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Mathematical Programs for Dynamic Pricing - Demand Based Management
Hrabec, Dušan ; Karpíšek, Zdeněk (oponent) ; Hoff, Arild (oponent) ; Haugen, Kjetil Kare (vedoucí práce)
The thesis deals with the development, modeling, and analysis of demand-based problems containing marketing, operations, and logistics decisions. The problems may be further extended to the concepts of dynamic pricing and marketing that drive the development. Two demand-based problems are presented in the thesis: a) the newsvendor problem, due to its simple structure as a suitable tool for illustrating how facets of marketing may affect decision-making concerning operational problems, and b) the transportation network design problem, where some results and knowledge gained from the newsvendor problem are applied. In the setting presented, the newsvendor is subsequently faced with pricing, advertising, and joint pricing and advertising-sensitive stochastic demand. A demand-related random element comprises the particular marketing decision(s) of a specific form (e.g., multiplicative or additive). It is assumed that a real pricing strategy is captured with a nonlinear decreasing demand function while a suitable advertising strategy results in increased sales. The properties of the obtained optimal decisions for particular models are discussed. The pricing-related results are applied to the stochastic transportation problem, where the stochastic demand is modeled using wait-and-see and here-and-now deterministic (scenario-based) reformulations. A hybrid algorithm composed of a heuristic (genetic) algorithm and an optimization software tool is proposed for solving of a mixed-integer linear as well as a mixed-integer nonlinear problem. Potential applications, especially in waste management, are also discussed at the end of the thesis.
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Mathematical Programs for Dynamic Pricing - Demand Based Management
Hrabec, Dušan ; Karpíšek, Zdeněk (oponent) ; Hoff, Arild (oponent) ; Haugen, Kjetil Kare (vedoucí práce)
The thesis deals with the development, modeling, and analysis of demand-based problems containing marketing, operations, and logistics decisions. The problems may be further extended to the concepts of dynamic pricing and marketing that drive the development. Two demand-based problems are presented in the thesis: a) the newsvendor problem, due to its simple structure as a suitable tool for illustrating how facets of marketing may affect decision-making concerning operational problems, and b) the transportation network design problem, where some results and knowledge gained from the newsvendor problem are applied. In the setting presented, the newsvendor is subsequently faced with pricing, advertising, and joint pricing and advertising-sensitive stochastic demand. A demand-related random element comprises the particular marketing decision(s) of a specific form (e.g., multiplicative or additive). It is assumed that a real pricing strategy is captured with a nonlinear decreasing demand function while a suitable advertising strategy results in increased sales. The properties of the obtained optimal decisions for particular models are discussed. The pricing-related results are applied to the stochastic transportation problem, where the stochastic demand is modeled using wait-and-see and here-and-now deterministic (scenario-based) reformulations. A hybrid algorithm composed of a heuristic (genetic) algorithm and an optimization software tool is proposed for solving of a mixed-integer linear as well as a mixed-integer nonlinear problem. Potential applications, especially in waste management, are also discussed at the end of the thesis.
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