The role of the agent's outside options in principal-agent relationships

We consider a principal-agent model of adverse selection where, in order to trade with the principal, the agent must undertake a relationship-specific investment which affects his outside option to trade, i.e. the payoff that he can obtain by trading with an alternative principal. This creates a distinction between the agent’s ex ante (before investment) and ex post (after investment) outside options to trade. We investigate the consequences of this distinction, and show that whenever an agent’s ex ante and ex post outside options differ, this may equip the principal with an additional tool for screening among different agent types, by randomizing over the probability with which trade occurs once the agent has undertaken the investment. In turn, this may enhance the efficiency of the optimal second-best contract

Pump scheduling in drinking water distribution networks with an LP/NLP-based branch and bound

This paper offers a novel approach for computing globally optimal solutions to the pump scheduling problem in drinking water distribution networks. A tailored integer linear relaxation of the original non-convex formulation is devised and solved by branch and bound where integer nodes are investigated through non-linear programming to check the satisfaction of the non-convex constraints and compute the actual cost. This generic method can tackle a large variety of networks, e.g. with variable-speed pumps. We also propose to specialize it for a common subclass of networks with several improving techniques, including a new primal heuristic to repair near-feasible integer relaxed solutions. Our approach is numerically assessed on various case studies of the literature and compared with recently reported results

Robust optimization for resource-constrained project scheduling with uncertain activity durations

The purpose of this paper is to propose models for project scheduling when there is considerable uncertainty in the activity durations, to the extent that the decision maker cannot with confidence associate probabilities with the possible scenarios. Our modeling techniques stem from robust optimization, which is a theoretical framework that enables the decision maker to produce solutions that will have a reasonably good objective value under any likely input data scenario. We develop and implement a scenario-relaxation algorithm and a scenario-relaxationbased heuristic. The first algorithm produces optimal solutions but requires excessive running times even for medium-sized instances; the second algorithm produces high-quality solutions for medium-sized instances and outperforms two benchmark heuristics

The SeqBin Constraint Revisited

We revisit the SEQBIN constraint [1]. This meta-constraint subsumes a number of important global constraints like CHANGE [2], SMOOTH [3] and INCREASINGNVALUE [4]. We show that the previously proposed filtering algorithm for SEQBIN has two drawbacks even under strong restrictions: it does not detect bounds disentailment and it is not idempotent. We identify the cause for these problems, and propose a new propagator that overcomes both issues. Our algorithm is based on a connection to the problem of finding a path of a given cost in a restricted n-partite graph. Our propagator enforces domain consistency in O(nd 2) and, for special cases of SEQBIN that include CHANGE,SMOOTH and INCREASINGNVALUE in O(nd) time