Early Versus Late Effort in Dynamic Agencies with Unverifiable Information

In this paper we analyze a dynamic agency problem where contracting parties do not know the agent's future productivity at the beginning of the relationship. We consider a two-period model where both the agent and the principal observe the agent's second-period productivity at the end of the first period. This observation is assumed to be non-verifiable information. We compare long-term contracts with short-term contracts with respect to their suitability to motivate effort in both periods. On the one hand, short-term contracts allow for a better fine-tuning of second-period incentives as they can be aligned with the agent's second-period productivity. On the other hand, in short-term contracts first-period effort incentives might be distorted as contracts have to be sequentially optimal. Hence, the difference between long-term and short-term contracts is characterized by a trade-off between inducing effort in the first and in the second period. We analyze the determinants of this trade-off and demonstrate its implications for performance measurement and information system design.

A Cooperative and Adaptive Variable Neighborhood Search for the Multi Depot Vehicle Routing Problem with Time Windows

In this paper we propose two cooperation schemes to compose new parallel variants of the Variable Neighborhood Search (VNS). On the one hand, a coarse-grained cooperation scheme is introduced which is well suited for being enhanced with a solution warehouse to store and manage the so far best found solutions and a self-adapting mechanism for the most important search parameters. This makes an a priori parameter tuning obsolete. On the other hand, a fine-grained scheme was designed to reproduce the successful properties of the sequential VNS. In combination with the use of parallel exploration threads all of the best solutions and 11 out of 20 new best solutions for the Multi Depot Vehicle Routing Problem with Time Windows were found.