STOCHASTIC SCENARIO-BASED TIME-STAGE OPTIMIZATION MODEL FOR THE LEAST EXPECTED TIME SHORTEST PATH PROBLEM

2013 ◽  
Vol 21 (supp01) ◽  
pp. 17-33 ◽  
Author(s):  
LIXING YANG ◽  
XIAOFEI YANG ◽  
CUILIAN YOU
Keyword(s):  
Shortest Path ◽  
Optimization Model ◽  
Optimal Solution ◽  
Shortest Path Problem ◽  
Travel Times ◽  
Expected Time ◽  
Second Stage ◽  
Time Threshold ◽  
Proposed Model ◽  
Time Invariant

Focusing on finding a pre-specified basis path in a network, this research formulates a two-stage stochastic optimization model for the least expected time shortest path problem, in which random scenario-based time-invariant link travel times are utilized to capture the uncertainty of the realworld traffic network. In this model, the first stage aims to find a basis path for the trip over all the scenarios, and the second stage intends to generate the remainder path adaptively when the realizations of random link travel times are updated after a pre-specified time threshold. The GAMS optimization software is introduced to find the optimal solution of the proposed model. The numerical experiments demonstrate the performance of the proposed approaches.

Adaptive Reliable Shortest Path in Discrete Stochastic Networks

2014 ◽  
Vol 587-589 ◽  
pp. 1854-1857
Author(s):  
Yi Yong Pan
Keyword(s):  
Shortest Path ◽  
Stochastic Networks ◽  
Shortest Path Problem ◽  
Successive Approximations ◽  
Stochastic Network ◽  
Route Guidance ◽  
Travel Times ◽  
Routing Policy ◽  
The Face ◽  
Stochastic Travel Times

This paper addresses adaptive reliable shortest path problem which aims to find adaptive en-route guidance to maximize the reliability of arriving on time in stochastic networks. Such routing policy helps travelers better plan their trips to prepare for the risk of running late in the face of stochastic travel times. In order to reflect the stochastic characteristic of travel times, a traffic network is modeled as a discrete stochastic network. Adaptive reliable shortest path problem is uniformly defined in a stochastic network. Bellman’s Principle that is the core of dynamic programming is showed to be valid if the adaptive reliable shortest path is defined by optimal-reliable routing policy. A successive approximations algorithm is developed to solve adaptive reliable shortest path problem. Numerical results show that the proposed algorithm is valid using typical transportation networks.

scholarly journals Dynamic Scheduling Model of Bike-Sharing considering Invalid Demand

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Liu He ◽  
Tangyi Guo ◽  
Kun Tang
Keyword(s):  
Optimization Model ◽  
Dynamic Scheduling ◽  
Demand Forecasting ◽  
Optimal Solution ◽  
Coupling Model ◽  
Service Level ◽  
Demand Prediction ◽  
Dynamic Relationship ◽  
Proposed Model ◽  
Bike Sharing

System resources allocation optimization through dynamic scheduling is key to improving the service level of bike-sharing. This study innovatively introduces three types of invalid demand with negative effect including waiting, transfer, and abandoning, which consists of the total demand of bike-sharing system. Through exploring the dynamic relationship among users’ travel demands, the quantity and capacity of bikes at the rental points, the records of bicycles borrowed and returned, and the vehicle scheduling schemes, a demand forecasting model for bike-sharing is established. According to the predicted bikes and the maximum capacity limit at each rental point, an optimization model of scheduling scheme is proposed to reduce the invalid demand and the total scheduling time. A two-layer dynamic coupling model with iterative feedback is obtained by combining the demand prediction model and scheduling optimization model and is then solved by Nicked Pareto Genetic Algorithm (NPGA). The proposed model is applied to a case study and the optimal solution set and corresponding Pareto front are obtained. The invalid demand is greatly reduced from 1094 to 26 by an effective scheduling of 3 rounds and 96 minutes. Empirical results show that the proposed model is able to optimize the resource allocation of bike-sharing, significantly reduce the invalid demand caused by the absence of bikes at the rental point such as waiting in a place, walking to other rental points, and giving up for other travel modes, and effectively improve the system service level.

A Bi-Objective Order Allocation Optimization Model for Horizontal Manufacturing Collaborative Alliance

2010 ◽  
Vol 102-104 ◽  
pp. 836-840 ◽  
Author(s):  
Fang Qi Cheng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Optimal Solution ◽  
Order Allocation ◽  
Nsga Ii ◽  
Manufacturing Enterprises ◽  
Proposed Model ◽  
Optimal Solution Set ◽  
Simulation Results ◽  
Enterprise Community

Horizontal manufacturing collaborative alliance is a dispersed enterprise community consisting of several enterprises which produce the same kind of products. To correctly assign order among member companies of horizontal manufacturing collaborative alliance is one of the most important ways to improve the agility and competitiveness of manufacturing enterprises. For the order allocation problem, a bi-objective optimization model is developed to minimize the comprehensive cost and balance the production loads among the selected manufacturing enterprises. Non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the optimization functions. The optimal solution set of Pareto is obtained. The simulation results indicate that the proposed model and algorithm is able to obtain satisfactory solutions.

A One to Multiple Order Allocation Optimization Model of Horizontal Manufacturing Collaborative Alliance

2012 ◽  
Vol 201-202 ◽  
pp. 996-999
Author(s):  
Jin Gao
Keyword(s):  
Optimization Model ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Order Allocation ◽  
Nsga Ii ◽  
Manufacturing Enterprises ◽  
Proposed Model ◽  
Optimal Solution Set ◽  
Simulation Results ◽  
Enterprise Community

Horizontal manufacturing collaborative alliance is a dispersed enterprise community consisting of several enterprises which produce the same kind of products. To correctly assign order among member companies of horizontal manufacturing collaborative alliance is one of the most important ways to improve the agility and competitiveness of manufacturing enterprises. For the order allocation problem, a multi-objective optimization model is developed to minimize the comprehensive cost and balance the production loads among the selected manufacturing enterprises. Non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the optimization functions. The optimal solution set of Pareto is obtained. The simulation results indicate that the proposed model and algorithm is able to obtain satisfactory solutions.

scholarly journals Robust Optimization Model with Shared Uncertain Parameters in Multi-Stage Logistics Production and Inventory Process

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 211
Author(s):  
Lijun Xu ◽  
Yijia Zhou ◽  
Bo Yu
Keyword(s):  
Robust Optimization ◽  
Optimization Model ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Uncertain Parameters ◽  
Worst Case ◽  
Robust Model ◽  
Multi Stage ◽  
Proposed Model ◽  
Dual Forms

In this paper, we focus on a class of robust optimization problems whose objectives and constraints share the same uncertain parameters. The existing approaches separately address the worst cases of each objective and each constraint, and then reformulate the model by their respective dual forms in their worst cases. These approaches may result in that the value of uncertain parameters in the optimal solution may not be the same one as in the worst case of each constraint, since it is highly improbable to reach their worst cases simultaneously. In terms of being too conservative for this kind of robust model, we propose a new robust optimization model with shared uncertain parameters involving only the worst case of objectives. The proposed model is evaluated for the multi-stage logistics production and inventory process problem. The numerical experiment shows that the proposed robust optimization model can give a valid and reasonable decision in practice.

An Optimization Model for Dynamic Speed Control in Urban Freeway Networks

2014 ◽  
Vol 668-669 ◽  
pp. 1458-1461
Author(s):  
Zhao Hong Zhang ◽  
Da Zhi Sun ◽  
Jin Peng Lv ◽  
Joseph Sai ◽  
M. Faruqi
Keyword(s):  
Optimization Model ◽  
Control Strategies ◽  
Optimal Solution ◽  
Speed Control ◽  
Speed Limits ◽  
Microscopic Traffic Simulation ◽  
Proposed Model ◽  
Average Travel Time ◽  
Freeway Networks ◽  
Urban Freeway

This paper introduced an optimization model to address dynamic speed control strategies for achieving network-wide speed harmonization. Genetic Algorithm (GA) was applied to search the optimal solution of the proposed model. During the search process, a computational fluid dynamics (CFD) based analytical model and microscopic traffic simulation VISSIM were applied to evaluate the performance of possible solutions. The proposed model can be used to determine the deployment of dynamic speed limits, the displayed speed limit, and the timing to change these speed limits. The proposed model was tested using VISSIM in an urban freeway network of about 12 miles long. Different simulation scenarios with varying AADT from 60,000 to 12,000 were tested. It was found that when properly implemented, dynamic speed control can improve traffic flow conditions, reduce congestion and emission, and enhance network throughput. For example, in the selected urban freeway network with the AADT of 80,000, the proposed dynamic speed control strategy can save 5% average travel time, reduce 9% of the vehicles with high collision risk and about 11% emission.

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