Benchmarks for basic scheduling problems

Taillard, Éric D.

doi:10.1016/0377-2217(93)90182-m

Cited by 1,995 publications

(1,104 citation statements)

References 6 publications

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“…In particular, we consider a subset of the problems proposed by Taillard [19], consisting of four families of problems of sizes 4 × 4, 5 × 5, 7 × 7 and 10 × 10 and where each family contains 10 problem instances. From each of these crisp problem instances, we generate 10 fuzzy instances, so we have 400 fuzzy problem instances in total.…”

Section: Resultsmentioning

confidence: 99%

A Genetic Algorithm for the Open Shop Problem with Uncertain Durations

Palacios

Puente

Vela

et al. 2009

Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira’s Scientific Legacy

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Abstract. We consider a variation of the open shop problem where task durations are allowed to be uncertain and where uncertainty is modelled using fuzzy numbers. We propose a genetic approach to minimise the expected makespan: we consider different possibilities for the genetic operators and analyse their performance, in order to obtain a competitive configuration. Finally, the performance of the proposed genetic algorithm is tested on several benchmark problems, modified so as to have fuzzy durations, compared with a greedy heuristic from the literature.

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Section: Resultsmentioning

confidence: 99%

A Genetic Algorithm for the Open Shop Problem with Uncertain Durations

Palacios

Puente

Vela

et al. 2009

Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira’s Scientific Legacy

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“…The tuning benchmark set contains the instances with 20 machines from the tuning benchmark set proposed by [9]. The testing benchmark set is an adaptation of the benchmark set proposed by Taillard [18], following what is traditionally done in the multi-objective PFSP literature [9,17]. Once the algorithms are tuned, we run them 10 times on each test instance, compute the average hypervolume, and compare the results using rank sum analysis and parallel coordinate plots.…”

Section: Methodsmentioning

confidence: 99%

Automatic Design of Evolutionary Algorithms for Multi-Objective Combinatorial Optimization

Bezerra

López-Ibáñez

Stützle

2014

Parallel Problem Solving From Nature – PPSN XIII

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Abstract. Multi-objective evolutionary algorithms (MOEAs) have been the subject of a large research effort over the past two decades. Traditionally, these MOEAs have been seen as monolithic units, and their study was focused on comparing them as blackboxes. More recently, a component-wise view of MOEAs has emerged, with flexible frameworks combining algorithmic components from different MOEAs. The number of available algorithmic components is large, though, and an algorithm designer working on a specific application cannot analyze all possible combinations. In this paper, we investigate the automatic design of MOEAs, extending previous work on other multi-objective metaheuristics. We conduct our tests on four variants of the permutation flowshop problem that differ on the number and nature of the objectives they consider. Moreover, given the different characteristics of the variants, we also investigate the performance of an automatic MOEA designed for the multi-objective PFSP in general. Our results show that the automatically designed MOEAs are able to outperform six traditional MOEAs, confirming the importance and efficiency of this design methodology.

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“…For the algorithms considered, the overall performance is assessed using a set of benchmark problems totaling 120 in number proposed by Taillard [12], and 250 in number proposed by Ruben Ruiz (2009). The processing time varies from 1 to 99 units and generated using a random number generator for a given seed.…”

Section: Heuristic Algorithms Analyzedmentioning

confidence: 99%

Analyzing a few Heuristic Algorithms Considering Machine Idle Time and Processing Time in Permutation Flow Shop Scheduling Problems

Baskar¹,

Xavior²

2013

IJAIS

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Flow Shop Scheduling has been an interesting field of research for over six decades. They are easy to formulate, yet difficult to solve. In a shop, there are 'm' machines arranged in series to process a set of 'n' jobs having different processing times. Each job has to pass through each machine, in order. The problem is to find a sequence of jobs to be processed in all the machines, so that a given performance parameter is optimized. The total number of schedules is (n!) m . If the order of machines is not to be changed, the problem is simplified, and the overall number of solutions is reduced to n!. This problem is referred to a permutation flow shop scheduling problem, or PFSP in short. Starting from two machines, 'n' jobs, various Heuristics have been proposed over the years. After the invention of meta heuristics and evolutionary algorithms, and increased computational capabilities available today, finding optimal/ near optimal solutions become comparatively easier. In this paper, a few heuristic algorithms have been analyzed for makespan criterion considering machine idle time and processing time, by comparing the results with the well known CDS algorithm. Benchmark problems proposed by Taillard and Ruben Ruiz are used for the performance analysis.

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Benchmarks for basic scheduling problems

Cited by 1,995 publications

References 6 publications

A Genetic Algorithm for the Open Shop Problem with Uncertain Durations

A Genetic Algorithm for the Open Shop Problem with Uncertain Durations

Automatic Design of Evolutionary Algorithms for Multi-Objective Combinatorial Optimization

Analyzing a few Heuristic Algorithms Considering Machine Idle Time and Processing Time in Permutation Flow Shop Scheduling Problems

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