A Fuzzy Mathematical Programming Model for Sustainable Supply Chain under Competition and Uncertainty Environment

Author(s):  
Mohammad Valipour Khatir ◽  
Abdol Hamid SafaeiGhadikolaei ◽  
Mojtaba Aghajani ◽  
Hassan Ali Aghajani
2014 ◽  
Vol 13 (01) ◽  
pp. 101-135 ◽  
Author(s):  
MUKESH KUMAR MEHLAWAT ◽  
PANKAJ GUPTA

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.


2017 ◽  
Vol 34 (05) ◽  
pp. 1750026 ◽  
Author(s):  
Yuxiang Yang ◽  
Zuqing Huang ◽  
Qiang Patrick Qiang ◽  
Gengui Zhou

A firm sets up his facilities including manufacturing/remanufacturing plants and distribution/collection centers, incorporating an existing closed-loop supply chain (CLSC) network. The entering firm has to compete with the existing firms in the existing network. The entering firm behaves as the leader of a Stackelberg game while the existing firms in the existing network are followers. We assume that the entering firm can anticipate the existing firms’ reaction to his potential location decision before choosing his optimal policy. We use a CLSC network equilibrium model in which the decision makers are faced with multiple objectives to capture the existing firms’ reaction. A mathematical programming model with equilibrium constraints is developed for this competitive CLSC network design problem by taking into account the market competition existing in the decentralized CLSC network. A solution method is developed by integrating Genetic algorithm with an inexact logarithmic-quadratic proximal augmented Lagrangian method. Finally, numerical examples and the related results are studied for illustration purpose.


Author(s):  
Aristotle T. Ubando ◽  
Joel L. Cuello ◽  
Mahmoud M. El-Halwagi ◽  
Alvin B. Culaba ◽  
Raymond R. Tan

A polygeneration approach is proposed to improve the economic viability of algal biofuel production through simultaneous production of co-products (i.e. electricity, heat, and other biochemicals). A multi-regional polygeneration supply chain consists of various array of processing plants in producing multiple bioenergy products given spatial constraints of each plant found in different regions. The inherent complexity of the polygeneration compounds the difficulty of designing the composite network of processing plants in multi-regions. Optimizing the design flow of the polygeneration supply chain considers multiple objectives, such as satisfying product demand, maximizing economic performance, and minimizing environmental footprint. In addition, the optimal strategic capacity design of the supply and distribution of biodiesel across multi-regions are considered. This study uses a fuzzy mathematical programming model to generate an optimized design of the polygeneration supply chain while satisfying all objectives. The developed model is demonstrated using a modified industrial case study comparing two cultivation alternatives. Results showed that all fuzzy multi-objective goals are satisfied and the flat-plate photobioreactor is the preferred cultivation system in terms of environmental footprints and economic performance.


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