scholarly journals Designing networks: A mixed-integer linear optimization approach

Networks ◽  
2016 ◽  
Vol 68 (4) ◽  
pp. 283-301 ◽  
Author(s):  
Chrysanthos E. Gounaris ◽  
Karthikeyan Rajendran ◽  
Ioannis G. Kevrekidis ◽  
Christodoulos A. Floudas
Keyword(s):  
Linear Optimization ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Mixed Integer Linear Optimization ◽  
Integer Linear Optimization

scholarly journals DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization

2020 ◽  
Author(s):  
Kentaro Kanamori ◽  
Takuya Takagi ◽  
Ken Kobayashi ◽  
Hiroki Arimura
Keyword(s):  
Cost Function ◽  
Empirical Data ◽  
Linear Optimization ◽  
Data Distribution ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Mixed Integer Linear Optimization ◽  
Post Hoc ◽  
New Framework ◽  
Integer Linear Optimization

Counterfactual Explanation (CE) is one of the post-hoc explanation methods that provides a perturbation vector so as to alter the prediction result obtained from a classifier. Users can directly interpret the perturbation as an "action" for obtaining their desired decision results. However, an action extracted by existing methods often becomes unrealistic for users because they do not adequately care about the characteristics corresponding to the empirical data distribution such as feature-correlations and outlier risk. To suggest an executable action for users, we propose a new framework of CE for extracting an action by evaluating its reality on the empirical data distribution. The key idea of our proposed method is to define a new cost function based on the Mahalanobis' distance and the local outlier factor. Then, we propose a mixed-integer linear optimization approach to extracting an optimal action by minimizing our cost function. By experiments on real datasets, we confirm the effectiveness of our method in comparison with existing methods for CE.

scholarly journals A scenario-based optimization model for planning and redesigning the sale and after-sales services closed-loop supply chain

2020 ◽  
Author(s):  
Nazanin Esmaeili ◽  
Ebrahim Teimoury ◽  
Fahimeh Pourmohammadi
Keyword(s):  
Supply Chain ◽  
Customer Satisfaction ◽  
Optimization Model ◽  
Linear Optimization ◽  
Spare Parts ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Warranty Period ◽  
Linear Optimization Model ◽  
Mixed Integer Linear Optimization

In today's competitive world, the quality of after-sales services plays a significant role in customer satisfaction and customer retention. Some after-sales activities require spare parts and owing to the importance of customer satisfaction, the needed spare parts must be supplied until the end of the warranty period. In this study, a mixed-integer linear optimization model is presented to redesign and plan the sale and after-sales services supply chain that addresses the challenges of supplying spare parts after the production is stopped due to demand reduction. Three different options are considered for supplying spare parts, including production/procurement of extra parts while the product is being produced, remanufacturing, and procurement of parts just in time they are needed. Considering the challenges of supplying spare parts for after-sales services based on the product's life cycle is one contribution of this paper. Also, this paper addresses the uncertainties associated with different parameters through Mulvey's scenario-based optimization approach. Applicability of the model is investigated using a numerical example from the literature. The results indicate that the production/procurement of extra parts and remanufacturing are preferred to the third option. Moreover, remanufacturing is recommended when the remanufacturing cost is less than 23% of the production cost.

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