Optimal strategy for determining pricing policy of a product under the influence of reworking during production

Kumar, Vijay; Sharma, Alpana; Taneja, Naman; Sharma, Himani

doi:10.1109/icrito.2014.7014727

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Sarkar et al [22] used Euler-Lagrange theory to derive the necessary and sufficient conditions for optimality of the dynamic variables. Recently, Kumar et al [23] proposed a model for determining optimal pricing policies of the product under the influence of reworking during production. Over a long survey of the literature and to the best of our knowledge, no effort has been made to develop the joint production-sales model which incorporates activities such as production, reworking of the imperfect items, and warranty during sale for a newly launched product.…”

Section: Notationmentioning

confidence: 99%

New Product Launching with Pricing, Free Replacement, Rework, and Warranty Policies via Genetic Algorithmic Approach

Kumar

Sarkar²,

Sharma

et al. 2019

IJCIS

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New products are appearing in the marketplace at an ever-increasing step. Their launching is either market driven, or technology driven. Pricing and warranty policies play a vital role in launching of a new product, consequently growth of a company. In this paper a decision model is proposed to determine the pricing and warranty polices of a newly launched product considering free replacement during warranty period, and reworking during production process. We have assumed that the reworking is performed for the defective items, which are produced when a machine shifts from in-control state to out-of-control state to make them perfect. Profit function is formulated by combining the diffusion models and cost model. Structured optimal policies are proposed using optimal control theory and genetic algorithm solution approach is employed to explore the optimum values of price and warranty for every period of the product's life cycle. Numerical example is presented considering different values of model parameters. Further, sensitivity analysis is performed to study the impact of model parameters on the profit model. The results of the paper will be greatly useful for the decision-makers, as it allows them to identify the role of the selected parameters during the entire life cycle of the product, and to study the long-term policy of a newly launched product.

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Section: Notationmentioning

confidence: 99%

New Product Launching with Pricing, Free Replacement, Rework, and Warranty Policies via Genetic Algorithmic Approach

Kumar

Sarkar²,

Sharma

et al. 2019

IJCIS

Self Cite

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“…INTRODUCTION Previous research has indicated that people tend to accept information shared by their friends more than advertisements presented in news or on TV [1]. With the growing popularity of social networks such as Facebook and Twitter, social networks currently play an important role in the spread of information or influence in the form of "word-of-mouth" [2], which has drawn increasing attention recently [3][4] [5]. To facilitate viral marketing, a company usually offers the free samples of a product to a "small" group of seed users according to the budget.…”

mentioning

confidence: 99%

Cross-Layer Design of Influence Maximization in Mobile Social Networks

Wang

Huang

Yang

et al. 2016

2016 IEEE Global Communications Conference (GLOBECOM)

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Abstract-Most prior algorithms for influence maximization focused are designed for Online Social Networks (OSNs) and require centralized computation. Directly deploying the above algorithms in distributed Mobile Social Networks (MSNs) will overwhelm the networks due to an enormous number of messages required for seed selection. In this paper, therefore, we design a new cross-layer strategy to jointly examine MSN and mobile ad hoc networks (MANETs) to facilitate efficient seed selection, by extracting a subset of nodes as agents to represent nearby friends during the distributed computation. Specifically, we formulate a new optimization problem, named Agent Selection Problem (ASP), to minimize the message overhead transmitted in MANET. We prove that ASP is NP-Hard and design an effectively distributed algorithm. Simulation results in real and synthetic datasets manifest that the message overhead can be significantly reduced compared with the existing approaches.

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