6533b839fe1ef96bd12a5b72

RESEARCH PRODUCT

Solutions for districting problems with chance-constrained balancing requirements

Carmela PiccoloAntonio DiglioFrancisco Saldanha-da-gamaJuanjo Peiró

subject

Mathematical optimizationInformation Systems and ManagementHeuristic (computer science)Computer scienceStrategy and Management0211 other engineering and technologiesStochastic programmingHeuristic02 engineering and technologyManagement Science and Operations ResearchPoisson distributionMeasure (mathematics)Contiguity (probability theory)Set (abstract data type)Contiguitysymbols.namesake0502 economics and business050210 logistics & transportation021103 operations research05 social sciencesStochastic programmingsymbolsProbability distributionDistrictingHeuristicsStochastic demand

description

Abstract In this paper, a districting problem with stochastic demands is investigated. The goal is to divide a geographic area into p contiguous districts such that, with some given probability, the districts are balanced with respect to some given lower and upper thresholds. The problem is cast as a p -median problem with contiguity constraints that is further enhanced with chance-constrained balancing requirements. The total assignment cost of the territorial units to the representatives of the corresponding districts is used as a surrogate compactness measure to be optimized. Due to the tantalizing purpose of deriving a deterministic equivalent for the problem, a two-phase heuristic is developed. In the first phase, the chance-constraints are ignored and a feasible solution is constructed for the relaxed problem; in the second phase, the solution is corrected if it does not meet the chance-constraints. In this case, a simulation procedure is proposed for estimating the probability of a given solution to yield a balanced districting. That procedure also provides information for guiding the changes to make in the solution. The results of a series of computational tests performed are discussed based upon a set of testbed instances randomly generated. Different families of probability distributions for the demands are also investigated, namely: Uniform, Log-normal, Exponential, and Poisson.

https://doi.org/10.1016/j.omega.2021.102430