New formulation and a branch-and-cut algorithm for the multiple allocation p-hub median problem

This article deals with the uncapacitated multiple allocation p-hub median problem, where p facilities (hubs) must be located among n available sites in order to minimize the transportation cost of sending a product between all pairs of sites. Each path between an origin and a destination can traverse any pair of hubs.     

Modeling fuzzy capacitated p-hub center problem and a genetic algorithm solution

Hub and spoke networks are used to switch and transfer commodities between terminal nodes in distribution systems at minimum cost and/or time. The p-hub center allocation problem is to minimize maximum travel time in networks by locating p hubs from a set of candidate hub locations and allocating demand and supply nodes to hubs. The capacities of the hubs are given. In previous studies, authors usually considered only quantitative parameters such as cost and time to find the optimum location. But it seems not to be sufficient and often the critical role of qualitative parameters like quality of service, zone traffic, environmental issues, capability for development in the future and etc. that are critical for decision makers (DMs), have not been incorporated into models. In many real world situations qualitative parameters are as much important as quantitative ones. We present a hybrid approach to the p-hub center problem in which the location of hub facilities is determined by both parameters simultaneously. Dealing with qualitative and uncertain data, Fuzzy systems are used to cope with these conditions and they are used as the basis of this work. We use fuzzy VIKOR to model a hybrid solution to the hub location problem. Results are used by a genetic algorithm solution to successfully solve a number of problem instances. Furthermore, this method can be used to take into account more desired quantitative variables other than cost and time, like future market and potential customers easily.     

A primal-dual approximation algorithm for stochastic facility location problem with service installation costs

We consider the stochastic version of the facility location problem with service installation costs. Using the primal-dual technique, we obtain a 7-approximation algorithm.     

Solving the uncapacitated multiple allocation hub location problem by means of a dual-ascent technique

This paper deals with the uncapacitated multiple allocation hub location problem. The dual problem of a four-indexed formulation is considered and a heuristic method, based on a dual-ascent technique, is designed. This heuristic, which is reinforced with several specifical subroutines and does not require any external linear problem solver, is the core tool embedded in an exact branch-and-bound framework. Besides, the heuristic provides the branch-and-bound algorithm with good lower bounds for the nodes of the branching tree. The results of the computational experience (with the classical CAB and AP data sets) are included, showing the great effectiveness of this approach: instances with up to 120 nodes are solved.     

随机容错设施选址问题的原始-对偶近似算法

研究两阶段随机容错设施选址问题,其中需要服务的顾客在第二阶段出现（在第一阶段不知道）．两个阶段中每个设施的开设费用可以不同,设施的开设依赖于阶段和需要服务的顾客集合（称为场景）．并且在出现的场景里的每个顾客都有相同的连接需求,即每个顾客需要由r个不同的设施服务．给定所有可能的场景及相应的概率,目标是在两个阶段分别选取开设的设施集合,将出现场景的顾客连接到r个不同的开设设施上,使得包括设施费用和连接费用的总平均费用最小．根据问题的特定结构,给出了原始。对偶（组合）3-近似算法．     

An exact decomposition algorithm for a chance-constrained new product risk model

We use the generalized two-sided Chebyshev inequality to reformulate a certain nonlinear, chance-constrained new product risk model. The problem has a linear cost objective and a constraint set featuring a probabilistic lower bound on an event which depends on a collection of mutually-independent, uniform random parameters. Our reformulation permits a reduction of the problem to a sequence of second-order cone programs. We, therefore, identify a new family of non-convex programs whose members are amenable to convex programming solution techniques.