An improved intelligent water drops algorithm for solving multi-objective job shop scheduling

Niu, Shaozhang; Ong, S. K.; Nee, A. Y. C.

doi:10.1016/j.engappai.2013.07.011

Cited by 47 publications

(19 citation statements)

References 19 publications

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“…IWD is a widely known swarm intelligence technique and GA belongs to the class of evolutionary algorithms. Both have been successfully applied to solve many optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Multi‐criteria workflow scheduling on clouds under deadline and budget constraints

Kalra

Singh

2019

Concurrency and Computation

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Cloud Computing is increasingly favored by scientists for executing large-scale workflow applications due to its appealing features such as availability of huge infrastructure, low operating costs, easy accessibility, and high scalability. Moreover, the resources in cloud are provisioned as per user's requirement following pay-per-use model. Scheduling workflows in cloud requires orchestration of tasks onto heterogeneous resources while complying with the task dependencies as well as user's Quality of Service (QoS) demands. The problem becomes challenging if there are multiple QoS requirements which conflict with each other. Recent studies attempted to minimize the makespan and execution cost under deadline and/or budget constraints. However, the failure of resources in cloud poses a serious threat to successful execution of these applications. Hence, it becomes essential to consider reliability of resources in addition to other parameters. This paper presents a workflow scheduling policy which minimizes makespan and execution cost while maximizing the reliability of executing workflows under user specified deadline and budget restraints. We have devised a hybrid of Intelligent Water Drops algorithm and Genetic Algorithm (IWD-GA) to accomplish the desired goal. It provides a set of non-dominated solutions with different makespan, cost, and reliability to offer users more flexibility in selecting a solution as per their preferences. The evaluation of the proposed algorithm has been carried based on two performance metrics, ie, hypervolume and two set coverage. The simulation results thus obtained using four diverse scientific workflows confirm that IWD-GA outperforms non-dominated sort genetic algorithm (NSGA-II) and hybrid particle swarm optimization (HPSO) with regard to accuracy and diversity.

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“…IWD is a widely known swarm intelligence technique and GA belongs to the class of evolutionary algorithms. Both have been successfully applied to solve many optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Multi‐criteria workflow scheduling on clouds under deadline and budget constraints

Kalra

Singh

2019

Concurrency and Computation

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“…However, the approaches they provided cannot be spread to a multiobjective context because they aim at a specific problem. Gradually research methods on multiobjective scheduling problem have converted from exact methods to the multiobjective decision-making theory [16,17] and modern heuristic search methods such as GA [18,19], particle swarm optimization (PSO) [20], simulated annealing (SA) [21,22], and water drops algorithm [23]. Literature [24] proposed an interactive solution to | | ( max , , , max , ), which is interactive with DM based on Tchebycheff-approximation in the first module and then using the greedy heuristic algorithm in the second module.…”

Section: Introductionmentioning

confidence: 99%

Cultural-Based Genetic Tabu Algorithm for Multiobjective Job Shop Scheduling

Yang

2014

Mathematical Problems in Engineering

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The job shop scheduling problem, which has been dealt with by various traditional optimization methods over the decades, has proved to be an NP-hard problem and difficult in solving, especially in the multiobjective field. In this paper, we have proposed a novel quadspace cultural genetic tabu algorithm (QSCGTA) to solve such problem. This algorithm provides a different structure from the original cultural algorithm in containing double brief spaces and population spaces. These spaces deal with different levels of populations globally and locally by applying genetic and tabu searches separately and exchange information regularly to make the process more effective towards promising areas, along with modified multiobjective domination and transform functions. Moreover, we have presented a bidirectional shifting for the decoding process of job shop scheduling. The computational results we presented significantly prove the effectiveness and efficiency of the cultural-based genetic tabu algorithm for the multiobjective job shop scheduling problem.

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“…Job shop scheduling problem (JSP) is a one of most important and difficult optimization problems in the production management of manufacturing processes, and exits almost ubiquitously in the industrial engineering world. The classical JSP can be described as follows [1]: there are n different jobs to be processed on m different machines. Each job needs m operations and each operation needs to be processed without preemption for a fixed processing time on a given machine.…”

Section: Introductionmentioning

confidence: 99%

An Improved Immune Algorithm for Multi-objective Flexible Job-shop Scheduling

Xue

Zhang²,

Wei³

et al. 2014

JNW

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Flexible job-shop scheduling problem derived from the job-shop scheduling problem, and is more realistic than the job-shop scheduling problem in the manufacturing enterprise. It should be utilized with high efficiency. But it is a multi-objective optimal problem. Three minimization objectives (Maximum completion time (makespan), Total workload of machines and Workload of the critical machine) are considered simultaneously. This multi-objective flexible job-shop scheduling problem is one of the hardest combinatorial optimization problems, and is a strongly NPhard problem. To solve the multi-objective flexible job-shop scheduling problem, a quantum immune algorithm based on the quantum and immune principles is proposed. According to the analysis of the experimental results, this algorithm is feasible and effective to solve the multi-objective flexible job-shop scheduling problem. It effectively relieves some disadvantages of the quantum and immune optimization.

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An improved intelligent water drops algorithm for solving multi-objective job shop scheduling

Cited by 47 publications

References 19 publications

Multi‐criteria workflow scheduling on clouds under deadline and budget constraints

Multi‐criteria workflow scheduling on clouds under deadline and budget constraints

Cultural-Based Genetic Tabu Algorithm for Multiobjective Job Shop Scheduling

An Improved Immune Algorithm for Multi-objective Flexible Job-shop Scheduling

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