Insertion Heuristics for Central Cycle Problems

A central cycle problem requires a cycle that is reasonably short and keeps a the maximum distance from any node not on the cycle to its nearest node on the cycle reasonably low. The objective may be to minimise maximumdistance or cycle length and the solution may have further constraints. Most classes of central cycle problems are NP-hard. This paper investigates insertion heuristics for central cycle problems, drawing on insertion heuristics for p-centres [7] and travelling salesman tours [21]. It shows that a modified farthest insertion heuristic has reasonable worstcase bounds for a particular class of problem. It then compares the performance of two farthest insertion heuristics against each other and against bounds (where available) obtained by integer programming on a range of problems from TSPLIB [20]. It shows that a simple farthest insertion heuristic is fast, performs well in practice and so is likely to be useful for a general problems or as the basis for more complex heuristics for specific problems

Solving the conditional and unconditional p-center problem with modified harmony search: A real case study

AbstractIn this paper, we solve the well-known conditional and unconditional p-center problem using a modified harmony search algorithm. This music inspired algorithm is a simple meta-heuristic that was proposed recently for solving combinatorial and large-scale engineering and optimization problems. This algorithm is applicable to both discrete and continuous search spaces. We have tested the present algorithm on ORLIB and TSP test problems and compared the results of the classic harmony search approach to those of the modified harmony search method. We also present some results for other meta-heuristic algorithms including the variable neighborhood search, the Tabu search, and the scatter search. Finally, we utilize this location model to locate bicycle stations in the historic city of Isfahan in Iran

Hybrid Meta-heuristics with VNS and Exact Methods: Application to Large Unconditional and Conditional Vertex p-Centre Problems

Large-scale unconditional and conditional vertex p-centre problems are solved using two meta-heuristics. One is based on a three-stage approach whereas the other relies on a guided multi-start principle. Both methods incorporate Variable Neighbourhood Search, exact method, and aggregation techniques. The methods are assessed on the TSP dataset which consist of up to 71,009 demand points with p varying from 5 to 100. To the best of our knowledge, these are the largest instances solved for unconditional and conditional vertex p-centre problems. The two proposed meta-heuristics yield competitive results for both classes of problems

Exact solution approaches for the discrete α\alpha-neighbor pp-center problem

The discrete $\alpha$-neighbor $p$-center problem (d-$\alpha$-$p$CP) is an emerging variant of the classical $p$-center problem which recently got attention in literature. In this problem, we are given a discrete set of points and we need to locate $p$ facilities on these points in such a way that the maximum distance between each point where no facility is located and its $\alpha$-closest facility is minimized. The only existing algorithms in literature for solving the d-$\alpha$-$p$CP are approximation algorithms and two recently proposed heuristics. In this work, we present two integer programming formulations for the d-$\alpha$-$p$CP, together with lifting of inequalities, valid inequalities, inequalities that do not change the optimal objective function value and variable fixing procedures. We provide theoretical results on the strength of the formulations and convergence results for the lower bounds obtained after applying the lifting procedures or the variable fixing procedures in an iterative fashion. Based on our formulations and theoretical results, we develop branch-and-cut (B&C) algorithms, which are further enhanced with a starting heuristic and a primal heuristic. We evaluate the effectiveness of our B&C algorithms using instances from literature. Our algorithms are able to solve 116 out of 194 instances from literature to proven optimality, with a runtime of under a minute for most of them. By doing so, we also provide improved solution values for 116 instances

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

Exact solution methodologies for the p-center problem under single and multiple allocation strategies

Ankara : The Department of Industrial Engineering and the Graduate School of Engineering and Science of Bilkent Univ., 2013.Thesis (Ph. D.) -- Bilkent University, 2013.Includes bibliographical references leaves 89-95.The p-center problem is a relatively well known facility location problem that involves locating p identical facilities on a network to minimize the maximum distance between demand nodes and their closest facilities. The focus of the problem is on the minimization of the worst case service time. This sort of objective is more meaningful than total cost objectives for problems with a time sensitive service structure. A majority of applications arises in emergency service locations such as determining optimal locations of ambulances, fire stations and police stations where the human life is at stake. There is also an increased interest in p-center location and related location covering problems in the contexts of terror fighting, natural disasters and human-caused disasters. The p-center problem is NP-hard even if the network is planar with unit vertex weights, unit edge lengths and with the maximum vertex degree of 3. If the locations of the facilities are restricted to the vertices of the network, the problem is called the vertex restricted p-center problem; if the facilities can be placed anywhere on the network, it is called the absolute p-center problem. The p-center problem with capacity restrictions on the facilities is referred to as the capacitated p-center problem and in this problem, the demand nodes can be assigned to facilities with single or multiple allocation strategies. In this thesis, the capacitated p-center problem under the multiple allocation strategy is studied for the first time in the literature. The main focus of this thesis is a modelling and algorithmic perspective in the exact solution of absolute, vertex restricted and capacitated p-center problems. The existing literature is enhanced by the development of mathematical formulations that can solve typical dimensions through the use of off the-shelf commercial solvers. By using the structural properties of the proposed formulations, exact algorithms are developed. In order to increase the efficiency of the proposed formulations and algorithms in solving higher dimensional problems, new lower and upper bounds are provided and these bounds are utilized during the experimental studies. The dimensions of problems solved in this thesis are the highest reported in the literature.Çalık, HaticePh.D

Not available

The p-center problem is a relatively well known facility location problem thatinvolves locating p identical facilities on a network to minimize the maximumdistance between demand nodes and their closest facilities. The focus of theproblem is on the minimization of the worst case service time. This sort ofobjective is more meaningful than total cost objectives for problems with a timesensitive service structure. A majority of applications arises in emergency servicelocations such as determining optimal locations of ambulances, fire stations andpolice stations where the human life is at stake. There is also an increasedinterest in p-center location and related location covering problems in the contextsof terror fighting, natural disasters and human-caused disasters. The p-centerproblem is NP-hard even if the network is planar with unit vertex weights, unitedge lengths and with the maximum vertex degree of 3. If the locations of thefacilities are restricted to the vertices of the network, the problem is called thevertex restricted p-center problem; if the facilities can be placed anywhere on thenetwork, it is called the absolute p-center problem. The p-center problem withcapacity restrictions on the facilities is referred to as the capacitated p-centerproblem and in this problem, the demand nodes can be assigned to facilities withsingle or multiple allocation strategies. In this thesis, the capacitated p-centerproblem under the multiple allocation strategy is studied for the first time in theliterature.The main focus of this thesis is a modelling and algorithmic perspective inthe exact solution of absolute, vertex restricted and capacitated p-center problems.The existing literature is enhanced by the development of mathematicalformulations that can solve typical dimensions through the use of off the-shelfcommercial solvers. By using the structural properties of the proposed formulations,exact algorithms are developed. In order to increase the effciency of theproposed formulations and algorithms in solving higher dimensional problems,new lower and upper bounds are provided and these bounds are utilized duringthe experimental studies. The dimensions of problems solved in this thesis arethe highest reported in the literature.Not availabl