An application of real-coded genetic algorithm (RCGA) for mixed integer non-linear programming in two-storage multi-item inventory model with discount policy

Maiti, Ajoy Kumar; Bhunia, Asoke Kumar; Maiti, Manoranjan

doi:10.1016/j.amc.2006.05.141

Cited by 46 publications

(22 citation statements)

References 22 publications

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“…Genetic algorithm, as a mature, efficient random search algorithm, is widely used to solve practical problems [5,6,14,16]. In practical applications, there have been many improvements such as different genetic expression, crossover and mutation operators, using special operators, different regeneration and selection methods and so on.…”

Section: Basic Genetic Evolution Algorithmmentioning

confidence: 99%

“…In this paper, we using the following method transformed the constrained optimization problem into unconstrained bi-objective optimization problem [2,5,6,8,12,13,14,29].…”

Section: Background Informationmentioning

confidence: 99%

“…Deep [5] improved crossover and mutation operator based on genetic algorithm, and used Deb constraint rules to choose the excellent individual into next generation. Maiti [14] proposed a RCGA algorithm with ranking selection, whole arithmetic crossover and uniform mutation to solve the problem. Lin [12] proposed MIHDE algorithm which contains the migration operation to avoid candidate individuals clustering together.…”

Section: Introductionmentioning

confidence: 99%

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Difference-genetic co-evolutionary algorithm for nonlinear mixed integer programming problems

Gao¹,

Wu²

2016

J. Nonlinear Sci. Appl.

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In this paper, the difference genetic co-evolutionary algorithm (D-GCE) is proposed for the mixed integer programming problems. First, the mixed integer programming problem with constrains converted to unconstrained bi-objective optimization problems. Secondly, selection mechanism combines the Pareto dominance and superiority of feasible solution methods to choose the excellent individual as the next generation. Final, differential evolution algorithm and genetic algorithm handle the continuous part and discrete part, respectively. Numerical experiments on 24 test functions have shown that the new approach is efficient. The comparison results among the D-GCE and other evolutionary algorithms indicate that the proposed D-GCE algorithm is competitive with and in some cases superior to, other existing algorithms in terms of the quality, efficiency, convergence rate, and robustness of the final solution.

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Section: Basic Genetic Evolution Algorithmmentioning

confidence: 99%

“…In this paper, we using the following method transformed the constrained optimization problem into unconstrained bi-objective optimization problem [2,5,6,8,12,13,14,29].…”

Section: Background Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Difference-genetic co-evolutionary algorithm for nonlinear mixed integer programming problems

Gao¹,

Wu²

2016

J. Nonlinear Sci. Appl.

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“…In this research the total cost of the system corresponding to each chromosome is the objective function. In order to use the conventional selection process as in Maiti et al (2006) and Maiti and Maiti (2007) there is a need for modification as the objective function in their research works are profit maximisation. In this research the fitness function of a solution is considered to be the total cost of the system incurred by that solution to the power of À1 therefore a chromosome with lower cost function has a higher chance to be selected (higher fitness function).…”

Section: Fitness Functionmentioning

confidence: 99%

“…Stop In order to implement this genetic algorithm similar to Maiti, Bhunia, and Maiti (2006) and Gupta, Bhunia, and Goyal (2007) the following components are considered:…”

Section: Optimisationmentioning

confidence: 99%

A two-echelon inventory model for a deteriorating item with stock-dependent demand, partial backlogging and capacity constraints

Ghiami

Williams

2013

European Journal of Operational Research

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An enhanced model for the integrated production and transportation problem in a multiple vehicles environment

et al. 2015

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Solving an integrated production and transportation problem (IPTP) is a very challenging task in semiconductor manufacturing with turnkey service. A wafer fabricator needs to coordinate with outsourcing factories in the processes including circuit probing testing, integrated circuit assembly, and final testing for buyers. The jobs are clustered by their product types, and they must be processed by groups of outsourcing factories in various stages in the manufacturing process. Furthermore, the job production cost depends on various product types and different outsourcing factories. Since the IPTP involves constraints on job clusters, jobcluster dependent production cost, factory setup cost, process capabilities, and transportation cost with multiple vehicles, it is very difficult to solve when the problem size becomes large. Therefore, heuristic tools may be necessary to solve the problem. In this paper, we first formulate the IPTP as a mixed integer linear programming problem to minimize the total production and transportation cost. An efficient genetic algorithm (GA) is proposed next to tackle the problem when it becomes too complicated. The objectives are to minimize total costs, where the costs include production cost and transportation cost, under the environment with backup capacities Communicated by V. Loia. and multiple vehicles, and to determine an appropriate production and distribution plan. The results demonstrate that the proposed GA model is an effective and accurate tool.

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An application of real-coded genetic algorithm (RCGA) for mixed integer non-linear programming in two-storage multi-item inventory model with discount policy

Cited by 46 publications

References 22 publications

Difference-genetic co-evolutionary algorithm for nonlinear mixed integer programming problems

Difference-genetic co-evolutionary algorithm for nonlinear mixed integer programming problems

A two-echelon inventory model for a deteriorating item with stock-dependent demand, partial backlogging and capacity constraints

An enhanced model for the integrated production and transportation problem in a multiple vehicles environment

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