Everything in Moderation: Foundations and Applications of the Satiation Model

Models in which current utility depends solely on current consumption (a.k.a. time-separable preferences) are widely acknowledged to be unrealistic, especially when attempting to describe preferences over consumption rates. Alternatively, one may stipulate that instant utility also depends on a state, for example, some stock of past consumption. Escaping the gravitational pull of time separability, however, is difficult because (1) the behavioral axioms that characterize the state and the instant utility are not known, (2) how to elicit the preference parameters—most notably the initial level of the state and the decay rate—is not known, and (3) managerial applications where state-dependent preferences produce interesting insights and solutions are scarce. This paper makes advances on these three fronts by proposing a novel set of axioms that characterize the satiation model, a proof of concept on how to elicit all preference parameters using consumption rates, and a mixed-integer linear formulation to solve the optimal design of experiential services under satiation. Our preferences introduce a de-satiation motive, absent in separable preferences, and we explore how to optimally manage this motive. This paper was accepted by David Simchi-Levi, decision analysis.

An MILP Formulation for the Thermal Unit Commitment Problem Considering Start-Up and Shut-Down Power Trajectories

This paper presents a mixed-integer linear formulation for the thermal unit commitment problem considering the start-up and shut-down power trajectories. A realistic and accurate modeling of the unit’s operating phase is given, which includes the phases of start-up, dispatchable and shut-down. The start-up type is decided by the unit’s prior off-line time. The start-up costs and power trajectories depend on the type of start-up. A new set of binary variables is introduced to represent the dispatchable status, which can decrease the binary variables and constraints significantly. Finally, a test case study is analyzed to verify the correctness and show the computational performance of the proposed formulation.