4,594 research outputs found
Locating a bioenergy facility using a hybrid optimization method
In this paper, the optimum location of a bioenergy generation facility for district energy applications is sought. A bioenergy facility usually belongs to a wider system, therefore a holistic approach is adopted to define the location that optimizes the system-wide operational and investment costs. A hybrid optimization method is employed to overcome the limitations posed by the complexity of the optimization problem. The efficiency of the hybrid method is compared to a stochastic (genetic algorithms) and an exact optimization method (Sequential Quadratic Programming). The results confirm that the hybrid optimization method proposed is the most efficient for the specific problem. (C) 2009 Elsevier B.V. All rights reserved
Consensus Strategies for Signed Profiles on Graphs
The median problem is a classical problem in Location Theory: one searches for a location that minimizes the average distance to the sites of the clients. This is for desired facilities as a distribution center for a set of warehouses. More recently, for obnoxious facilities, the antimedian was studied. Here one maximizes the average distance to the clients. In this paper the mixed case is studied. Clients are represented by a profile, which is a sequence of vertices with repetitions allowed. In a signed profile each element is provided with a sign from {+,-}. Thus one can take into account whether the client prefers the facility (with a + sign) or rejects it (with a - sign). The graphs for which all median sets, or all antimedian sets, are connected are characterized. Various consensus strategies for signed profiles are studied, amongst which Majority, Plurality and Scarcity. Hypercubes are the only graphs on which Majority produces the median set for all signed profiles. Finally, the antimedian sets are found by the Scarcity Strategy on e.g. Hamming graphs, Johnson graphs and halfcubes.median;consensus function;median graph;majority rule;plurality strategy;Graph theory;Hamming graph;Johnson graph;halfcube;scarcity strategy;Discrete location and assignment;Distance in graphs
The obnoxious facilities planar p-median problem
In this paper we propose the planar obnoxious p-median problem. In the
p-median problem the objective is to find p locations for facilities that
minimize the weighted sum of distances between demand points and their closest
facility. In the obnoxious version we add constraints that each facility must
be located at least a certain distance from a partial set of demand points
because they generate nuisance affecting these demand points. The resulting
problem is extremely non-convex and traditional non-linear solvers such as
SNOPT are not efficient. An efficient solution method based on Voronoi diagrams
is proposed and tested. We also constructed the efficient frontiers of the test
problems to assist the planers in making location decisions
The planar multiple obnoxious facilities location problem: A Voronoi based heuristic
Consider the situation where a given number of facilities are to be located in a convex polygon with the objective of maximizing the minimum distance between facilities and a given set of communities with the important additional condition that the facilities have to be farther than a certain distance from one another. This continuous multiple obnoxious facility location problem, which has two variants, is very complex to solve using commercial nonlinear optimizers. We propose a mathematical formulation and a heuristic approach based on Voronoi diagrams and an optimally solved binary linear program. As there are no nonlinear optimization solvers that guarantee optimality, we compare our results with a popular multi-start approach using interior point, genetic algorithm (GA), and sparse non-linear optimizer (SNOPT) solvers in Matlab. These are state of the art solvers for dealing with constrained non linear problems. Each instance is solved using 100 randomly generated starting solutions and the overall best is then selected. It was found that the proposed heuristic results are much better and were obtained in a fraction of the computer time required by the other methods.The multiple obnoxious location problem is a perfect example where all-purpose non-linear non-convex solvers perform poorly and hence the best way forward is to design and analyze heuristics that have the power and the exibility to deal with such a high level of complexity
Integer programming models for the semi-obnoxious p-median problem
The p-median problem concerns the location of facilities so that the sum of
distances between the demand points and their nearest facility is minimized. We
study a variant of this classic location problem where minimum distance
constraints exist both between the facilities and between the facilities and
the demand points. This specific type of problem can be used to model
situations where the facilities to be located are semi-obnoxious. But despite
its relevance to real life scenarios, it has received little attention within
the vast literature on location problems. We present twelve ILP models for this
problem, coupling three formulations of the p-median problem with four
formulations of the distance constraints. We utilize Gurobi Optimizer v9.0.3 in
order to compare these ILP models on a large dataset of problems. Experimental
results demonstrate that the classic p-median model proposed by ReVelle \&
Swain and the model proposed by Rosing et al. are the best performers
Less is more approach: basic variable neighborhood search for the obnoxious p-median problem
The goal of the less is more approach (LIMA) for solving optimization problems that has recently been proposed in Mladenović et al. (2016) is to find the minimum number of search ingredients that make a heuristic more efficient than the currently best. In this paper, LIMA is successfully applied to solve the obnoxious p-median problem (OpMP). More precisely, we developed a basic variable neighborhood search for solving the OpMP, where the single search ingredient, the interchange neighborhood structure, is used. We also propose a new simple local search strategy for solving facility location problems, within the interchange neighborhood structure, which is in between the usual ones: first improvement and best improvement strategies. We call it facility best improvement local search. On the basis of experiments, it appeared to be more efficient and effective than both first and best improvement. According to the results obtained on the benchmark instances, our heuristic turns out to be highly competitive with the existing ones, establishing new state-of-the-art results. For example, four new best-known solutions and 133 ties are claimed in testing the set with 144 instances. © 2019 The Authors. International Transactions in Operational Research © 2019 International Federation of Operational Research Societies15YJC630097Higher Education Discipline Innovation ProjectMinistarstvo Prosvete, Nauke i TehnoloÅ¡kog Razvoja, MPNTR: BR05236839, 044006, 174010National Natural Science Foundation of China, NSFC: 71601065, 71690235, 71521001, 71871080, 71501058This research is partially supported by Serbian Ministry of Education, Science and Technological Development under the grants nos. 174010 and 044006. In addition, this research is partially supported by the framework of the grant number BR05236839 ?Development of information technologies and systems for stimulation of personality's sustainable development as one of the bases of development of digital Kazakhstan? and by the National Natural Science Foundation of China (Nos. 71871080, 71601065, 71690235, 71501058), Innovative Research Groups of the National Natural Science Foundation of China (71521001), the Humanities and Social Sciences Foundation of the Chinese Ministry of Education (No. 15YJC630097), and the Base of Introducing Talents of Discipline to Universities for Optimization and Decision-Making in the Manufacturing Process of Complex Product (111 project)
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