Mixed-Integer Nonlinear Programming Models and Algorithms for Large-Scale Supply Chain Design with Stochastic Inventory Management

2008 ◽  
Vol 47 (20) ◽  
pp. 7802-7817 ◽  
Author(s):  
Fengqi You ◽  
Ignacio E. Grossmann
Keyword(s):  
Supply Chain ◽  
Nonlinear Programming ◽  
Inventory Management ◽  
Large Scale ◽  
Mixed Integer Nonlinear Programming ◽  
Supply Chain Design ◽  
Programming Models ◽  
Mixed Integer ◽  
Stochastic Inventory ◽  
Integer Nonlinear Programming

scholarly journals Investigation of Bottlenecks in Supply Chain System for Minimizing Total Cost by Integrating Manufacturing Modelling Based on MINLP Approach

2019 ◽  
Vol 9 (6) ◽  
pp. 1185 ◽  
Author(s):  
Mahboobehalsadat Hajmirfattahtabrizi ◽  
Huaming Song
Keyword(s):  
Supply Chain ◽  
Nonlinear Programming ◽  
Mixed Integer Nonlinear Programming ◽  
Decision Makers ◽  
Mixed Integer ◽  
Chain Process ◽  
Total Cost ◽  
Multi Objective ◽  
Integer Nonlinear Programming ◽  
Production And Distribution

In recent years, due to high pressure of expenses on supply chain systems and members, the decision makers in these situations are seeking to create policy and strategies to minimize the total cost for their supply chain process with low target price and future demands. Regarding this crucial issue, their studies on the implementation of supply chain issues and bottlenecks have observed the enormous and direct impact on company’s financial performance and improve it systemically. In this paper, a well-known mixed integer nonlinear programming by multi-objective function is proposed to decide on reliable results for supply, production, and distribution system problems. The proposed mixed integer nonlinear programming model for multi-objective supply, production, and distribution problems is used to minimize the total cost for incurred sections and terms by decision makers. The obtained optimum solution and result are fulfilled by investigators and producers for planning how to adjust the investment and gain more accurate performances and implementations. Numerical results in plots and throughputs from MATLAB, using MINLP, showed that integrating the supply chain and mitigating the bottlenecks led to improve the system and decrease the total cost approximately (19.73%), while running without negative effects of supply chain disturbances on total cost.

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