Optimizing the Joint Replenishment and Delivery Scheduling Problem under Fuzzy Environment Using Inverse Weight Fuzzy Nonlinear Programming Method

In reality, decision-makers are always in front of imprecise and vague operational conditions. We propose a practical multiobjective joint replenishment and delivery scheduling (JRD) model with deterministic demand and fuzzy cost. This model minimizes the total cost defuzzified by the signed distanc...

Full description

Saved in:
Bibliographic Details
Published inAbstract and Applied Analysis Vol. 2014; no. 2014; pp. 404 - 416-1541
Main Authors Fu, Qing-Liang, Xu, Xian-Hao, Wang, Lin, Zeng, Yu-Rong
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Limiteds 01.01.2014
Hindawi Publishing Corporation
John Wiley & Sons, Inc
Hindawi Limited
Subjects
Budgets
Cash flow
Colleges & universities
Cost control
Experiments
Fuzzy algorithms
Fuzzy logic
Fuzzy systems
Heuristic
Information management
Inventory
Mathematical models
Methods
Nonlinear programming
Objectives
Optimization algorithms
Scheduling (Management)
Online AccessGet full text

Cover

More Information
Summary:In reality, decision-makers are always in front of imprecise and vague operational conditions. We propose a practical multiobjective joint replenishment and delivery scheduling (JRD) model with deterministic demand and fuzzy cost. This model minimizes the total cost defuzzified by the signed distance method and maximizes the credibility that the total cost does not exceed the budget level. Then, an inverse weight fuzzy nonlinear programming (IWFNLP) method is adopted to formulate the proposed model. This method embeds the idea of inverse weights into the Max-Min fuzzy model. Thirdly, the fuzzy simulation approach and differential evolution algorithm (DE) are utilized to solve this problem. Results show that solutions derived from the IWFNLP method satisfy the decision-maker’s desirable achievement level of the cost objective and credibility objective. It is an effective decision tool since it can really reflect the relative importance of each fuzzy component. Our study also shows that the improved DE outperforms DE with a faster convergence speed.
ISSN:1085-3375
1687-0409
DOI:10.1155/2014/904240