T he connected facility location (ConFL) problem arises in a number of applications that relate to the design of telecommunication networks as well as data distribution and management problems on networks. It combines features of the uncapacitated facility location problem with the Steiner tree problem and is known to be NP-complete. In this setting, we wish to install a set of facilities on a communication network and assign customers to the installed facilities. In addition, the set of selected facilities needs to be connected by a Steiner tree. In this paper, we propose a dual-based local search heuristic that combines dual ascent and local search, which together yield strong lower and upper bounds to the optimal solution. Our procedure is applied to a slightly more general version of the ConFL problem that embraces a family of four different problemsthe Steiner tree-star problem, the general Steiner tree-star problem, the ConFL problem, and the rent-or-buy problem-that combine facility location decisions with connectivity requirements. Consequently, our solution methodology successfully applies to all of them. We discuss a wide range of computational experiments that indicate that our heuristic is a very effective procedure that finds high-quality solutions very rapidly.

Railroad classification yards play a significant role in freight transportation: shipments are consolidated to benefit from economies of scales. However, the disassembling of inbound trains, the classification of railcars and reassembling of outbound trains add significant time to the overall transportation. Determining the operational schedule of a railroad classification yard to ensure that railcars pass as quickly as possible through the yard to continue with their journey to their final destination is a challenging problem. In this paper, we create a simulation model to mimic the dynamics of a classification yard and investigate the effect of two simple but practical priority rules (train length and arrival time) for the sequencing of inbound trains through the humping operation. We monitor the effect of these rules on performance measures such as average wait time (dwell time) at the yard and daily throughput as the complexity and frequency of the trains vary. We run the simulation on four data sets with low and high complexity of trains and low and high frequency of trains.

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The sample average approximation (SAA) approach is a widely used technique, based on Monte‐Carlo simulation, often applied to large‐scale stochastic optimization problems. In this approach, a set of sample average problems with multiple copies of sampled scenarios are generated and solved exactly. In other words, there is an implicit assumption that the sample average problems are solvable to optimality. In some instances, however, the sample average problems might be NP‐hard problems, often difficult or impractical to solve to optimality. In this article, we broaden the scope of the SAA approach and show that even without solving the sample problems to optimality, by combining a heuristic and a lower bounding approach, high‐quality solutions with tight confidence bounds on the optimal solution value can be obtained. We demonstrate this “inexact SAA approach” on two problems. First, we apply it to the Stochastic Connected Facility Location (SConFL) problem, the motivating application for this article, that arises in the design of telecommunications networks. As an additional application, we also use it for the Stochastic Uncapacitated Facility Location (SUFL) problem. Our computational results demonstrate the effectiveness of the inexact SAA approach. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 70(1), 19–33 2017

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