Optimizing decentralized production-distribution planning problem in a multi-period supply chain network under uncertainty

Decentralized supply chain management is found to be significantly relevant in today's competitive markets. Production and distribution planning is posed as an important optimization problem in supply chain networks. Here, we propose a multi-period decentralized supply chain network model with...

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Bibliographic Details
Published in:Journal of industrial engineering international Vol. 14; no. 2; pp. 367 - 382
Main Authors: Nourifar, Raheleh, Mahdavi, Iraj, Mahdavi-Amiri, Nezam, Paydar, Mohammad Mahdi
Format: Journal Article
Language:English
Published: Heidelberg Springer 01.06.2018
Springer Berlin Heidelberg
Islamic Azad University, South Tehran Branch
Islamic Azad University
Subjects:
Algorithms
Bi-level approach
Convolution
Decentralized supply chain
Engineering
Engineering Economics
Facility Management
Heuristic methods
Industrial and Production Engineering
Integer programming
Linear programming
Logistics
Marketing
Mathematical and Computational Engineering
Mathematical models
Mixed integer
Network design
Optimization
Organization
Original Research
Parameter uncertainty
Production-distribution planning problem
Quality Control
Reliability
Safety and Risk
Sensitivity analysis
Supply chains
Production–distribution planning problem
Decentralized supply chain
Network design
Bi-level approach
ISSN:2251-712X, 1735-5702, 2251-712X
Online Access:Get full text
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Summary:Decentralized supply chain management is found to be significantly relevant in today's competitive markets. Production and distribution planning is posed as an important optimization problem in supply chain networks. Here, we propose a multi-period decentralized supply chain network model with uncertainty. The imprecision related to uncertain parameters like demand and price of the final product is appropriated with stochastic and fuzzy numbers. We provide mathematical formulation of the problem as a bi-level mixed integer linear programming model. Due to problem's convolution, a structure to solve is developed that incorporates a novel heuristic algorithm based on Kth-best algorithm, fuzzy approach and chance constraint approach. Ultimately, a numerical example is constructed and worked through to demonstrate applicability of the optimization model. A sensitivity analysis is also made.
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content type line 14
ISSN:2251-712X
1735-5702
2251-712X
DOI:10.1007/s40092-017-0229-3