scholarly journals Optimization of a multi-constraint transport problem using a heuristic approach

2022 ◽  
Vol 10 (1) ◽  
pp. 001-021
Author(s):  
Ngnassi Djami Aslain Brisco ◽  
Nzié Wolfgang ◽  
Doka Yamigno Serge

A Linear transport problem can be defined as the action of transporting products from "m origins" (or units) to "n destinations" (or customers) at the lowest cost. So the solution to a transportation problem is to organize the transportation in such a way as to minimize its cost. The objective of this paper is to determine the quantity sent from each source (origin) to each destination while minimizing transport costs. Achieving this objective requires a methodology which consists in deploying an algorithm whose purpose is the search for an optimal solution, based on an initial solution. The application is made on a factory producing mechanical parts.

2018 ◽  
Vol 7 (4.10) ◽  
pp. 364
Author(s):  
N. Srinivasan ◽  
A. Seethalakshmy

This Method is proposed for obtaining an optimal solution for transportation problem. This method gives the optimal solution in lesser iteration. Here find the difference between two consecutive maximum for row-wise and column-wise. In that find the maximum value, for which the minimum is allocated by the minimum supply or demand. Illustration for this method is given with some examples at the end. 


2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Rudy Santosa Sudirga

<p>The famous method to determine and solve transportation problem is the transportation model and the assignment model. We see how to develop an initial solution to the transportation problem with VAM (Vogel’s Approximation Method) and MODI (Modified Distribution) and Northwest Corner rule and the Stepping-Stone method. VAM is not quite as simple as the Northwest Corner approach, but it facilitates a very good initial solution, as a matter of fact, one that is often the optimal solution. The Assignment method, which is simple and faster to solve the transportation problem by reducing the numbers (cost) in the table/tableau until a series of zeros is found, or zero opportunity costs, which means that we will reach the optimal cost allocations. Once we have reached the optimal cost allocations, we then allocate each sources or supply according to some points of demand (destinations). Assignment Method is a specialized form of optimization linear programming model that attempts to assign limited capacity to various demand points in a way that minimizes costs. The special cases of transportation problem included degeneracy (a condition that occurs when the number of occupied squares in any solution is less than the number of rows plus the number of columns minus 1 in a transportation table), unbalanced problems, and multiple optimal solutions. At this opportunity, we would like to demonstrate the multiple optimal solutions. We will see how the Assignment method may be viewed as a special case of solving the transportation problem.</p><p> </p><p>Keywords : Assignment method, VAM and MODI, Northwest Corner and Stepping-Stone.</p>


2020 ◽  
Vol 8 (4) ◽  
Author(s):  
Qazi Shoeb Ahmad

A transportation problem deals with two different problems balanced transportation problem or unbalanced transportation problem. This paper points out how Goyal’s modification of Vogel’s approximation method for the unbalanced transportation problem can be improved by subtracting or adding suitable constants to the cost matrix, rows and columns of the cost matrix. In this paper, a new method is proposed for solving unbalanced transportation problem which gives optimal or very near to optimal solution.


2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Rudy Santosa Sudirga

<p>The famous method to determine and solve transportation problem is the transportation model VAM and MODI method, the Northwest-Corner and Stepping-Stone method, and the Assignment method. We see how to develop an initial aolution to the transportation problem with VAM (Vogel’s Approximation Method) and MODI (Modified Distribution). VAM is not quite as simple as the Northwest Corner approach. But it facilitates a very good initial solution, as a matter of fact, one that is often the optimal solution.</p><p>VAM method trackles the problem of finding a good initial solution by taking into account the cost the costs associated with each route alternative. This is something that Northwest Corner Rules does not do. To apply VAM, we first compute for each row and column the penalty faced if we should ship over the second-best route instead of the least-cost route. After the initial of VAM solution has been found, you should evaluate it with either the Stepping-Stone method or the MODI method. The MODI (Modified Distribution) method allows us to compute improvement indices quickly for each unused square without drawing all of the closed paths. Because of this, it can often provide considerable time savings over the Stepping-Stone method for solving transportation problems. If there is a negative index indicating an imporovemet can be made, then only one Stepping-Stone path must be found. This is used as it was before to determine what changes should be made to obtain the improved solution.</p><p>In the Northwest-corner rule, the largest possible allocation is made to the cell in the upper left-hand corner of the tableau, followed by allocations to adjacent feasible celss. While the Stepping-stone method is an interactive technique for moving from an initial feasible solution to an optimal feasible solution, and continues will until the optimal solution is reached. The Stepping-stone path method is used to calculate improvement indices for the empty cells. Improved solutions are developed using a Stepping-stone path.</p><p>The assignment method, which is simple and faster to solve the transportation problem by reducing the numbers (cost) in the table/tableau until a series of zeros is found, or zero opportunity costs, which mean that we will reach the optimal cost allocations. Once we have reached the optimal cost allocations, we the allocate each sources or supply according to some point of demand (destinations). Assignment Method is a specialized form of optimization linear programming model that attempts to assign limited capacity to various demand points in a way that minimizes costs.</p><p>The special cases of transportation problem included degeneracy (a condition that occurs when the number of occupied squares in any solution is less than the number of rows plus the number of columns minus 1 in a transportation table), unbalanced problems, and multiple optimal solutions. We will see how the VAM and MODI method may be viewed as a special case of solving the multiple optimal solutions of the transportation problem.</p><p>Keywords : transportation, VAM and MODI</p>


2020 ◽  
Vol 5 (1) ◽  
pp. 456
Author(s):  
Tolulope Latunde ◽  
Joseph Oluwaseun Richard ◽  
Opeyemi Odunayo Esan ◽  
Damilola Deborah Dare

For twenty decades, there is a visible ever forward advancement in the technology of mobility, vehicles and transportation system in general. However, there is no "cure-all" remedy ideal enough to solve all life problems but mathematics has proven that if the problem can be determined, it is most likely solvable. New methods and applications will keep coming to making sure that life problems will be solved faster and easier. This study is to adopt a mathematical transportation problem in the Coca-Cola company aiming to help the logistics department manager of the Asejire and Ikeja plant to decide on how to distribute demand by the customers and at the same time, minimize the cost of transportation. Here, different algorithms are used and compared to generate an optimal solution, namely; North West Corner Method (NWC), Least Cost Method (LCM) and Vogel’s Approximation Method (VAM). The transportation model type in this work is the Linear Programming as the problems are represented in tables and results are compared with the result obtained on Maple 18 software. The study shows various ways in which the initial basic feasible solutions to the problem can be obtained where the best method that saves the highest percentage of transportation cost with for this problem is the NWC. The NWC produces the optimal transportation cost which is 517,040 units.


10.26524/cm46 ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 40-48
Author(s):  
Ravi J ◽  
Dickson S ◽  
Sathya K

Author(s):  
Amit Kumar ◽  
Amarpreet Kaur

There are several methods, in literature, for finding the fuzzy optimal solution of fully fuzzy transportation problems (transportation problems in which all the parameters are represented by fuzzy numbers). In this paper, the shortcomings of some existing methods are pointed out and to overcome these shortcomings, two new methods (based on fuzzy linear programming formulation and classical transportation methods) are proposed to find the fuzzy optimal solution of unbalanced fuzzy transportation problems by representing all the parameters as trapezoidal fuzzy numbers. The advantages of the proposed methods over existing methods are also discussed. To illustrate the proposed methods a fuzzy transportation problem (FTP) is solved by using the proposed methods and the obtained results are discussed. The proposed methods are easy to understand and to apply for finding the fuzzy optimal solution of fuzzy transportation problems occurring in real life situations.


Author(s):  
Sankar Kumar Roy ◽  
Deshabrata Roy Mahapatra

In this chapter, the authors propose a new approach to analyze the Solid Transportation Problem (STP). This new approach considers the multi-choice programming into the cost coefficients of objective function and stochastic programming, which is incorporated in three constraints, namely sources, destinations, and capacities constraints, followed by Cauchy's distribution for solid transportation problem. The multi-choice programming and stochastic programming are combined into a solid transportation problem, and this new problem is called Multi-Choice Stochastic Solid Transportation Problem (MCSSTP). The solution concepts behind the MCSSTP are based on a new transformation technique that will select an appropriate choice from a set of multi-choice, which optimize the objective function. The stochastic constraints of STP converts into deterministic constraints by stochastic programming approach. Finally, the authors construct a non-linear programming problem for MCSSTP, and by solving it, they derive an optimal solution of the specified problem. A realistic example on STP is considered to illustrate the methodology.


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