Parallel ant colony optimization for resource constrained job scheduling

Thiruvady, Dhananjay; Ernst, Andreas T.; Singh, Gaurav

doi:10.1007/s10479-014-1577-7

Cited by 48 publications

(36 citation statements)

References 21 publications

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“…Evolutionary Computation [236], Genetic Algorithm [187], Evolution Strategy [128], Evolutionary Programming [237], Genetic Programming [238], Estimation of Distribution Algorithm [35], Differential Evolution [239], Coevolutionary Algorithm [156], Scatter Search [50,240], Swarm Intelligence [241], Ant Colony [242], Bacterial Foraging Optimization Algorithm [243], Bee Colony [244], Artificial Immune System [245], Particle Swarm Optimization [141], and Biogeography-based Optimization [246] are population-based metaheuristics.…”

Section: Population-based Metaheuristicsmentioning

confidence: 99%

A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

Fei

Shen

2017

Scientific Programming

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Over the past decades, optimization in operations management has grown ever more popular not only in the academic literature but also in practice. However, the problems have varied a lot, and few literature reviews have provided an overview of the models and algorithms that are applied to the optimization in operations management. In this paper, we first classify crucial optimization areas of operations management from the process point of view and then analyze the current status and trends of the studies in those areas. The purpose of this study is to give an overview of optimization modelling and resolution approaches, which are applied to operations management.

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Section: Population-based Metaheuristicsmentioning

confidence: 99%

A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

Fei

Shen

2017

Scientific Programming

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“…The tests confirmed the superiority of the combined technique above all the techniques reported in the literature. Thiruvady et al (2016) consider a resource constrained scheduling problem for a mining supply chain. The authors propose two popular metaheuristics, ant colony optimization (ACO) and simulated annealing, to investigate how best to parallelize the proposed methods on a shared memory architecture consisting of several cores.…”

Section: Overview Of Papers In This Special Issuementioning

confidence: 99%

Evolutionary algorithms for supply chain management

Govindan

2016

Ann Oper Res

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“…Thiruvady et al proposed a parallel ACO algorithm to efficiently solve the resource constrained scheduling problem for mining supply chains [34]. • Genetic algorithm is used to solve the optimization problem based on a natural selection process that mimics biological evolution.…”

Section: Resource Management Approachesmentioning

confidence: 99%

Real-Time Multisensor Data Retrieval for Cloud Robotic Systems

Wang

Liu

Meng

2015

IEEE Trans. Automat. Sci. Eng.

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Cloud technology elevates the potential of robotics with which robots possessing various capabilities and resources may share data and combine new skills through cooperation. With multiple robots, a cloud robotic system enables intensive and complicated tasks to be carried out in an optimal and cooperative manner. Multisensor data retrieval (MSDR) is one of the key fundamental tasks to share the resources. Having attracted wide attention, MSDR is facing severe technical challenges. For example, MSDR is particularly difficult when cloud cluster hosts accommodate unpredictable data requests triggered by multiple robots operating in parallel. In these cases, near real-time responses are essential while addressing the problem of the synchronization of multisensor data simultaneously. In this paper, we present a framework targeting near real-time MSDR, which grants asynchronous access to the cloud from the robots. We propose a market-based management strategy for efficient data retrieval. It is validated by assessing several quality-of-service (QoS) criteria, with emphasis on facilitating data retrieval in near real-time. Experimental results indicate that the MSDR framework is able to achieve excellent performance under the proposed management strategy in typical cloud robotic scenarios.Note to Practitioners-This paper was motivated by the problem of sharing resources in cloud robotic systems efficiently for accomplishing real-time tasks. Existing approaches to cloud robotics bear very strict assumptions that the resources are unconstrained and ubiquitous. However, there are technical challenges for multirobot systems to access the cloud and retrieve resources in near real-time. This paper presents a general framework for setting up cloud robotic system with a novel resource management strategy. We mathematically formulate the problem of multisensor data retrieval through the cloud as a Stackelberg game, and propose an optimal solution with proof. We then define the QoS criteria for evaluation considering the constraints of robotic tasks. In the experimental scenarios, our management mechanism significantly improves the performance for multisensor data retrieval in the evaluation of QoS, CPU load, and bandwidth usage.

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Parallel ant colony optimization for resource constrained job scheduling

Cited by 48 publications

References 21 publications

A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

Evolutionary algorithms for supply chain management

Real-Time Multisensor Data Retrieval for Cloud Robotic Systems

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