An EPQ model with price discounted promotional demand in an imprecise planning horizon via Genetic Algorithm

Pal, Sova; Maiti, Manas Kumar; Maiti, Manoranjan

doi:10.1016/j.cie.2008.11.016

Cited by 41 publications

(15 citation statements)

References 12 publications

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“…In order to solve the model, they provided a hybrid genetic algorithm that combines self-adapting crossover operator and mutation operator. There are several interesting and relevant papers related to the application of GA in inventory problems such as Stockton and Quinn [10], Mondal and Maiti [11], Hou et al [12], Gupta et al [13], Lotfi [14], Pal et al [15], and Taleizadeh et al [16][17][18][19].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

A parameter-tuned genetic algorithm for multi-product economic production quantity model with space constraint, discrete delivery orders and shortages

Pasandideh

Niaki

Yeganeh

2010

Advances in Engineering Software

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Section: Introduction and Literature Reviewmentioning

confidence: 99%

A parameter-tuned genetic algorithm for multi-product economic production quantity model with space constraint, discrete delivery orders and shortages

Pasandideh

Niaki

Yeganeh

2010

Advances in Engineering Software

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“…This phenomenon is known as the 'learning effect', in the literature. Although, different types of learning effects have been studied in various areas (Kuo and Yang, 2006), it has rarely been studied in the context of inventory control problems (Pal et al, 2009). …”

Section: Literature Reviewmentioning

confidence: 99%

“…This process of boosting a product is commonly practiced by the manufacturer especially when a product is newly launched in the market. Pal et al (2009) developed an EPQ model incorporating price discounted promotional demand in a fuzzy planning horizon. They assume that producer offers price discount in every production cycle for some time, but ignore the effect of product availability in stimulating demand, though this effect is well established (Chung et al, 2000;Maiti and Maiti, 2006).…”

Section: Literature Reviewmentioning

confidence: 99%

A fuzzy lifetime-based particle swarm optimisation with varying swarm size to solve a production inventory model

Guchhait

Maiti²

2016

IJCCIA

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Here, a modified particle swarm optimisation (MPSO) algorithm with varying swarm size for constrained optimisation problem is proposed. In this MPSO, a life time is assigned to each particle at the time of generation depending on its fitness. After completion of a generation, if no movement is made by the particle, its age is increased by unity. When age of a particle exceeds the lifetime, it is discarded from the swarm. Diversity in the swarm is maintained using information entropy theory. A fuzzy possibility/necessity-based fitness evolution is proposed to deal with fuzzy optimisation problems using this MPSO. Efficiency of the algorithm is tested against a list of crisp valued standard benchmark nonlinear test functions. This algorithm is used to solve a production inventory model with fuzzy costs, where lifetime of the product is random in nature. At the beginning of planning horizon price discount is offered to the customers for few cycles to boost the demand. Demand also depends on stock and selling price. The model is illustrated with numerical examples and some sensitivity analyses have been made. Keywords: modified PSO algorithm; fuzzy lifetime; varying swarm size; possibility/necessity measure; EPQ model; price discounted promotional demand; random planning horizon.Reference to this paper should be made as follows: Guchhait, P. and Maiti, M.K. (2016)

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“…They assumed that demand is function of the retail price and advertising by both players. Pal et al (2009) developed an economic production quantity model for a newly launched product in an imprecise planning horizon. They assumed that price discount is offered at the beginning of each cycle to boost the demand.…”

Section: Introductionmentioning

confidence: 99%

Integrated model with imperfect production process under the effect of learning

Yadav¹,

Singh²,

Vandana³

2018

Malaya J. Mat.

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A multi-item production-inventory model for a two-echelon supply chain model is developed in this paper. In supply chain we consider two players one is manufacturer and other is retailer. In the proposed model it is assumed that manufacturing process in not perfect and unit manufacturing cost is the function of production rate. During transportation, due to improper handling lot received by the retailer contains imperfect items. So, to maintain the goodwill in market screening process is carried out at retailer ends. Due to learning phenomenon, percentage of defective items present in each lot follows the learning curve. Partial back ordering is considered at retailer's ends. To increase the demand, supports of promotional activity such as advertisement is taken by the decision maker and cost of advertisement is bear by both the player. Two different approaches, one is integrated approach and other is leader-follower approach optimal solution of the supply chain has been obtained. Proposed model is illustrated with the help of numerical example and sensitivity analysis is carried out with respect to different parameters.

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An EPQ model with price discounted promotional demand in an imprecise planning horizon via Genetic Algorithm

Cited by 41 publications

References 12 publications

A parameter-tuned genetic algorithm for multi-product economic production quantity model with space constraint, discrete delivery orders and shortages

A parameter-tuned genetic algorithm for multi-product economic production quantity model with space constraint, discrete delivery orders and shortages

A fuzzy lifetime-based particle swarm optimisation with varying swarm size to solve a production inventory model

Integrated model with imperfect production process under the effect of learning

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