Two NEH Heuristic Improvements for Flowshop Scheduling Problem with Makespan Criterion

Sauvey, Christophe; Sauer, Nathalie

doi:10.3390/a13050112

Cited by 17 publications

(7 citation statements)

References 32 publications

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“…If there are differences in the experimental results, differences in input parameters will undoubtedly affect the results. The results obtained will be compared with the upper limit of Eric Taillard's website, and the order of work that is not changed is the order of the work, which is named with a simple "makespan" [7], [20]. Experimental testing was carried out in each case.…”

Section: B Experimental Resultsmentioning

confidence: 99%

A Bee Colony Algorithm based Solver for Flow Shop Scheduling Problem

Halim

Nugraheni

2021

JOIV : Int. J. Inform. Visualization

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Flow Shop Scheduling (FSS) is scheduled to involve n jobs and m machines in the same process sequence, where each machine processes precisely one job in a certain period. In FSS, when a machine is doing work, other machines cannot do the same job simultaneously. The solution to this problem is the job sequence with minimal total processing time. Many algorithms can be used to determine the order in which the job is performed. In this paper, the algorithm used to solve the flow shop scheduling problem is the bee colony algorithm. The bee colony algorithm is an algorithm that applies the metaheuristic method and performs optimization according to the workings of the bee colony. To measure the performance of this algorithm, we conducted some experiments by using Taillard's Benchmark as problem instances. Based on experiments that have been carried out by changing the existing parameter values, the size of the bee population, the number of iterations, and the limit number of bees can affect the candidate solutions obtained. The limit is a control parameter for a bee when looking for new food sources. The more the number of bees, the more iterations, and the limit used, the better the final time of the sequence of work. The bee colony algorithm can reach the upper limit of the Taillard case in some cases in the number of machines 5 and 20 jobs. The more the number of machines and jobs to optimize, the worse the total processing time.

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Section: B Experimental Resultsmentioning

confidence: 99%

A Bee Colony Algorithm based Solver for Flow Shop Scheduling Problem

Halim

Nugraheni

2021

JOIV : Int. J. Inform. Visualization

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“…Constructive methods have great relevance to solving problems with variable job processing time. The case of the NEH algorithm is known, which showed good performance with problems with a high number of machines [103] and minimized the makespan as an objective function (among the most common objective functions).…”

Section: Heuristicsmentioning

confidence: 99%

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

2022

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In traditional scheduling problems, job processing times are considered constant and known in advance. This assumption is, however, a simplification when it comes to hand-intensive real-life production contexts because workers usually induce variability in the job processing times due to several factors such as learning, monotony, fatigue, psychological factors, etc. These effects can decrease or increase the actual processing time when workers execute a job. The academic literature has reported several modeling and resolution approaches to deal with the phenomenon in a variety of configurations. However, there is no comprehensive review of these research outputs to the best of our knowledge. In this paper, we follow a systematic approach to review relevant contributions addressing the scheduling problem with learning and deterioration effects. Modeling approaches for learning and deterioration effects, objective functions, and solution methods employed in the literature are the main topics for the taxonomy proposed in this review. A total of 455 papers from 1999 to 2021 are included and analyzed. Different areas of interest are presented, and some opportunities for future research are identified.

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“…Due to succeeding achieved by the NEH algorithm, the researchers have been work on improving its performance or integrating it with other optimization algorithms for overcoming this problem [3,4]. Before speaking of the optimization algorithms, we start reviewing the literature which is devoted to the improvement of the standard NEHheuristic method.…”

Section: Introductionmentioning

confidence: 99%

“…This improvement is summarized as follows: (1) using the priority rule, which integrates the average processing time of jobs and their standard deviations to replace the one in the standard NEH, and (2) using the new tie-breaking strategy to significantly improve the performance of the standard one. Furthermore, Sauvey, and Sauer [3] proposed an improvement on the standard NEH algorithm using two strategies: (1) the first one used the factorial basis decomposition method to ensure testing all the possible orders for the small-scale instances, and allow to randomly choose a particular order of all the possible orders, and (2) the second strategy was based on keeping a list of the best partial sequences, rather than just one.…”

Section: Introductionmentioning

confidence: 99%

A Simple and Effective Approach for Tackling the Permutation Flow Shop Scheduling Problem

et al. 2021

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In this research, a new approach for tackling the permutation flow shop scheduling problem (PFSSP) is proposed. This algorithm is based on the steps of the elitism continuous genetic algorithm improved by two strategies and used the largest rank value (LRV) rule to transform the continuous values into discrete ones for enabling of solving the combinatorial PFSSP. The first strategy is combining the arithmetic crossover with the uniform crossover to give the algorithm a high capability on exploitation in addition to reducing stuck into local minima. The second one is re-initializing an individual selected randomly from the population to increase the exploration for avoiding stuck into local minima. Afterward, those two strategies are combined with the proposed algorithm to produce an improved one known as the improved efficient genetic algorithm (IEGA). To increase the exploitation capability of the IEGA, it is hybridized a local search strategy in a version abbreviated as HIEGA. HIEGA and IEGA are validated on three common benchmarks and compared with a number of well-known robust evolutionary and meta-heuristic algorithms to check their efficacy. The experimental results show that HIEGA and IEGA are competitive with others for the datasets incorporated in the comparison, such as Carlier, Reeves, and Heller.

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Two NEH Heuristic Improvements for Flowshop Scheduling Problem with Makespan Criterion

Cited by 17 publications

References 32 publications

A Bee Colony Algorithm based Solver for Flow Shop Scheduling Problem

A Bee Colony Algorithm based Solver for Flow Shop Scheduling Problem

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

A Simple and Effective Approach for Tackling the Permutation Flow Shop Scheduling Problem

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