A novel parallel quantum genetic algorithm for stochastic job shop scheduling

Gu, Jinwei; Gu, Xingsheng; Gu, Manzhan

doi:10.1016/j.jmaa.2008.12.065

Cited by 100 publications

(43 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At this point one also needs to de-fine the terminating condition so that the algorithm stops running once an acceptable solution is found [5].…”

Section: Initializationmentioning

confidence: 99%

“…Mutation alters one individual, parent, to produce a single new individual, child. Let pm be the probability of mutation, then as in the crossover routine, we first determine whether we are going to perform mutation on the current pair of parent chromosomes [5]. If a mutation operation is called for, we select a mutating point m point, and then change a true to a false (1 to 0) or vice versa.…”

Section: VIImentioning

confidence: 99%

See 1 more Smart Citation

Genetic Algorithm by using MATLAB Program

Alenazi¹

2015

International Journal of Advanced Research in Computer and Comm

View full text Add to dashboard Cite

Abstract:In this paper, an attractive approach for teaching genetic algorithm (GA) is presented. This approach is based primarily on using MATLAB in implementing the genetic operators: initialization, crossover, mutation, evaluation and selection. A detailed illustrative examples is presented to demonstrate that how to solve Traveling Salesman Problem (TSP) and Drawing the largest possible circle in a space of stars without enclosing any of them.

show abstract

“…At this point one also needs to de-fine the terminating condition so that the algorithm stops running once an acceptable solution is found [5].…”

Section: Initializationmentioning

confidence: 99%

Section: VIImentioning

confidence: 99%

Genetic Algorithm by using MATLAB Program

Alenazi¹

2015

International Journal of Advanced Research in Computer and Comm

View full text Add to dashboard Cite

show abstract

“…QGA has recently been developed for solving such problems and has been quite effective too. QGA performs better than GA as it explores in 0-1 hyperspace whereas GA explores directly in search space [17,18,19]. A novel method of task scheduling using QGA has been proposed and experimental study reveals that the performance of QGA based model is better than GA based model.…”

Section: Related Workmentioning

confidence: 99%

A Review on Resource Scheduling Models to Optimize Quality of Service Parameters in Grid Computing using Meta-heuristics

PrasadSahu¹,

Singh²,

Prakash³

2015

IJCA

View full text Add to dashboard Cite

Computational Grid (CG) is a wide network of computational resources that provides a distributed platform for high end compute intensive applications. The resources in the computational grid are usually heterogeneous and being a highly heterogeneous system, Computational Grid poses a number of constraints. It is difficult to allocate and schedule the applications properly to achieve the benefit of the grid resources from the applications point of view, as the resources are heterogeneous and dynamic in nature. There are no common scheduling strategies that fulfill all the needs with respect to both, user and the system. The available scheduling implementations consider specific characteristics of the available resources and the application. The complexity of application, user requirements and system heterogeneity prevents any scheduling procedure in achieving its best performance. The aim of a grid scheduling algorithm is to find an appropriate set of resources and maintain its userdemanded Quality of Service (QoS) requirements. Scheduling in CG is an NP-hard problem which requires an efficient solution. The problem, considered in this work, is task scheduling in Computational Grid (CG). Task scheduling in CG is a complex problem as many QoS parameters and system constraints are involved. This paper deliberates over the problem and various tools used in order to solve this problem.

show abstract

“…In addition, a multi-objective genetic algorithm is proposed in [16] for stochastic job shop scheduling problems in which the makespan and the total tardiness ratio should be minimized. In [17,18], quantum genetic algorithms are proposed to solve SJSSP with expected makespan criterion. In [19], a simulation-based decision support system is presented for the production control of a stochastic flexible job shop manufacturing system.…”

Section: The Stochastic Job Shop Scheduling Problemmentioning

confidence: 99%

An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times

Zhang

2011

Entropy

View full text Add to dashboard Cite

Due to the influence of unpredictable random events, the processing time of each operation should be treated as random variables if we aim at a robust production schedule. However, compared with the extensive research on the deterministic model, the stochastic job shop scheduling problem (SJSSP) has not received sufficient attention. In this paper, we propose an artificial bee colony (ABC) algorithm for SJSSP with the objective of minimizing the maximum lateness (which is an index of service quality). First, we propose a performance estimate for preliminary screening of the candidate solutions. Then, the K-armed bandit model is utilized for reducing the computational burden in the exact evaluation (through Monte Carlo simulation) process. Finally, the computational results on different-scale test problems validate the effectiveness and efficiency of the proposed approach.

show abstract

A novel parallel quantum genetic algorithm for stochastic job shop scheduling

Cited by 100 publications

References 17 publications

Genetic Algorithm by using MATLAB Program

Genetic Algorithm by using MATLAB Program

A Review on Resource Scheduling Models to Optimize Quality of Service Parameters in Grid Computing using Meta-heuristics

An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times

Contact Info

Product

Resources

About