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Jia Shu - One of the best experts on this subject based on the ideXlab platform.

  • stochastic transportation inventory Network Design Problem
    Operations Research, 2005
    Co-Authors: Jia Shu, Chung-piaw Teo, Zuojun Max Shen
    Abstract:

    We study the stochastic transportation-inventory Network Design Problem involving one supplier and multiple retailers. Each retailer faces some uncertain demand, and safety stock must be maintained to achieve suitable service levels. However, risk-pooling benefits may be achieved by allowing some retailers to serve as distribution centers for other retailers. The Problem is to determine which retailers should serve as distribution centers and how to allocate the other retailers to the distribution centers. Shen et al. (2003) formulated this Problem as a set-covering integer-programming model. The pricing Problem that arises from the column generation algorithm gives rise to a new class of the submodular function minimization Problem. In this paper, we show that by exploiting certain special structures, we can solve the general pricing Problem in Shen et al. efficiently. Our approach utilizes the fact that the set of all lines in a two-dimension plane has low VC-dimension. We present computational results on several instances of sizes ranging from 40 to 500 retailers. Our solution technique can be applied to a wide range of other concave cost-minimization Problems.

  • Warehouse-Retailer Network Design Problem
    Operations Research, 2004
    Co-Authors: Chung-piaw Teo, Jia Shu
    Abstract:

    In this paper, we study the distribution Network Design Problem integrating transportation and infinite horizon multiechelon inventory cost function. We consider the trade-off between inventory cost, direct shipment cost, and facility location cost in such a system. The Problem is to determine how many warehouses to set up, where to locate them, how to serve the retailers using these warehouses, and to determine the optimal inventory policies for the warehouses and retailers. The objective is to minimize the total multiechelon inventory, transportation, and facility location costs. To the best of our knowledge, none of the papers in the area of distribution Network Design has explicitly addressed the issues of the 2-echelon inventory cost function arising from coordination of replenishment activities between the warehouses and the retailers. We structure this Problem as a set-partitioning integer-programming model and solve it using column generation. The pricing subProblem that arises from the column generation algorithm gives rise to a new class of the submodular function minimization Problem. We show that this pricing subProblem can be solved in O(n log n) time, where n is the number of retailers. Computational results show that the moderate size distribution Network Design Problem can be solved efficiently via this approach.

Anthony Chen - One of the best experts on this subject based on the ideXlab platform.

  • transport Network Design Problem under uncertainty a review and new developments
    Transport Reviews, 2011
    Co-Authors: Anthony Chen, Piya Chootinan, Zhong Zhou, Chao Yang, Seungkyu Ryu, S. C. Wong
    Abstract:

    This paper aims to provide a state-of-the-art review of the transport Network Design Problem (NDP) under uncertainty and to present some new developments on a bi-objective-reliable NDP (BORNDP) model that explicitly optimizes the capacity reliability and travel time reliability under demand uncertainty. Both are useful performance measures that can describe the supply-side reliability and demand-side reliability of a road Network. A simulation-based multi-objective genetic algorithm solution procedure, which consists of a traffic assignment algorithm, a genetic algorithm, a Pareto filter, and a Monte-Carlo simulation, is developed to solve the proposed BORNDP model. A numerical example based on the capacity enhancement Problem is presented to demonstrate the tradeoff between capacity reliability and travel time reliability in the NDP.

  • stochastic multi objective models for Network Design Problem
    Expert Systems With Applications, 2010
    Co-Authors: Anthony Chen, Juyoung Kim, Seungjae Lee, Youngchan Kim
    Abstract:

    Transportation Network Design Problem (NDP) is inherently multi-objective in nature, because it involves a number of stakeholders with different needs. In addition, the decision-making process sometimes has to be made under uncertainty where certain inputs are not known exactly. In this paper, we develop three stochastic multi-objective models for Designing transportation Network under demand uncertainty. These three stochastic multi-objective NDP models are formulated as the expected value multi-objective programming (EVMOP) model, chance constrained multi-objective programming (CCMOP) model, and dependent chance multi-objective programming (DCMOP) model in a bi-level programming framework using different criteria to hedge against demand uncertainty. To solve these stochastic multi-objective NDP models, we develop a solution approach that explicitly optimizes all objectives under demand uncertainty by simultaneously generating a family of optimal solutions known as the Pareto optimal solution set. Numerical examples are also presented to illustrate the concept of the three stochastic multi-objective NDP models as well as the effectiveness of the solution approach.

  • Biobjective Reliable Network Design Problem
    2008
    Co-Authors: Anthony Chen, Piya Chootinan, Zhong Zhou, Sze Chun Wong
    Abstract:

    Capacity reliability is concerned with the probability that the Network capacity can accommodate a certain travel demand at a required level of service; while travel time reliability is concerned with the probability that a trip between a given origin-destination (O-D) pair can be made successfully within a specified interval of time for a given level of travel demand in the Network. Both are useful performance measures that can describe the supply-side reliability and demand-side reliability of a road Network. In this paper, we propose a bi-objective reliable Network Design Problem (BORNDP) model that explicitly considers these two interdependent reliabilities under demand uncertainty. A simulation-based multi-objective genetic algorithm (SMOGA) solution procedure, which consists of a traffic assignment algorithm, a genetic algorithm, a Pareto filter, and a Monte-Carlo simulation, is developed to solve the proposed BORNDP model. A numerical example based on the capacity enhancement Problem is also presented to demonstrate the tradeoff between capacity reliability and travel time reliability in the Network Design Problem.

  • a simulation based multi objective genetic algorithm smoga procedure for bot Network Design Problem
    Optimization and Engineering, 2006
    Co-Authors: Anthony Chen, Kitti Subprasom
    Abstract:

    Solving optimization Problems with multiple objectives under uncertainty is generally a very difficult task. Evolutionary algorithms, particularly genetic algorithms, have shown to be effective in solving this type of complex Problems. In this paper, we develop a simulation-based multi-objective genetic algorithm (SMOGA) procedure to solve the build-operate-transfer (BOT) Network Design Problem with multiple objectives under demand uncertainty. The SMOGA procedure integrates stochastic simulation, a traffic assignment algorithm, a distance-based method, and a genetic algorithm (GA) to solve a multi-objective BOT Network Design Problem formulated as a stochastic bi-level mathematical program. To demonstrate the feasibility of SMOGA procedure, we solve two mean-variance models for determining the optimal toll and capacity in a BOT roadway project subject to demand uncertainty. Using the inter-city expressway in the Pearl River Delta Region of South China as a case study, numerical results show that the SMOGA procedure is robust in generating ‘good’ non-dominated solutions with respect to a number of parameters used in the GA, and performs better than the weighted-sum method in terms of the quality of non-dominated solutions.

  • a reliability based Network Design Problem
    Journal of Advanced Transportation, 2005
    Co-Authors: Piya Chootinan, S. C. Wong, Anthony Chen
    Abstract:

    This paper presents a reliability-based Network Design Problem. A Network reliability concept is embedded into the continuous Network Design Problem in which travelers' route choice behavior follows the stochastic user equilibrium assumption. A new capacity-reliability index is introduced to measure the probability that all of the Network links are operated below their capacities when serving different traffic patterns deviating from the average condition. The reliability-based Network Design Problem is formulated as a bi-level program in which the lower level sub-program is the probit-based stochastic user equilibrium Problem and the upper level sub-program is the maximization of the new capacity reliability index. The lower level sub-program is solved by a variant of the method of successive averages using the exponential average to represent the learning process of Network users on a daily basis that results in the daily variation of traffic-flow pattern, and Monte Carlo stochastic loading. The upper level sub-program is tackled by means of genetic algorithms. A numerical example is used to demonstrate the concept of the proposed framework.

Chung-piaw Teo - One of the best experts on this subject based on the ideXlab platform.

  • stochastic transportation inventory Network Design Problem
    Operations Research, 2005
    Co-Authors: Jia Shu, Chung-piaw Teo, Zuojun Max Shen
    Abstract:

    We study the stochastic transportation-inventory Network Design Problem involving one supplier and multiple retailers. Each retailer faces some uncertain demand, and safety stock must be maintained to achieve suitable service levels. However, risk-pooling benefits may be achieved by allowing some retailers to serve as distribution centers for other retailers. The Problem is to determine which retailers should serve as distribution centers and how to allocate the other retailers to the distribution centers. Shen et al. (2003) formulated this Problem as a set-covering integer-programming model. The pricing Problem that arises from the column generation algorithm gives rise to a new class of the submodular function minimization Problem. In this paper, we show that by exploiting certain special structures, we can solve the general pricing Problem in Shen et al. efficiently. Our approach utilizes the fact that the set of all lines in a two-dimension plane has low VC-dimension. We present computational results on several instances of sizes ranging from 40 to 500 retailers. Our solution technique can be applied to a wide range of other concave cost-minimization Problems.

  • Warehouse-Retailer Network Design Problem
    Operations Research, 2004
    Co-Authors: Chung-piaw Teo, Jia Shu
    Abstract:

    In this paper, we study the distribution Network Design Problem integrating transportation and infinite horizon multiechelon inventory cost function. We consider the trade-off between inventory cost, direct shipment cost, and facility location cost in such a system. The Problem is to determine how many warehouses to set up, where to locate them, how to serve the retailers using these warehouses, and to determine the optimal inventory policies for the warehouses and retailers. The objective is to minimize the total multiechelon inventory, transportation, and facility location costs. To the best of our knowledge, none of the papers in the area of distribution Network Design has explicitly addressed the issues of the 2-echelon inventory cost function arising from coordination of replenishment activities between the warehouses and the retailers. We structure this Problem as a set-partitioning integer-programming model and solve it using column generation. The pricing subProblem that arises from the column generation algorithm gives rise to a new class of the submodular function minimization Problem. We show that this pricing subProblem can be solved in O(n log n) time, where n is the number of retailers. Computational results show that the moderate size distribution Network Design Problem can be solved efficiently via this approach.

Zuojun Max Shen - One of the best experts on this subject based on the ideXlab platform.

  • stochastic transportation inventory Network Design Problem
    Operations Research, 2005
    Co-Authors: Jia Shu, Chung-piaw Teo, Zuojun Max Shen
    Abstract:

    We study the stochastic transportation-inventory Network Design Problem involving one supplier and multiple retailers. Each retailer faces some uncertain demand, and safety stock must be maintained to achieve suitable service levels. However, risk-pooling benefits may be achieved by allowing some retailers to serve as distribution centers for other retailers. The Problem is to determine which retailers should serve as distribution centers and how to allocate the other retailers to the distribution centers. Shen et al. (2003) formulated this Problem as a set-covering integer-programming model. The pricing Problem that arises from the column generation algorithm gives rise to a new class of the submodular function minimization Problem. In this paper, we show that by exploiting certain special structures, we can solve the general pricing Problem in Shen et al. efficiently. Our approach utilizes the fact that the set of all lines in a two-dimension plane has low VC-dimension. We present computational results on several instances of sizes ranging from 40 to 500 retailers. Our solution technique can be applied to a wide range of other concave cost-minimization Problems.

Youngchan Kim - One of the best experts on this subject based on the ideXlab platform.

  • stochastic multi objective models for Network Design Problem
    Expert Systems With Applications, 2010
    Co-Authors: Anthony Chen, Juyoung Kim, Seungjae Lee, Youngchan Kim
    Abstract:

    Transportation Network Design Problem (NDP) is inherently multi-objective in nature, because it involves a number of stakeholders with different needs. In addition, the decision-making process sometimes has to be made under uncertainty where certain inputs are not known exactly. In this paper, we develop three stochastic multi-objective models for Designing transportation Network under demand uncertainty. These three stochastic multi-objective NDP models are formulated as the expected value multi-objective programming (EVMOP) model, chance constrained multi-objective programming (CCMOP) model, and dependent chance multi-objective programming (DCMOP) model in a bi-level programming framework using different criteria to hedge against demand uncertainty. To solve these stochastic multi-objective NDP models, we develop a solution approach that explicitly optimizes all objectives under demand uncertainty by simultaneously generating a family of optimal solutions known as the Pareto optimal solution set. Numerical examples are also presented to illustrate the concept of the three stochastic multi-objective NDP models as well as the effectiveness of the solution approach.