion time variance of jobs in flowshops

In this work, the flowshop scheduling problem is considered with the objective of minimising the completion-time variance (CTV) of jobs, and an Ant Colony Optimisation (ACO) algorithm is presented. Two implementations of the Modified Ant Colony Optimisation algorithm (MACO-I and MACO-II) are proposed to solve the permutation flowshop scheduling problem. The proposed ant-colony-algorithm implementations have been tested on 90 benchmark flowshop scheduling problems. The solutions yielded by the proposed MACO implementations are compared with various algorithms and with the best CTV of jobs reported in the literature. The proposed MACO implementations are found to perform very well in minimising the chosen performance measure.

Effect of Varying Levels of Setup Time on Makespan Criteria: A Comparative Study of Two Algorithms on SDST Flow Shop

Scheduling problems with setup time consideration are the realistic versions of classical scheduling problems. This paper analyses the effect of setup time level variation on the performance of two constructive algorithms for flow shop scheduling with sequence dependent setup time. The two constructive algorithms considered in the study differ in their approach in constructing the sequence; the first algorithm is processing time based while the second algorithm is setup time and processing time based. The setup time based algorithm known as fictitious job setup ranking algorithm is developed as a part of the present study. Taillard benchmark flow shop scheduling problems are used for development of benchmark problems for flow shop scheduling with sequence dependent setup time. The two algorithms are tested for the set of benchmark problem instances developed. The results are analyzed graphically and statistically for the varying levels of setup time. The study reveals that the setup time based algorithm improves its performance when the setup time increases as percentage of processing time. This paper proposes a framework namely setup dominance matrix, which serves as an aid to identify the algorithm that is suitable for a specific size flow shop scheduling problem at a particular level of setup time.

Joint scheduling of jobs and Preventive Maintenance operations in the flowshop sequencing problem: a resolution with sequential and integrated strategies

This paper presents a comparative study on joint production and Preventive Maintenance (PM) scheduling according to the most interesting joint scheduling strategies: sequential and integrated ones regarding Flow Shop Problems (FSPs). For each strategy, we present three heuristics: Genetic Algorithms (GAs), Taboo Search (TS) and NEH heuristic. The proposed heuristics have been applied to non-standard test problems, which represent joint production and maintenance benchmark flowshop scheduling problems taken from Benbouzid et al. (2003). A comparison of the solutions yielded by the heuristics developed in this paper with the heuristic solutions given by Taillard (1993) is undertaken with respect to the minimisation of performance loss after maintenance insertion. The comparison shows that the proposed integrated GAs are clearly superior to all the analysed algorithms.

Discrete particle swarm optimisation algorithms for minimising the completion-time variance of jobs in flowshops

In this paper, the problem of scheduling in the permutation flowshop scheduling problem is considered with the objective of minimising the completion-time variance of jobs (CTV). Two particle swarm optimisation algorithms (PSOAs) are proposed and analysed. The first PSOA is inspired from the solution construction procedures that are used in ant colony optimisation algorithms. The second algorithm is a newly developed one. The proposed algorithms are applied to a set of benchmark flowshop scheduling problems, and performances of the algorithms are evaluated by comparing the obtained results with the results published in the literature. The performance analysis demonstrates the effectiveness of the proposed algorithms in solving the permutation flowshop sequencing problem with the CTV objective.

A genetic algorithm for flowshop scheduling with multiple objectives

In this paper, we study the permutation flowshop scheduling problem with the objectives of minimizing the makespan and total flowtime of jobs. An attempt is made to solve the multi-objective problem by using the Proposed Genetic Algorithm (ProGA). In order to evaluate the ProGA, we have made use of benchmark flowshop scheduling problems, and the heuristically non-dominated solutions for these problems are obtained from each of the existing methodologies and the ProGA. These non-dominated solutions are combined to obtain a net heuristic non-dominated front for a given problem. It is found that most of the solutions on the net non-dominated front are yielded by the ProGA, bringing out the superiority of the proposed genetic algorithm.

A multi-objective genetic algorithm for scheduling in flow shops to minimize the makespan and total flow time of jobs

In this paper the problem of permutation flow shop scheduling with the objectives of minimizing the makespan and total flow time of jobs is considered. A Pareto-ranking based multi-objective genetic algorithm, called a Pareto genetic algorithm (GA) with an archive of non-dominated solutions subjected to a local search (PGA-ALS) is proposed. The proposed algorithm makes use of the principle of non-dominated sorting, coupled with the use of a metric for crowding distance being used as a secondary criterion. This approach is intended to alleviate the problem of genetic drift in GA methodology. In addition, the proposed genetic algorithm maintains an archive of non-dominated solutions that are being updated and improved through the implementation of local search techniques at the end of every generation. A relative evaluation of the proposed genetic algorithm and the existing best multi-objective algorithms for flow shop scheduling is carried by considering the benchmark flow shop scheduling problems. The non-dominated sets obtained from each of the existing algorithms and the proposed PGA-ALS algorithm are compared, and subsequently combined to obtain a net non-dominated front. It is found that most of the solutions in the net non-dominated front are yielded by the proposed PGA-ALS.