scholarly journals Application of fuzzy programming techniques to solve solid transportation problem with additional constraints

2020 ◽  
Vol 30 (1) ◽  
Author(s):  
Sharmistha Halder (Jana) ◽  
Biswapati Jana
Keyword(s):  
Transportation Problem ◽  
Real Life ◽  
Transportation Cost ◽  
Fuzzy Programming ◽  
Solid Transportation Problem ◽  
Generalized Gradient ◽  
Proposed Model ◽  
Programming Techniques ◽  
Total Transportation Cost ◽  
Additional Constraints

An innovative, real-life solid transportation problem is explained in a non-linear form. As in real life, the total transportation cost depends on the procurement process or type of the items and the distance of transportation. Besides, an impurity constraint is considered here. The proposed model is formed with fuzzy imprecise nature. Such an interesting model is optimised through two different fuzzy programming techniques and fractional programming methods, using LINGO-14.0 tools followed by the generalized gradient method. Finally, the model is discussed concerning these two different methods.

Multi-Objective Fixed-Charge Transportation Problem with Random Rough Variables

2018 ◽  
Vol 26 (06) ◽  
pp. 971-996 ◽  
Author(s):  
Sankar Kumar Roy ◽  
Sudipta Midya ◽  
Vincent F. Yu
Keyword(s):  
Transportation Problem ◽  
Deterministic Model ◽  
Real Life ◽  
Optimal Solution ◽  
Fuzzy Programming ◽  
Fixed Charge ◽  
Optimal Solutions ◽  
Multi Objective ◽  
Proposed Model ◽  
Fixed Charge Transportation Problem

This paper considers a multi-objective fixed-charge transportation problem (MOFCTP) in which the parameters of the objective functions are random rough variables, while the supply and the demand parameters are rough variables. In real-life situations, the parameters of a multi-objective fixed-charge transportation problem may not be defined precisely, because of globalization of the market, uncontrollable factors, etc. As such, the multi-objective fixed-charge transportation problem is proposed under rough and random rough environments. To tackle uncertain (rough and random rough) parameters, the proposed model employs an expected value operator. Furthermore, a procedure is developed for converting the uncertain multi-objective fixed-charge transportation problem into a deterministic form and then solving the deterministic model. Three different methods, namely, the fuzzy programming, global criterion, and ϵ-constrained methods, are used to derive the optimal compromise solutions of the suggested model. To provide the preferable optimal solution of the formulated problem, a comparison is drawn among the optimal solutions that are extracted from different methods. Herein, the ϵ-constrained method derives a set of optimal solutions and generates an exact Paretofront. Finally, in order to show the applicability and feasibility of the proposed model, the paper includes a real-life example of a multi-objective fixed-charge transportation problem. The main contribution of the paper is that it deals with MOFCTP using two types of uncertainties, thus making the decision making process more flexible.

Fuzzy Transportation Problem by Using Triangular, Pentagonal and Heptagonal Fuzzy Numbers With Lagrange's Polynomial to Approximate Fuzzy Cost for Nonagon and Hendecagon

2020 ◽  
Vol 9 (1) ◽  
pp. 112-129
Author(s):  
Ashok Sahebrao Mhaske ◽  
Kirankumar Laxmanrao Bondar
Keyword(s):  
Transportation Problem ◽  
Operational Research ◽  
Fuzzy Numbers ◽  
Real Life ◽  
Transportation Cost ◽  
Fuzzy Transportation Problem ◽  
Total Transportation Cost ◽  
Fuzzy Cost

The transportation problem is a main branch of operational research and its main objective is to transport a single uniform good which are initially stored at several origins to different destinations in such a way that the total transportation cost is minimum. In real life applications, available supply and forecast demand, are often fuzzy because some information is incomplete or unavailable. In this article, the authors have converted the crisp transportation problem into the fuzzy transportation problem by using various types of fuzzy numbers such as triangular, pentagonal, and heptagonal fuzzy numbers. This article compares the minimum fuzzy transportation cost obtained from the different method and in the last section, the authors introduce the Lagrange's polynomial to determine the approximate fuzzy transportation cost for the nanogon (n = 9) and hendecagon (n = 11) fuzzy numbers.

Solving the fully fuzzy multi-objective transportation problem based on the common set of weights in DEA

2020 ◽  
Vol 39 (3) ◽  
pp. 3099-3124
Author(s):  
M. Bagheri ◽  
A. Ebrahimnejad ◽  
S. Razavyan ◽  
F. Hosseinzadeh Lotfi ◽  
N. Malekmohammadi
Keyword(s):  
Relative Efficiency ◽  
Transportation Problem ◽  
Real Life ◽  
Transportation Cost ◽  
Multi Objective ◽  
Decision Making Unit ◽  
Common Set Of Weights ◽  
The Common ◽  
Total Transportation Cost ◽  
Fuzzy Dea

A transportation problem basically deals with the problem which aims to minimize the total transportation cost or maximize the total transportation profit of distributing a product from a number of sources or origins to a number of destinations. While, in general, most of the real life applications are modeled as a transportation problem (TP) with the multiple, conflicting and incommensurate objective functions. On the other hand, for some reason such as shortage of information, insufficient data or lack of evidence, the data of the mentioned problem are not always exact but can be fuzzy. This type of problem is called fuzzy multi-objective transportation problem (FMOTP). There are a few approaches to solve the FMOTPs. In this paper, a new fuzzy DEA based approach is developed to solve the Fully Fuzzy MOTPs (FFMOTPs) in which, in addition to parameters of the MOTPs, all of the variables are considered fuzzy. This approach considers each arc in a FFMOTP as a decision making unit which produces multiple fuzzy outputs using the multiple fuzzy inputs. Then, by using the concept of the common set of weights (CSW) in DEA, a unique fuzzy relative efficiency is defined for each arc. In the following, the unique fuzzy relative efficiency is considered as the only attribute for the arcs. In this way, a single objective fully fuzzy TP (FFTP) is obtained that can be solved using the existing standard algorithms for solving this kind of TPs. A numerical example is provided to illustrate the developed approach.

scholarly journals A Synchronous Optimization Model for Multiship Shuttle Tanker Fleet Design and Scheduling Considering Hard Time Window Constraint

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Zhenfeng Jiang ◽  
Dongxu Chen ◽  
Zhongzhen Yang
Keyword(s):  
Time Window ◽  
Programming Model ◽  
Transportation Cost ◽  
Exact Algorithm ◽  
Mixed Integer ◽  
Routing Problem ◽  
Hard Time ◽  
Proposed Model ◽  
Total Transportation Cost ◽  
Time Window Constraints

A Synchronous Optimization for Multiship Shuttle Tanker Fleet Design and Scheduling is solved in the context of development of floating production storage and offloading device (FPSO). In this paper, the shuttle tanker fleet scheduling problem is considered as a vehicle routing problem with hard time window constraints. A mixed integer programming model aiming at minimizing total transportation cost is proposed to model this problem. To solve this model, we propose an exact algorithm based on the column generation and perform numerical experiments. The experiment results show that the proposed model and algorithm can effectively solve the problem.

scholarly journals Optimization of total transportation cost

2020 ◽  
Vol 26 (1) ◽  
pp. 57-63
Author(s):  
Adamu Isah Kamba ◽  
Suleiman Mansur Kardi ◽  
Yunusa Kabir Gorin Dikko
Keyword(s):  
Transportation Problem ◽  
Research Work ◽  
Minimum Cost ◽  
Transportation Cost ◽  
Strategic Decision ◽  
Distribution Method ◽  
North West ◽  
Total Transportation Cost ◽  
Transportation Schedule ◽  
Furniture Factory

In this research work, the study used transportation problem techniques to determine minimum cost of transportation of Gimbiya Furniture Factory using online software, Modified Distribution Method (MODI). The observation made was that if Gimbiya furniture factory, Birnin Kebbi could apply this model to their transportation schedule, it will help to minimize transportation cost at the factory to ₦1,125,000.00 as obtained from North west corner method, since it was the least among the two methods, North west corner method and Least corner method. This transportation model willbe useful for making strategic decision by the logistic managers of Gimbiya furniture factory, in making optimum allocation of the production from the company in Kebbi to various customers (key distributions) at a minimum transportation cost. Keywords: North West corner, Least corner, Transportation problem, minimum transportation.

scholarly journals Multi-Objective Capacitated Solid Transportation Problem with Uncertain Variables

2021 ◽  
Vol 6 (5) ◽  
pp. 1406-1422
Author(s):  
Vandana Y. Kakran ◽  
Jayesh M. Dhodiya
Keyword(s):  
Transportation Problem ◽  
Optimal Solution ◽  
Fuzzy Programming ◽  
Solid Transportation Problem ◽  
Programming Technique ◽  
Distance Method ◽  
Multi Objective ◽  
Uncertain Variables ◽  
Value Model ◽  
Single Objective

This paper investigates a multi-objective capacitated solid transportation problem (MOCSTP) in an uncertain environment, where all the parameters are taken as zigzag uncertain variables. To deal with the uncertain MOCSTP model, the expected value model (EVM) and optimistic value model (OVM) are developed with the help of two different ranking criteria of uncertainty theory. Using the key fundamentals of uncertainty, these two models are transformed into their relevant deterministic forms which are further converted into a single-objective model using two solution approaches: minimizing distance method and fuzzy programming technique with linear membership function. Thereafter, the Lingo 18.0 optimization tool is used to solve the single-objective problem of both models to achieve the Pareto-optimal solution. Finally, numerical results are presented to demonstrate the application and algorithm of the models. To investigate the variation in the objective function, the sensitivity of the objective functions in the OVM model is also examined with respect to the confidence levels.

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