An artificial immune algorithm for multiple-route job shop scheduling problem

Golmakani, Hamid Reza; Namazi, Ali

doi:10.1007/s00170-011-3886-1

Cited by 13 publications

(4 citation statements)

References 35 publications

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“…The generation of antibody population will no longer use random number mode [3], but the chaotic map model, taking into account the traversal effect, In this paper, we will use sin model and Kent model instead of Logistic model and tent model;…”

Section: A Basic Steps Of Chaos Immune Algorithmmentioning

confidence: 99%

An improved chaos immune algorithm based on Hadoop framework to solve job-shop scheduling problem

Wang

Xuan

et al. 2013

Proceedings of 2013 3rd International Conference on Computer Science and Network Technology

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Based on the analysis of the inadequate of chaos immune algorithm, an improved chaos immune algorithm for solving job-shop problem is presented. Use combined chaos mapping to increase the diversity of the initial population and Map-Reduce method to reduce the time complexity of algorithm based on Hadoop framework. Through the experiment of antibody population initialization and classic JSSP benchmark, the increase of the performance and effect of the algorithm is verified.

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Section: A Basic Steps Of Chaos Immune Algorithmmentioning

confidence: 99%

An improved chaos immune algorithm based on Hadoop framework to solve job-shop scheduling problem

Wang

Xuan

et al. 2013

Proceedings of 2013 3rd International Conference on Computer Science and Network Technology

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“…RCPSP-MR problems can be regarded in the scheduling of projects, such as mechanizing various production line, launching SCADA systems, launching smart urban systems, dispatching liquid reservoirs fuel, launching different vehicle tracking systems, developing educational simulators, developing electronic Siebel for shooting training and reverse engineering of critical and important equipment. Also, RCPSP-MR is defined and described in this paper is indicated in flexible manufacturing cells (FMCs) according to the related literature [e.g., [42][43][44][45][46][47][48][49][50][51][52][53][54][55].…”

Section: Introductionmentioning

confidence: 99%

Designing a resource-constrained project scheduling model considering multiple routes for flexible project activities: meta-heuristic algorithms

2019

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Resource constrained project scheduling problem with multiple routes for flexible project activities (RCPSP-MR) is a generalization of the RCPSP, in which for the implementation of each flexible activity in main structure of the project, several exclusive sub-networks are considered. Each sub-network is regarded as a route for the flexible activity. The routes are considered for each flexible activity that are varied in terms of: 1) Number of activities required to execute; 2) Precedence relationship between activates; 3) Allocation of different renewable and nonrenewable resources to each activity; and 4) Effectiveness on the duration and cost of project completion. In this paper, a new mathematical formulation of RCPSP-MR is firstly presented. Then, two solving approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) are proposed to minimize costs of project completion. To evaluate the effectiveness of these proposed approaches, 50 problems (in very small, small, medium, and large-sized test problems) are designed and then are solved. Finally, comparisons are provided. Computational results show that the proposed GA generates high-quality solutions in a timely fashion. Keywords: Resource constrained project scheduling problem (RCPSP), flexible activities, multiple routes, particle swarm optimization (PSO), genetic algorithm (GA) 0,1, 2,..., 1 JN  1 J Set of total project fixed activities, 1 JJ  2 J Set of total flexible project activities, 21 J J J    Set of routes of each flexible activity 2 J   h  Set of total sub network for flexible activity

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“…(7,9,10) is the first aluminum ladle, (14,15,16) is the second aluminum ladle, etc. The 6th aluminum ladle only contains one electrolytic cell (8).…”

Section: The Gene Code Rule and The Initializationmentioning

confidence: 99%

“…A more simple and efficient operator needs to be put forward for the whole optimization process. Here, a novel hybrid optimization algorithm, composed of an immune chaos clone algorithm [8][9] and an artificial experience, is proposed.…”

Section: Model Solution Based On Hybrid Chaos Clone Algorithmmentioning

confidence: 99%

A New Approach to Blending and Loading Problem of Molten Aluminum

Wei²

2014

j.eng.technol.sci.

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Abstract. The problems of blending electrolyzer and multi-constraint optimization of electrolytic aluminum scheduling in the electrolytic aluminum production process were addressed. Based on a mathematical model analysis, a novel hybrid optimization algorithm is proposed for optimization of blending together the molten aluminum in different electrolytic cells. An affinity degree function was designed to represent the path of aluminum scheduling. The mutation operators were designed to implement the transformation of electrolyzer combination and change the route of loading. A typical optimization example from an aluminum plant in northwest China is given in this paper, the results of which demonstrate the effectiveness of the proposed method.

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An artificial immune algorithm for multiple-route job shop scheduling problem

Cited by 13 publications

References 35 publications

An improved chaos immune algorithm based on Hadoop framework to solve job-shop scheduling problem

An improved chaos immune algorithm based on Hadoop framework to solve job-shop scheduling problem

Designing a resource-constrained project scheduling model considering multiple routes for flexible project activities: meta-heuristic algorithms

A New Approach to Blending and Loading Problem of Molten Aluminum

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