Reducing the risks believed to be associated with product availability can be critical to increasing consumer retention rates. This study considers the role that perceptions of channel integration have on such beliefs and their impact on purchasing decisions. Surveys distributed to purchasers of specific goods both online and in-store provide data used in the analysis of these effects. The findings suggest that firms simultaneously managing both online and instore channels should not only reassess the repercussions of availability failures but also consider efforts that encourage the transparency of channel integration.

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In this paper we study the nonlinear resource allocation problem, defined as the minimization of a convex function over one convex constraint and bounded integer variables. This problem is encountered in a variety of applications, including capacity planning in manufacturing and computer networks, production planning, capital budgeting, and stratified sampling. Despite its importance to these and other applications, the nonlinear resource allocation problem has received little attention in the literature. Therefore, we develop a branch-and-bound algorithm to solve this class of problems. First we present a general framework for solving the continuous-variable problem. Then we use this framework as the basis for our branch-and-bound method. We also develop reoptimization procedures and a heuristic that significantly improve the performance of the branch-and-bound algorithm. In addition, we show how the algorithm can be modified to solve nonconvex problems so that a concave objective function can be handled. The general algorithm is specialized for the applications mentioned above and computational results are reported for problems with up to 200 integer variables. A computational comparison with a 0, 1 linearization approach is also provided.

Legislators at the state and national levels are addressing renewed concerns over the adequacy of hospital nurse staffing to provide quality care and ensure patient safety. At the same time, the well-known nursing shortage remains an ongoing problem. To address these issues, we reexamine the nurse scheduling problem and consider how recent health care legislation impacts nursing workforce management decisions. Specifically, we develop a scheduling model and perform computational experiments to evaluate how mandatory nurse-to-patient ratios and other policies impact schedule cost and schedule desirability (from the nurses' perspective). Our primary findings include the following: (i) nurse wage costs can be highly nonlinear with respect to changes in mandatory nurseto-patient ratios of the type being considered by legislators; (ii) the number of undesirable shifts can be substantially reduced without incurring additional wage cost; (iii) more desirable scheduling policies, such as assigning fewer weekends to each nurse, have only a small impact on wage cost; and (iv) complex policy statements involving both singleperiod and multiperiod service levels can sometimes be relaxed while still obtaining good schedules that satisfy the nurse-to-patient ratio requirements. The findings in this article suggest that new directions for future nurse scheduling models, as it is likely 39 40Reexamining the Nurse Scheduling Problem that nurse-to-patient ratios and nursing shortages will remain a challenge for health care organizations for some time.