scholarly journals TIGA JAWABAN PENYELESAIAN OPTIMAL DALAM PROBLEM TRANSPORTASI DENGAN VAM AND MODI METHOD

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>

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>


2017 ◽  
Vol 5 (1) ◽  
Author(s):  
Rudy Santosa Sudirga

<p>The famous methods to determine and solve transportation problem are the Northwest-corner rule/method and the Stepping-stone Method. In the Northwest-Corner Rule, the largest possible allocation is made to the cell in the upper left-hand corner of the tableau, followerd by allocations to adjacent feasible cells; while the Stepping-Stone Method is an iterative technique for moving from an initial feasible solution to an optimum feasible solution which continues untul the optimum solution is reached. However, inn some cases, sometimes we have been facing some difficulties to solve transportation problem with these two methods, therefore we intoduce the <strong>Assignment Method, </strong>which is simpler and faster in solving 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 will reach the optimum cost allocations. Once reach the optimum cost allocations, the next step is to allocate each source or supply according to some points of demand (destinations). <strong>Assignment Method </strong>is a specialized form of optimization linear programming model that attempts to assign limited capacitiy to various demand points in a way that minimizes costs.</p><p>Keywords :</p><p>assignment method, linear programming, lowest opportunity cost, minimize time required</p>


Author(s):  
Bhabani Mallia ◽  
Manjula Das ◽  
C. Das

Transportation Problem is a linear programming problem. Like LPP, transportation problem has basic feasible solution (BFS) and then from it we obtain the optimal solution. Among these BFS the optimal solution is developed by constructing dual of the TP. By using complimentary slackness conditions the optimal solutions is obtained by the same iterative principle. The method is known as MODI (Modified Distribution) method. In this paper we have discussed all the aspect of transportation problem.


Author(s):  
Priyanka Nagar ◽  
Pankaj Kumar Srivastava ◽  
Amit Srivastava

The transportation of big species is essential to rescue or relocate them and it requires the optimized cost of transportation. The present study brings out an optimized way to handle a special class of transportation problem called the Pythagorean fuzzy species transportation problem. To deal effectively with uncertain parameters, a new method for finding the initial fuzzy basic feasible solution (IFBFS) has been developed and applied. To test the optimality of the solutions obtained, a new approach named the Pythagorean fuzzy modified distribution method is developed. After reviewing the literature, it has been observed that till now the work done on Pythagorean fuzzy transportation problems is solely based on defuzzification techniques and so the optimal solutions obtained are in crisp form only. However, the proposed study is focused to get the optimal solution in its fuzzy form only. Getting results in the fuzzy form will lead to avoid any kind of loss of information during the defuzzification process. A comparative study with other defuzzification-based methods has been done to validate the proposed approach and it confirms the utility of the proposed methodology.


Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2027
Author(s):  
Abd Allah A. Mousa ◽  
Yousria Abo-Elnaga

This paper investigates the solution for an inverse of a parametric nonlinear transportation problem, in which, for a certain values of the parameters, the cost of the unit transportation in the basic problem are adapted as little as possible so that the specific feasible alternative become an optimal solution. In addition, a solution stability set of these parameters was investigated to keep the new optimal solution (feasible one) is unchanged. The idea of this study based on using a tuning parameters λ∈Rm in the function of the objective and input parameters υ∈Rl in the set of constraint. The inverse parametric nonlinear cost transportation problem P(λ,υ), where the tuning parameters λ∈Rm in the objective function are tuned (adapted) as less as possible so that the specific feasible solution x∘ has been became the optimal ones for a certain values of υ∈Rl, then, a solution stability set of the parameters was investigated to keep the new optimal solution x∘ unchanged. The proposed method consists of three phases. Firstly, based on the optimality conditions, the parameter λ∈Rm are tuned as less as possible so that the initial feasible solution x∘ has been became new optimal solution. Secondly, using input parameters υ∈Rl resulting problem is reformulated in parametric form P(υ). Finally, based on the stability notions, the availability domain of the input parameters was detected to keep its optimal solution unchanged. Finally, to clarify the effectiveness of the proposed algorithm not only for the inverse transportation problems but also, for the nonlinear programming problems; numerical examples treating the inverse nonlinear programming problem and the inverse transportation problem of minimizing the nonlinear cost functions are presented.


2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Nebojša V. Stojković ◽  
Predrag S. Stanimirović ◽  
Marko D. Petković ◽  
Danka S. Milojković

This paper discusses the importance of starting point in the simplex algorithm. Three different methods for finding a basic feasible solution are compared throughout performed numerical test examples. We show that our two methods on theNetlibtest problems have better performances than the classical algorithm for finding initial solution. The comparison of the introduced optimization softwares is based on the number of iterative steps and on the required CPU time. It is pointed out that on average it takes more iterations to determine the starting point than the number of iterations required by the simplex algorithm to find the optimal solution.


2020 ◽  
Vol 14 (1) ◽  
pp. 40
Author(s):  
Nurul Iftitah ◽  
Pardi Affandi ◽  
Akhmad Yusuf

(demand). the method that could be used for solving the transportation problem is to directly find the optimal solution. The direct method that used in this study id the ASM method for solving the balance transportation problem and revised ASM method for solving the unbalance transportation problem. This study aims to construct a transportation model using those methods and it solution. The method on this study is to identify the transportation model, construct the transportation model matrixes, construct an algorithm table using ASM method and to determine the optimal solution of the transportation problem. The obtained result from this study was the model ASM method could determine the optimum value without using initial feasible solution. On solving the unbalance transportation problem, there is an addition of dummy cell or column step. Then reducing the cost of cell and column and change the dummy cost with the biggest cost of reduced cell or column.


2017 ◽  
Vol 16 (4) ◽  
pp. 6895-6902
Author(s):  
Nidhi Joshi ◽  
Surjeet Singh Chauhan (Gonder) ◽  
Raghu Raja

The present paper attempts to obtain the optimal solution for the fuzzy transportation problem with mixed constraints. In this paper, authors have proposed a new innovative approach for obtaining the optimal solution of mixed constraint fuzzy transportation problem. The method is illustrated using a numerical example and the logical steps are highlighted using a simple flowchart. As maximum transportation problems in real life have mixed constraints and these problems cannot be truly solved using general methods, so the proposed method can be applied for solving such mixed constraint fuzzy transportation problems to obtain the best optimal solutions.


2007 ◽  
Vol 17 (1) ◽  
pp. 125-133 ◽  
Author(s):  
Ilija Nikolic

This paper shows the total transportation time problem regarding the time of the active transportation routes. If the multiple optimal solutions exist, it is possible to include other criteria as second level of criteria and find the corresponding solutions. Furthermore, if there is a multiple solution, again, the third objective can be optimized in lexicographic order. The methods of generation of the optimal solution in selected cases are developed. The numerical example is included. .


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.


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