Mobile phone operators need to plan and schedule field force personnel for maintenance and repair tasks on mobile phone base stations across the country on a daily basis. In this paper, we will introduce the field force scheduling problem with priorities. Motivated by the rising popularity of mobile field force management solutions, we compare online and offline heuristics as well as hybrids to solve the problem. The results help understand the benefits of dynamic scheduling based on realtime position information as compared to traditional daily offline planning.

Combinatorial auctions are used in a variety of application domains such as transportation or industrial procurement using a variety of bidding languages and different allocation constraints. This flexibility in the bidding languages and the allocation constraints is essential in these domains, but has not been considered in the theoretical literature so far. In this paper, we analyze different pricing rules for ascending combinatorial auctions which allow for such flexibility: winning levels, and deadness levels. We determine the computational complexity of these pricing rules and show that deadness levels actually satisfy an ex-post equilibrium, while winning levels do not allow for a strong game-theoretical solution concept. We investigate the relationship of deadness levels and the simple price update rules used in efficient ascending combinatorial auction formats.We show that ascending combinatorial auctions with deadness level pricing rules maintain a strong game theoretical solution concept and reduce the number of bids and rounds required at the expense of higher computational effort. The calculation of exact deadness levels is a Π P 2 -complete problem. Nevertheless, numerical experiments show that for mid-sized auctions this is a feasible approach. The paper provides a foundation for allocation constraints in combinatorial auctions and a theoretical framework for recent Information Systems contributions in this field.

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Ascending combinatorial auctions are being used in an increasing number of spectrum sales worldwide, as well as in other multi-item markets in procurement and logistics. Much research has focused on pricing and payment rules in such ascending auctions. However, recent game-theoretical research has shown that such auctions can even lead to inefficient perfect Bayesian equilibria with risk-neutral bidders. There is a fundamental free-rider problem without a simple solution, raising the question whether ascending combinatorial auctions can be expected to be efficient in the field. Risk aversion is arguably a significant driver of bidding behavior in high-stakes auctions. We analyze the impact of risk aversion on equilibrium bidding strategies and efficiency in a threshold problem with one global and several local bidders. Due to the underlying free-rider problem, the impact of risk-aversion on equilibrium bidding strategies of local bidders is not obvious. We characterize the necessary and sufficient conditions for the perfect Bayesian equilibria of the ascending auction mechanism to have the local bidders to drop at the reserve price. Interestingly, in spite of the free-riding opportunities of local bidders, risk-aversion reduces the scope of the non-bidding equilibrium. The results help explain the high efficiency of ascending combinatorial auctions observed in the lab.