We develop local search algorithms for routing problems with time windows. The presented algorithms are based on the k-interchange concept. The presence of time windows introduces feasibility constraints, the checking of which normally requires O(N) time. Our method reduces this checking effort to 0(1) time. We also consider the problem of finding initial solutions. A complexity result is given and an insertion heuristic is described.

We investigate the implementation of edge-exchange improvement methods for the vehicle routing problem with time windows with minimization of route duration as the objective. The presence of time windows as well as the chosen objective cause verification of the feasibility and profitability of a single edge-exchange to require an amount of computing time that is linear in the number of vertices. We show how this effort can, on the average, be reduced to aconstant.

We investigate the algorithmic and implementation issues related to the e ective and e cient use of lifted cover inequalities and lifted GUB cover inequalities in a branch-and-cut algorithm for 0-1 integer programming. We have tried various strategies on several test problems and we identify the best ones for use in practice.

In the first part of the paper, we present a framework for describing basic techniques to improve the representation of a mixed integer programming problem. We elaborate on identification of infeasibility and redundancy, improvement of bounds and coefficients, and fixing of binary variables. In the second part of the paper, we discuss recent extensions to these basic techniques and elaborate on the investigation and possible uses of logical consequences. In the first part of the paper, we present a framework for describing basic techniques to improve the representation of a mixed integer programming problem. We elaborate on identification of infeasibility and redundancy, improvement of bounds and coefficients, and fixing of binary variables. In the second part of the paper, we discuss recent extensions to these basic techniques and elaborate on the investigation and possible uses of logical consequences. Preprocessing and Probing Techniques for Mixed Integer Programming Problems

Although downsizing has become an integral part of organizational life in the U.S., there is little serious theoretical or empirical work on this issue. Nearly all of the completed work addresses the effects of downsizing, which usually are negative. Therefore, an important yet unanswered question is: Why do organizations downsize in the first place? In addressing this question, I offer some systematic thoughts on the causes of downsizing. Specifically, I develop a conceptual framework for studying organizational innovation that draws on two overlooked dimensions associated with this phenomenon, the basis of social action (rational versus arational) and social context (organizational versus extraorganizational). I then characterize downsizing as an organizational innovation and develop propositions that explain why organizations downsize. Finally, I emphasize that empirical evaluation of these propositions will help us to understand a pivotal organizational development of recent decades.