The Experts below are selected from a list of 4131 Experts worldwide ranked by ideXlab platform
Nenad Mladenovic - One of the best experts on this subject based on the ideXlab platform.
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Variable Neighborhood Search for stochastic linear programming problem with quantile criterion
Journal of Global Optimization, 2019Co-Authors: Sergey V Ivanov, Nenad Mladenovic, Andrey I Kibzun, Dragan UrosevicAbstract:We consider the stochastic linear programming problem with quantile criterion and continuous distribution of random parameters. Using the sample approximation, we obtain a stochastic programming problem with discrete distribution of random parameters. It is known that the solution to this problem provides an approximate solution to the problem with continuous random parameters if the size of the sample is large enough. Applying the confidence method, we reduce the problem to a mixed integer programming problem, which is linear with respect to continuous Variables. Integer Variables determine confidence sets, and we describe the structure of the optimal confidence set. This property allows us to take into account only confidence sets that may be optimal. To find an approximate solution to the problem, we suggest a modification of the Variable Neighborhood Search and determine structures of Neighborhoods used in the Search. Also, we discuss a method to find a good initial solution and give results of numerical experiments. We apply the developed algorithm to solve a problem of optimization of a hospital budget.
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continuous Variable Neighborhood Search c vns for solving systems of nonlinear equations
Informs Journal on Computing, 2019Co-Authors: Nenad Mladenovic, Jun Pei, Zorica Dražic, Milan Dražic, Panos M PardalosAbstract:In this paper, we propose the continuous Variable Neighborhood Search method for finding all the solutions to a nonlinear system of equations (NSEs). We transform the NSE problem into an equivalent...
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Variable Neighborhood Search for a two stage stochastic programming problem with a quantile criterion
Automation and Remote Control, 2019Co-Authors: Sergey V Ivanov, Nenad Mladenovic, Andrey I KibzunAbstract:We consider a two-stage stochastic programming problem with a bilinear loss function and a quantile criterion. The problem is reduced to a single-stage stochastic programming problem with a quantile criterion. We use the method of sample approximations. The resulting approximating problem is considered as a stochastic programming problem with a discrete distribution of random parameters. We check convergence conditions for the sequence of solutions of approximating problems. Using the confidence method, the problem is reduced to a combinatorial optimization problem where the confidence set represents an optimization strategy. To Search for the optimal confidence set, we adapt the Variable Neighborhood Search method. To solve the problem, we develop a hybrid algorithm based on the method of sample approximations, the confidence method, Variable Neighborhood Search.
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solving the capacitated clustering problem with Variable Neighborhood Search
Annals of Operations Research, 2019Co-Authors: Jack Brimberg, Nenad Mladenovic, Raca Todosijevic, Dragan UrosevicAbstract:Variable Neighborhood Search (VNS) is a proven heuristic framework for finding good solutions to combinatorial and global optimization problems. In this paper two VNS-based heuristics are proposed for solving the capacitated clustering problem. The first follows a standard VNS approach, and the second a skewed VNS that allows moves to inferior solutions. The performance of the two heuristics is assessed on benchmark instances from the literature. We also compare their performance against a recently published iterated VNS procedure. All VNS procedures outperform the state-of-the-art, but the Skewed VNS is best overall. This would suggest that using acceptance criteria before allowing moves to inferior solutions in Skewed VNS is preferable to the random shaking approach that is used in Iterated VNS to move to new regions of the solution space.
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Variable Neighborhood Search variants for min power symmetric connectivity problem
Les Cahiers du GERAD, 2016Co-Authors: Adil I Erzin, Nenad Mladenovic, Roman V PlotnikovAbstract:We consider the problem of optimal communication tree construction in a given undirected weighted graph. Such a problem occurs while minimizing the power consumption of data transmission in different distributed networks in the case when network elements are able to adjust their transmission ranges. In this paper, the most general strongly NP-hard formulation, when edge weights have arbitrary non-negative values, is considered. We propose new heuristics, mostly based on Variable Neighborhood Search, for getting an approximate solution of the problem. Extensive comparative analysis between the proposed methods was performed. Numerical experiments demonstrated the high efficiency of the proposed heuristics. HighlightsProposed new local Search that is based on elementary tree transformation (ETT). In terms of solution quality it significantly outperforms the previous one (named as LI), but uses more computation time.Several basic VNS- and general VNS-based heuristics are proposed and tested. Some of these new heuristics give results of better quality than the recent state-of-the-art (hybrid heuristic 4) technique, especially for solving more realistic large size problems.A simulation has been executed. Its results demonstrated high efficiency of the majority of the proposed methods.
Dragan Urosevic - One of the best experts on this subject based on the ideXlab platform.
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Variable Neighborhood Search for stochastic linear programming problem with quantile criterion
Journal of Global Optimization, 2019Co-Authors: Sergey V Ivanov, Nenad Mladenovic, Andrey I Kibzun, Dragan UrosevicAbstract:We consider the stochastic linear programming problem with quantile criterion and continuous distribution of random parameters. Using the sample approximation, we obtain a stochastic programming problem with discrete distribution of random parameters. It is known that the solution to this problem provides an approximate solution to the problem with continuous random parameters if the size of the sample is large enough. Applying the confidence method, we reduce the problem to a mixed integer programming problem, which is linear with respect to continuous Variables. Integer Variables determine confidence sets, and we describe the structure of the optimal confidence set. This property allows us to take into account only confidence sets that may be optimal. To find an approximate solution to the problem, we suggest a modification of the Variable Neighborhood Search and determine structures of Neighborhoods used in the Search. Also, we discuss a method to find a good initial solution and give results of numerical experiments. We apply the developed algorithm to solve a problem of optimization of a hospital budget.
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solving the capacitated clustering problem with Variable Neighborhood Search
Annals of Operations Research, 2019Co-Authors: Jack Brimberg, Nenad Mladenovic, Raca Todosijevic, Dragan UrosevicAbstract:Variable Neighborhood Search (VNS) is a proven heuristic framework for finding good solutions to combinatorial and global optimization problems. In this paper two VNS-based heuristics are proposed for solving the capacitated clustering problem. The first follows a standard VNS approach, and the second a skewed VNS that allows moves to inferior solutions. The performance of the two heuristics is assessed on benchmark instances from the literature. We also compare their performance against a recently published iterated VNS procedure. All VNS procedures outperform the state-of-the-art, but the Skewed VNS is best overall. This would suggest that using acceptance criteria before allowing moves to inferior solutions in Skewed VNS is preferable to the random shaking approach that is used in Iterated VNS to move to new regions of the solution space.
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less is more basic Variable Neighborhood Search for minimum differential dispersion problem
Information Sciences, 2016Co-Authors: Nenad Mladenovic, Raca Todosijevic, Dragan UrosevicAbstract:Abstract In this paper, we propose a basic Variable Neighborhood Search for solving Minimum differential dispersion problem using only the swap Neighborhood structure in both descent (intensification) and shaking (diversification) steps. It has become a trend in the metaheuristic literature to use hybrid metaheuristics, i.e., combination of several metaheuristic paradigms, for solving some particular optimization problem. We show that our simple method, which relies on the basic Variable Neighborhood Search, significantly outperforms the hybrid one that combines GRASP, Variable Neighborhood Search, and Exterior path relinking metaheuristics. Thus, simplicity is not only the desired user friendly property of a heuristic but can lead to more efficient and effective method than if complex hybrid metaheuristic is used: less is more.
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a general Variable Neighborhood Search for solving the uncapacitated r allocation p hub median problem
Les Cahiers du GERAD, 2015Co-Authors: Raca Todosijevic, Nenad Mladenovic, Dragan Urosevic, Saïd HanafiAbstract:The \(p\)-hub median problem consists of choosing \(p\) hub locations from a set of nodes with pairwise traffic demands in order to route the traffic between the origin-destination pairs at minimum cost. We accept general assumption that transportation between non-hub nodes is possible only via \(r\)-hub nodes, to which non-hub nodes are assigned. In this paper we propose a general Variable Neighborhood Search heuristic to solve the problem in an efficient and effective way. Moreover, for the first time full nested Variable Neighborhood descent is applied as a local Search within Variable Neighborhood Search. Computational results outperform the current state-of-the-art results obtained by GRASP based heuristic.
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solving the maximally diverse grouping problem by skewed general Variable Neighborhood Search
Information Sciences, 2015Co-Authors: Jack Brimberg, Nenad Mladenovic, Dragan UrosevicAbstract:The maximally diverse grouping problem requires finding a partition of a given set of elements into a fixed number of mutually disjoint subsets (or groups) in order to maximize the overall diversity between elements of the same group. In this paper we develop a new variant of Variable Neighborhood Search for solving the problem. The extensive computational results show that our new heuristic significantly outperforms the current state of the art. Moreover, the best known solutions have been improved on 531 out of 540 test instances from the literature.
Dennis Huisman - One of the best experts on this subject based on the ideXlab platform.
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a Variable Neighborhood Search heuristic for rolling stock rescheduling
EURO Journal on Transportation and Logistics, 2021Co-Authors: Rowan Hoogervorst, Gábor Maróti, Twan Dollevoet, Dennis HuismanAbstract:We present a Variable Neighborhood Search heuristic for the rolling stock rescheduling problem. Rolling stock rescheduling is needed when a disruption leads to cancellations in the timetable. In rolling stock rescheduling, one must then assign duties, i.e., sequences of trips, to the available train units in such a way that both passenger comfort and operational performance are taken into account. For our heuristic, we introduce three Neighborhoods, which focus on swapping duties between train units, on improving the individual duties and on changing the shunting that occurs between trips, respectively. These Neighborhoods are used for both a Variable Neighborhood Descent local Search procedure and for perturbing the current solution in order to escape from local optima. Moreover, we show that the heuristic can be extended to the setting of flexible rolling stock turnings at ending stations by introducing a fourth Neighborhood. We apply our heuristic to instances of Netherlands Railways (NS). The results show that the heuristic is able to find high-quality solutions within one minute of solving time. This allows rolling stock dispatchers to use our heuristic in real-time rescheduling.
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a Variable Neighborhood Search heuristic for rolling stock rescheduling
Econometric Institute Research Papers, 2019Co-Authors: Rowan Hoogervorst, Gábor Maróti, Twan Dollevoet, Dennis HuismanAbstract:textabstractWe present a Variable Neighborhood Search heuristic for the rolling stock rescheduling problem. Rolling stock rescheduling is needed when a disruption leads to cancellations in the timetable. In rolling stock rescheduling, we then assign duties, i.e., sequences of trips, to the available train units in such a way that both passenger comfort and operational performance are taken into account. For our heuristic, we introduce three Neighborhoods that can be used for rolling stock rescheduling, which respectively focus on swapping duties between train units, on improving the individual duties and on changing the shunting that occurs between trips. These Neighborhoods are used for both a Variable Neighborhood Descent local Search procedure and for perturbing the current solution in order to escape from local optima. We apply our heuristic to instances of Netherlands Railways (NS). The results show that the heuristic is able to find high-quality solutions in a reasonable amount of time. This allows rolling stock dispatchers to use our heuristic in real-time rescheduling.
Pierre Hansen - One of the best experts on this subject based on the ideXlab platform.
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Variable Neighborhood Search: basics and variants
EURO Journal on Computational Optimization, 2017Co-Authors: Pierre Hansen, Nenad Mladenović, Raca Todosijević, Saïd HanafiAbstract:Variable Neighborhood Search (VNS) is a framework for building heuristics, based upon systematic changes of Neighborhoods both in a descent phase, to find a local minimum, and in a perturbation phase to escape from the corresponding valley. In this paper, we present some of VNS basic schemes as well as several VNS variants deduced from these basic schemes. In addition, the paper includes parallel implementations and hybrids with other metaheuristics.
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Variable Neighborhood Search for minimum cost berth allocation
European Journal of Operational Research, 2008Co-Authors: Pierre Hansen, Nenad Mladenovic, Ceyda OguzAbstract:Abstract The berth allocation problem is to allocate space along the quayside to incoming ships at a container terminal in order to minimize some objective function. We consider minimization of total costs for waiting and handling as well as earliness or tardiness of completion, for all ships. We assume ships can arrive at any given time, i.e., before or after the berths become available. The resulting problem, which subsumes several previous ones, is expressed as a linear mixed 0–1 program. As it turns out to be too time-consuming for exact solution of instances of realistic size, a Variable Neighborhood Search (VNS) heuristic is proposed, and compared with Multi-Start (MS), a Genetic Search algorithm (GA) and a Memetic Search algorithm (MA). VNS provides optimal solutions for all instances solved to optimality in a previous paper of the first two authors and outperforms MS, MA and GA on large instances.
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Variable Neighborhood Search methods
Les Cahiers du GERAD, 2007Co-Authors: Pierre Hansen, Nenad MladenovicAbstract:Main methods, algorithms and applications of the Variable Neighborhood Search metaheuristic are surveyed, in view of a chapter of the Encyclopedia of Optimization.
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Variable Neighborhood Search and local branching
Computers & Operations Research, 2006Co-Authors: Pierre Hansen, Nenad Mladenovic, Dragan UrosevicAbstract:In this paper we develop a Variable Neighborhood Search (VNS) heuristic for solving mixed-integer programs (MIPs). It uses CPLEX, the general-purpose MIP solver, as a black-box. Neighborhoods around the incumbent solution are defined by adding constraints to the original problem, as suggested in the recent local branching (LB) method of Fischetti and Lodi (Mathematical Programming Series B 2003;98:23-47). Both LB and VNS use the same tools: CPLEX and the same definition of the Neighborhoods around the incumbent. However, our VNS is simpler and more systematic in Neighborhood exploration. Consequently, within the same time limit, we were able to improve 14 times the best known solution from the set of 29 hard problem instances used to test LB.
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Cooperative Parallel Variable Neighborhood Search for the p-Median
Journal of Heuristics, 2004Co-Authors: Teodor Gabriel Crainic, Pierre Hansen, Michel Gendreau, Nenad MladenovićAbstract:We propose a cooperative multi-Search method for the Variable Neighborhood Search (VNS) meta-heuristic based on the central-memory mechanism that has been successfully applied to a number of difficult combinatorial problems. In this approach, several independent VNS meta-heuristics cooperate by asynchronously exchanging information about the best solutions identified so far, thus conserving the simplicity of the original, sequential VNS ideas. The p -median problem (PM) serves as test case. Extensive experimentations have been conducted on the classical TSPLIB benchmark problem instances with up to 11948 customers and 1000 medians, without any particular calibration of the parallel method. The results indicate that, compared to sequential VNS, the cooperative strategy yields significant gains in terms of computation time without a loss in solution quality.
Mostafa Zandieh - One of the best experts on this subject based on the ideXlab platform.
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Flexible job-shop scheduling with parallel Variable Neighborhood Search algorithm
Expert Systems with Applications, 2010Co-Authors: Mehdi Yazdani, Maghsoud Amiri, Mostafa ZandiehAbstract:Flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem. FJSP is NP-hard and mainly presents two difficulties. The first one is to assign each operation to a machine out of a set of capable machines, and the second one deals with sequencing the assigned operations on the machines. This paper proposes a parallel Variable Neighborhood Search (PVNS) algorithm that solves the FJSP to minimize makespan time. Parallelization in this algorithm is based on the application of multiple independent Searches increasing the exploration in the Search space. The proposed PVNS uses various Neighborhood structures which carry the responsibility of making changes in assignment and sequencing of operations for generating neighboring solutions. The results obtained from the computational study have shown that the proposed algorithm is a viable and effective approach for the FJSP.
