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The objective of this article is to illustrate how managers in the hotel industry can analyze and improve their brands’market efficiency using data envelopment analysis (DEA). The authors evaluated the competitive market efficiency of 46 hotel brands in terms of customer satisfaction and customer value. Their DEA results show that 23 of the 46 brands studied generated less customer satisfaction and customer value for the same level of inputs relative to their more efficient competitors. In particular, the competitive market-inefficient hotel brands suffered more guest complaints, employed a lower quality staff, did not maintain their properties as well, and charged prices higher than justified by their market offerings. Furthermore, the competitive market-inefficient hotels had a less-than-optimal number of rooms and properties in the chain. The results illustrate how managers can use DEA to improve the relative market efficiency of their brands.

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The multiple traveling salesperson problem (MTSP) involves scheduling m > 1 salespersons to visit a set of n > m locations. Thus, the n locations must be divided into m groups and arranged so that each salesperson has an ordered set of cities to visit. The grouping genetic algorithm (GGA) is a type of genetic algorithm (GA) designed particularly for grouping problems. It has been successfully applied to a variety of grouping problems. This paper focuses on the application of a GGA to solve the MTSP. Our GGA introduces a new chromosome representation to indicate which salesperson is assigned to each tour and the ordering of the cities within each tour. We compare our method to standard GAs that employ either the one-chromosome or two-chromosome representation for MTSP. This research demonstrates that our GGA with its new chromosome representation is capable of solving a variety of MTSP problems from the literature and can outperform the traditional encodings of previously published GA methods.

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