scholarly journals Possibilistic Distribution for Selection of Critical Path in Multi Objective Multi-Mode Problem with Trapezoidal Fuzzy Number

2019 ◽  
Vol 8 (4) ◽  
pp. 10833-10842
Keyword(s):  
Fuzzy Numbers ◽  
Critical Path ◽  
Fuzzy Programming ◽  
Decision Makers ◽  
Programming Approach ◽  
Management Problem ◽  
Linguistic Variables ◽  
Multi Objective ◽  
Fuzzy Environment ◽  
Multi Mode

Execution of any project with optimum duration, cost, quality and risk is very significant for project administrators in recent very competitive commercial situation. Sometimes it is not possible to have detailed earlier statistics about project criteria. In such situations, estimation of different Decision makers are considered in linguistic variables and altered into triangular fuzzy numbers as fuzzy numbers have ability to deal with vagueness. In this paper, we frame a new multi-mode multi objective critical path problem and suggest a possibilistic methodology to find critical path for a project where three decision makers’ views are considered as three modes of execution in terms of linguistic variables. In this paper have formulated model of multiple mode in project network problem and find its solution with fuzzy programming approach with exponential membership and linear membership function. The proposed approach is useful to solve multi-mode project management problem which calculates optimal critical path according to four criteria- time, cost, risk and quality with three activities modes of execution in fuzzy environment

scholarly journals Multi Objective Multi Mode Project Management Problem in Triangular Fuzzy Environment

2020 ◽  
Vol 9 (2) ◽  
pp. 1772-1781
Keyword(s):  
Project Management ◽  
Fuzzy Numbers ◽  
Critical Path ◽  
Decision Makers ◽  
Programming Approach ◽  
Management Problem ◽  
Linguistic Variables ◽  
Multi Objective ◽  
Fuzzy Environment ◽  
Multi Mode

Execution of any project with optimum duration, cost, quality and risk is very significant for project administrators in recent very competitive commercial situation. Sometimes it is not possible to have detailed earlier statistics about project criteria. In such situations, estimation of different Decision makers are considered in linguistic variables and altered into triangular fuzzy numbers as fuzzy numbers have ability to deal with vagueness. In this paper, we frame a new multi-mode multi objective critical path problem and suggest a possibilistic methodology to find critical path for a project where three decision makers’ views are considered as three modes of execution in terms of linguistic variables. We have formulated model of multiple mode in project network problem and find its solution with fuzzy programming approach with exponential membership and linear membership function. The proposed approach is useful to solve multi-mode project management problem which calculates optimal critical path according to four criteria- time, cost, risk and quality with three activities modes of execution in fuzzy environment.

scholarly journals Extended TOPSIS Method for Supplier Selection under Picture Hesitant Fuzzy Environment Using Linguistic Variables

2021 ◽  
Vol 2021 ◽  
pp. 1-28
Author(s):  
Rabia Ambrin ◽  
Muhammad Ibrar ◽  
Manuel De La Sen ◽  
Ihsan Rabbi ◽  
Asghar Khan
Keyword(s):  
Fuzzy Numbers ◽  
Fuzzy Topsis ◽  
Vital Role ◽  
Decision Makers ◽  
Linguistic Variables ◽  
Comparison Analysis ◽  
Topsis Method ◽  
Practical Applications ◽  
Fuzzy Environment ◽  
Intuitionistic Fuzzy Topsis

The main purpose of this planned manuscript is to establish an algorithm for the solution of multiattribute decision-making (MADM) issues, where the experts utilizing linguistic variables provide the information about attributes in the form of picture hesitant fuzzy numbers. So, for the solution of these kinds of issues, we develop the TOPSIS algorithm under picture hesitant fuzzy environment using linguistic variables, which plays a vital role in practical applications, notably MADM issues, where the decision information is arranged by the decision-makers (DMs) in the form of picture hesitant fuzzy numbers. Finally, a sample example is given as an application and appropriateness of the planned method. At the end, we conduct comparison analysis of the planned method with picture fuzzy TOPSIS method and intuitionistic fuzzy TOPSIS method.

Goal programming approach for solving multi-objective fractional transportation problem with fuzzy parameters

2019 ◽  
Vol 53 (1) ◽  
pp. 157-178 ◽  
Author(s):  
Paraman Anukokila ◽  
Bheeman Radhakrishnan ◽  
Antony Anju
Keyword(s):  
Goal Programming ◽  
Transportation Problem ◽  
Fuzzy Numbers ◽  
Decision Makers ◽  
Programming Approach ◽  
Multi Objective ◽  
Proposed Model ◽  
Fuzzy Goal ◽  
Fuzzy Parameters ◽  
Interval Valued

In this paper, authors studied a goal programming approach for solving multi-objective fractional transportation problem by representing the parameters (γ, δ) in terms of interval valued fuzzy numbers. Fuzzy goal programming problem with multiple objectives is difficult for the decision makers to determine the goal valued of each objective precisely. The proposed model presents a special type of non-linear (hyperbolic) membership functions to solve multi-objective fractional transportation problem with fuzzy parameters. To illustrate the proposed method numerical examples are solved.

scholarly journals The LR-Type Fuzzy Multi-Objective Vendor Selection Problem in Supply Chain Management

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1621
Author(s):  
Irfan Ali ◽  
Armin Fügenschuh ◽  
Srikant Gupta ◽  
Umar Muhammad Modibbo
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Real Life ◽  
Selection Problem ◽  
Programming Approach ◽  
Vendor Selection ◽  
Chain Management ◽  
Multi Objective ◽  
Fuzzy Environment ◽  
Proposed Model

Vendor selection is an established problem in supply chain management. It is regarded as a strategic resource by manufacturers, which must be managed efficiently. Any inappropriate selection of the vendors may lead to severe issues in the supply chain network. Hence, the desire to develop a model that minimizes the combination of transportation, deliveries, and ordering costs under uncertainty situation. In this paper, a multi-objective vendor selection problem under fuzzy environment is solved using a fuzzy goal programming approach. The vendor selection problem was modeled as a multi-objective problem, including three primary objectives of minimizing the transportation cost; the late deliveries; and the net ordering cost subject to constraints related to aggregate demand; vendor capacity; budget allocation; purchasing value; vendors’ quota; and quantity rejected. The proposed model input parameters are considered to be LR fuzzy numbers. The effectiveness of the model is illustrated with simulated data using R statistical package based on a real-life case study which was analyzed using LINGO 16.0 optimization software. The decision on the vendor’s quota allocation and selection under different degree of vagueness in the information was provided. The proposed model can address realistic vendor selection problem in the fuzzy environment and can serve as a useful tool for multi-criteria decision-making in supply chain management.

scholarly journals Significance of multi-objective optimization in logistics problem for multi-product supply chain network under the intuitionistic fuzzy environment

2021 ◽  
Author(s):  
Srikant Gupta ◽  
Ahteshamul Haq ◽  
Irfan Ali ◽  
Biswajit Sarkar
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Real Life ◽  
Decision Makers ◽  
Supply Chain Network ◽  
Multi Objective ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Hierarchical Decision

AbstractDetermining the methods for fulfilling the continuously increasing customer expectations and maintaining competitiveness in the market while limiting controllable expenses is challenging. Our study thus identifies inefficiencies in the supply chain network (SCN). The initial goal is to obtain the best allocation order for products from various sources with different destinations in an optimal manner. This study considers two types of decision-makers (DMs) operating at two separate groups of SCN, that is, a bi-level decision-making process. The first-level DM moves first and determines the amounts of the quantity transported to distributors, and the second-level DM then rationally chooses their amounts. First-level decision-makers (FLDMs) aimed at minimizing the total costs of transportation, while second-level decision-makers (SLDM) attempt to simultaneously minimize the total delivery time of the SCN and balance the allocation order between various sources and destinations. This investigation implements fuzzy goal programming (FGP) to solve the multi-objective of SCN in an intuitionistic fuzzy environment. The FGP concept was used to define the fuzzy goals, build linear and nonlinear membership functions, and achieve the compromise solution. A real-life case study was used to illustrate the proposed work. The obtained result shows the optimal quantities transported from the various sources to the various destinations that could enable managers to detect the optimum quantity of the product when hierarchical decision-making involving two levels. A case study then illustrates the application of the proposed work.

A FUZZY PROGRAMMING APPROACH FOR SUPPLY CHAIN NETWORK DESIGN

2007 ◽  
Vol 15 (supp02) ◽  
pp. 75-87 ◽  
Author(s):  
XIAOYU JI ◽  
XIANDE ZHAO ◽  
DEMING ZHOU
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Cost Optimization ◽  
Fuzzy Programming ◽  
Network Design Problem ◽  
Programming Approach ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Fuzzy Environment ◽  
Stochastic Parameters

This paper presents a fuzzy programming method to design supply chain network, in which the customer demands and transportation costs are assumed to be fuzzy parameters. Existing researches on supply chain network design problem are either restricted on deterministic environment or only address stochastic parameters. In this paper, we consider this problem in fuzzy environment. Under different criteria, we format three types of models for the decision makers: expected cost optimization model, chance-constrained model and chance maximization model. A genetic algorithm based on fuzzy simulation is developed to solve the proposed fuzzy models. Moreover, some numerical examples are presented to illustrate the effectiveness of models and solution algorithm.

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