• 아태비즈니스연구
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• 제1권2호
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• pp.23-46
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• 2010

Despite the explosive growth of no-frill airline industry, very little is known about how consumers make purchase decision in such settings. Today's airline industry requires choice models consistent with consumers' true preference sets. This study used conjoint analysis to identify these ideal choice models. 38 percent of the subjects were found to use compensatory and 62 percent non-compensatory models. Our findings suggest a need to base choice-making promotions on ideal choice models if the promotion is to lead consumers to decisions consistent with true preferences.

• 대한교통학회지
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• 제8권1호
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• pp.55-71
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• 1990

The behavioral mechanism underlying the traffic assignment model is a choice, or decision-making process of traveling paths between origins and destinations. The deterministic approach to traffic assignment assumes that travelers choose shortest path from their origin-destination pair. Although this assumption seems reasonable, it presumes that all travelers have perfect information regarding travel time, that they make consistently correct decision, and that they all behave in identical fashion. Stochastic user equilibrium assignment relaxes these presumptions by including a random component in traveler's perception of travel time. The objective of this study is to compare "A Model of Deterministic User Equilibrium Assignment" with "Models of Stochastic User Equilibrium Assignment" in the theoretical and practical aspects. Specifically, SUE models are developed to logit and probit based models according to discrete choice functions. The models were applied to sioux Falls net ork consisting of 24 zones, 24 nodes and 76 links. The distribution of perceived travel time was obtained by using the relationship between speed and traffic flow.

• 대한교통학회지
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• 제11권1호
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• pp.67-86
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• 1993

A formulation of dynamic traffic assignment between multiple origins and single destination was first introduced in 1987 by Merchant and Nemhauser, and then expanded for multiple destination in the late 1980's (Carey, 1987). Based on behavioral choice theory which provides proper demand elasticities with respect to changes in policy variables, traffic phenomena can be analysed more realistically, especially in peak periods. However, algorithms for these models are not well developed so far(working with only small toy network) and solutions of these models are not unique. In this paper, a new model is developed which keeps the simplicity of static models, but provides the sensitivity of dynamic models with changes of O-D flows over time. It can be viewed as a joint departure time and route choice model, in the given time periods(6-7, 7-8, 8-9 and 9-10 am). Standard multinomial logit model has been used for simulating the choice behavior of destination, mode, route and departure time within a framework of the incremental network assignment model. The model developed is workable in a PC 386 with 175 traffic zones and 3581 links of Seoul and tested for evaluating the exclusive use of Namsan tunnel for HOV and the left-turn prohibition. Model's performance results and their statistical significance are also presented.

• 대한교통학회지
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• 제13권4호
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• pp.79-103
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• 1995

The elasticity of a model is considered most important. Without showing the proper elasticity any model cannot provide useful information for decision making. This paper demonstrates a macro model which can generate dynamic transport informations every 15 minutes. Without the Wardrop principles and the monotonicity assumptions for the link travel time and link volume relationship, the basic elements of this new modeling approache are composed of link density simulation, stochastic incremental route choice, departure time choice, destination choice and mode choice. The elasticity of the proposed model is examined based on elasticity equations and simulation results. Also the transferability from a mega city like Seoul to a big city like Daejon is demonstrated for the choice model. The issues centering around the dynamic relations among density(k), speed(u), and flow rate(v) are also discussed for the modeling of highly congested situations.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2006년도 학술발표회 논문집
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• pp.354-359
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• 2006

The analysis of large-scale water resources systems is often complicated by the presence of multiple reservoirs and diversions, the uncertainty of unregulated inflows and demands, and conflicting objectives. Reinforcement learning is presented herein as a new approach to solving the challenging problem of stochastic optimization of multi-reservoir systems. The Q-Learning method, one of the reinforcement learning algorithms, is used for generating integrated monthly operation rules for the Keum River basin in Korea. The Q-Learning model is evaluated by comparing with implicit stochastic dynamic programming and sampling stochastic dynamic programming approaches. Evaluation of the stochastic basin-wide operational models considered several options relating to the choice of hydrologic state and discount factors as well as various stochastic dynamic programming models. The performance of Q-Learning model outperforms the other models in handling of uncertainty of inflows.

• Coupled systems mechanics
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• 제7권2호
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• pp.211-232
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• 2018

In this work we present an upscaling technique for multi-scale computations based on a stochastic model calibration technique. We consider a coarse-scale continuum material model described in the framework of generalized standard materials. The model parameters are considered uncertain, and are determined in a Bayesian framework for the given fine scale data in a form of stored energy and dissipation potential. The proposed stochastic upscaling approach is independent w.r.t. the choice of models on coarse and fine scales. Simple numerical examples are shown to demonstrate the ability of the proposed approach to calibrate coarse scale elastic and inelastic material parameters.

• Management Science and Financial Engineering
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• 제15권1호
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• pp.51-69
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• 2009

A network model and a Genetic Algorithm (GA) is proposed to solve the simultaneous estimation of the trip distribution and traffic assignment from traffic counts in the congested networks in a logit-based Stochastic User Equilibrium (SUE). The model is formulated as a problem of minimizing a non-linear objective function with the linear constraints. In the model, the flow-conservation constraints are utilized to restrict the solution space and to force the link flows become consistent to the traffic counts. The objective of the model is to minimize the discrepancies between two sets of link flows. One is the set of link flows satisfying the constraints of flow-conservation, trip production from origin, trip attraction to destination and traffic counts at observed links. The other is the set of link flows those are estimated through the trip distribution and traffic assignment using the path flow estimator in the logit-based SUE. In the proposed GA, a chromosome is defined as a real vector representing a set of Origin-Destination Matrix (ODM), link flows and route-choice dispersion coefficient. Each chromosome is evaluated by the corresponding discrepancies. The population of the chromosome is evolved by the concurrent simplex crossover and random mutation. To maintain the feasibility of solutions, a bounded vector shipment technique is used during the crossover and mutation.

• Management Science and Financial Engineering
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• 제15권1호
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• pp.33-49
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• 2009

We examine the effectiveness of the conventional (Q, r) model in managing production-inventory systems with finite capacity, stochastic demand, and stochastic order processing times. We show that, for systems with finite production capacity, order replenishment lead times are highly sensitive to loading and order quantity. Consequently, the choice of optimal order quantity and optimal reorder point can vary significantly from those obtained under the usual assumption of a load-independent lead time. More importantly, we show that for a given (Q, r) policy the conventional model can grossly under or over-estimate the actual cost of the policy. In cases where a setup time is associated with placing a production order, we show that the optimal (Q, r) policy derived from the conventional model can, in fact, be infeasible.

• 경영과학
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• 제29권2호
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• pp.91-103
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• 2012

Revenue management problems originated in the 1970's in the context of the airline industry have been successfully introduced in airline industries. It has started on the capacity control by booking classes for available seats, and has been recognized as a powerful tool to maximize the total revenue. Changing customer behavior and airline market environments, however, has required a new mechanism for improving the revenue. Dynamic pricing is one of innovative tools which is to adjust prices according to the market status. In this paper, we consider a dynamic pricing and seat control problem for discrete time horizon. The problem can be modeled as a stochastic programming problem. Applying the linear approximation technique and given the price set for each time, we suggest a mixed Integer Programming model to solve our problem efficiently. From the simulation results, we can find our model makes good performance and can be expanded to other comprehensive problems.

• 대한교통학회:학술대회논문집
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• 대한교통학회 2000년도 제37회 학술발표회논문집
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• pp.25-47
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• 2000

In route guidance systems fastest-path routing has typically been adopted because of its simplicity. However, empirical studies on route choice behavior have shown that drivers use numerous criteria in choosing a route. The objective of this study is to develop computationally efficient algorithms for identifying a manageable subset of the nondominated (i.e. Pareto optimal) paths for real-time vehicle routing which reflect the drivers' preferences and route choice behaviors. We propose two pruning algorithms that reduce the search area based on a context-dependent linear utility function and thus reduce the computation time. The basic notion of the proposed approach is that ⅰ) enumerating all nondominated paths is computationally too expensive, ⅱ) obtaining a stable mathematical representation of the drivers' utility function is theoretically difficult and impractical, and ⅲ) obtaining optimal path given a nonlinear utility function is a NP-hard problem. Consequently, a heuristic two-stage strategy which identifies multiple routes and then select the near-optimal path may be effective and practical. As the first stage, we utilize the relaxation based pruning technique based on an entropy model to recognize and discard most of the nondominated paths that do not reflect the drivers' preference and/or the context-dependency of the preference. In addition, to make sure that paths identified are dissimilar in terms of links used, the number of shared links between routes is limited. We test the proposed algorithms in a large real-life traffic network and show that the algorithms reduce CPU time significantly compared with conventional multi-criteria shortest path algorithms while the attributes of the routes identified reflect drivers' preferences and generic route choice behaviors well.