interdiction scenario with his limited resources in order to minimize the covering of the demand points. A bi-level formulation is presented to model this two-player game problem. To solve this problem we use a combination of exact linear programming optimization technique and comprehensive enumeration. To reduce the computing time a heuristic approach based on local search is proposed. Keywords: Maximal Covering Location Problem (MCLP)‚ Multi- level interdiction‚ Fortification‚ Dummy facility 1. INTRODUCTION
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Assignment Name - Pulkit Course – MMS Batch 2 Roll no. – 100 Topic : “Use of dual linear programming problem in decision making.” Applications of Linear Programming Linear programming has been applied to a wide variety of constrained optimization problems. Some of these are: Optimal process selection Most products can be manufactured by using a number of processes‚ each requiring a different technology and combination of inputs. Given input prices and the quantity of the commodity
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Differential Evolution Algorithm for Solving Multi-Stages Assignment Problem: a case study of chicken transportation. I. Introduction The transportation in this case study is a chicken transportation from chicken farm to egg farm. There are 4 types of truck which are ten-wheel truck‚ eight-wheel truck‚ modified four-wheel truck and ordinary four-wheel truck. Each type of truck has different capacity moreover each hen’s farm has different in quantity supplied as same as each egg’s farm also have
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Portfolio Optimization A Selection of Stocks from the Heng Seng Index 17 August 2012 Introduction A typical investor purchases an asset with the hope that it will generate income or appreciate in the future. Given the plethora of choices in the market‚ a rational investor would choose an investment with the highest expected return. The Hong Kong Stock Exchange is the sixth largest stock exchange in the world with 1‚477 listed companies and a combined market capitalization of HKD 17 trillion
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programming problem? Ans: Linear programming (LP‚ or linear optimization) is a mathematical method for determining a way to achieve the best outcome (such as maximum profit or lowest cost) in a given mathematical model for some list of requirements represented as linear relationships. Linear programming is a specific case of mathematical programming (mathematical optimization). More formally‚ linear programming is a technique for the optimization of a linear objective function‚ subject to linear equality
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What is a linear programming problem? Discuss the scope and role of linear programming in solving management problems. Discuss and describe the role of linear programming in managerial decision-making bringing out limitations‚ if any. 2. Explain the concept and computational steps of the simplex method for solving linear programming problems. How would you identify whether an optimal solution to a problem obtained using simplex algorithm is unique or not? a) What is the difference between a
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wide variety of optimization strategies for different quer y languages and implementation environments. However‚ very little is known about how to design and structure the query optimization component to implement these strategies. This paper proposes a first step towards the design of a modular query optimizer. We describe its operations by transformation rules which generate different QEPs from initial query specifications. As we distinguish different aspects of the query optimization process‚ our
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Society 2003;54(12):1283–95. [35] Donaldson H‚ Johnson EL‚ Ratliff‚ HD‚ Zhang M. Schedule-driven crossdocking networks. Technical report 9904‚ Atlanta‚ GA‚ USA: Georgia Institute of Technology; 1999. [36] Musa R‚ Arnaout JP‚ Jung H. Ant colony optimization algorithm to solve for the transportation problem of cross-docking network. Computers & Industrial Engineering 2010;59(1):85–92. [37] Sung CS‚ Yang W. An exact algorithm for a cross-docking supply chain network design problem. Journal of the Operational
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assign‚ in the objective function‚ coefficients to the artificial variables that are either very small (maximization problem) or very large (minimization problem); whatever this value‚ let us call it Big M. In fact‚ this notion is an old trick in optimization in general; we simply associate a penalty value with variables that we do not want to be part of an ultimate solution (unless such an outcome Is unavoidable). Indeed‚ the penalty is so costly that unless any of the respective variables’ inclusion
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TOPIC: NATURE AND APPLICATIONS OF OPERATIONAL RESEARCH SUBMITTED TO: SUBMITTED BY: MS. AMANDEEP SAVEENA (LECTURER OF O.R) MBA-3RD (11-148) INTRODUCTION & HISTORY OF OPERATIONAL RESEARCH Operational research came into existence in 1885 when Frederick W. Taylor emphasised the application of scientific analysis to methods of production. The name (OR)
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