scholarly journals A Multiparametric Mixed-integer Bi-level Optimization Strategy for Supply Chain Planning Under Demand Uncertainty * *We are grateful to the Department of Chemical Engineering and the Faculty of Engineering of Imperial College London for an EPSRC-funded Doctoral Training Partnership (DTP) studentship. Financial support from Texas A & M University and Texas A & M Energy Institute is also gratefully acknowledged

2017 ◽  
Vol 50 (1) ◽  
pp. 10178-10183 ◽  
Author(s):  
Styliani Avraamidou ◽  
Efstratios N. Pistikopoulos
Keyword(s):  
Supply Chain ◽  
Financial Support ◽  
Demand Uncertainty ◽  
Chemical Engineering ◽  
Imperial College ◽  
Mixed Integer ◽  
Doctoral Training ◽  
Optimization Strategy ◽  
Supply Chain Planning

FUZZY LINEAR PROGRAMMING FOR SUPPLY CHAIN PLANNING UNDER UNCERTAINTY

2010 ◽  
Vol 09 (03) ◽  
pp. 373-392 ◽  
Author(s):  
DAVID PEIDRO ◽  
JOSEFA MULA ◽  
RAÚL POLER
Keyword(s):  
Linear Programming ◽  
Supply Chain ◽  
Demand Uncertainty ◽  
Programming Model ◽  
Fuzzy Model ◽  
Fuzzy Linear Programming ◽  
Mixed Integer ◽  
Planning Problem ◽  
Planning Under Uncertainty ◽  
Supply Chain Planning

A new fuzzy mathematical programming model for supply chain planning under supply, process and demand uncertainty is proposed in this paper. A tactical supply chain planning problem has been formulated as a fuzzy mixed integer linear programming model where data are ill-known and modeled by fuzzy numbers with modified s-curve membership functions. The fuzzy model provides alternative decision plans to the decision maker (DM) for different degrees of satisfaction. Finally, the proposed model is tested by using data from a real automobile supply chain.

Optimal Design and Operation of Supply Chain Networks under Demand Uncertainty

2011 ◽  
pp. 73-108
Author(s):  
Michael C. Georgiadis ◽  
Pantelis Longinidis
Keyword(s):  
Supply Chain ◽  
Demand Uncertainty ◽  
Mathematical Formulation ◽  
Life Time ◽  
Mixed Integer ◽  
Global Optimality ◽  
Supply Chain Networks ◽  
Integer Linear Programming Problem ◽  
Complex Interactions ◽  
Varying Demand

This chapter considers a detailed mathematical formulation for the problem of designing supply chain networks comprising multiproduct production facilities with shared production resources, warehouses, distribution centers and customer zones and operating under time varying demand uncertainty. Uncertainty is captured in terms of a number of likely scenarios possible to materialize during the life time of the network. The problem is formulated as a mixed-integer linear programming problem and solved to global optimality using standard branch-and-bound techniques. A case study concerned with the establishment of Europe-wide supply chain is used to illustrate the applicability and efficiency of the proposed approach. The results obtained provide a good indication of the value of having a model that takes into account the complex interactions that exist in such networks and the effect of inventory levels to the design and operation.

Intel Realizes $25 Billion by Applying Advanced Analytics from Product Architecture Design Through Supply Chain Planning

2021 ◽  
Vol 51 (1) ◽  
pp. 9-25
Author(s):  
John Heiney ◽  
Ryan Lovrien ◽  
Nicholas Mason ◽  
Irfan Ovacik ◽  
Evan Rash ◽  
...  
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Product Architecture ◽  
Mixed Integer ◽  
Architecture Design ◽  
Product Portfolio ◽  
Supply Chain Planning ◽  
Design Alternatives ◽  
Physics Simulation ◽  
Advanced Analytics

Due to its scale, the complexity of its products and manufacturing processes, and the capital-intensive nature of the semiconductor business, efficient product architecture design integrated with supply chain planning is critical to Intel’s success. In response to an exponential increase in complexities, Intel has used advanced analytics to develop an innovative capability that spans product architecture design through supply chain planning with the dual goals of maximizing revenue and minimizing costs. Our approach integrates the generation and optimization of product design alternatives using genetic algorithms and device physics simulation with large-scale supply chain planning using problem decomposition and mixed-integer programming. This corporate-wide capability is fast and effective, enabling analysis of many more business scenarios in much less time than previous solutions, while providing superior results, including faster response time to customers. Implementation of this capability over the majority of Intel’s product portfolio has increased annual revenue by an average of $1.9 billion and reduced annual costs by $1.5 billion, for a total benefit of $25.4 billion since 2009, while also contributing to Intel’s sustainability efforts.

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