Food hubs are of interest in regional and local food system development because they potentially enhance the sustainability of food supply chains. Expanding on earlier literature, this study introduces economies of scale into an aggregation hub location model and disaggregates production into four seasons to account for geographic and seasonal variation of US fresh produce production. A mixed integer linear programming model is formulated with the objective of minimizing total costs of assembly and first-handler operations. Results suggest scale economies have significant effects on the optimal number, locations, and sizes of aggregation hubs. We model regional and local food systems in a manner more consistent with economic theory and provide a richer framework for policy analysis.

The paper addresses a multi-item, multi-plant lot-sizing problem with transfer costs andcapacity constraints. The problem is reformulated according to a multi-commodity flowformalism, and decomposed, through Lagrangean relaxation, into a master facility locationproblem and a slave minimal cost multi-commodity flow problem. The decomposition frameworkgives rise in a natural way to designing a Lagrangean based heuristic. Numericalexperiments showing the efficiency of the proposed approach are reported.

Given the probability measure ν over the given region $\Omega\subset \mathbb{R}^n$ , we consider the optimal location of a setΣ composed by n points in Ω in order to minimize theaverage distance $\Sigma\mapsto \int_\Omega \mathrm{dist}\,(x,\Sigma)\,{\rm d}\nu$ (theclassical optimal facility location problem). The paper compares twostrategies to find optimal configurations: the long-term one whichconsists in placing all n points at once in an optimal position, and the short-term one which consists in placing the points one by one addingat each step at most one point and preserving the configurationbuilt at previous steps. We show that the respective optimizationproblems exhibit qualitatively different asymptotic behavior as $n\to\infty$ , although the optimization costs in both cases have the same asymptotic orders of vanishing.

The soft-capacitated facility location problem, where each facility is composed of a variable number of fixed-capacity production units, has been recently studied in several papers, especially in the metric case. In this paper, we only consider the general problem where connection costs do not systematically satisfy the triangle inequality property. We show that an adaptation of the set covering greedy heuristic, where the subproblem is approximately solved by a fully polynomial-time approximation scheme based on cost scaling and dynamic programming, achieves a logaritmic approximation ratio of (1 + ε)H(n) for the problem, where n is the number of customers to be served and H is the harmonic series. This improves the previous bound of 2H(n) for this problem.

Public inoculation centers are examples of facilities providing service to customers whose demand is elastic to travel and waiting time. That is, people will not travel too far, or stay in line for too long to obtain the service. The goal, when planning such services, is to maximize the demand they attract, by locating centers and staffing them so as to reduce customers' travel time and time spent in queue. In the case of inoculation centers, the goal is to maximize the people that travel to the centers and stay in line until inoculated. We propose a procedure for the allocation of multiple servers to centers, so that this goal is achieved. An integer programming model is formulated. Since demand is elastic, a supply-demand equilibrium equation must be explicitly included in the optimization model, which then becomes nonlinear. As there are no exact procedures to solve such problems, we propose a heuristic procedure, based on Heuristic Concentration, which finds a good solution to this problem. Numerical examples are presented.