Several social, economic and political factors have contributed to the increasing diversity of today's workforce. In addition, in an era when organizations are continuously redesigning their work and restructuring their operations to achieve their goals with fewer resources, performing work in teams has become commonplace. These trends have increased the need for managing diverse work teams effectively. There are several existing models in the management science literature that help managers to assign employees to work groups in order to maximize the groups' diversity and hence, facilitate their effectiveness. This paper introduces a new model that recasts the problem of managing diversity in a different way: it is assumed that the population comes partitioned into `families' with a high degree of intra-familial similarity and inter-familial dissimilarity. The objective of the assignment then is to disperse these family members as evenly into the workgroups as possible. A little known network flow problem, known as the dining problem, is used to develop an efficient algorithm to produce solutions to this new model. This is followed by a report on an experimental application of the developed model to assign Master of Business Administration students in a business school to different projects in a course. As a part of this empirical report, an attractive feature of this model is also demonstrated; namely, how to conduct sensitivity analysis to determine the optimal levels of diversity in the presence of resource constraints. Finally, the paper concludes by discussing limitations of this new model and how they may be addressed in future research on this topic. Keywords: Decision making; Personnel/human resources; Manpower planning; Set partitioning; Optimization; Dining problem Article: 1. Introduction Among the many environmental trends affecting organizations in the 1990s is the rapidly changing composition of the workforce, a phenomenon known as workforce diversity. The word `diversity' refers to differences in a range of human qualities among individuals [17]. The traditional view of an organization characterized by workforce diversity is one in which there are increasing numbers of nondominant or minority social groups based on gender, race, ethnicity or nationality, resulting in heterogeneity in socio-cultural perspectives, world views, life styles, language and behavior [17]. However, recent approaches have also attempted to extend the concept of diversity to include other factors besides race, gender and ethnicity. As Thomas, Jr. argues in [25], employees differ on a variety of other dimensions such as age, functional and
Abstract:This paper develops two heuristics for solving the centroid problem on a plane with discrete demand points. The methods are based on the alternating step well known in location methods. Extensive computational testing with the heuristics reveals that they converge rapidly, giving good solutions to problems that are up to twice as large as those reported in the literature. The testing also provides some managerial insight into the problem and its solution.Keywords: Competitive location model; Medianoid; Centroid; Stackelberg solution; Heuristic methods
Article: Scope and purposeWhen dealing with competitive location models, one popular solution concept is the Stackelberg solution. It assumes that one (group of) firm(s) acts as leader, while the other(s) act(s) as follower. In the locational context, the follower takes the locations of the leader as given and optimizes on that basis, whereas the leader will exercise foresight and take into account that a follower will subsequently locate additional facilities. It is commonly assumed that the leader knows how many facilities the follower will locate. In this bilevel programming problem, the leader's problem is called a centroid problem, whereas the follower faces a so-called medianoid problem. In both cases, the objective of the facility planner is to capture as much of the market as possible. Since centroid problems are inherently difficult, it is necessary to devise heuristic methods for all but the smallest models. This paper presents two such heuristics for the planar version of the centroid problem that are based on the repeated application of a medianoid solution, a much simpler problem. Computational results attesting to their performance are also included.
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