Lesson 6 - InventoryModels NonLinearModels (NLP and/ The

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Non Linear Programming Inventory Models
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2 Rutgers Business School Non Linear Models Introduction to Nonlinear Programming (NLP) An NLP problem has a nonlinear objective function  and/or one or more nonlinear constraints. NLP problems are formulated and implemented in  virtually the same way as linear problems. The mathematics involved in solving NLPs is quite  different than for LPs. Solver tends to mask this different but it is important to  understand the difficulties that may be encountered  when solving NLPs.
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A Note About “Optimal” Solutions When solving a NLP problem, Solver normally stops when the first of three  numerical tests is satisfied, causing one of the following three completion  messages to appear: 1)  “Solver found a solution.  All constraints and  optimality  conditions are satisfied.”   This means Solver found a local optimal  solution, but does not guarantee that the  solution is the global optimal solution.  
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A Note About “Optimal” Solutions When solving a NLP problem, Solver normally stops when the first of three  numerical tests is satisfied, causing one of the following three completion  messages to appear: 2)  “Solver has converged to the current solution.   All constraints  are satisfied.” This means the objective function value  changed very slowly for the last few iterations.
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A Note About “Optimal” Solutions When solving a NLP problem, Solver normally stops when the first of three  numerical tests is satisfied, causing one of the following three completion  messages to appear: 3) “ Solver cannot improve the current solution.   All constraints  are satisfied .” This rare message means the your model is  degenerate and the Solver is cycling.   Degeneracy can often be eliminated by  removing redundant constraints in a model.  
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The Economic Order Quantity  (EOQ) Problem Involves determining the optimal quantity to purchase when orders  are placed. Small orders result in: low inventory levels & carrying costs frequent orders & higher ordering costs Large orders result in: higher inventory levels & carrying costs infrequent orders & lower ordering costs
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7 Rutgers Business School Non Linear Models Involves determining the optimal quantity to purchase when  orders are placed. Small orders result in: low inventory levels & carrying costs frequent orders & higher ordering costs Large orders result in: higher inventory levels & carrying costs infrequent orders & lower ordering costs
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8 Rutgers Business School Non Linear Models Basic EOQ Model Basic problem
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