Meta-heuristic algorithms for solving a fuzzy single-period problem

Meta-heuristic algorithms for solving a fuzzy single-period problem
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DOI：
10.1016/j.mcm.2011.03.038
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发表时间：
2011-09
期刊：
Math. Comput. Model.
影响因子：
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通讯作者：
A. Taleizadeh;F. Barzinpour;H. Wee
A. Taleizadeh;F. Barzinpour;H. Wee
中科院分区：
其他
文献类型：
--
作者：
A. Taleizadeh;F. Barzinpour;H. Wee

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单周期问题（SPP）是近年来兴起的一种经典的随机库存模型。在本研究中，我们开发了一个具有模糊环境的SPP。将每个产品的需求考虑为lr模糊变量（根据左右偏差程度对模糊数进行排序）和多个约束（包括服务水平、批量订单、预算、空间和每个订单的上限）。本文的研究目标是在增量折扣策略下使总期望利润最大化。提出了五种基于模糊仿真和元启发式方法的混合智能算法；它们分别是蜂群优化（BCO）、和谐搜索（HS）、粒子群优化（PSO）、遗传算法（GA）和模拟退火（SA）。给出了三个数值算例来说明算法的性能。研究表明，BCO-FS混合方法的性能优于HS-FS、GA-FS、PSO-FS和SA-FS混合方法。
Single-period problem (SPP) is a classical stochastic inventory model that has become very popular recently. In this research, we developed a SPP with fuzzy environment. The demand of each product is considered as LR-fuzzy variables (ranking fuzzy numbers based on the left and right deviation degrees), and multiple constraints (including service level, batch order, budget, space and upper limit for each order). The aim of this paper is to maximize the total expected profit under incremental discount strategy. Five hybrid intelligent algorithms based on fuzzy simulation (FS) and meta-heuristic methods are presented; they are bees colony optimization (BCO), harmony search (HS), particle swarm optimization (PSO), genetic algorithm (GA) and simulated annealing (SA). Three numerical examples are presented to illustrate the performance of the algorithms. Our study shows that the BCO-FS hybrid method performs better than the HS-FS, GA-FS, PSO-FS, and SA-FS hybrid methods.