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Restart strategies for GRASP with pathrelinking heuristics
 Optimization Letters
"... Abstract. GRASP with pathrelinking is a hybrid metaheuristic, or stochastic local search (Monte Carlo) method, for combinatorial optimization. A restart strategy in GRASP with pathrelinking heuristics is a set of iterations {i1, i2, . . .} on which the heuristic is restarted from scratch using a ..."
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Abstract. GRASP with pathrelinking is a hybrid metaheuristic, or stochastic local search (Monte Carlo) method, for combinatorial optimization. A restart strategy in GRASP with pathrelinking heuristics is a set of iterations {i1, i2, . . .} on which the heuristic is restarted from scratch using a new seed for the random number generator. Restart strategies have been shown to speed up stochastic local search algorithms. In this paper, we propose a new restart strategy for GRASP with pathrelinking heuristics. We illustrate the speedup obtained with our restart strategy on GRASP with pathrelinking heuristics for the maximum cut problem, the maximum weighted satisfiability problem, and the private virtual circuit routing problem.
Randomized heuristics for the family traveling salesperson problem
 International Transactions in Operational Research
, 2013
"... Abstract. This paper introduces the family traveling salesperson problem (FTSP), a variant of the generalized traveling salesman problem. In the FTSP, a subset of nodes must be visited for each node cluster in the graph. The objective is to minimize the distance traveled. We describe an integer prog ..."
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Abstract. This paper introduces the family traveling salesperson problem (FTSP), a variant of the generalized traveling salesman problem. In the FTSP, a subset of nodes must be visited for each node cluster in the graph. The objective is to minimize the distance traveled. We describe an integer programming formulation for the FTSP and show that the commercialgrade integer programming solver CPLEX 11 can only solve small instances of the problem in reasonable running time. We propose two randomized heuristics for finding optimal and nearoptimal solutions of this problem. These heuristics are a biased randomkey genetic algorithm and a GRASP with evolutionary pathrelinking. Computational results comparing both heuristics are presented. 1.
MULTISTART METHODS FOR COMBINATORIAL OPTIMIZATION
"... Abstract. Multistart methods strategically sample the solution space of an optimization problem. The most successful of these methods have two phases that are alternated for a certain number of global iterations. The first phase generates a solution and the second seeks to improve the outcome. Each ..."
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Abstract. Multistart methods strategically sample the solution space of an optimization problem. The most successful of these methods have two phases that are alternated for a certain number of global iterations. The first phase generates a solution and the second seeks to improve the outcome. Each global iteration produces a solution that is typically a local optimum, and the best overall solution is the output of the algorithm. The interaction between the two phases creates a balance between search diversification (structural variation) and search intensification (improvement), to yield an effective means for generating highquality solutions. This survey briefly sketches historical developments that have motivated the field, and then focuses on modern contributions that define the current stateoftheart. We consider two categories of multistart methods: memorybased and memoryless procedures. The former are based on identifying and recording specific types of information (attributes) to exploit in future constructions. The latter are based on order statistics of sampling and generate unconnected solutions. An interplay between the features of these two categories provides an inviting area for future exploration. 1.
Pareto Local Search with VNS (2PPLSVNS), Multiobjective Variable Neighborhood
"... approaches for the openpit ..."
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Hybridizing VNS and pathrelinking on a particle swarm framework to minimize total flowtime
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RANDOMIZED HEURISTICS FOR HANDOVER MINIMIZATION IN MOBILITY NETWORKS
"... Abstract. A mobile device connects to the cell tower (base station) from which it receives the strongest signal. As the device moves it may connect to a series of towers. The process in which the device changes the base station it is connected to is called handover. A cell tower is connected to a ra ..."
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Abstract. A mobile device connects to the cell tower (base station) from which it receives the strongest signal. As the device moves it may connect to a series of towers. The process in which the device changes the base station it is connected to is called handover. A cell tower is connected to a radio network controller (RNC) which controls many of its operations, including handover. Each cell tower handles an amount of traffic and each radio network controller has capacity to handle a maximum amount of traffic from all base stations connected to it. Handovers between base stations connected to different RNCs tend to fail more often than handovers between base stations connected to the same RNC. Handover failures result in dropped connections and therefore should be minimized. The Handover Minimization Problem is to assign towers to RNCs such that RNC capacity is not violated and the number of handovers between base stations connected to different RNCs is minimized. We describe an integer programming formulation for the handover minimization problem and show that stateoftheart integer programming solvers can solve only very small instances of the problem. We propose several randomized heuristics for finding approximate solutions of this problem, including a GRASP with pathrelinking for the generalized quadratic assignment problem, a GRASP with evolutionary pathrelinking, and a biased randomkey genetic algorithm. Computational results are presented. 1.
A SURVEY OF MULTISTART METHODS FOR COMBINATORIAL OPTIMIZATION
"... Abstract. Multistart methods strategically sample the solution space of an optimization problem. The most successful of these methods have two phases that are alternated for a certain number of global iterations. The first phase generates a solution and the second seeks to improve the outcome. Each ..."
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Abstract. Multistart methods strategically sample the solution space of an optimization problem. The most successful of these methods have two phases that are alternated for a certain number of global iterations. The first phase generates a solution and the second seeks to improve the outcome. Each global iteration produces a solution that is typically a local optimum, and the best overall solution is the output of the algorithm. The interaction between the two phases creates a balance between search diversification (structural variation) and search intensification (improvement), to yield an effective means for generating highquality solutions. This survey briefly sketches historical developments that have motivated the field, and then focuses on modern contributions that define the current stateoftheart. We consider two categories of multistart methods: memorybased and memoryless procedures. The former are based on identifying and recording specific types of information (attributes) to exploit in future constructions. The latter are based on order statistics of sampling and generate unconnected solutions. An interplay between the features of these two categories provides an inviting area for future exploration. 1.
GRASP WITH PATHRELINKING FOR FACILITY LAYOUT
"... Abstract. This paper proposes a mathematical formulation for the facility layout problem (FLP) based on the generalized quadratic assignment problem (GQAP). The GQAP is a generalization of the NPhard quadratic assignment problem (QAP) that allows multiple facilities to be assigned to a single locat ..."
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Abstract. This paper proposes a mathematical formulation for the facility layout problem (FLP) based on the generalized quadratic assignment problem (GQAP). The GQAP is a generalization of the NPhard quadratic assignment problem (QAP) that allows multiple facilities to be assigned to a single location as long as the capacity of the location allows. As a case study, we adapt the GRASP with pathrelinking (GRASPPR) heuristic introduced in Mateus et al. (2011) for the hospital layout problem (HLP). In the HLP we are given a set of areas in a hospital where medical facilities, such as surgery and recovery rooms, can be located and a set of medical facilities, each facility with a required area, that must be located in the hospital. Furthermore, we are given a matrix specifying, for each ordered pair of facilities, the number of patients that transition from the first to the second facility. The objective of the assignment is to minimize the total distance traveled by the patients. We illustrate our algorithm with a numerical example. 1.
AN INTEGRATED SIMULATION MODEL AND EVOLUTIONARY ALGORITHM FOR TRAIN TIMETABLING PROBLEM WITH CONSIDERING TRAIN STOPS FOR PRAYING
"... This paper presents a simulationbased optimization approach for railway timetabling, which is made interesting by the need for trains to stop periodically to allow passengers to pray. The developed framework is based on integration of a simulation model and an evolutionary path relinking algorithm ..."
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This paper presents a simulationbased optimization approach for railway timetabling, which is made interesting by the need for trains to stop periodically to allow passengers to pray. The developed framework is based on integration of a simulation model and an evolutionary path relinking algorithm with the capability of scheduling trains, subject to the capacity constraints in order to minimize the total waiting times. A customized deadlock avoidance method has been developed which is based on a conditional capacity allocation. The proposed lookahead deadlock avoidance approach is effective and easy to implement in the simulation model. A case study of the Iranian Railway (RAI) is selected for examining the efficiency of the metaheuristic algorithm. The result shows that proposed algorithm has the capability of generating good quality solution in realworld problems. 1
An ILSbased Algorithm to Solve a Largescale Real Heterogeneous Fleet VRP with Multitrips and Docking Constraints
"... Distribution planning is crucial for most companies since goods are rarely produced and consumed at the same place. Distribution costs, in addition, can be an important component of the final cost of the goods. In this paper, we study a VRP variant inspired on a real case of a large distribution co ..."
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Distribution planning is crucial for most companies since goods are rarely produced and consumed at the same place. Distribution costs, in addition, can be an important component of the final cost of the goods. In this paper, we study a VRP variant inspired on a real case of a large distribution company. In particular, we consider a VRP with a heterogeneous fleet of vehicles that are allowed to perform multiple trips. The problem also includes docking constraints in which some vehicles are unable to serve some particular customers. Given the combinatorial nature and the size of the problem, which discard the use of efficient exact methods for its resolution, a novel heuristic algorithm is proposed. The proposed algorithm, called GILSVND, combines Iterated Local Search (ILS), Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Descent (VND) procedures. Our method obtains better solutions than other approaches found in the related literature, and improves the solutions used by the company leading to