A test of a linear programming model as an optimal solution to the problem of combining methods of reading instruction

1973 ◽  
Vol 2 (2) ◽  
pp. 215-233 ◽  
Author(s):  
James W. Mills ◽  
Philip K. Jensen ◽  
Martin Hershkowitz
Keyword(s):  
Linear Programming ◽  
Reading Instruction ◽  
Programming Model ◽  
Optimal Solution ◽  
Linear Programming Model ◽  
Combining Methods

A Linear Programming Model for Multiple-use Planning

1975 ◽  
Vol 5 (3) ◽  
pp. 485-491 ◽  
Author(s):  
William A. Leuschner ◽  
John R. Porter ◽  
Marion R. Reynolds ◽  
Harold E. Burkhart
Keyword(s):  
Linear Programming ◽  
Programming Model ◽  
Geographical Location ◽  
Optimal Solution ◽  
Linear Programming Model ◽  
Sensitivity Analyses ◽  
Multiple Use ◽  
Planning Unit ◽  
Wide Range ◽  
Choice Variable

Multiple-use planning is useful in matching forest production possibilities and social desires. A linear programming model for multiple-use planning is presented. Planning is approached as a set of production objectives which have a set of management activities to help achieve them and a set of constraints which limit the management activities. Timber yield is the objective function and other multiple-use objectives are stated as constraints. Production can be increased by certain management activities, but these activities are limited by budget, cutting, and other technical constraints. Size of the area cut is the choice variable and the solution is tied to 21.6-acre (8.74-ha) grid cells thereby identifying the geographical location of management and production activities. The model was tested on a planning unit and sensitivity analyses were performed on the initial optimal solution. These indicated that on this planning unit, there is a wide range of production alternatives which will not affect other multiple-use production possibilities and that production is sensitive to budget changes.

scholarly journals Use of Linear Programming Model to Determine the Optimum Cropping Pattern for an Irrigation Scheme in Masvingo, Zimbabwe

2013 ◽  
Vol 1 (4) ◽  
pp. 450-452
Author(s):  
Majeke F ◽  
Mubvuma S M T ◽  
J. Chirima, K. Makaza ◽  
T. Hungwe R. Gwazan ◽  
Nyoni ◽  
...  
Keyword(s):  
Linear Programming ◽  
Optimization Problems ◽  
Programming Model ◽  
Optimal Solution ◽  
Linear Programming Model ◽  
No Production ◽  
Cropping Pattern ◽  
Irrigation Scheme ◽  
Irrigation Planning ◽  
Optimal Cropping

Agricultural systems are often faced by challenges such as crop selection and irrigation planning which can be formulated as optimization problems. Decisions have to be made on the proper set of crops to be cultivated and a proper irrigation scheme. The objectives of such decisions are to maximize net profit or to minimize water waste. In this study, a linear programming model was developed that helped to determine the optimal cropping pattern for an irrigation scheme in Masvingo, Zimbabwe. Crops which considered were wheat, sugar beans for winter and cotton and maize for summer for the 2012/13 agricultural season. The linear programming model was solved by using Microsoft Excel (2007). The model recommended no production of wheat and cotton. Sugar beans and maize gained acreage by 50 percent and 88 percent respectively. On the whole, the optimal cropped acreage did not change as compared to the existing cropping plan. As a result of the optimal solution, a farmer‘s income could be increased by $1,668.60. The optimal income increased from existing level of $1,919.40 to $3,588.00 showing an improvement of 87 percent. The results show that LP models solutions are worthy implementing.

scholarly journals Linear programming problems with some multi-choice fuzzy parameters

2018 ◽  
Vol 28 (2) ◽  
pp. 249-264 ◽  
Author(s):  
Avik Pradhan ◽  
Biswal Prasad
Keyword(s):  
Linear Programming ◽  
Fuzzy Numbers ◽  
Programming Model ◽  
Real Life ◽  
Optimal Solution ◽  
Linear Programming Model ◽  
Mixed Integer ◽  
Non Linear Programming ◽  
A Value ◽  
Linear Programming Problems

In this paper, we consider some Multi-choice linear programming (MCLP) problems where the alternative values of the multi-choice parameters are fuzzy numbers. There are some real-life situations where we need to choose a value for a parameter from a set of different choices to optimize our objective, and those values of the parameters can be imprecise or fuzzy. We formulate these situations as a mathematical model by using some fuzzy numbers for the alternatives. A defuzzification method based on incentre point of a triangle has been used to find the defuzzified values of the fuzzy numbers. We determine an equivalent crisp multi-choice linear programming model. To tackle the multi-choice parameters, we use Lagranges interpolating polynomials. Then, we establish a transformed mixed integer nonlinear programming problem. By solving the transformed non-linear programming model, we obtain the optimal solution for the original problem. Finally, two numerical examples are presented to demonstrate the proposed model and methodology.

A Class of Collocating Problem in Production and its Optimization Models

2014 ◽  
Vol 513-517 ◽  
pp. 1215-1220
Author(s):  
Dai Lun Tan ◽  
Hong Xia Chen ◽  
Ze Jian Cui
Keyword(s):  
Linear Programming ◽  
Combinatorial Optimization ◽  
Integer Linear Programming ◽  
Programming Model ◽  
Optimal Solution ◽  
Linear Programming Model ◽  
Optimization Models ◽  
General Description ◽  
Integer Linear Programming Model ◽  
Model Based

The paper gives a general description of a typical combinatorial optimization problem-the collocation problem of production, and respectively establish the integer linear programming model based on the collocation ways and the integer linear programming model based on the optimal collocation amounts and the integer nonlinear programming model based on the local optimum. In the solving methods of the models, we have mainly analyzed the enumeration method and the method by using LINGO optimization software to solve the model. The instance shows that the models and solutions are all effective. The first model reflects the mechanism of the collocation problem better and gets the global optimal solution most likely, but more difficult to solve large-scale; the second model can quickly obtain the optimal the numbers of the finished products and all kinds of materials consumptions, but still need to enumerate the collocation ways step by step; the third model can gets the local optimal solution and the specific collocation ways through solving the model circularly. The optimization models and solution methods in this paper can be extended to the similar sub-problems of the combinatorial optimization problems, such as assembly problem, packing problem, the cutting problem, etc.

scholarly journals Analysis of Postponement Practice in Cement Supply Chain: A Case Study

2020 ◽  
Vol 19 (2) ◽  
pp. 176-185
Author(s):  
Fauzan Romadlon ◽  
Hadi Kurniawan ◽  
Ridho Ananda
Keyword(s):  
Linear Programming ◽  
Ordinary Portland Cement ◽  
Programming Model ◽  
Model Simulation ◽  
Optimal Solution ◽  
Cement Industry ◽  
Linear Programming Model ◽  
Observational Method ◽  
Investment Cost

This research focuses on assessing implementing postponement in a cement industry especially for OPC (Ordinary Portland Cement) in Indonesia. The company offers three postponement points; Ciawi and Kampung Rambutan, and Jatiwarna. This research has been done with observational method to map customer condition. Moreover, implementing linear programming model is used to gain optimal solution for allocated truck. The results are, highest proportion of OPC customer is in Jakarta, followed Karawang, and Bogor. The result from linear programming model simulation, Ciawi point is assigned to serve Jakarta and Bogor’ customers, Kampung Rambutan point is assigned for Jakarta and Kawarang’ customers, and Jatiwarna point is prepared for Jakarta, Karawang, Bekasi, Lampung, Pekalongan, Tangerang, and Serang’ customers. So, implementing postponement can increase customer satisfaction without adding higher cost such as investment cost. The cement industry encounter effectiveness and responsiveness even tough, some problem shall be taken such as double administration process (manual and computerized).

A Linear Programming Model for Product Mix Profit Maximization in A Small Medium Enterprise Company

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
S Mohd Baki ◽  
Jack Kie Cheng
Keyword(s):  
Linear Programming ◽  
Production Planning ◽  
Business Performance ◽  
Programming Model ◽  
Profit Maximization ◽  
Optimal Level ◽  
Linear Programming Model ◽  
Product Mix ◽  
Small Medium Enterprises ◽  
Medium Enterprises

Production planning is often challenging for small medium enterprises (SMEs) company. Most of the SMEs are having difficulty in determining the optimal level of the production output which can affect their business performance. Product mix optimization is one of the main key for production planning. Many company have used linear programming model in determining the optimal combination of various products that need to be produced in order to maximize profit. Thus, this study aims for profit maximization of a SME company in Malaysia by using linear programming model. The purposes of this study are to identify the current process in the production line and to formulate a linear programming model that would suggest a viable product mix to ensure optimum profitability for the company. ABC Sdn Bhd is selected as a case study company for product mix profit maximization study. Some conclusive observations have been drawn and recommendations have been suggested. This study will provide the company and other companies, particularly in Malaysia, an exposure of linear programming method in making decisions to determine the maximum profit for different product mix.

A Linear Programming Model to Evaluate Gas Availability for Vital Industries in Saudi Arabia

1992 ◽  
Vol 43 (11) ◽  
pp. 1035-1045
Author(s):  
S O Duffuaa ◽  
J A Al-Zayer ◽  
M A Al-Marhoun ◽  
M A Al-Saleh
Keyword(s):  
Saudi Arabia ◽  
Linear Programming ◽  
Programming Model ◽  
Linear Programming Model
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