scholarly journals Matrix Maxima Method to Solve Multi-objective Transportation Problem with a Pareto Optimality Criteria

2019 ◽  
Vol 8 (11) ◽  
pp. 1929-1932
Keyword(s):  
Pareto Optimality ◽  
Transportation Problem ◽  
Geometric Mean ◽  
Optimal Solution ◽  
Optimality Criteria ◽  
Pareto Optimal Solution ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Pareto Optimal ◽  
Fast Method

In this paper we proposed a new method (Matrix Maxima Method) using Geometric mean approach to solve multiobjective transportation problem with a Pareto Optimality Criteria. Fuzzy membership function is used to convert objectives into membership values and then we take Geomertic mean of membership values. We used a different criteria to find Pareto Optimal Solution. This is an easy and fast method to find the Pareto Optimal solution. The method is illustrated by numerical examples. The result is compared with some other available methods in the literature.

scholarly journals Geometric Mean Method to Solve Multi-Objective Transportation Problem Under Fuzzy Environment

2020 ◽  
Vol 9 (5) ◽  
pp. 1739-1744
Keyword(s):  
Transportation Problem ◽  
Geometric Mean ◽  
Method Comparison ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Pareto Optimal ◽  
Numerical Examples ◽  
Multi Objective ◽  
Fuzzy Environment ◽  
Single Objective

Transportation problem is a very common problem for a businessman. Every businessman wants to reduce cost, time and distance of transportation. There are several methods available to solve the transportation problem with single objective but transportation problems are not always with single objective. To solve transportation problem with more than one objective is a typical task. In this paper we explored a new method to solve multi criteria transportation problem named as Geometric mean method to Solve Multi-objective Transportation Problem Under Fuzzy Environment. We took a problem of transportation with three objectives cost, time and distance. We converted objectives into membership values by using a membership function and then geometric mean of membership values is taken. We also used a procedure to find a pareto optimal solution. Our method gives the better values of objectives than other methods. Two numerical examples are given to illustrate the method comparison with some existing methods is also made.

scholarly journals A Fuzzy Approach Using Generalized Dinkelbach’s Algorithm for Multiobjective Linear Fractional Transportation Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Nurdan Cetin ◽  
Fatma Tiryaki
Keyword(s):  
Transportation Problem ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Pareto Optimal ◽  
Fuzzy Approach ◽  
Operator Model ◽  
Numerical Example

We consider a multiobjective linear fractional transportation problem (MLFTP) with several fractional criteria, such as, the maximization of the transport profitability like profit/cost or profit/time, and its two properties are source and destination. Our aim is to introduce MLFTP which has not been studied in literature before and to provide a fuzzy approach which obtain a compromise Pareto-optimal solution for this problem. To do this, first, we present a theorem which shows that MLFTP is always solvable. And then, reducing MLFTP to the Zimmermann’s “min” operator model which is the max-min problem, we construct Generalized Dinkelbach’s Algorithm for solving the obtained problem. Furthermore, we provide an illustrative numerical example to explain this fuzzy approach.

A Pareto-Optimal Solution for a Multi-Objective Scheduling Problem with Periodic Maintenance Requirement

2012 ◽  
Vol 3 (2) ◽  
pp. 24-45 ◽  
Author(s):  
Deniz Mungan ◽  
Junfang Yu ◽  
Bhaba R. Sarker ◽  
Mohammad Anwar Rahman
Keyword(s):  
Objective Function ◽  
Single Machine ◽  
Pareto Optimality ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Neighborhood Search ◽  
Pareto Optimal ◽  
Scheduling Problem ◽  
Objective Functions ◽  
Periodic Maintenance

A Pareto-optimal solution is developed in this paper for a scheduling problem on a single machine with periodic maintenance and non-preemptive jobs. Most of the scheduling problems address only one objective function, while in the real world, such problems are always associated with more than one objective. In this paper, both multi-objective functions and multi-maintenance periods are considered for a machine scheduling problem. To avoid complexities, multiple objective functions are consolidated and transformed into a single objective function after they are weighted and assigned proper weighting factors. In addition, periodic maintenance schedules are also considered in the model. The objective of the model addressed is to minimize the weighted function of the total job flow time, the maximum tardiness, and the machine idle time in a single machine problem with periodic maintenance and non-preemptive jobs. An algorithm is developed to solve this multiple criterion problem and to construct the Pareto-set. The parametric analysis of the trade-offs of all solutions with all possible weighted combination of the criteria is performed. A neighborhood search heuristic is also developed. Results are provided to explore the best schedule among all the Pareto-optimality sets and to compare the result of the modified Pareto-optimality algorithm with the result of the neighborhood search heuristic.

Multi-Objective Optimization Algorithm Design and Implementation of Mobile Base Station Site Selection

2010 ◽  
Vol 29-32 ◽  
pp. 2496-2502
Author(s):  
Min Wang ◽  
Jun Tang
Keyword(s):  
Site Selection ◽  
Engineering Application ◽  
Optimal Solution ◽  
Base Station ◽  
Pareto Optimal Solution ◽  
Antibody Concentration ◽  
Pareto Optimal ◽  
Memory Cells ◽  
Multi Objective Optimization ◽  
Multi Objective

The number of base station location impact the network quality of service. A new method is proposed based on immune genetic algorithm for site selection. The mathematical model of multi-objective optimization problem for base station selection and the realization of the process were given. The use of antibody concentration selection ensures the diversity of the antibody and avoiding the premature convergence, and the use of memory cells to store Pareto optimal solution of each generation. A exclusion algorithm of neighboring memory cells on the updating and deleting to ensure that the Pareto optimal solution set of the distribution. The experiments results show that the algorithm can effectively find a number of possible base station and provide a solution for the practical engineering application.

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