Two-stage Dynamic Model Research Articles

Location of Customer Order Decoupling Point in the Supply Chain of Deteriorated Food Based on Dynamic Model

Considering the supply chain of deteriorated food, a two-stage dynamic model in the product life cycle is proposed. The optimal conditions and results are obtained by using the maximum principle. On this basis, the effect of location of CODP on the total cost is researched in the product life cycle. By the data simulation, the model constructs the relations of CODP, inventory policy and demand type. The results show that CODP locates in the downstream of the product life cycle which is a linear function of the product life cycle. The demand forecast is the main factors influencing the total cost. The model can reflect the relation between the total cost and inventory, demand of the supply chain for deteriorated food.

适于机载环境对地目标跟踪的粒子滤波设计

To improve the robustness while tracking a ground target with strong mobility in the cabin environment, a particle filter was taken as tracking framework, and its dynamic model and observation model were investigated. According to the two-stage dynamic model proposed by Kristan et al, a two-stage acceleration (TSA) dynamic model was proposed for the characteristics of cabin environment and the strong mobility of the tracking target. According to the idea of asymmetric kernel function proposed by Yilmaz et al., a method was proposed by using Snake algorithm to extract the object contour and to construct an asymmetric kernel function based on contour information to solve the real-time moving target problem. Finally, the TSA-AK particle filter tracking algorithm was proposed based on above methods. The proposed algorithm was tested on the video tracking ground target in cabin environment. The results show that the proposed algorithm can stably track target moving in a wide range of velocity. The targeting accuracy is 98%, and the computing frame rate is 26 frame/s when the object scale is 25 pixel30 pixel.

A game theoretical analysis of the design options of the real-time electricity market

In this paper we study the economic consequences of two real-time electricity market designs (with or without penalties) taking into account the opportunistic behaviors of market players. We implement a two-stage dynamic model to consider the interaction between the forward market and the real-time market where market players compete in a Nash manner and rely on supply/demand function oligopoly competition. Dynamic programming is used to deal with the stochastic environment of the market and the mixed complementarity problem is employed to find a solution to the game. Numerical examples are presented to illustrate how the optimal competitor’s strategies could change according to the adoption or no adoption of a balancing mechanism and to the level of the penalty imposed on imbalances, regarding a variety of producers’ cost structures. The main finding of this study is that implementing balancing mechanisms would increase forward contracts while raising electricity prices. Moreover, possible use of market power would not be reduced when imbalances are penalized.

Dynamic Models and Coordination Analysis of Reverse Supply Chain with Remanufacturing

In this paper, we establish a reverse chain system with one manufacturer and one retailer under demand uncertainties. Distinguishing between the recycling process of the retailer and the remanufacturing process of the manufacturer, we formulate a two-stage dynamic model for reverse supply chain based on remanufacturing. Using buyback contract as coordination mechanism and applying dynamic programming the optimal decision problems for each stage are analyzed. It concluded that the reverse supply chain system could be coordinated under the given condition. Finally, we carry out numerical calculations to analyze the expected profits for the manufacturer and the retailer under different recovery rates and recovery prices and the outcomes validate the theoretical analyses.

The investment strategies for a dynamic supply chain under stochastic demands

Abstract A successful supply chain management (SCM) should aim to maximize the net present value of joint profits along the supply chain. However, the volatile market conditions cause the future cash flows along the supply chain more difficult to anticipate. To obtain higher supply chain value, the supplier and the retailer should cooperatively determine the optimal entry time. This paper proposes a two-stage dynamic optimization model by using a real option approach and then performs the sensitivity analyses for the option value and the investment threshold. The impacts of some critical factors, including the growth rate and the volatility of demand shock, sunk cost, and relating operational costs (cost rates, fix costs, holding costs of inventory, and shipping costs), are investigated.

Gaining information from commercial catch for a Bayesian two-stage biomass dynamic model: application to Bay of Biscay anchovy

Abstract Ibaibarriaga, L., Fernández, C., and Uriarte, A. 2011. Gaining information from commercial catch for a Bayesian two-stage biomass dynamic model: application to Bay of Biscay anchovy. – ICES Journal of Marine Science, 68: 1435–1446. A two-stage biomass dynamic model for Bay of Biscay anchovy is presented. Compared with the model currently applied by ICES for the assessment of that stock, the new model separates the growth and natural mortality processes and allows them to differ by age class. Stochastic equations involving the observed weights by age class in surveys are incorporated to provide information on growth rates. The fishing process is modelled separating fishing mortality into year and age-class effects in each semester, and observation equations are introduced for total catch and catch proportion by age class (in biomass) by semester. The model is first tested on simulated data, then applied to real data for the years 1987–2008. Although the results are affected by survey catchability and natural mortality assumptions, estimates of population trends, when expressed in relation to the value in a given year, are robust. The new model has significantly more parameters, requiring longer computational time for its fitting, which is done in a Bayesian context. However, it does allow the testing of different assumptions on natural mortality, which is of special interest after the recent fishery closure, and estimating new parameters, which could provide further insight on stock and fleet dynamics.

A two-stage dynamic sales forecasting model for the fashion retail

The difficulty with fashion retail forecasting is due to a number of factors such as the season, region and fashion effect and causes a nonlinear change in the original sales rules. To improve the accuracy of fashion retail forecasting, a two-stage dynamic forecasting model is proposed, which is combined with both long-term and short-term predictions. The model introduces the improved adjustment methods, the main adjustment model and error forecasting model in the adjustment system collaborated with each other. The real-time data are demonstrated by applying the model in wireless mobile environment. The experiment shows that the model provides good results for fashion retail forecasting.

A Two-Stage Dynamic Model for Visual Tracking

We propose a new dynamic model which can be used within blob trackers to track the target's center of gravity. A strong point of the model is that it is designed to track a variety of motions which are usually encountered in applications such as pedestrian tracking, hand tracking, and sports. We call the dynamic model a two-stage dynamic model due to its particular structure, which is a composition of two models: a liberal model and a conservative model. The liberal model allows larger perturbations in the target's dynamics and is able to account for motions in between the random-walk dynamics and the nearly constant-velocity dynamics. On the other hand, the conservative model assumes smaller perturbations and is used to further constrain the liberal model to the target's current dynamics. We implement the two-stage dynamic model in a two-stage probabilistic tracker based on the particle filter and apply it to two separate examples of blob tracking: 1) tracking entire persons and 2) tracking of a person's hands. Experiments show that, in comparison to the widely used models, the proposed two-stage dynamic model allows tracking with smaller number of particles in the particle filter (e.g., 25 particles), while achieving smaller errors in the state estimation and a smaller failure rate. The results suggest that the improved performance comes from the model's ability to actively adapt to the target's motion during tracking.

On determining optimal fleet size and vehicle transfer policy for a car rental company

This paper considers how to determine the optimal fleet size and vehicle transfer policy for a rental-car company that serves two cities. In each city, there are single-trip and round-trip customers, where the former is given a higher priority. Because of the single-trip traffic, the number of cars at these two cities may become unbalanced. Hence, the central planner in each day needs to decide whether to transfer any cars from one city to the other. We develop a two-stage dynamic programming model, in which we determine the vehicle transfer policy in the second stage and the optimal fleet size in the first stage. Although the objective function could be neither concave nor quasi-concave due to lost sales, we can find the optimal fleet size and vehicle transfer policy by solving a series of linear programming problems. We propose a heuristic solution, which is based on a special case analysis, for the fleet size problem. A numerical study reveals that our heuristic solution for the fleet size performs well. However, if the corresponding vehicle transfer policy is not appropriate, the overall performance can drastically deteriorate even with the optimal fleet size. Several extensions of our basic model are also analyzed.

A two-stage biomass dynamic model for Bay of Biscay anchovy: a Bayesian approach

Abstract Ibaibarriaga, L., Fernández, C., Uriarte, A., and Roel, B. A. 2008. A two-stage biomass dynamic model for Bay of Biscay anchovy: a Bayesian approach. – ICES Journal of Marine Science, 65: 191–205. A two-stage biomass-based state-space model with stochastic recruitment processes and deterministic dynamics was developed for the Bay of Biscay anchovy population. It is fitted in a Bayesian context with posterior computations carried out using Markov chain Monte Carlo techniques. The model is tested first on a simulated dataset and the effects of different modelling assumptions and of missing values evaluated. Then, it is applied to a real historical series of commercial catch and survey data from 1987 to 2006. Results are compared with those obtained by the standard assessment model for this stock, integrated catch-at-age analysis (ICA). From the posterior distribution of biomass in the latest year (2006), the distribution of unexploited biomass in 2007 can be derived assuming the distribution of recruitment in 2007 to be a mixture of the posterior distributions of past series recruitment. Hence, the effect of different catch options on future biomass levels can be quantified in probabilistic terms. Finally, directions for possible further improvements are indicated.

A two-stage dynamic model on allocation of construction facilities with genetic algorithm

By their very nature, activities within the construction site are generally highly dynamic and complex. Hence, it is highly desirable to be able to formulate the optimal strategy for allocating site-level facilities at different times of the project. The principal objective is to minimize the total cost, which comprises the transportation, handling, capital, and operating costs at potential intermediate transfer centers of various plant and material resources over the entire project duration. The problem can be formulated as a mixed integer program, which entails enormous computational effort for the solution, in particular when the problem size is large. In this paper, a two-stage dynamic model is developed to assist construction planners to formulate the optimal strategy for establishing potential intermediate transfer centers for site-level facilities such as batch plants, lay-down yards, receiving warehouses, various workshops, etc. Under this approach, the solution of the problem is split into two stages, namely, a lower-level stage and an upper-level stage. The former can be solved by a standard linear programming method, whereas the latter is solved by a genetic algorithm. The efficiency of the proposed algorithm is demonstrated through case examples.

A two-stage dynamic model on allocation of construction facilities with genetic algorithm

By their very nature, activities within the construction site are generally highly dynamic and complex. Hence, it is highly desirable to be able to formulate the optimal strategy for allocating site-level facilities at different times of the project. The principal objective is to minimize the total cost, which comprises the transportation, handling, capital, and operating costs at potential intermediate transfer centers of various plant and material resources over the entire project duration. The problem can be formulated as a mixed integer program, which entails enormous computational effort for the solution, in particular when the problem size is large. In this paper, a two-stage dynamic model is developed to assist construction planners to formulate the optimal strategy for establishing potential intermediate transfer centers for site-level facilities such as batch plants, lay-down yards, receiving warehouses, various workshops, etc. Under this approach, the solution of the problem is split into two stages, namely, a lower-level stage and an upper-level stage. The former can be solved by a standard linear programming method, whereas the latter is solved by a genetic algorithm. The efficiency of the proposed algorithm is demonstrated through case examples.

Water Quality Control in Watercourses

Different approaches to studying hierarchical problems are compared with the use of a two-stage dynamic model of water quality control in the case of point pollution sources. Variations in pollutant concentrations in a watercourse are described by partial differential equations. The study embraces the cases of a single and multiple point pollution sources and different types of pollutants.

Dislocation dynamics and brittle-to-ductile transitions

The validity of different types of models for crack-dislocation interactions and their effect on fracture is discussed. The brittle-to-ductile transition (BDT) in precracked silicon and sapphire can be described by a two-stage dynamic model based on dislocation motion to and around the crack tip. In silicon and germanium, where the stress-temperature-velocity relations for the test materials are known, the model allows accurate prediction of the transition temperature. Computer simulations of the dynamic development of near-crack tip dislocation arrays then allows prediction of the shape of the BDT given the pre-existing dislocation arrangements in the crystals; we have performed experiments to confirm these predictions. In sapphire, where these data are not available, the model allowed prediction of dislocation velocities from fracture tests. These predictions were then checked by measuring dislocation velocities and were found to be a close fit to experiment. We have also studied the brittle-ductile transition in semibrittle materials (b.c.c. metals and oxides), where pre-existing dislocations appear to control the transition behaviour via slow, stable crack growth. In molybdenum the stable crack growth is relatively small in extent and our model predicts the fracture stress-temperature curve to good accuracy.

A Dynamic Model for the Analysis of Labour Turnover

SUMMARY A two-stage dynamic model has been developed to represent the process of labour turnover. The model provides a good fit to a wide range of published turnover data. Labour turnover is defined in terms of primary and secondary termination processes separated by an induction period. The rates of the two termination processes vary markedly between firms; firms with low initial labour turnover rates do not necessarily have low secondary labour turnover rates and vice versa. The rates at which recruits adapt to the ways of different firms are markedly similar. The parameters controlling the turnover process seem to have a realistic significance in terms of quantifying some of the factors involved in the interaction of personnel and organizations. If the model is valid, then its use in the analysis of labour turnover data would make it relatively easy for a particular firm to pinpoint (and control) those factors which result in its particular type and level of labour turnover. Examples are given of the use of the model for evaluating the likely consequences of parameter modification (policy changes).

Dynamic Modeling and Estimation of Carbon in a Basic Oxygen Furnace

This paper describes an approach to dynamic modeling and on-line carbon estimation based on waste gas analysis in a basic oxygen furnace. As is well known, the basic oxygen process for steelmaking is highly stochastic in nature. There is, at present, no satisfactory model for predicting the decarbonization rate of the process. The first part of this paper considers approaches to on-line dynamic modeling. A two-stage dynamic model is developed using time-series analysis and empirical information on the decarbonization rate. This model is put in a state-vector form which is ideal for online estimation and control. The second part of the paper applies linear and nonlinear filtering to estimate the carbon and the decarbonization rate of the process during each heat. Since there is a time delay between the bath conditions and the measurements, the state of the system must be predicted. Two different methods for prediction are given. One is based on the present estimate of the carbon in the bath, and the other is based on the present estimate of the decarbonization rate of the bath. A combination of the two schemes is shown to provide better results than those achieved so far in practice. A total of 175 heats from a 220-ton BOF are processed and analyzed, and the results are discussed.