13 research outputs found
Staffing decisions for heterogeneous workers with turnover
In this paper we consider a firm that employs heterogeneous workers to meet demand for its product or service. Workers differ in their skills, speed, and/or quality, and they randomly leave, or turn over. Each period the firm must decide how many workers of each type to hire or fire in order to meet randomly changing demand forecasts at minimal expense. When the number of workers of each type can by continuously varied, the operational cost is jointly convex in the number of workers of each type, hiring and firing costs are linear, and a random fraction of workers of each type leave in each period, the optimal policy has a simple hire- up-to/fire-down-to structure. However, under the more realistic assumption that the number of workers of each type is discrete, the optimal policy is much more difficult to characterize, and depends on the particular notion of discrete convexity used for the cost function. We explore several different notions of discrete convexity and their impact on structural results for the optimal policy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45844/1/186_2005_Article_33.pd
Recent Progress on Integrally Convex Functions
Integrally convex functions constitute a fundamental function class in
discrete convex analysis, including M-convex functions, L-convex functions, and
many others. This paper aims at a rather comprehensive survey of recent results
on integrally convex functions with some new technical results. Topics covered
in this paper include characterizations of integral convex sets and functions,
operations on integral convex sets and functions, optimality criteria for
minimization with a proximity-scaling algorithm, integral biconjugacy, and the
discrete Fenchel duality. While the theory of M-convex and L-convex functions
has been built upon fundamental results on matroids and submodular functions,
developing the theory of integrally convex functions requires more general and
basic tools such as the Fourier-Motzkin elimination.Comment: 50 page