scholarly journals A Heuristic Approach to Obtain an Optimal Solution for Unbalanced Transportation Problem

2018 ◽  
Vol 7 (4.10) ◽  
pp. 364
Author(s):  
N. Srinivasan ◽  
A. Seethalakshmy
Keyword(s):  
Transportation Problem ◽  
Optimal Solution ◽  
Heuristic Approach ◽  
Maximum Value ◽  
The Difference

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. 

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
Keyword(s):  
Transportation Problem ◽  
Optimal Solution ◽  
Heuristic Approach ◽  
Initial Solution ◽  
Transport Problem ◽  
Transport Costs ◽  
Mechanical Parts ◽  
Linear Transport

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.

OPTIMAL FEASIBLE SOLUTIONS TO A ROAD FREIGHT TRANSPORTATION PROBLEM

2020 ◽  
Vol 5 (1) ◽  
pp. 456
Author(s):  
Tolulope Latunde ◽  
Joseph Oluwaseun Richard ◽  
Opeyemi Odunayo Esan ◽  
Damilola Deborah Dare
Keyword(s):  
Transportation Problem ◽  
Optimal Solution ◽  
Transportation Cost ◽  
North West ◽  
Life Problems ◽  
Basic Feasible Solutions ◽  
Feasible Solutions ◽  
Road Freight Transportation ◽  
The Cost ◽  
Cost Of Transportation

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.

Magnetocrystalline Anisotropy and Twinning Stress of 10M and 2M Martensites in Ni-Mn-Ga System

2006 ◽  
Vol 512 ◽  
pp. 195-200 ◽  
Author(s):  
Nariaki Okamoto ◽  
Takashi Fukuda ◽  
Tomoyuki Kakeshita ◽  
Tetsuya Takeuchi
Keyword(s):  
Magnetic Field ◽  
Shear Stress ◽  
Magnetocrystalline Anisotropy ◽  
Anisotropy Constant ◽  
The Other ◽  
Magnetic Shear ◽  
Maximum Value ◽  
Twinning Plane ◽  
Twinning Shear ◽  
The Difference

Ni2MnGa alloy with 10M martensite exhibits rearrangement of martensite variants (RMV) by magnetic field, but Ni2.14Mn0.92Ga0.94 with 2M martensite does not. In order to explain the difference, we measured uniaxial magnetocrystalline anisotropy constant Ku and the stress required for twinning plane movement τreq in these alloys. Concerning the former alloy, the maximum value of magnetic shear stress acting across twinning plane τmag, which is evaluated as |Ku| divided by twinning shear, becomes larger than τr eq. On the other hand, concerning the latter alloy, the maximum of τmag is only one-tenth of τreq at any temperature examined. Obviously, the relation, τmag> τr eq, is satisfied when RMV occurs by magnetic field and vice versa. In this martensite, the large twinning shear of 2M martensite is responsible for small τmag and large τreq.

Optimal Solution for Hyper-Sphere Integral Classification Process of Big Data

2014 ◽  
Vol 687-691 ◽  
pp. 1462-1465
Author(s):  
Zhi Liang Zhang
Keyword(s):  
Big Data ◽  
Calculation Method ◽  
Optimal Solution ◽  
Constraint Condition ◽  
Target Problem ◽  
Maximum Value ◽  
Dual Programming ◽  
Optimal Classification ◽  
Classification Function ◽  
Optimal Calculation

This paper mainly discusses the optimal solution for hyper-sphere integral classification process of big data. The paper proposes an optimal calculation method for the target problem. Through statistics and analysis of big data, we get the constraint condition, and calculate a maximum value of data characteristic. Then, by the dual programming of Quadratic Programming, we obtain the optimal classification function for hyper-sphere integral classification process of big data. The experiment results show that the proposed algorithm can significantly improve the accuracy of the classification hyper-sphere integral for big data.

Prophet Inequalities for Independent and Identically Distributed Random Variables from an Unknown Distribution

2021 ◽  
Author(s):  
José Correa ◽  
Paul Dütting ◽  
Felix Fischer ◽  
Kevin Schewior
Keyword(s):  
Stopping Time ◽  
Random Variables ◽  
Optimal Solution ◽  
Random Order ◽  
Secretary Problem ◽  
Maximum Value ◽  
Optimal Stopping Theory ◽  
Long Time ◽  
Object Of Study ◽  
Independent And Identically Distributed

A central object of study in optimal stopping theory is the single-choice prophet inequality for independent and identically distributed random variables: given a sequence of random variables [Formula: see text] drawn independently from the same distribution, the goal is to choose a stopping time τ such that for the maximum value of α and for all distributions, [Formula: see text]. What makes this problem challenging is that the decision whether [Formula: see text] may only depend on the values of the random variables [Formula: see text] and on the distribution F. For a long time, the best known bound for the problem had been [Formula: see text], but recently a tight bound of [Formula: see text] was obtained. The case where F is unknown, such that the decision whether [Formula: see text] may depend only on the values of the random variables [Formula: see text], is equally well motivated but has received much less attention. A straightforward guarantee for this case of [Formula: see text] can be derived from the well-known optimal solution to the secretary problem, where an arbitrary set of values arrive in random order and the goal is to maximize the probability of selecting the largest value. We show that this bound is in fact tight. We then investigate the case where the stopping time may additionally depend on a limited number of samples from F, and we show that, even with o(n) samples, [Formula: see text]. On the other hand, n samples allow for a significant improvement, whereas [Formula: see text] samples are equivalent to knowledge of the distribution: specifically, with n samples, [Formula: see text] and [Formula: see text], and with [Formula: see text] samples, [Formula: see text] for any [Formula: see text].

Fuzzy-neural-network-based fluctuation smoothing rule for reducing the cycle times of jobs with various priorities in a wafer fabrication plant: A simulation study

2009 ◽  
Vol 223 (8) ◽  
pp. 1033-1043 ◽  
Author(s):  
T Chen
Keyword(s):  
Neural Network ◽  
Cycle Time ◽  
Fuzzy Neural Network ◽  
Optimal Solution ◽  
Wafer Fabrication ◽  
Estimation Accuracy ◽  
Cycle Times ◽  
Fuzzy Neural ◽  
The Difference ◽  
Fluctuation Smoothing

This paper presents a fuzzy-neural-network-based fluctuation smoothing rule to further improve the performance of scheduling jobs with various priorities in a wafer fabrication plant. The fuzzy system is modified from the well-known fluctuation smoothing policy for a mean cycle time (FSMCT) rule with three innovative treatments. First, the remaining cycle time of a job is estimated by applying an existing fuzzy-neural-network-based approach to improve the estimation accuracy. Second, the components of the FSMCT rule are normalized to balance their importance. Finally, the division operator is applied instead of the traditional subtraction operator in order to magnify the difference in the slack and to enhance the responsiveness of the FSMCT rule. To evaluate the effectiveness of the proposed methodology, production simulation is applied to generate some test data. According to the experimental results, the proposed methodology outperforms six existing approaches in the reduction of the average cycle times. In addition, the new rule is shown to be a Pareto optimal solution for scheduling jobs in a semiconductor manufacturing plant.

scholarly journals An Optimal Solution for Transportation problem-DFSD

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

Methods for Solving Fully Fuzzy Transportation Problems Based on Classical Transportation Methods

2013 ◽  
pp. 328-347 ◽  
Author(s):  
Amit Kumar ◽  
Amarpreet Kaur
Keyword(s):  
Transportation Problem ◽  
Fuzzy Numbers ◽  
Real Life ◽  
Optimal Solution ◽  
Fuzzy Linear Programming ◽  
Programming Formulation ◽  
Transportation Problems ◽  
Fuzzy Optimal Solution ◽  
Linear Programming Formulation ◽  
Fuzzy Transportation Problem

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.

Solving Solid Transportation Problems With Multi-Choice Cost and Stochastic Supply and Demand

2018 ◽  
pp. 137-170 ◽  
Author(s):  
Sankar Kumar Roy ◽  
Deshabrata Roy Mahapatra
Keyword(s):  
Stochastic Programming ◽  
Objective Function ◽  
Transportation Problem ◽  
Supply And Demand ◽  
Optimal Solution ◽  
Programming Approach ◽  
Solid Transportation Problem ◽  
New Approach ◽  
Non Linear Programming ◽  
The Cost

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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