A particle swarm optimization for solving joint pricing and lot-sizing problem with fluctuating demand and unit purchasing cost

Authors:
Chung-Yuan Dye;Tsu-Pang Hsieh
Affiliations:
Department of Business Management, Shu-Te University, Yen Chao, Kaohsiung 824, Taiwan, ROC;Department of Business Management, Aletheia University, Tamsui, Taipei 251, Taiwan, ROC
Venue:
Computers & Mathematics with Applications
Year:
2010

Citing 7
Cited 1

Price Protection in the Personal Computer Industry

Management Science
Optimal replenishment policies with allowable shortages for a product life cycle

Computers & Mathematics with Applications
A numerical approach to a multi-objective optimal inventory control problem for deteriorating multi-items under fuzzy inflation and discounting

Computers & Mathematics with Applications
Price determination for an EOQ model for deteriorating items under perfect competition

Computers & Mathematics with Applications
A simple method to locate the optimal solution for exponentially deteriorating items under trade credit financing

Computers & Mathematics with Applications
A new algorithm for optimally determining lot-sizing policies for a deteriorating item in an integrated production-inventory system

Computers & Mathematics with Applications
Economic design of an inventory policy for non-instantaneous deteriorating items under permissible delay in payments

Journal of Computational and Applied Mathematics

A possibilistic multiple objective pricing and lot-sizing model with multiple demand classes

Fuzzy Sets and Systems

Quantified Score

Hi-index	0.09

Visualization

Abstract

In this paper, we extend the classical economic order quantity model to allow for not only a function of price-dependent and time-varying demand but also fluctuating unit purchasing cost. The joint replenishment problem is subject to continuous decay and a general partial backlogging rate. The objective is to find the optimal replenishment number, time scheduling and periodic selling price to maximize the discounted total profit. An effective search procedure is provided to find the optimal solution by employing the properties derived in this paper and particle swarm optimization algorithm. Several numerical examples are used to illustrate the features of the proposed model.