In this paper, we present the stochastic version of Maximal Covering Location Problem which optimizes both location and allocation decisions, concurrently. It’s assumed that traveling time between customers and distribution centers (DCs) is uncertain and described by normal distribution function and if this time is less than coverage time, the customer can be allocated to DC. In classical mod...

With emergencies being, unfortunately, part of our lives, it is crucial to efficiently plan and allocate emergency response facilities that deliver effective and timely relief to people most in need. Emergency Medical Services (EMS) allocation problems deal with locating EMS facilities among potential sites to provide efficient and effective services over a wide area with spatially distributed ...

The study of the facility location problem in the presence of self-interested agents has recently emerged as the benchmark problem in the research on mechanism design without money. In the setting studied in the literature so far, agents are single-parameter in that their type is a single number encoding their position on a real line. We here initiate a more realistic model for several real-lif...

In this paper we apply theoretical and practical results from facility location theory to the problem of community detection in networks. The result is an algorithm that computes bounds on a minimization variant of local modularity. We also define the concept of an edge support and a new measure of the goodness of community structures with respect to this concept. We present preliminary results...

In this study, we consider facility location decisions for a humanitarian relief chain responding to quick-onset disasters. In particular, we develop a model that determines the number and locations of distribution centres in a relief network and the amount of relief supplies to be stocked at each distribution centre to meet the needs of people affected by the disasters. Our model, which is a v...

1.2 Hardness of k-median Theorem 1. It is hard to approximate k-median within 1+ 2 e1/c for any c < 1. This theorem is equivalent to the 1− 1/e− hardness shown in [2] and follows from a standard reduction from set cover. The main idea is to take an approximation algorithm for k-median, and use it to obtain a partial set cover. Then repeat this process, again partially covering the remaining ite...

We study the problem of locating a public facility on a real line or an interval, when agents’ costs are their (expected) distances from the location of the facility. Our goal is to minimize the maximum envy over all agents, which we will refer to as the minimax envy objective, while at the same time ensuring that agents will report their most preferred locations truthfully. First, for the prob...