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W e study the multi-item capacitated lot sizing problem with setup times. Based on two strong reformulations of the problem, we present a transformed reformulation and valid inequalities that speed up column generation and Lagrange relaxation. We demonstrate computationally how both ideas enhance the performance of our algorithm and show theoretically how they are related to dual space reduction techniques. We compare several solution methods and propose a new efficient hybrid scheme that combines column generation and Lagrange relaxation in a novel way. Computational experiments show that the proposed solution method for finding lower bounds is competitive with textbook approaches and state-of-the-art approaches found in the literature. Finally, we design a branch-and-price-based heuristic and report computational results. The heuristic scheme compares favorably or outperforms other approaches.

Failure is a common occurrence in many operational contexts involving knowledge work. Concentrating on highly critical cardiac surgery operations, we investigate how failure affects subsequent performance over time. In addressing our research questions, we draw on the sensemaking perspective and incorporate behavioral aspects of failure that are often overlooked. We develop three hypotheses on the effects of failure (i.e., in‐hospital mortality of a patient) and test them with a unique data set of 4,306 cardiac surgery operations from a large European hospital, spanning five years. Our findings show that while failure promotes learning over time and improves task execution quality (as measured by patients' reduced length of stay) in the long term, its effect is the opposite in the short term. Our work also unravels how relational dynamics (i.e., familiarity) may reduce the short‐term effects of failure. We find evidence that team familiarity mitigates the detrimental effects of recent failures. This implies that certain team assignment strategies after failure (e.g., putting individuals into familiar teams) may be preferable than others. We explore and illustrate this by conducting a policy simulation based on our data. This article provides new insights into how operations managers can support their employees in moving forward after failure.

We introduce Horizon Decomposition in the context of Dantzig-Wolfe Decomposition, and apply it to the Capacitated Lot-Sizing Problem with Setup Times. We partition the problem horizon in contiguous overlapping intervals and create subproblems identical to the original problem, but of smaller size. The user has the flexibility to regulate the size of the master problem and the subproblem via two scalar parameters. We investigate empirically which parameter configurations are efficient, and assess their robustness at different problem classes. Our branch-and-price algorithm outperforms state-of-the-art branch-and-cut solvers when tested to a new dataset of challenging instances that we generated. Our methodology can be generalized to mathematical programs with a generic constraint structure.

Project teams are regularly assembled by a variety of organizations in order to perform knowledge-intensive tasks. Previous shared experiences among their members can have a significant impact on team performance. In this study, we use a unique and detailed dataset of 6206 cardiac surgeries from a private hospital in Europe, property of an American non-profit organization, in order to examine how past shared experiences of individuals affect future team productivity. Using transactive memory system as theoretical framework, we first decompose overall team familiarity into horizontal familiarity (e.g. surgeon to surgeon) and hierarchical familiarity (e.g. surgeon to nurse) and find that the former one is more beneficial for team productivity than the latter one. Next, we observe that horizontal familiarity of high-power, high-status individuals has a higher impact on team productivity than the one among subordinate individuals. Finally, we investigate how past failure experiences of individuals in the same team can increase future team productivity more than past shared successes. Our results provide useful insights for managers who aim to increase team productivity via better team allocation strategies.