scholarly journals Modelling agricultural risk in a large scale positive mathematical programming model

2020 ◽  
Vol 10 (1) ◽  
pp. 2
Author(s):  
Iván Arribas ◽  
Kamel Louhichi ◽  
Angel Perni ◽  
José Vila ◽  
Sergio Gómez y Paloma
Keyword(s):  
Mathematical Programming ◽  
Large Scale ◽  
Programming Model ◽  
Mathematical Programming Model ◽  
Agricultural Risk ◽  
Positive Mathematical Programming

scholarly journals How do fishery policies affect Hawaii's longline fishing industry? Calibrating a positive mathematical programming model

2017 ◽  
Vol 30 (2) ◽  
pp. e12127 ◽  
Author(s):  
Jonathan R. Sweeney ◽  
Richard E. Howitt ◽  
Hing Ling Chan ◽  
Minling Pan ◽  
PingSun Leung
Keyword(s):  
Mathematical Programming ◽  
Programming Model ◽  
Fishing Industry ◽  
Mathematical Programming Model ◽  
Positive Mathematical Programming

scholarly journals A Mathematical Programming Model to Determine Objective Weights for the Interval Extension of TOPSIS

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Hai Shen ◽  
Lingyu Hu ◽  
Kin Keung Lai
Keyword(s):  
Mathematical Programming ◽  
Input Data ◽  
Programming Model ◽  
Multiple Criteria Decision Making ◽  
Decision Makers ◽  
Mathematical Programming Model ◽  
Topsis Method ◽  
Data Set ◽  
Interval Extension ◽  
Interval Valued

Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method has been extended in previous literature to consider the situation with interval input data. However, the weights associated with criteria are still subjectively assigned by decision makers. This paper develops a mathematical programming model to determine objective weights for the implementation of interval extension of TOPSIS. Our method not only takes into account the optimization of interval-valued Multiple Criteria Decision Making (MCDM) problems, but also determines the weights only based upon the data set itself. An illustrative example is performed to compare our results with that of existing literature.

CREDIBILITY-BASED FUZZY MATHEMATICAL PROGRAMMING MODEL FOR PORTFOLIO SELECTION UNDER UNCERTAINTY

2014 ◽  
Vol 13 (01) ◽  
pp. 101-135 ◽  
Author(s):  
MUKESH KUMAR MEHLAWAT ◽  
PANKAJ GUPTA
Keyword(s):  
Mathematical Programming ◽  
Portfolio Selection ◽  
Programming Model ◽  
Fuzzy Optimization ◽  
Chance Constrained Programming ◽  
Fuzzy Mathematical Programming ◽  
Mathematical Programming Model ◽  
Two Phase ◽  
Solution Approach ◽  
Practical Applications

In this paper, we develop a hybrid bi-objective credibility-based fuzzy mathematical programming model for portfolio selection under fuzzy environment. To deal with imprecise parameters, we use a hybrid credibility-based approach that combines the expected value and chance constrained programming techniques. The model simultaneously maximizes the portfolio return and minimizes the portfolio risk. We also consider an additional important criterion, namely, portfolio liquidity as a constraint in the model to make it better suited for practical applications. The proposed fuzzy optimization model is solved using a two-phase approach. An empirical study is included to demonstrate applicability of the proposed model and the solution approach in real-world applications of portfolio selection.

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