Scatter search for mixed blocking flowshop scheduling

Riahi, Vahid; Khorramizadeh, Mostafa; Newton, M. A. Hakim; Sattar, Abdul

doi:10.1016/j.eswa.2017.02.027

Cited by 58 publications

(48 citation statements)

References 51 publications

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“…The reason is that different neighbourhood operators produce different landscapes and hence different local optima. We use insert and swap operators since they are widely used when solutions are represented as permutations e.g., in the flowshop scheduling problems [11,12] and the order scheduling problems [13].…”

Section: Intensification Methodsmentioning

confidence: 99%

Constraint-Guided Local Search for Single Mixed-Operation Runway

Riahi

Newton

Sattar

2018

AI 2018: Advances in Artificial Intelligence

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Aircraft sequencing problem (ASP) is to schedule the operation times of departing and arriving aircraft such that their deviation from the desired operation times are minimised. There are two types of hard constraint which make this problem very challenging: time window constraint for the operation time of each aircraft, and minimum separation time between each pair of aircraft. ASP is known to be NP-Hard. Although some progress has been made in recent years in solving ASP, existing techniques still rely on generic algorithms that usually lack problem specific knowledge. This leads to either finding low quality solutions or scrambling with large-sized problems. In this work, we propose a constraint-guided local search algorithm that advances ASP search by injecting the specific knowledge of the problem into its different phases. In the intensification phase, we propose a greedy approach that gives more priorities to aircraft that are more problematic and create more delays. In the diversification phase, we employ a bounded-diversification technique that controls the new position of each selected aircraft and does not allow them to move very far away from their current positions. Computational results show that the proposed algorithm outperforms the existing state-of-the-art methods with considerable margin.

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Section: Intensification Methodsmentioning

confidence: 99%

Constraint-Guided Local Search for Single Mixed-Operation Runway

Riahi

Newton

Sattar

2018

AI 2018: Advances in Artificial Intelligence

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“…There are three approaches used for handling the JSP: Heuristic, classical optimization, and Artificial Intelligence (AI) approaches . Typically, resource allocation in JSP is considered as an optimization problem, and several heuristic algorithms have been proposed based on different assumptions and constraints . These algorithms directly influence the volume, frequency, and intensity of data exchange in job‐shop as well as the scheduling quality .…”

Section: Related Workmentioning

confidence: 99%

“…21 Typically, resource allocation in JSP is considered as an optimization problem, and several heuristic algorithms have been proposed based on different assumptions and constraints. 18,[22][23][24][25][26][27][28] These algorithms directly influence the volume, frequency, and intensity of data exchange in job-shop as well as the scheduling quality. 21 Most of heuristic algorithms can be classified into three types with respect to the real-time processing.…”

Section: Related Workmentioning

confidence: 99%

Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

Hasan

Al‐Rizzo

2019

Concurrency and Computation

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Summary Internet of Things (IoT) is steadily growing in support of current and projected real‐time distributed Internet applications in civilian and military applications, while Cloud Computing has the ability to meet the performance expectations of these applications. In this paper, we present the implementation of logistics management applications relying on cooperative resources with optimized performances. To dynamically incorporate smart manufacturing objects into logistics management IoT applications within a ubiquitous environment, task scheduling must be provided for resource allocation in an optimized way. Within such environment, we propose a task scheduling algorithm based on a robust Canonical Particle Swarm Optimization (CPSO) algorithm to solve the problem of resource allocation and management in both homogeneous and heterogeneous IoT Cloud Computing. Our objective is to satisfy the Makespan by performing optimal task scheduling while considering different policies of incoming tasks. Performance evaluation from simulation experiments reveals that optimizing the Makespan can be significantly improved by Longest Processing Time (LPT), Shortest Processing Time (SPT), Earliest Computation Time (ECT), Earliest Starting Time (EST), Earliest Deadline First (EDF), and Earliest Duedate (EDD) using our CPSO algorithm as compared with traditional list task scheduling algorithms.

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“…Como contextualização na parte histórica, Cheng, Gupta e Wang (2009), Vallada, Ruiz e Framinan (2015), e Riahi et al (2017) enfatizam que o pioneiro na área de pesquisa da permutação flowshop foi Johnson (1954), para o problema de duas máquinas com o critério de minimização do tempo total de processamento (makespan).…”

Section: Flowshopunclassified

Avaliação de heurísticas para o problema flowshop com setup separado e independente da sequência

Branco¹,

Correia²

2019

R. Gest. Industr.

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O presente trabalho caracteriza-se pelo estudo comparativo de desempenho das heurísticas LPT, SPT e NEH adaptadas para a resolução de problemas de sequenciamento de tarefas flowshop, com tempo de setup separado e independente da seqüência. Para realizar a análise de desempenho é considerado não só o tempo de processamento de cada tarefa para cada máquina disponível, mas também o tempo necessário para realizar a configuração das máquinas utilizadas. Com o intuito de identificar o melhor método de sequenciamento a ser utilizado, analisou-se em um banco de dados o desempenho dos métodos baseado nos resultados de makespan, de acordo com ferramentas estatísticas. Após os cálculos realizados, observou-se na primeira análise, dos métodos de ordenação SPT e LPT, o melhor desempenho do método heurístico LPT com a consideração do tempo de setup das tarefas somado aos tempos de processamento, enquanto que para o método construtivo NEH, a ordenação LPT sem considerar o tempo de setup para definição do sequenciamento das tarefas se mostrou mais efetivo. Concluiu-se também a melhor performance dos métodos construtivos NEH quando comparado aos métodos de ordenação SPT e LPT.

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Scatter search for mixed blocking flowshop scheduling

Cited by 58 publications

References 51 publications

Constraint-Guided Local Search for Single Mixed-Operation Runway

Constraint-Guided Local Search for Single Mixed-Operation Runway

Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

Avaliação de heurísticas para o problema flowshop com setup separado e independente da sequência

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