A Monte Carlo simulation based chaotic differential evolution algorithm for scheduling a stochastic parallel processor system

Mokhtari, Hadi; Salmasnia, Ali

doi:10.1016/j.eswa.2015.05.015

Cited by 25 publications

(10 citation statements)

References 60 publications

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“…It has great capabilities to forecast the uncertainties, and to offer more accurate solutions of the results generated. For the scheduling problem, MC simulation experiments can generate some sample values of processing time that are utilized to calculate the expected makespan [44]- [46]. A system in a stochastic context is more realistic than in a deterministic one and works remain to be done when concerning the stochastic version, Mokhtari and Salmasnia [44] executed MC simulation to solve the parallel processor problem with stochastic processing time.…”

Section: Literature Review a Flow Shop And Job Shop Scheduling Pmentioning

confidence: 99%

“…For the scheduling problem, MC simulation experiments can generate some sample values of processing time that are utilized to calculate the expected makespan [44]- [46]. A system in a stochastic context is more realistic than in a deterministic one and works remain to be done when concerning the stochastic version, Mokhtari and Salmasnia [44] executed MC simulation to solve the parallel processor problem with stochastic processing time. Juan et al [45] adopted MC and iterative local search methods to solve permutation flow shop scheduling problem with stochastic processing time.…”

Section: Literature Review a Flow Shop And Job Shop Scheduling Pmentioning

confidence: 99%

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Multi-Objective Migrating Birds Optimization Algorithm for Stochastic Lot-Streaming Flow Shop Scheduling With Blocking

Han

Gong

et al. 2019

IEEE Access

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Blocking lot-streaming flow shop scheduling problem with the stochastic processing time has a wide range of applications in various industrial systems. However, this problem has not yet been well studied. In this paper, the above-mentioned problem is transformed into a determinate multi-objective optimization one using the Monte Carlo sampling method. A Multi-Objective Migrating Birds Optimization (MOMBO) algorithm is then proposed to solve the above-mentioned re-formulated multi-objective scheduling problem, in which the multiple-based PFE is proposed to yield the initial solutions with high quality, the information of the non-dominated solutions is learned and sampled to improve the global searching ability of MOMBO, and a reference-point-assisted local search method for multi-objective optimization is applied to further enhance the exploitation capability of MOMBO. To evaluate the performance of the MOMBO, several comparative experiments are executed on 180 test scheduling instances. The experimental results demonstrate that the MOMBO outperforms the compared algorithms in convergence and distributivity and has capacities to tackle the uncertainties. INDEX TERMS Scheduling, multi-objective, blocking lot-streaming flow shop, stochastic processing time, migrating birds optimization.

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Section: Literature Review a Flow Shop And Job Shop Scheduling Pmentioning

confidence: 99%

Section: Literature Review a Flow Shop And Job Shop Scheduling Pmentioning

confidence: 99%

Multi-Objective Migrating Birds Optimization Algorithm for Stochastic Lot-Streaming Flow Shop Scheduling With Blocking

Han

Gong

et al. 2019

IEEE Access

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“…That is to say, a slight change may yield the chaotic system to great differences in the output. 67 CMs [68][69][70] are usually used for modeling chaos. A CM is a dynamical discrete-time continuous value function.…”

Section: Cmsmentioning

confidence: 99%

Quantum-inspired hybrid algorithm for integrated process planning and scheduling

Liu

Wen

2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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Process planning and job shop scheduling problems are the two classical but crucial activities in manufacturing system. With the approach of integrated process planning and scheduling, the two actual activities are combined to conduct operation selection and operation sequencing with the constraints of practical job shop status. In this article, a quantuminspired hybrid algorithm with the objective of minimum makespan is proposed, aiming to solve integrated process planning and scheduling problems in dynamic manufacturing systems. A hybrid-coding representation is suggested, which is a three-layer structure in numerical representation and Q-bit representation adopted from quantum-inspired evolutionary algorithm. Based on the hybrid-coding representation, customized converting and repairing rules and methods are presented to generate feasible individuals. Q-gate rotation and group leader optimization algorithm are integrated systematically for the population evolution to accelerate the convergence speed of the proposed algorithm. In order to increase the diversity of population, a chaotic map called logistic map is introduced, bringing the stochastic initial individuals. Experiments show that the proposed hybrid algorithm can generate outstanding outcomes for integrated process planning and scheduling instances.

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“…Therefore, a novel optimization-simulation approach to solving CCP problem is proposed. Optimization-simulation approaches are not new to optimization, and their aim is to integrate optimization methods with Monte Carlo simulation experiments for solving stochastic problems [33,18,19,45,16,7,1,32,31]. Their number has increased significantly only recently, due to increased computational abilities of computers.…”

Section: Introductionmentioning

confidence: 99%

An Optimization-Simulation Approach to Chance Constraint Programming

Markovic¹,

Vujošević

Makajić-Nikolić

2018

ITC

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This paper considers a stochastic programming problem with a number of random parameters in the set of constraints. The method used for solving the problem is the iterative optimization-simulation approach. It consists of two phases: optimization phase, which includes solving a deterministic counterpart of the original chance-constrained problem, and a simulation phase in which the original constraints are checked using Monte Carlo simulation. One iteration corresponds to one scenario. If the decision maker is dissatisfied with the results, a new scenario is generated in which the deterministic values of stochastic parameters are changed in the direction that will provide a more robust solution. The deterministic counterpart in the new scenario is formulated depending on the result of the previous iteration. To that end, different heuristics are considered. The main goal is to provide a good insight on the optimization problem under uncertainty by performing a relatively small number of iterations. The general approach and results of the proposed framework are illustrated on an example of advertisement placement.

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A Monte Carlo simulation based chaotic differential evolution algorithm for scheduling a stochastic parallel processor system

Cited by 25 publications

References 60 publications

Multi-Objective Migrating Birds Optimization Algorithm for Stochastic Lot-Streaming Flow Shop Scheduling With Blocking

Multi-Objective Migrating Birds Optimization Algorithm for Stochastic Lot-Streaming Flow Shop Scheduling With Blocking

Quantum-inspired hybrid algorithm for integrated process planning and scheduling

An Optimization-Simulation Approach to Chance Constraint Programming

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