A key decision in scheduling problems is deciding when to perform certain operations, and the quality of solutions depends on how time is represented. The two main classes of time representation are discrete-time approaches (with uniform or nonuniform discretization schemes) and continuous-time approaches. In this work, we compare the performance of these two classes for short-term scheduling of multipurpose facilities with single purpose machines, constant processing times, discrete batches, material splitting, multitasking, and no batch mixing. In addition, the different discretization schemes were compared against each other. We show that, for the modeling framework proposed in this work, the selected discrete-time formulation typically obtained higher quality solutions, and required less time to solve as compared to the selected continuous-time formulation, as the continuous-time formulation exhibited detrimental trade-off between computational time and solution quality. We also show that within the scope of this study, nonuniform discretization schemes typically yielded solutions of similar quality as compared to a fine uniform discretization scheme, but required only a fraction of the computational time. A total of 190 small and industrialsized instances, comprised of 1030 runs, were considered for this study.
We study a problem in which a firm sets prices for products based on the transaction data, i.e., which product past customers chose from an assortment and what were the historical prices that they observed.Our approach does not impose a model on the distribution of the customers' valuations and only assumes, instead, that purchase choices satisfy incentive-compatible constraints. The individual valuation of each past customer can then be encoded as a polyhedral set, and our approach maximizes the worst-case revenue assuming that new customers' valuations are drawn from the empirical distribution implied by the collection of such polyhedra. We show that the optimal prices in this setting can be approximated at any arbitrary precision by solving a compact mixed-integer linear program. Moreover, we study special practical cases where the program can be solved efficiently, and design three approximation strategies that are of low computational complexity and interpretable. Comprehensive numerical studies based on synthetic and real data suggest that our pricing approach is uniquely beneficial when the historical data has a limited size or is susceptible to model misspecification.
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