A production inventory model with stock dependent demand incorporating learning and inflationary effect in a random planning horizon: A fuzzy genetic algorithm with varying population size approach

Roy, Arindam; Pal, Sova; Maiti, Manas Kumar

doi:10.1016/j.cie.2009.07.008

Cited by 27 publications

(9 citation statements)

References 16 publications

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“…They used a Fuzzy Logic Controller (FLC) to adopt the crossover probability as a function of the chromosomes age. Also Fdez et al [36], Roy et al [35] used it in some refinement of the mechanism on inventory control system. They used the age of the chromosome in fuzzy environment.…”

Section: Rough Set-based Age-dependent Selectionmentioning

confidence: 99%

Rough Genetic Algorithm for Constrained Solid TSP with Interval Valued Costs and Times

Maity

Roy²,

Maiti

2018

Fuzzy Information and Engineering

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This paper presents new rough set based genetic algorithms (RSGAs) to solve constrained solid travelling salesman problems (CSTSPs) with restricted conveyances (CSTSPwR) having uncertain costs and times as interval values. To grow the impreciseness in soft computing (SC), the proposed RSGAs, a rough set based age-dependent selection technique and an age-oriented min-point crossover are used along with three types of probability, p m-dependent random mutations. A number of benchmark problems from standard data set, TSPLIB are tested against the proposed algorithms and existing simple GA (SGA). CSTSPwRs are formulated as constrained linear programming problems and solved by both proposed RSGAs and SGA. These are illustrated numerically by some empirical data and the results from the above methods are compared. Statistical significance of the proposed algorithms are demonstrated through statistical analysis using standard deviation. Moreover, the non-parametric test, Friedman test, is performed with the proposed algorithms. In addition, a post hoc paired comparison is applied and the out performance of the RSGAs.

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Section: Rough Set-based Age-dependent Selectionmentioning

confidence: 99%

Rough Genetic Algorithm for Constrained Solid TSP with Interval Valued Costs and Times

Maity

Roy²,

Maiti

2018

Fuzzy Information and Engineering

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“…Last and Eyal (2005) developed a GA with varying population size, where chromosomes are classified into young, middle-age, and old according to their age and lifetime. During last few years different authors use GA to solve different inventory control problems (Mandal & Maiti, 2003;Maiti & Maiti, 2005a, 2006Roy et al, 2009;and others).…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…Influence of displayed inventory on demand is well established (Hsieh & Dye, 2010;Levin, Mclaughlin, Lamone, & Kothas, 1972;Panda, Senapati, & Basu, 2008;Roy, Pal, & Maiti, 2009;Wolfe, 1968). But this phenomenon puts a retailer of the items made of glass, ceramic, china clay, etc., in a conflicting situation.…”

Section: Introductionmentioning

confidence: 99%

Multi-item inventory model of breakable items with stock-dependent demand under stock and time dependent breakability rate

Guchhait

Maiti²,

Maiti

2010

Computers & Industrial Engineering

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“…Thus, in the recent competitive market, the inventory stock is decoratively exhibited and displayed through electronic media to attract the customers and thus to boost the sale. For this reason, a group of researchers have considered inventory control systems with stock-dependent demand in their research such as Datta and Pal (1992), Mandal and Phaujdar (1989a, b), Giri et al (1996), Hou (2006), Roy et al (2009a), and others. They considered linear form of stock-dependent demand.…”

Section: Introductionmentioning

confidence: 99%

A production-inventory model with permissible delay incorporating learning effect in random planning horizon using genetic algorithm

Kar¹,

Bera

Das

et al. 2015

J Ind Eng Int

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This paper presents a production-inventory model for deteriorating items with stock-dependent demand under inflation in a random planning horizon. The supplier offers the retailer fully permissible delay in payment. It is assumed that the time horizon of the business period is random in nature and follows exponential distribution with a known mean. Here learning effect is also introduced for the production cost and setup cost. The model is formulated as profit maximization problem with respect to the retailer and solved with the help of genetic algorithm (GA) and PSO. Moreover, the convergence of two methods-GA and PSO-is studied against generation numbers and it is seen that GA converges rapidly than PSO. The optimum results from methods are compared both numerically and graphically. It is observed that the performance of GA is marginally better than PSO. We have provided some numerical examples and some sensitivity analyses to illustrate the model.

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A production inventory model with stock dependent demand incorporating learning and inflationary effect in a random planning horizon: A fuzzy genetic algorithm with varying population size approach

Cited by 27 publications

References 16 publications

Rough Genetic Algorithm for Constrained Solid TSP with Interval Valued Costs and Times

Rough Genetic Algorithm for Constrained Solid TSP with Interval Valued Costs and Times

Multi-item inventory model of breakable items with stock-dependent demand under stock and time dependent breakability rate

A production-inventory model with permissible delay incorporating learning effect in random planning horizon using genetic algorithm

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