Scheduling in service systems: an application in the event-tourism industry

This paper deals with an event-bus tour booked by Bollywood film fans. During the tour, the participants visit selected locations of famous Bollywood films at various sites in Switzerland. Moreover, the tour includes stops for lunch and shopping. Each day, up to five buses operate the tour; for organizational reasons, two or more buses cannot stay at the same location simultaneously. The planning problem is how to compute a feasible schedule for each bus such that the total waiting time (primary objective) and the total travel time (secondary objective) are minimized. We formulate this problem as a mixed-integer linear program, and we report on computational results obtained with the Gurobi solver.

Thematic thinking and individual performance in research and development

Individuals differ in their preference for processing information on the basis of taxonomic, feature-based similarity, or thematic, relation-based similarity. These differences, which have been investigated in a recently emerging research stream in cognitive psychology, affect innovative behavior and thus constitute an important antecedent of individual performance in research and development (R&D) that has been overlooked so far in the literature on innovation management. To fill this research gap, survey and test data from the employees of a multinational information technology services firm are used to examine the relationship between thematic thinking and R&D professionals' individual performance. A moderated mediation model is applied to investigate the proposed relationships of thematic thinking and individual-level performance indicators. Results show a positive relationship between thematic thinking and innovativeness, as well as individual job performance. While the results do not support the postulated moderation of the innovativeness–job performance relationship by employees' political skill, they show that the relationship between thematic thinking and job performance is fully mediated by R&D professionals' innovativeness. The present study is thus the first to reveal a positive relationship between thematic thinking and innovative performance.

Index Tracking Using Data-Mining Techniques and Mixed-Binary Linear Programming

Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage approach that at first identifies promising subsets by employing data-mining techniques, then determines the stock weights in the subsets using mixed-binary linear programming, and finally evaluates the subsets based on cross validation. The best subset is returned as the tracking portfolio. Our approach outperforms state-of-the-art methods in terms of out-of-sample performance and running times.