Scheduling two-stage hybrid flow shops with parallel batch, release time, and machine eligibility constraints (original) (raw)
Related papers
2020
This paper presents an effective memetic algorithm (MA) for a hybrid flow shop scheduling problem with multiprocessor tasks (HFSMT) to minimize the makespan. The problem is modeled as deterministic by a mixed graph. This problem has at least two production stages, each of which has several machines, operating in parallel. Two sub-problems are considered for solving this problem: determining the sequence of jobs in the first stage and reducing the idle time of the processors in the next stages. The developed algorithm uses an operator called Bad Selection Operator. This operator holds the worst chromosomes of each generation and uses them to search in the space of other problems at predetermined timescales. Besides, this algorithm uses a dynamic adjustment structure to improve the ratio of crossover and mutation operators that could reduce the execution time. The efficiency of the proposed MA is investigated by testing it on well-known benchmark instances and also compared with other...
The hybrid flow shop scheduling problem
2010
The scheduling of flow shops with multiple parallel machines per stage, usually referred to as the Hybrid Flow Shop (HFS), is a complex combinatorial problem encountered in many real world applications. Given its importance and complexity, the HFS problem has been intensively studied. This paper presents a literature review on exact, heuristic and metaheuristic methods that have been proposed for its solution. The paper discusses several variants of the HFS problem, each in turn considering different assumptions, constraints and objective functions. Research opportunities in HFS are also discussed.
Invited Review The hybrid flow shop scheduling problem
The scheduling of flow shops with multiple parallel machines per stage, usually referred to as the hybrid flow shop (HFS), is a complex combinatorial problem encountered in many real world applications. Given its importance and complexity, the HFS problem has been intensively studied. This paper presents a literature review on exact, heuristic and metaheuristic methods that have been proposed for its solution. The paper briefly discusses and reviews several variants of the HFS problem, each in turn considering different assumptions, constraints and objective functions. Research opportunities in HFS are also discussed.
Three Algorithms for Flexible Flow-shop Scheduling
American Journal of Applied Sciences, 2007
Scheduling is an important process widely used in manufacturing, production, management, computer science, and so on. Appropriate scheduling can reduce material handling costs and time. Finding good schedules for given sets of jobs can thus help factory supervisors effectively control job flows and provide solutions for job sequencing. In simple flow shop problems, each machine operation center includes just one machine. If at least one machine center includes more than one machine, the scheduling problem becomes a flexible flow-shop problem. Flexible flow shops are thus generalization of simple flow shops. In this paper, we propose three algorithms to solve flexible flow-shop problems of more than two machine centers. The first one extends Sriskandarajah and Sethi's method by combining both the LPT and the search-and-prune approaches to get a nearly optimal makespan. It is suitable for a medium-sized number of jobs. The second one is an optimal algorithm, entirely using the search-and-prune technique. It can work only when the job number is small. The third one is similar to the first one, except that it uses Petrov's approach (PT) to deal with job sequencing instead of searchand-prune. It can get a polynomial time complexity, thus being more suitable for real applications than the other two. Experiments are also made to compare the three proposed algorithms. A trade-off can thus be achieved between accuracy and time complexity.
A two-stage flow shop batch-scheduling problem with the option of using Not-All-Machines
International Journal of Production Economics, 2013
ABSTRACT The decision whether to use all the available machines in the shop becomes very relevant when the capacity exceeds the demand. In such cases, it might be optimal to use only a subset of the machines. We study this option in a two-stage flowshop environment. Jobs are assumed to be identical, and are processed in batches, where a machine-dependent setup time is required when starting a new batch. The objective function is minimum makespan. We introduce an exact efficient dynamic programming algorithm, which is shown numerically to be able to solve medium size instances in very reasonable time. For the solution of large instances, we propose an asymptotically optimal heuristic procedure and a lower bound on the makespan value, which produce extremely small optimality gaps.
A multi-level hybrid framework applied to the general flow-shop scheduling problem
Computers & Operations Research, 2002
The ability to provide better solutions is determined by search performance. The goal is to create a multi-level hybrid system which is able to improve the results of exisiting methods. The paper explores the effective and efficient interaction of intensifying and diversifying strategies within the context of the general flow-shop scheduling problem. The techiques of scatter search and path relinking, marrying ideas from tabu search and evolutionary algorithms, provide a unifying environment in which these techniques are created.
Computers and Operations Research, Vol. 36, No.2, 2009, 358 - 378
This paper considers a flexible flow shop scheduling problem, where at least one production stage is made up of unrelated parallel machines. Moreover, sequence- and machine-dependent setup times are given. The objective is to find a schedule that minimizes a convex sum of makespan and the number of tardy jobs in a static flexible flow shop environment. For this problem, a 0-1 mixed integer program is formulated. The problem is however a combinatorial optimization problem which is too difficult to be solved optimally for large problem sizes, and hence heuristics are used to obtain good solutions in a reasonable time. The proposed constructive heuristics for sequencing the jobs start with the generation of the representatives of the operation time for each operation. Then some dispatching rules and flow shop makespan heuristics are developed. To improve the solutions obtained by the constructive algorithms, fast polynomial heuristic improvement algorithms based on shift moves and pairwise interchanges of jobs are applied. In addition, metaheuristics are suggested, namely simulated annealing, tabu search and genetic algorithms. The basic parameters of each metaheuristic are briefly discussed in this paper. The performance of the heuristics is compared relative to each other on a set of test problems with up to 50 jobs and 20 stages and with an optimal solution for small-size problems. We have found that among the constructive algorithms the insertion based approach is superior to the others, whereas the proposed simulated annealing algorithms are better than tabu search and genetic algorithms among the iterative metaheuristic algorithms.
Dynamic set-up rules for hybrid flow shop scheduling with parallel batching machines
International Journal of Production Research, 2013
An S-stage hybrid (or flexible) flowshop, with sequence-independent uniform setup times, parallel batching machines with compatible parallel batch families (like in casting or heat treatments in furnaces, chemical or galvanic baths, painting in autoclave etc.) has been analyzed with the purpose of reducing the number of tardy jobs (and the makespan); in Graham's notation: FPB(m 1 , m 2 , … , m S )|p-batch, ST si,b |ΣU i . Jobs are sorted dynamically (at each new delivery); batches are closed within sliding (or rolling) time windows and processed in parallel by multiple identical machines. Computation experiments have shown the better performance on benchmarks of the two proposed heuristics based on new formulations of the critical ratio (CR setup ) considering the ratio of allowance setup and processing time in the scheduling horizon, which improves the weighted modified operation due date rule (WMOD).
The goal of this paper is to investigate scheduling heuristics to seek the minimum of a positively weighted convex sum of makespan and the number of tardy jobs in a static hybrid flow shop environment where at least one production stage is made up of unrelated parallel machines. In addition, sequence -and machine -dependent setup times are considered. The problem is a combinatorial optimization problem which is too difficult to be solved optimally for large problem sizes, and hence heuristics are used to obtain good solutions in a reasonable time. Some dispatching rules and flow shop makespan heuristics are developed. Then this solution may be improved by fast polynomial heuristic improvement algorithms based on shift moves and pairwise interchanges. In addition, metaheuristic proposed is a tabu search algorithm. Three basic parameters (i.e., number of neighbors, neighborhood structure, and size of tabu list in each iteration) of a tabu search algorithm are briefly discussed in this paper. The performance of the heuristics is compared relative to each other on a set of test problems with up to 50 jobs and 20 stages.
Flow shop scheduling with two batch processing machines and nonidentical job sizes
The International Journal of Advanced Manufacturing Technology, 2009
A batch processing machine can process several jobs simultaneously. In this research, we consider the problem of a two-stage flow shop with two batch processing machines to minimize the makespan. We assume that the processing time of a batch is the longest processing time among all the jobs in that batch and the sizes of the jobs are nonidentical. There is a limitation on batch sizes and the sum of job sizes in a batch must be less than or equal to the machine capacity. Since this problem is strongly nondeterministic polynomial time hard, we propose two heuristic algorithms. The first one is knowledge-based and the other is based on the batch first fit heuristic proposed previously. To further enhance the solution quality, two different simulated annealing (SA) algorithms based on the two constructive heuristics is also developed. Since heuristic methods for this problem has not been proposed previously, a lower bound is developed for evaluating the performance of the proposed methods. Several test problems have been solved by SAs and lower bound method and the results are compared. Computational studies show that both algo-rithms provide good results but the first SA (ARSA) algorithm considerably outperforms the second one (FLSA). In addition, the results of ARSA algorithm, optimal solutions, and lower bounds are compared for several small problems. The comparisons show that except for one instance, the ARSA could find the optimal solutions and the proposed lower bound provides small gaps comparing with the optimal solutions.