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变质物品的最优定价与营销规划

Optimal pricing and marketing planning for deteriorating items.

作者信息

Moosavi Tabatabaei Seyed Reza, Sadjadi Seyed Jafar, Makui Ahmad

机构信息

Department of Industrial Engineering, Center of excellence in optimization and manufacturing, Iran University of Science and Technology, Tehran, Iran.

出版信息

PLoS One. 2017 Mar 17;12(3):e0172758. doi: 10.1371/journal.pone.0172758. eCollection 2017.

DOI:10.1371/journal.pone.0172758
PMID:28306750
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5357006/
Abstract

Optimal pricing and marketing planning plays an essential role in production decisions on deteriorating items. This paper presents a mathematical model for a three-level supply chain, which includes one producer, one distributor and one retailer. The proposed study considers the production of a deteriorating item where demand is influenced by price, marketing expenditure, quality of product and after-sales service expenditures. The proposed model is formulated as a geometric programming with 5 degrees of difficulty and the problem is solved using the recent advances in optimization techniques. The study is supported by several numerical examples and sensitivity analysis is performed to analyze the effects of the changes in different parameters on the optimal solution. The preliminary results indicate that with the change in parameters influencing on demand, inventory holding, inventory deteriorating and set-up costs change and also significantly affect total revenue.

摘要

最优定价与营销规划在变质物品的生产决策中起着至关重要的作用。本文提出了一个三级供应链的数学模型,该供应链包括一个生产商、一个经销商和一个零售商。所提出的研究考虑了变质物品的生产,其中需求受价格、营销支出、产品质量和售后服务支出的影响。所提出的模型被表述为一个具有5度难度的几何规划问题,并使用优化技术的最新进展来求解该问题。该研究得到了几个数值例子的支持,并进行了敏感性分析,以分析不同参数变化对最优解的影响。初步结果表明,随着影响需求、库存持有、库存变质和设置成本的参数变化,总收入也会发生变化,并且这些参数变化也会对总收入产生显著影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46da/5357006/f1212d85d1f9/pone.0172758.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46da/5357006/9019a61e12dd/pone.0172758.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46da/5357006/40c99062b9e7/pone.0172758.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46da/5357006/6da5d03fbd4b/pone.0172758.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46da/5357006/6e5cc0fb2793/pone.0172758.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46da/5357006/fcad2c02a759/pone.0172758.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46da/5357006/5b2f8d13ed05/pone.0172758.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46da/5357006/f1212d85d1f9/pone.0172758.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46da/5357006/9019a61e12dd/pone.0172758.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46da/5357006/40c99062b9e7/pone.0172758.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46da/5357006/6da5d03fbd4b/pone.0172758.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46da/5357006/6e5cc0fb2793/pone.0172758.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46da/5357006/fcad2c02a759/pone.0172758.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46da/5357006/5b2f8d13ed05/pone.0172758.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46da/5357006/f1212d85d1f9/pone.0172758.g007.jpg

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