Using Location-Allocation Algorithms to Distribute Multioutlet Hydrants in Irrigation Networks Design

Location-allocation algorithms allow for situating services in an efficient way in zones where the demand is dispersed across the area. In the present work, the minisum location-allocation algorithm is used to optimize the placement multioutlet hydrants, which are needed to supply irrigation to the region. The objective function aims to minimize the total access costs of the service. Because the total number of hydrants is the same as what currently exists the results can be compared with the presented solution. The proposed model reduces the total distance run to access the service, from 16,177 m to 13,560 m (16.17%), and the objective function (proportional to the cost) by 28.95%. © 2012 American Society of Civil Engineers.González Villa, FJ.; García Prats, A. (2011). Using Location-Allocation Algorithms to Distribute Multioutlet Hydrants in Irrigation Networks Design. Journal of Irrigation and Drainage Engineering. 274-283. doi:10.1061/(ASCE)IR.1943-4774.0000405S27428

Revisiting several problems and algorithms in continuous location with lp norms

This paper addresses the general continuous single facility location problems in finite dimension spaces under possibly different ℓp norms in the demand points. We analyze the difficulty of this family of problems and revisit convergence properties of some well-known algorithms. The ultimate goal is to provide a common approach to solve the family of continuous ℓp ordered median location problems in dimension d (including of course the ℓp minisum or Fermat-Weber location problem for any p ≥ 1). We prove that this approach has a polynomial worse case complexity for monotone lambda weights and can be also applied to constrained and even non-convex problems.Junta de AndalucíaFondo Europeo de Desarrollo RegionalMinisterio de Ciencia e Innovació

Supply facility and input/output point locations in the presence of barriers

This paper studies a facility location model in which two-dimensional Euclidean space represents the layout of a shop floor. The demand is generated by fixed rectangular-shaped user sites and served by a single supply facility. It is assumed that (i) communication between the supply point and a demand facility occurs at an input/output (I/O) point on the demand facility itself, (ii) the facilities themselves pose barriers to travel and (iii) distance measurement is as per the L1-metric. The objective is to determine optimal locations of the supply facility as well as I/O points on the demand facilities, in order to minimize total transportation costs. Several, increasingly more complex, versions of the model are formulated and polynomial time algorithms are developed to find the optimal locations in each case. Scope and purpose In a facility layout setting, often a new central supply facility such as a parts supply center or tool crib needs to be located to serve the existing demand facilities (e.g., workstations or maintenance areas). The demand facilities are physical entities that occupy space, that cannot be traveled through, and that receive material from the central facility, through a perimeter I/O (input/output or drop-off/pick-up) point. This paper addresses the joint problem of locating the central facility and determining the I/O point on each demand facility to minimize the total material transportation cost. Different versions of this problem are considered. The solution methods draw from and extend results of location theory for a class of restricted location problems. For practitioners, simple results and polynomial time algorithms are developed for solving these facility (re) design problems

Robustness in facility location

Facility location concerns the placement of facilities, for various objectives, by use of mathematical models and solution procedures. Almost all facility location models that can be found in literature are based on minimizing costs or maximizing cover, to cover as much demand as possible. These models are quite efficient for finding an optimal location for a new facility for a particular data set, which is considered to be constant and known in advance. In a real world situation, input data like demand and travelling costs are not fixed, nor known in advance. This uncertainty and uncontrollability can lead to unacceptable losses or even bankruptcy. A way of dealing with these factors is robustness modelling. A robust facility location model aims to locate a facility that stays within predefined limits for all expectable circumstances as good as possible. The deviation robustness concept is used as basis to develop a new competitive deviation robustness model. The competition is modelled with a Huff based model, which calculates the market share of the new facility. Robustness in this model is defined as the ability of a facility location to capture a minimum market share, despite variations in demand. A test case is developed by which algorithms can be tested on their ability to solve robust facility location models. Four stochastic optimization algorithms are considered from which Simulated Annealing turned out to be the most appropriate. The test case is slightly modified for a competitive market situation. With the Simulated Annealing algorithm, the developed competitive deviation model is solved, for three considered norms of deviation. At the end, also a grid search is performed to illustrate the landscape of the objective function of the competitive deviation model. The model appears to be multimodal and seems to be challenging for further research

Mixed planar and network single-facility location problems

We consider the problem of optimally locating a single facility anywhere in a network to serve both on-network and off-network demands. Off-network demands occur in a Euclidean plane, while on-network demands are restricted to a network embedded in the plane. On-network demand points are serviced using shortest-path distances through links of the network (e.g., on-road travel), whereas demand points located in the plane are serviced using more expensive Euclidean distances. Our base objective minimizes the total weighted distance to all demand points. We develop several extensions to our base model, including: (i) a threshold distance model where if network distance exceeds a given threshold, then service is always provided using Euclidean distance, and (ii) a minimax model that minimizes worst-case distance. We solve our formulations using the “Big Segment Small Segment” global optimization method, in conjunction with bounds tailored for each problem class. Computational experiments demonstrate the effectiveness of our solution procedures. Solution times are very fast (often under one second), making our approach a good candidate for embedding within existing heuristics that solve multi-facility problems by solving a sequence of single-facility problems. © 2016 Wiley Periodicals, Inc. NETWORKS, Vol. 68(4), 271–282 2016