Unrelated parallel machine scheduling using local search

Glass, Celia A.; Potts, Chris N.; Shade, P.

doi:10.1016/0895-7177(94)90205-4

Cited by 93 publications

(48 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Este algoritmo será chamado de Algoritmo de Eficiência. Os testes foram feitos usando o conjunto de dados proposto por (GLASS et al, 1994). As instâncias foram geradas considerando as seguintes três estruturas para os tempos de processamento: Para cada uma das estruturas propostas foram geradas dez instâncias de cada combinação de número de tarefas, n = 50, 100, 150 e 200, e de número de processadores, m = 2, 3, 5, 10, 25 e 50.…”

Section: Resultados Computacionaisunclassified

“…Os resultados apresentados são uma média dos dez resultados obtidos para cada combinação. A melhor solução conhecida para o problema foi obtida utilizando as meta-heurísticas descritas em (GLASS et al, 1994) e (PIERSMA; VAN DIJK, 1996), com o tempo limite de 500 segundos como critério de parada.…”

Section: Resultados Computacionaisunclassified

“…(GLASS et al, 1994) apresentam um estudo comparativo de diferentes metaheurísticas para o R||C max (Busca Tabu, Algoritmos Genéticos e Simulated Annealing). (PIERSMA; VAN DIJK, 1996) apresentam um algoritmo de busca local que utiliza o conceito de eficiência (que será apresentado na seção 4) aliado a um grande número de ordenações intermediárias.…”

Section: Métodos Para Máquinas Paralelas Não-relacionadasunclassified

“…(PIERSMA; VAN DIJK, 1996) apresentam um algoritmo de busca local que utiliza o conceito de eficiência (que será apresentado na seção 4) aliado a um grande número de ordenações intermediárias. Este algoritmo consegue soluções de qualidade comparável às obtidas pelas metaheurísticas estudadas em (GLASS et al, 1994).…”

Section: Métodos Para Máquinas Paralelas Não-relacionadasunclassified

See 3 more Smart Citations

Algoritmo para o problema de seqüenciamento em máquinas paralelas não-relacionadas

Müller

Dias

Araújo

2002

Prod.

View full text Add to dashboard Cite

This work deals with the problem of scheduling n independent jobs on m unrelated parallel machines with the objective of minimizing the makespan (the total elapsed time from the start of execution until all jobs are completed). In this work we propose a new local search algorithm in connection with a powerful neighborhood scheme that uses a structure of intervals and uses the efficiency of the machines for each job. The proposed algorithm, called Mutat, is compared with other algorithms in order to evaluate the quality of the solutions obtained. The new approach finds solutions that overcome, in quality and computational time, the best algorithm of local search found in the literature for this problem. Key wordsScheduling, Unrelated machines, Local search, Heuristics. FELIPE MARTINS MÜLLER ResumoEste trabalho trata do problema de seqüenciamento de n tarefas independentes em m máquinas paralelas não-relacionadas com o objetivo de minimizar o tempo de execução da máquina mais carregada (makespan). É proposto um novo algoritmo de busca local em conexão com um esquema de vizinhança que usa estrutura de intervalos e o conceito de eficiência das máquinas para cada tarefa. O algoritmo proposto, denominado Mutat, é comparado com outros algoritmos para avaliar a qualidade das soluções obtidas. A nova abordagem encontra soluções que superam, em qualidade e tempo computacional, o melhor algoritmo de busca local encontrado na literatura para este problema. Palavras-chaveSeqüenciamento, Máquinas não-relacionadas, Busca local, Heurísticas.Algoritmo para o problema de seqüenciamento em máquinas paralelas não-relacionadas

show abstract

Section: Resultados Computacionaisunclassified

Section: Métodos Para Máquinas Paralelas Não-relacionadasunclassified

See 2 more Smart Citations

Algoritmo para o problema de seqüenciamento em máquinas paralelas não-relacionadas

Müller

Dias

Araújo

2002

Prod.

View full text Add to dashboard Cite

show abstract

“…van de Velde (1993) proposed an exact algorithm and an iterated local search metaheuristic. Glass et al (1994) proposed Genetic Algorithm (GA), Tabu Search (TS) and Simulated Annealing (SA) methods. Piersma and van Dijk (1996) showed a SA and a TS with initialisations coming from the aforementioned heuristics of Hariri and Potts (1991) and Davis and Jaffe (1981).…”

Section: Introductionmentioning

confidence: 99%

Size-reduction heuristics for the unrelated parallel machines scheduling problem

Fanjul-Peyro

Ruíz

2011

Computers & Operations Research

View full text Add to dashboard Cite

In this paper we study the unrelated parallel machines problem where n independent jobs must be assigned to one out of m parallel machines and the processing time of each job differs from machine to machine. We deal with the objective of the minimisation of the maximum completion time of the jobs, usually referred to as makespan or C max . This is a type of assignment problem that has been frequently studied in the scientific literature due to its many potential applications. We propose a set of metaheuristics based on a size-reduction of the original assignment problem that produce solutions of very good quality in a short amount of time. The underlying idea is to consider only a few of the best possible machine assignments for the jobs and not all of them. The results are simple, yet powerful methods. We test the proposed algorithms with a large benchmark of instances and compare them with current state-of-the-art methods. In most cases, the proposed size-reduction algorithms produce results that are statistically proven to be better by a significant margin.

show abstract

Constraint‐based human resource allocation in software projects

2011

View full text Add to dashboard Cite

Resource allocation in a software project is crucial for successful software development. Among various types of resources, human resource is the most important as software development is a human‐intensive activity. Human resource allocation is very complex owing to the human characteristics of developers. The human characteristics affecting allocation can be grouped into individual‐level characteristics and team‐level characteristics. At the individual level, familiarity with tasks needs to be taken into account as it affects the performance of developers. In addition, developers have different levels of productivity, depending on their capability and experience; the productivity of developers also varies according to tasks. At the team level, characteristics such as team cohesion, communication overhead, and collaboration and management also affect human resource allocation. As these characteristics affect the efficiency of project execution, we treat them as constraints of human resource allocation in our approach. We identify individual‐level constraints and team‐level constraints based on the literature and interviews with experts in the industry. With these constraints, our approach optimizes the scheduling of human resource allocations, resulting in more realistic and efficient allocations. We also provide a guideline supporting various factors, with respect to roles and module characteristics, to estimate the productivity of developers based on COCOMO II. As productivity data are hard to obtain and manage, our guideline can provide a useful direction for human resource allocation in case of software projects. To validate our proposed approach, we document a case study using real project data. Copyright © 2011 John Wiley & Sons, Ltd.

show abstract

Unrelated parallel machine scheduling using local search

Cited by 93 publications

References 28 publications

Algoritmo para o problema de seqüenciamento em máquinas paralelas não-relacionadas

Algoritmo para o problema de seqüenciamento em máquinas paralelas não-relacionadas

Size-reduction heuristics for the unrelated parallel machines scheduling problem

Constraint‐based human resource allocation in software projects

Contact Info

Product

Resources

About