Mean and CV reduction methods on Gaussian type-2 fuzzy set and its application to a multilevel profit transportation problem in a two-stage supply chain network

2016 ◽  
Vol 28 (9) ◽  
pp. 2703-2726 ◽  
Author(s):  
Dipak Kumar Jana ◽  
Sutapa Pramanik ◽  
Manoranjan Maiti
Keyword(s):  
Supply Chain ◽  
Fuzzy Set ◽  
Transportation Problem ◽  
Supply Chain Network ◽  
Two Stage ◽  
Gaussian Type ◽  
Reduction Methods

Meta-heuristic approaches for fixed-charge solid transportation problem in two-stage supply chain network

2017 ◽  
Vol 20 (1) ◽  
pp. 447-471 ◽  
Author(s):  
Mojtaba Akbari ◽  
Saber Molla-Alizadeh-Zavardehi ◽  
Sadegh Niroomand
Keyword(s):  
Supply Chain ◽  
Transportation Problem ◽  
Fixed Charge ◽  
Supply Chain Network ◽  
Solid Transportation Problem ◽  
Two Stage ◽  
Heuristic Approaches

A Parametric Programming Method on Gaussian Type-2 Fuzzy Set and Its Application to a Multilevel Supply Chain

2016 ◽  
Vol 24 (03) ◽  
pp. 451-477 ◽  
Author(s):  
Dipak Kumar Jana ◽  
Sutapa Pramanik ◽  
Manoranjan Maiti
Keyword(s):  
Supply Chain ◽  
Transportation Problem ◽  
Optimization Problems ◽  
Fuzzy Variable ◽  
Distribution Center ◽  
Distribution Centers ◽  
Deterministic Models ◽  
Gaussian Type ◽  
Soft Computing Techniques

The transportation problem is an important and relevant supply chain optimization problem in the traffic engineering. This paper minimizes shipping costs of a three channel distribution system comprised of plants, distribution centers, and customers. Plants manufacture several products that are delivered to distribution centers. If a distribution center is used then fixed cost is charged. Customers are replenished by an only one distribution center. To characterize the uncertainty that typically occurs in many practical decision environments, this paper considers the supply capacities, demands as Gaussian type-2 fuzzy variables. To provide a modelling framework for optimization problems with multi-fold uncertainty, different reduction methods are proposed to transform a Gaussian type-2 fuzzy variable into a type-1 fuzzy variable by mean reduction method. Then the transportation problem is reformulated as a chance-constrained expected value model enlightened by the credibility optimization method. The deterministic models are then solved using two different soft computing techniques (i) Generalized Reduced Gradient (Lingo-14.0), and (ii) modified Particle Swarm Optimization(PSO), where the position of each particle is adjusted according to its own experience and that of its neighbors. The numerical experiments illustrate the application and effectiveness of the proposed solution approaches.

A model of resilient supply chain network design: A two-stage programming with fuzzy shortest path

2014 ◽  
Vol 41 (1) ◽  
pp. 39-49 ◽  
Author(s):  
Yohanes Kristianto ◽  
Angappa Gunasekaran ◽  
Petri Helo ◽  
Yuqiuqe Hao
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Shortest Path ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Two Stage ◽  
Resilient Supply Chain

A two-stage chance-constrained stochastic programming model for a bio-fuel supply chain network

2018 ◽  
Vol 195 ◽  
pp. 27-44 ◽  
Author(s):  
Md Abdul Quddus ◽  
Sudipta Chowdhury ◽  
Mohammad Marufuzzaman ◽  
Fei Yu ◽  
Linkan Bian
Keyword(s):  
Supply Chain ◽  
Stochastic Programming ◽  
Programming Model ◽  
Supply Chain Network ◽  
Two Stage ◽  
Fuel Supply ◽  
Stochastic Programming Model
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