Development of an inexact optimization model for coupled coal and power management in North China
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Goal programming
The optimal timing of air-to-ground tasks for Unmanned Aerial Vehicles is undertaken.In this paper,a mixed integer linear program(MILP) is posed to resolve the optimal task assignment and timing problem.The simulation indicates a successful implementation of a MILP task allocation solution for a team of Unmanned Aerial Vehicles.
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Linear programming relaxation
Branch and cut
Subgradient method
Subroutine
Criss-cross algorithm
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A linear programming model for optimizing about compensative regulation in multi-storage system, which is to make regulation time interval by time interval, is formulated here in this paper. The deviations occurred from the fact that non-linear functions are replaced by linear functions will be put right in current time interval, and the solution procedure for next time interval will then be introduced. A practical example is studied to show that this model is quite powerful.
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Interval arithmetic
Interval data
Constant (computer programming)
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A Novel Alternative Algorithm for Solving Integer Linear Programming Problems Having Three Variables
Abstract In this study, a novel alternative method based on parameterization for solving Integer Linear Programming (ILP) problems having three variables is developed. This method, which is better than the cutting plane and branch boundary method, can be applied to pure integer linear programming problems with m linear inequality constraints, a linear objective function with three variables. Both easy to understand and to apply, the method provides an effective tool for solving three variable integer linear programming problems. The method proposed here is not only easy to understand and apply, it is also highly reliable, and there are no computational difficulties faced by other methods used to solve the three-variable pure integer linear programming problem. Numerical examples are provided to demonstrate the ease, effectiveness and reliability of the proposed algorithm.
Cutting-plane method
Branch and cut
Criss-cross algorithm
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In branch and bound algorithm for integer linear programming the usual approach is incorporating dual simplex method to achieve feasibility for each sub-problem. Although one can also employ the phase 1 simplex method but the simplicity and easy implementation of the dual simplex method bounds the users to use it. In this paper a new technique for handling sub-problems in branch and bound method has been presented, which is an efficient alternative of dual simplex method.
Branch and cut
Branch and bound
Simplex
Revised simplex method
Linear programming relaxation
Simplicity
Criss-cross algorithm
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In this paper, we present a new method for solving linear goal programming (or linear integer goal programming) by measuring priority factors with number. The linear goal programming, (or linear integer goal programming) whose priority factors have been numbered, can be solved using the LP Process for solving linear progrmming in SAS/OR software. Some examples are given for illuminating the algorithm and the programme. Finally several other questions of research led by this method are list.
Goal programming
Factor (programming language)
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Time, raw materials and labour are some of the nite resources in the world. Due to this, Linear Programming* (LP) is adopted by key decision-markers as an innovative tool to wisely consume these resources. This paper test the strength of linear programming models and presents an optimal solution to a diet problem on a multi-shop system formulated as linear, integer linear and mixed-integer linear programming models. All three models gave different least optimal values, that is, in linear programming, the optimal cost was GHS15.26 with decision variables being continuous (R+) and discrete (Z+). The cost increased to GHS17.50 when the models were formulated as mixed-integer linear programming with decision variables also being continuous (R+) and discrete (Z+) and lastly GHS17.70 for integer linear programming with discrete (Z+) decision variables. The difference in optimal cost for the same problem under different search spaces sufficiently establish that, in programming, the search space undoubtedly affect the optimal value. Applications to most problems like the diet and scheduling problems periodically require both discrete and continuous decision variables. This makes integer and mixed-integer linear programming models also an effective way of solving most problems. Therefore, Linear Programming* is applicable to numerous problems due to its ability to provide different required solutions.
Robustness
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