The Experts below are selected from a list of 39 Experts worldwide ranked by ideXlab platform

Hotman Simbolon - One of the best experts on this subject based on the ideXlab platform.

  • On Solving the Capacitated Open Vehicle Routing Problems with Uncertainty Demand
    2020
    Co-Authors: Hotman Simbolon, Herman Mawengkang
    Abstract:

    In open vehicle routing problems, the vehicles are not required to return to the depot after completing service. In this paper, we extend the problem to be more realistic by including the uncertainty of customer demands. Each customer has a demand and each customer must be serviced by a single vehicle and no vehicle may serve a set of customers whose total demand exceeds its capacity. Each vehicle route must start at the depot and end at the last customer it serves. The objective is to define the set of vehicle routes that minimizes the total costs. We solve the stochastic model using a strategy of releasing Nonbasic Variables from their bounds, combined with the "active constraint" method. This strategy is used to force the appropriate non-integer basic Variables to move to their neighborhood integer points. A study of criteria for choosing a Nonbasic Variable to work with in the integerizing strategy has also been made.

  • DEVELOPING A DIRECT SEARCH ALGORITHM FOR SOLVING THE CAPACITATED OPEN VEHICLE ROUTING PROBLEM
    2011
    Co-Authors: Hotman Simbolon
    Abstract:

    In open vehicle routing problems, the vehicles are not required to return to the depot after completing service. In this paper, we present the first exact optimization algorithm for the open version of the well‐known capacitated vehicle routing problem (CVRP). The strategy of releasing Nonbasic Variables from their bounds, combined with the “active constraint” method and the notion of superbasics, has been developed for efficiently requirements; this strategy is used to force the appropriate non‐integer basic Variables to move to their neighborhood integer points. A study of criteria for choosing a Nonbasic Variable to work with in the integerizing strategy has also been made.

Herman Mawengkang - One of the best experts on this subject based on the ideXlab platform.

  • An Improved Approach For Solving Mixed-Integer Nonlinear Programming Problems
    2020
    Co-Authors: Abil Mansyur, Herman Mawengkang
    Abstract:

    This special class of a nonlinear mathematical programming problem which is addressed in this paper has a structure characterized by a subset of Variables restricted to assume discrete values, which are linear and separable from the continuous Variables. The strategy of releasing Nonbasic Variables from their bounds, combined with the "active constraint" method and the notion of superbasics, has been developed for efficiently tackling the problem. After solving the problem by ignoring the integrality requirements, this strategy is used to force the appropriate non-integer basic Variables to move to their neighborhood integer points. A study of criteria for choosing a Nonbasic Variable to work with in the integerizing strategy has also been made. Successful implementation of these algorithms was achieved on various test problems. The results show that the proposed integerizing strategy is promising in tacking certain classes of mixed integer nonlinear programming problems.

  • On Solving the Capacitated Open Vehicle Routing Problems with Uncertainty Demand
    2020
    Co-Authors: Hotman Simbolon, Herman Mawengkang
    Abstract:

    In open vehicle routing problems, the vehicles are not required to return to the depot after completing service. In this paper, we extend the problem to be more realistic by including the uncertainty of customer demands. Each customer has a demand and each customer must be serviced by a single vehicle and no vehicle may serve a set of customers whose total demand exceeds its capacity. Each vehicle route must start at the depot and end at the last customer it serves. The objective is to define the set of vehicle routes that minimizes the total costs. We solve the stochastic model using a strategy of releasing Nonbasic Variables from their bounds, combined with the "active constraint" method. This strategy is used to force the appropriate non-integer basic Variables to move to their neighborhood integer points. A study of criteria for choosing a Nonbasic Variable to work with in the integerizing strategy has also been made.

Pengarapen Bangun - One of the best experts on this subject based on the ideXlab platform.

  • AN IMPROVED SEARCH ALGORITHM FOR SOLVING MIXED-INTEGER NON LINEAR PROGRAMMING PROBLEM
    2015
    Co-Authors: Pengarapen Bangun
    Abstract:

    The special nonlinear mathematical programming problem which is addressed in this paper has a structure characterized by a subset of Variables restricted to assume discrete values, which are linear and seperable from the continuous Variables. The strategy of releasing Nonbasic Variables from their bounds, combined with the "active conshaint" method and the notion of superbasics, has been developed for efficiently tackling such a problem by ignoring the integrality requirements, this strategy is used to force the appropriate non-integer basic Variables to move to their neighbourhood integer points. A study of criteria for choosing a Nonbasic Variable to work with in the integerizing shategy has also been made. Successful implementation of these algorithms was achieved on various test problems. The result show that the proposed integerizing strategy is promosing in tackling certain classes of mixed integer programming problems.

Krung Sinapiromsaran - One of the best experts on this subject based on the ideXlab platform.

  • Max-out-in pivot rule with cycling prevention for the simplex method
    Scienceasia, 2020
    Co-Authors: Monsicha Tipawanna, Krung Sinapiromsaran
    Abstract:

    A max-out-in pivot rule is designed to solve a linear programming (LP) problem with a non-zero right- hand side vector. It identifies the maximum of the leaving basic Variable before selecting the associated entering Nonbasic Variable. Our method guarantees convergence after a finite number of iterations. The improvement of our pivot rule over Bland's rule is illustrated by some cycling LP examples. In addition, we report computational results obtained from two sets of LP problems. Among 100 simulated LP problems, the max-out-in pivot rule is significantly better than Bland's rule and Dantzig's rule according to the Wilcoxon signed rank test. Based on these results, we conclude that our method is best suited for degenerate LP problems.

Chengjun Li - One of the best experts on this subject based on the ideXlab platform.