The Experts below are selected from a list of 1074 Experts worldwide ranked by ideXlab platform

Peter Merz - One of the best experts on this subject based on the ideXlab platform.

  • improved Construction Heuristics and iterated local search for the routing and wavelength assignment problem
    European conference on Evolutionary Computation in Combinatorial Optimization, 2008
    Co-Authors: Kerstin Bauer, Thomas Fischer, Sven O Krumke, Katharina Gerhardt, Stephan Westphal, Peter Merz
    Abstract:

    This paper deals with the design of improved Construction Heuristics and iterated local search for the Routing and Wavelength Assignment problem (RWA). Given a physical network and a set of communication requests, the static RWA deals with the problem of assigning suitable paths and wavelengths to the requests. We introduce benchmark instances from the SND library to the RWA and argue that these instances are more challenging than previously used random instances. We analyze the properties of several instances in detail and propose an improved Construction Heuristic to handle 'problematic' instances. Our iterated local search finds the optimum for most instances.

  • EvoCOP - Improved Construction Heuristics and iterated local search for the routing and wavelength assignment problem
    Evolutionary Computation in Combinatorial Optimization, 2008
    Co-Authors: Kerstin Bauer, Thomas Fischer, Sven O Krumke, Katharina Gerhardt, Stephan Westphal, Peter Merz
    Abstract:

    This paper deals with the design of improved Construction Heuristics and iterated local search for the Routing and Wavelength Assignment problem (RWA). Given a physical network and a set of communication requests, the static RWA deals with the problem of assigning suitable paths and wavelengths to the requests. We introduce benchmark instances from the SND library to the RWA and argue that these instances are more challenging than previously used random instances. We analyze the properties of several instances in detail and propose an improved Construction Heuristic to handle 'problematic' instances. Our iterated local search finds the optimum for most instances.

  • SAINT - A Multilevel Approach for the Routing and Wavelength Assignment Problem
    2008 International Symposium on Applications and the Internet, 2008
    Co-Authors: Thomas Fischer, Kerstin Bauer, Peter Merz
    Abstract:

    In this paper we present a multilevel approach for the static routing and wavelength assignment (RWA) problem. The RWA deals with the problem of assigning paths and wavelengths to requests in optical communication networks. The multilevel approach is a general solution strategy involving stepwise coarsening the original problem instance, solving a simplified instance and expanding the solution back to the original size. We propose both a multilevel-inspired Construction Heuristic and a multilevel algorithm using iterated local search for refinement. These algorithms significantly improve previous approaches regarding time consumption and solution quality for large instances.

  • new genetic local search operators for the traveling salesman problem
    Parallel Problem Solving from Nature, 1996
    Co-Authors: Bernd Freisleben, Peter Merz
    Abstract:

    In this paper, an approach is presented to incorporate problem specific knowledge into a genetic algorithm which is used to compute near-optimum solutions to traveling salesman problems (TSP). The approach is based on using a tour Construction Heuristic for generating the initial population, a tour improvement Heuristic for finding local optima in a given TSP search space, and new genetic operators for effectively searching the space of local optima in order to find the global optimum. The quality and efficiency of solutions obtained for a set of TSP instances containing between 318 and 1400 cities are presented.

  • PPSN - New Genetic Local Search Operators for the Traveling Salesman Problem
    Parallel Problem Solving from Nature — PPSN IV, 1996
    Co-Authors: Bernd Freisleben, Peter Merz
    Abstract:

    In this paper, an approach is presented to incorporate problem specific knowledge into a genetic algorithm which is used to compute near-optimum solutions to traveling salesman problems (TSP). The approach is based on using a tour Construction Heuristic for generating the initial population, a tour improvement Heuristic for finding local optima in a given TSP search space, and new genetic operators for effectively searching the space of local optima in order to find the global optimum. The quality and efficiency of solutions obtained for a set of TSP instances containing between 318 and 1400 cities are presented.

Günther R. Raidl - One of the best experts on this subject based on the ideXlab platform.

  • Districting and Routing for Security Control
    Hybrid Metaheuristics, 2016
    Co-Authors: Michael Prischink, Christian Kloimüllner, Benjamin Biesinger, Günther R. Raidl
    Abstract:

    Regular security controls on a day by day basis are an essential and important mechanism to prevent theft and vandalism in business buildings. Typically, security workers patrol through a set of objects where each object requires a particular number of visits on all or some days within a given planning horizon, and each of these visits has to be performed in a specific time window. An important goal of the security company is to partition all objects into a minimum number of disjoint clusters such that for each cluster and each day of the planning horizon a feasible route for performing all the requested visits exists. Each route is limited by a maximum working time, must satisfy the visits’ time window constraints, and any two visits of one object must be separated by a minimum time difference. We call this problem the Districting and Routing Problem for Security Control. In our Heuristic approach we split the problem into a districting part where objects have to be assigned to districts and a routing part where feasible routes for each combination of district and period have to be found. These parts cannot be solved independently though. We propose an exact mixed integer linear programming model and a routing Construction Heuristic in a greedy like fashion with variable neighborhood descent for the routing part as well as a districting Construction Heuristic and an iterative destroy & recreate algorithm for the districting part. Computational results show that the exact algorithm is only able to solve small routing instances and the iterative destroy & recreate algorithm is able to reduce the number of districts significantly from the starting solutions.

  • EUROCAST - A Scalable Approach for the K -Staged Two-Dimensional Cutting Stock Problem with Variable Sheet Size
    Computer Aided Systems Theory – EUROCAST 2015, 2015
    Co-Authors: Frederico Dusberger, Günther R. Raidl
    Abstract:

    We present a new scalable approach for the K-staged two-dimensional cutting stock problem with variable sheet size, particularly aiming to solve large-scale instances from industry. A Construction Heuristic exploiting the congruency of subpatterns efficiently computes sheet patterns of high quality. This Heuristic is embedded in a beam-search framework to allow for a meaningful selection from the available sheet types. Computational experiments on benchmark instances show the effectiveness of our approach and demonstrate its scalability.

  • EUROCAST (1) - A PILOT/VND/GRASP Hybrid for the Static Balancing of Public Bicycle Sharing Systems
    Computer Aided Systems Theory - EUROCAST 2013, 2013
    Co-Authors: Petrina Papazek, Günther R. Raidl, Marian Rainer-harbach
    Abstract:

    Due to varying user demands in bicycle sharing systems, operators need to actively shift bikes between stations by a fleet of vehicles. We address the problem of finding efficient vehicle tours by an extended version of an iterated greedy Construction Heuristic following the concept of the PILOT method and GRASP and applying a variable neighborhood descend (VND) as local improvement. Computational results on benchmark instances derived from the real-world scenario in Vienna with up to 700 stations indicate that our PILOT/GRASP hybrid especially scales significantly better to very large instances than a previously proposed variable neighborhood search (VNS) approach. Applying only one iteration, the PILOT Construction Heuristic followed by the VND provides good solutions very quickly, which can be potentially useful for urgent requests.

  • EUROCAST (1) - A multilevel Heuristic for the rooted delay-constrained minimum spanning tree problem
    Computer Aided Systems Theory – EUROCAST 2011, 2012
    Co-Authors: Martin Berlakovich, Mario Ruthmair, Günther R. Raidl
    Abstract:

    The rooted delay-constrained minimum spanning tree problem is an NP-hard combinatorial optimization problem. The problem appears in practice for example when designing a distribution network with a guarantee of timely delivery. Another example is be a centralized broadcasting network where the delaybound represents a quality of service constraint. We introduce a multilevel-based Construction Heuristic which uses a new measurement for the suitability of edges to create a solution for the problem. In comparison to existing Heuristics the main intention is not to create a minimum cost spanning tree, but a solution with a high potential for further improvement. Experimental results indicate that in most cases our approach produces solutions that after local improvement are of higher quality than those of other existing Construction techniques.

  • variable neighborhood search and ant colony optimization for the rooted delay constrained minimum spanning tree problem
    Parallel Problem Solving from Nature, 2010
    Co-Authors: Mario Ruthmair, Günther R. Raidl
    Abstract:

    The rooted delay-constrained minimum spanning tree problem is an NP-hard combinatorial optimization problem arising for example in the design of centralized broadcasting networks where quality of service constraints are of concern. We present two new approaches to solve this problem Heuristically following the concepts of ant colony optimization (ACO) and variable neighborhood search (VNS). The ACO uses a fast Construction Heuristic based on node delays and local improvement exploiting two different neighborhood structures. The VNS employs the same neighborhood structures but additionally applies various kinds of shaking moves. Experimental results indicate that both metaHeuristics outperform existing approaches whereas the ACO produces mostly the best solutions.

François Guertin - One of the best experts on this subject based on the ideXlab platform.

  • Coupling a Greedy Route Construction Heuristic with a Genetic Algorithm for the Vehicle Routing Problem with Time Windows
    Operations Research Computer Science Interfaces Series, 1997
    Co-Authors: Jean-yves Potvin, François Guertin
    Abstract:

    Vehicle routing algorithms can be divided into three broad classes: route Construction Heuristics that “build” routes through the insertion of new customers, route improvement Heuristics that modify the location of customers within the existing routes through exchange procedures, and composite Heuristics that mix route Construction and route improvement procedures. In this paper, a greedy route Construction Heuristic for the vehicle routing problem with time windows is described. This Heuristic inserts customers one by one into the routes using a fixed a priori ordering of the customers. Then, a genetic algorithm is proposed to identify the ordering that produces the best routes.

  • A Genetic Algorithm for Vehicle Routing with Backhauling
    Applied Intelligence, 1996
    Co-Authors: Jean-yves Potvin, Christophe Duhamel, François Guertin
    Abstract:

    In this paper, a greedy route Construction Heuristic for a vehicle routing problem with backhauling is described. This Heuristic inserts customers one by one into the routes using a fixed a priori ordering of customers. Then, a genetic algorithm is used to identify an ordering that produces good routes. Numerical comparisons are provided with an exact algorithm and with other Heuristic approaches.

Paul Mcmullan - One of the best experts on this subject based on the ideXlab platform.

  • a new neural network based Construction Heuristic for the examination timetabling problem
    Parallel Problem Solving from Nature, 2006
    Co-Authors: Patrick H Corr, Barry Mccollum, M A J Mcgreevy, Paul Mcmullan
    Abstract:

    This paper examines the application of neural networks as a Construction Heuristic for the examination timetabling problem. Building on the Heuristic ordering technique, where events are ordered by decreasing scheduling difficulty, the neural network allows a novel dynamic, multi-criteria approach to be developed. The difficulty of each event to be scheduled is assessed on several characteristics, removing the dependence of an ordering based on a single Heuristic. Furthermore, this technique allows the ordering to be reviewed and modified as each event is scheduled; a necessary step since the timetable and constraints are altered as events are placed. Our approach uses a Kohonen self organising neural network and is shown to have wide applicability. Results are presented for a range of examination timetabling problems using standard benchmark datasets.

  • PPSN - A new neural network based Construction Heuristic for the examination timetabling problem
    Parallel Problem Solving from Nature - PPSN IX, 2006
    Co-Authors: Patrick H Corr, Barry Mccollum, M A J Mcgreevy, Paul Mcmullan
    Abstract:

    This paper examines the application of neural networks as a Construction Heuristic for the examination timetabling problem. Building on the Heuristic ordering technique, where events are ordered by decreasing scheduling difficulty, the neural network allows a novel dynamic, multi-criteria approach to be developed. The difficulty of each event to be scheduled is assessed on several characteristics, removing the dependence of an ordering based on a single Heuristic. Furthermore, this technique allows the ordering to be reviewed and modified as each event is scheduled; a necessary step since the timetable and constraints are altered as events are placed. Our approach uses a Kohonen self organising neural network and is shown to have wide applicability. Results are presented for a range of examination timetabling problems using standard benchmark datasets.

Gao Cheng-xiu - One of the best experts on this subject based on the ideXlab platform.