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This work focuses on a broad class of facility location problems in the context of adaptive robust stochastic optimization under the state-dependent demand uncertainty. The demand is assumed to be significantly affected by related state information, such as the seasonal or socio-economic information. In particular, a state-wise ambiguity set is adopted for modeling the distributional uncertainty associated with the demand in different states. The conditional distributional characteristics in each state are described by a support, as well as by mean and dispersion measures, which are assumed to be conic representable. A robust sensitivity analysis is performed, in which, on the one hand, we analyze the impact of the change in ambiguity-set parameters (e.g., state probabilities, mean value abounds, and dispersion bounds in different states) onto the optimal worst-case expected total cost using the ambiguity dual variables. On the other hand, we analyze the impact of the change in location design onto the worst-case expected second-stage cost and show that the sensitivity bounds are fully described as the worst-case expected shadow-capacity cost. As for the solution approach, we propose a nested Benders decomposition algorithm for solving the model exactly, which leverages the subgradients of the worst-case expected second-stage cost at the location decisions formed insightfully by the associated worst-case distributions. The nested Benders decomposition approach ensures a finite-step convergence, which can also be regarded as an extension of the classic L-shaped algorithm for two-stage stochastic programming to our state-wise, robust stochastic facility location problem with conic representable ambiguity. Finally, the results of a series of numerical experiments are presented that justify the value of the state-wise distributional information incorporated in our robust stochastic facility location model, the robustness of the model, and the performance of the exact solution approach. [ABSTRACT FROM AUTHOR]

This article aims at contributing to the celebration of the 25th Anniversary of Transportation Research Part E: Logistics and Transportation Review (TRE). It provides an overview of the role of Facility Location in Logistics and Transportation highlighting the contribution of TRE to such an enduring relationship. Several conventional problems are revisited showing that the three above fields have been intertwined for a long time. Nevertheless, the role of Facility Location has become even stronger in the past decades due to challenges posed by new technological developments together with a fast economy globalization and a strong increase in environmental concerns. This has called for the study of more complex problems and the development of comprehensive mathematical models leading to major advances in areas such as reverse and green logistics, humanitarian supply chains, and multimodal transportation, to mention a few. These and other related topics are discussed. Hedging against uncertainty has gained much practical relevance and thus it will be much in focus throughout the paper. Several current trends and future challenges are thoroughly discussed. These include but are not limited to the steps already made and those still missing for paving the way from Industry 4.0 to Industry 5.0, as well as the challenges posed by data-driven decision making in the Era of big data. • An overview of the role of Facility Location in Logistics and Transportation is provided. • The contribution of TRE to this multi-disciplinary area is emphasized. • Several conventional problems are revisited and more complex ones are discussed. • Different application areas are covered. • Current trends and future challenges are discussed. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including the findings for schedule optimization problem at the public transit terminals, the multi-criteria approach on the location of public facilities due to tsunamis and the context of Huff-like Stackelberg location problem.

Third authors' name should appear as: Francisco Saldanha-da-Gama. [ABSTRACT FROM AUTHOR]

In this paper a stochastic districting problem is investigated. Demand is assumed to be represented by a random vector with a given joint probability distribution function. A two-stage mixed-integer stochastic programming model is proposed. The first stage comprises the decision about the initial territory design: the districts are defined and all the territory units assigned to one and exactly one of them. In the second stage, i.e., after demand becomes known, balancing requirements are to be met. This is ensured by means of two recourse actions: outsourcing and reassignment of territory units. The objective function accounts for the total expected cost that includes the cost for the first-stage territory design plus the expected cost incurred at the second stage by outsourcing and reassignment. The (re)assignment costs are associated with the distances between territory units, i.e., the focus is put on the compactness of the solution. The model is then extended in different ways to account for aspects of practical relevance such as a maximum desirable dispersion, reallocation constraints, or similarity of the second-stage solution w.r.t. the first-stage one. The new modeling framework proposed is tested computationally using instances built using real geographical data. [ABSTRACT FROM AUTHOR]

We propose a cutting-plane approach (namely, Benders decomposition) for a class of capacitated multi-period facility location problems. The novelty of this approach lies on the use of a specialized interior-point method for solving the Benders subproblems. The primal block-angular structure of the resulting linear optimization problems is exploited by the interior-point method, allowing the (either exact or inexact) efficient solution of large instances. The consequences of different modeling conditions and problem specifications on the computational performance are also investigated both theoretically and empirically, providing a deeper understanding of the significant factors influencing the overall efficiency of the cutting-plane method. The methodology proposed allowed the solution of instances of up to 200 potential locations, one million customers and three periods, resulting in mixed integer linear optimization problems of up to 600 binary and 600 millions of continuous variables. Those problems were solved by the specialized approach in less than one hour and a half, outperforming other state-of-the-art methods, which exhausted the (144 GB of) available memory in the largest instances. [ABSTRACT FROM AUTHOR]

In this paper, a modeling framework is proposed for multi-period hub location. The problems to be studied are extensions of classical hub location problems to the situation in which the hub network can be progressively built and its capacity gradually expanded over time. Both the single allocation and the multiple allocation cases are considered. For each case, a mixed-integer linear programming formulation is proposed and a set of valid inequalities is derived for enhancing the corresponding model. The results of a set of computational tests performed using the formulations proposed and their enhancements are reported. The value of the multi-period solution is discussed as a measure for evaluating the relevance of considering a multi-period model instead of a static counterpart. [ABSTRACT FROM AUTHOR]

In this paper we investigate one of the most recent extensions of the Resource Constrained Project Scheduling Problem (RCPSP): the Multi-Skill Resource Constrained Project Scheduling Problem (MSRCPSP). For this complex problem we propose the use of a parallel scheduling scheme. Such scheme has been successfully applied to the RCPSP. Nevertheless, in order to apply it to the MSRCPSP two new concepts are developed: resource weight and activity grouping. We discuss such concepts and use them for the new heuristic framework proposed. A series of computational tests performed using a large number of instances and reported in this paper shows that the new heuristic is very effective in finding high quality solutions within very small CPU times. [ABSTRACT FROM AUTHOR]

In this paper, we study a variant of the resource-constrained project scheduling problem in which resources are flexible, i.e., each resource has several skills. Each activity in the project may need several resources for each required skill. We present a mixed-integer linear programming formulation for this problem. Several sets of additional inequalities are also proposed. Due to the fact that some of the above-mentioned inequalities require a valid upper bound to the problem, a heuristic procedure is proposed. Computational experience is reported based on randomly generated data, showing that for instances of reasonable size the proposed model enlarged with the additional inequalities can be solved efficiently. [ABSTRACT FROM AUTHOR]

Abstract: Hub location problems are network design problems which are solved as part of a strategic decision making process. In strategic planning, decisions may have a long lasting effect and the implementation may take considerable time. Moreover, input data is not precisely known in advance. Hence, decisions have to be made anticipating uncertainty. In this paper, we address several aspects concerning hub location problems under uncertainty. Two sources of uncertainty are considered: the set-up costs for the hubs and the demands to be transported between the nodes. Generic models are presented for single and multiple allocation versions of the problems. Firstly, the two sources of uncertainty are analyzed separately and afterwards a more comprehensive model is proposed considering all sources of uncertainty. Using a set of computational tests performed, we analyze the changes in the solutions driven by the different sources of uncertainty considered isolated and combined. [Copyright &y& Elsevier]

Abstract: In this paper, an extension of the capacitated single-allocation hub location problem is considered in which the capacity of the hubs is part of the decision making process and balancing requirements are imposed on the network. The decisions to be made comprise (i) the selection of the hubs, (ii) the allocation of the spoke nodes to the hubs, (iii) the flow distribution through the sub network defined by the hubs and (iv) the capacity level at which each hub should operate. In the latter case, for each potential hub, a set of available capacities is considered among which one can be chosen. The objective is to minimize the total cost, which includes the setup cost for the hubs as well as the flow routing cost. Economies of scale are assumed for the costs. Balancing requirements are imposed to the network. In particular, a value is considered for the maximum difference between the maximum and minimum number of spoke nodes that are allocated to the hubs. Two mixed-integer linear programming formulations are proposed and analyzed for this problem. The results of a set of computational experiments using an off-the-shelf commercial solver are presented. These tests aim at evaluate the possibility of solving the problem to optimality using such a solver with a particular emphasis to the impact of the balancing requirements. The tests also allow an analysis of the gap of the bounds provided by linear relaxation. [Copyright &y& Elsevier]

Abstract: In this paper, an extension of the classical capacitated single-allocation hub location problem is studied in which the size of the hubs is part of the decision making process. For each potential hub a set of capacities is assumed to be available among which one can be chosen. Several formulations are proposed for the problem, which are compared in terms of the bound provided by the linear programming relaxation. Different sets of inequalities are proposed to enhance the models. Several preprocessing tests are also presented with the goal of reducing the size of the models for each particular instance. The results of the computational experiments performed using the proposed models are reported. [Copyright &y& Elsevier]

In this work, we propose a heuristic procedure for a stochastic version of the uncapacitated r-allocation p-hub median problem with nonstop services. In particular, we assume that the number of hubs to which a terminal can be allocated is bounded from above by r. Additionally, we consider the possibility of shipping traffic directly between terminals (nonstop services). Uncertainty is associated with the traffic to be shipped between nodes and with the transportation costs. If we assume that such uncertainty can be captured by a finite set of scenarios, each of which with a probability known in advance, it is possible to develop a compact formulation for the deterministic equivalent problem. However, even for small instances of the problem, the model becomes too large to be tackled by a general solver. This fact motivates the development of a heuristic procedure, whose starting point is the determination of a feasible solution to every (deterministic) single-scenario problem. These solutions are then embedded into a process based on the path relinking methodology: gradually an initial solution to the overall problem is transformed by the incorporation of attributes from some guiding solutions. In our case, the guiding solutions are those found for the single-scenario problems. We report and discuss the results of the numerical experiments performed using instances randomly generated for the new problem from the well-known CAB and AP data sets. [ABSTRACT FROM AUTHOR]

Highlights • A broad family of hub location problems is investigated. • Improved formulations are proposed and discussed. • A polyhedral study is conducted rendering many facet-defining inequalities. • Additional valid inequalities are derived, whose separation is analyzed. • A cutting-plane approach and a branch-and-cut algorithm are devised. Abstract This work focuses on a broad class of uncapacitated p -hub median problems that includes non-stop services and setup costs for the network structures. In order to capture both the single and the multiple allocation patterns as well as any intermediate case of interest, we consider the so-called r -allocation pattern with r denoting the maximum number of hubs a terminal can be allocated to. We start by revisiting an optimization model recently proposed for the problem. For that model, we introduce several families of valid inequalities as well as optimality cuts. Moreover, we consider a relaxation of the model that contains several sets of set packing constraints. This motivates a polyhedral study that we perform and that leads to the identification of many families of facets and other valid inequalities to the relaxed problem that, in turn, provide valid inequalities for the original model. Some of these families are too large for being handled directly. For those cases, separation algorithms are also presented. Finally, we gather all the above elements in a branch-and-cut procedure that we devise and implement for tackling the problem. The methodological developments proposed are tested computationally using data generated from the well-known AP data set. [ABSTRACT FROM AUTHOR]

In this paper, we present a modeling framework for hub location problems with a service time limit considering congestion at hubs. Service time is modeled taking the traveling time on the hub network as well as the handling time and the delay caused by congestion at hubs into account. We develop mixed-integer linear programming formulations for the single and multiple allocation versions of this problem. We further extend the multiple allocation model with a possibility of direct shipments. We test our models on the well-known AP data set and analyze the effects of congestion and service time on costs and hub network design. We introduce a measure for the value of modeling congestion and show that not considering the effects of congestion may result in increased costs as well as in building infeasible hub networks. [ABSTRACT FROM AUTHOR]

This paper explores the concept of capacity transfer in the context of capacitated facility location problems. This is accomplished by assuming that facilities with surplus capacity/production can cooperate with those facing shortage by transferring part of that capacity/production. Such a transfer incurs a cost that nonetheless may be compensated by savings both in the installation costs and in the distribution costs. Mixed-integer mathematical programming models are proposed for the problem. A distinction is made between the case in which the triangle inequality holds for the transfer costs and the case in which it does not. We present compact models, which are enhanced with valid inequalities that are separated in a branch-and-cut fashion. A comprehensive computational study with several hundreds of instances is reported showing the value of transferring capacities. Overall, this work investigates a problem that is at the core of more comprehensive models emerging in the context of logistics network design. [ABSTRACT FROM AUTHOR]