Simulation-Based Optimization: A Case Study for Airline's Cargo Service Call Center

2008 ◽  
pp. 153-172
Author(s):  
Tiravat Assavapokee ◽  
Ibrahim Mourtada
Keyword(s):  
Call Center ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
Service Call

scholarly journals Incentives for Ridesharing: A Case Study of Welfare and Traffic Congestion

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Changle Song ◽  
Julien Monteil ◽  
Jean-Luc Ygnace ◽  
David Rey
Keyword(s):  
Social Welfare ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Transmission Model ◽  
Fixed Point Algorithm ◽  
Optimization Framework ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
The Impact

Traffic congestion is largely due to the high proportion of solo drivers during peak hours. Ridesharing, in the sense of carpooling, has emerged as a travel mode with the potential to reduce congestion by increasing the average vehicle occupancy rates and reduce the number of vehicles during commuting periods. In this study, we propose a simulation-based optimization framework to explore the potential of subsidizing ridesharing users, drivers, and riders, so as to improve social welfare and reduce congestion. We focus our attention on a realistic case study representative of the morning commute on Sydney’s M4 Motorway in Australia. We synthesize a network model and travel demand data from open data sources and use a multinomial logistic model to capture users’ preferences across different travel roles, including solo drivers, ridesharing drivers, ridesharing passengers, and a reserve option that does not contribute to congestion on the freeway network. We use a link transmission model to simulate traffic congestion on the freeway network and embed a fixed-point algorithm to equilibrate users’ mode choice in the long run within the proposed simulation-based optimization framework. Our numerical results reveal that ridesharing incentives have the potential to improve social welfare and reduce congestion. However, we find that providing too many subsidies to ridesharing users may increase congestion levels and thus be counterproductive from a system performance standpoint. We also investigate the impact of transaction fees to a third-party ridesharing platform on social welfare and traffic congestion. We observe that increasing the transaction fee for ridesharing passengers may help in mitigating congestion effects while improving social welfare in the system.

scholarly journals Efficient Simulation-Based Toll Optimization for Large-Scale Networks

2021 ◽  
Author(s):  
Carolina Osorio ◽  
Bilge Atasoy
Keyword(s):  
Analytical Model ◽  
Nonlinear Equations ◽  
Network Model ◽  
Large Scale ◽  
Structural Information ◽  
Optimization Technique ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
Large Scale Networks

This paper proposes a simulation-based optimization technique for high-dimensional toll optimization problems of large-scale road networks. We formulate a novel analytical network model. The latter is embedded within a metamodel simulation-based optimization (SO) algorithm. It provides analytical and differentiable structural information of the underlying problem to the SO algorithm. Hence, the algorithm no longer treats the simulator as a black box. The analytical model is formulated as a system of nonlinear equations that can be efficiently evaluated with standard solvers. The dimension of the system of equations scales linearly with network size. It scales independently of the dimension of the route choice set and of link attributes such as link length. Hence, it is a scalable formulation suitable for the optimization of large-scale networks. For instance, the model is used in the case study of the paper for toll optimization of a Singapore network with more than 4,050 OD (origin-destination) pairs and 18,200 feasible routes. The corresponding analytical model is implemented as a system of 860 nonlinear equations. The analytical network model is validated based on one-dimensional toy network problems. It captures the main trends of the simulation-based objective function and, more importantly, accurately locates the global optimum for all experiments. The proposed SO approach is then used to optimize a set of 16 tolls for the network of expressways and major arterials of Singapore. The proposed method is compared with a general-purpose algorithm. The proposed method identifies good quality solutions at the very first iteration. The benchmark method identifies solutions with similar performance after 2 days of computation or similarly after more than 30 points have been simulated. The case study indicates that the analytical structural information provided to the algorithm by the analytical network model enables it to (i) identify good quality solutions fast and (ii) become robust to both the quality of the initial points and to the stochasticity of the simulator. The final solutions identified by the proposed algorithm outperform those of the benchmark method by an average of 18%.

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