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Knowledge Based Optimization of a Stochastic Inventory Problem Using Dynamic Programming (Masters Thesis)

Contributor(s): Material type: TextTextLanguage: English Publication details: Karachi : NED University of Engineering and Technology Department of Industrial and Manufacturing Engineering, c2002Description: XI, vp. : ill +1 FloppySubject(s): DDC classification:
  • 519.703378242 IRF
Summary: ABSTRACT This research involves the development of a computer-based decision-support system for optimal lot-sizing of finished goods over a multi-stage planning horizon. The product demand across the planning horizon is variable and symbolic in nature. Methods commonly employed for the lot-sizing problem (deterministic demand) are heuristics that produce near- optimal solutions. However, an optimal solution is guaranteed by the Wagner-Whitin method that can lend itself to be solved by dynamic programming. The dynamic programming formulation is highly structured and facilitates the development of a computer code. A key feature of this decision-support system is to permit incorporation of feedback information into the decision -making process by exploiting the 'principle of optimality' of dynamic programming, which identifies optimal policies for each and every state in the problem, regardless of how that state was achieved. The situation demand considered here is a company contemplating a price reduction decision and supporting it with a promotion action. A set of rules of the type "If X is low, then Y is low," for example, involving several interrelated variables that influence demand have been used. To incorporate this reasoning, a fuzzy logic approach has been used.
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ABSTRACT

This research involves the development of a computer-based decision-support system for optimal lot-sizing of finished goods over a multi-stage planning horizon. The product demand across the planning horizon is variable and symbolic in nature.

Methods commonly employed for the lot-sizing problem (deterministic demand) are heuristics that produce near- optimal solutions. However, an optimal solution is guaranteed by the Wagner-Whitin method that can lend itself to be solved by dynamic programming. The dynamic programming formulation is highly structured and facilitates the development of a computer code.

A key feature of this decision-support system is to permit incorporation of feedback information into the decision -making process by exploiting the 'principle of optimality' of dynamic programming, which identifies optimal policies for each and every state in the problem, regardless of how that state was achieved.

The situation demand considered here is a company contemplating a price reduction decision and supporting it with a promotion action. A set of rules of the type "If X is low, then Y is low," for example, involving several interrelated variables that influence demand have been used. To incorporate this reasoning, a fuzzy logic approach has been used.