A two-stage model for a day-ahead paratransit planning problem

We consider a dynamic planning problem for paratransit transportation. The focus is on a decision to take one day ahead: which requests to serve with own vehicles, and which requests to subcontract to taxis? We call this problem the day-ahead paratransit planning problem. The developed model is a non-standard two-stage integer recourse model. Both stages consist of two consecutive optimization problems: the clustering of requests into routes, and the assignment of these routes to vehicles. To solve this model, a genetic algorithm approach is used. Computational results are presented for randomly generated data sets.     

A simple model for a two-stage chemical reaction with diffusion

We analyse the evolution of a two-stage chemical reaction betweentwo neighbouring plumes of reactants. Under the assumption thatthe plumes are approximately Gaussian we derive a system ofordinary differential equations for the total amount, the centroidand the variance of each reactant. We compare the solution ofthese equations with full numerical simulation of the reaction.Excellent agreement is obtained, with solution of the near-Gaussianmodel requiring considerably less computational effort thanthe full simulations. Of key importance is the yield of thereaction, and we discuss this feature in particular.     

A leader-follower model and analysis for a two-stage network of oligopolies

This paper is concerned with the existence, uniqueness and computation of leader-follower equilibrium solutions for an industry involved with two major stages of production. We assume that there exists one set of firms performing the first stage of production, which produces a semi-finished product. This semi-finished product is converted to a final good by a second set of firms performing the second stage of production. Furthermore, also competing in the final product market is a third set of firms, which are vertically integrated through the two stages of production and which are assumed to lead the second set of firms by explicitly considering the reaction or response of these latter firms to their own outputs. We model such an industry as a two-stage network of oligopolies, and define equilibrium solutions based on assumed market structures. Our analysis examines the existence and uniqueness of such equilibrium solutions, characterizes the nature of the production strategies of the various firms at an equilibrium, and prescribes algorithms to compute such solutions. This provides the machinery required to perform sensitivity analyses for studying the effects of various mergers or integrations on individual firm profits, and on the industry outputs and prices at equilibrium. The presentation is self-contained, and does not necessarily require any significant prior preparation in economic theory on the part of the reader.This paper is based on work done for the Minerals and Mining Resources Research Institute, under the sponsorship of the Bureau of Mines, Department of the Interior.     

A two-stage stochastic model for labour wastage

A stochastic model for labour wastage is presented which accounts for the employee's variability on the propensity to leave through dependence on both his length of service and his tenure-in-current-state. The basic assumption underlying the model is that the employee's personal characteristics, the job characteristics and the external labour market conditions are stochastic over time, therefore affecting his decision to stay or leave the company. It is shown that the model provides a good fit to a variety of observed leaving patterns for several companies reported in the literature, explains the relationships among a number of important occupational variables, and is useful for planning purposes in predicting future developments.     

A two-stage prognosis model in condition based maintenance

We often observe in practice that the life of a piece of production equipment can be divided into two stages. The first stage is referred to as the normal working stage where no significant deviation from the normal operating state is observed. The second stage is called the failure delay period, since a defect may be initiated, and progressively develop into an actual failure, i.e., the equipment is in a defective stage but still working during this stage. With the help of condition monitoring, hidden defects already present in the equipment may be detected, but for maintenance planning purposes, the prediction of the initiation point of the second stage, and more importantly, the residual life thereafter is important. This paper reports on the development of a probability model to predict the initiation point of the second stage and the remaining life based on available condition monitoring information. The method for model parameters estimation is discussed and applied to real data.     

Dynamically integrated regression model for online auction data

Science China Mathematics - We propose a dynamically integrated regression model to predict the price of online auctions, including the final price. Different from existing models, the proposed...     

A two-stage analytical extension for porothermoelastic model under axisymmetric loadings

Porothermoelastic responses of saturated porous media find wide applications in geotechnical engineering. However, the coupled partial differential equations describing the conservation of momentum, mass, and energy in the porous medium posed mathematical difficulties in obtaining analytical solutions. In this paper, we provided a two-stage porothermoelastic model for comprehensive solutions under axisymmetric loadings. At the first stage, the governing equations were decoupled by the Laplace–Hankel transform, which yielded the explicit expressions of the temperature, pore pressure, displacements, and the vertical and shear stresses. At the second stage, the radial and tangential strains were obtained after the numerical inversion of the volumetric strain and displacements. We also found that the volumetric strain played an important role in this model: (1) coupled displacements with the pore pressure and temperature at the first stage; (2) combined the vertical, radial, and tangential strains at the second stage. Results of a finite layer under a disk thermal loading showed that this model could capture the thermal expansion and contraction in terms of displacements, strains and stresses, and such mechanical interactions could give rise to the buildup and dissipation of pore pressures during the thermal conduction.     

A discrete two-stage population model: continuous versus seasonal reproduction

A discrete two-stage model which describes the dynamics of a population where juveniles and adults compete for different resources is developed. A motivating example is the green tree frog (Hyla cinerea) where tadpoles and adult frogs feed on separate resources. First, continuous breeding is assumed and the asymptotic behavior of the resulting autonomous model is fully analyzed. It is shown that the unique interior equilibrium is globally asymptotically stable when the inherent net reproductive number is greater than one. However, when the inherent net reproductive number is less than one, the population becomes extinct. Then a seasonal breeding described by a periodic birth rate with period 2 is assumed. It is proved that for this nonautonomous model a period two solution is globally asymptotically stable when the inherent net reproductive number is greater than one and when the inherent net reproductive number is less than one the population becomes extinct. Finally, the advantage (in terms of maximizing the number of juveniles and adults in the population over a fixed time period) of having a seasonal breeding is studied by comparing the average of the juvenile and adult numbers of the periodic solution for the nonautonomous model to the equilibrium solution of the autonomous model. Our results indicate that for high birth rates the equilibrium of the autonomous model is higher than the average of the two cycle solution. Therefore, all other factors being equal, seasonal breeding appears to be deleterious to populations with high birth rates. However, for low birth rates seasonal breeding can be beneficial. It is also shown that for a range of birth rates the nonautnomous model is persistent while the solution to the autonomous model goes to extinction.     

A bargaining game model for measuring performance of two-stage network structures

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs), where the internal structures of DMUs are treated as a black-box. Recently DEA has been extended to examine the efficiency of DMUs that have two-stage network structures or processes, where all the outputs from the first stage are intermediate measures that make up the inputs to the second stage. The resulting two-stage DEA model not only provides an overall efficiency score for the entire process, but also yields an efficiency score for each of the individual stages. The current paper develops a Nash bargaining game model to measure the performance of DMUs that have a two-stage structure. Under Nash bargaining theory, the two stages are viewed as players and the DEA efficiency model is a cooperative game model. It is shown that when only one intermediate measure exists between the two stages, our newly developed Nash bargaining game approach yields the same results as applying the standard DEA approach to each stage separately. Two real world data sets are used to demonstrate our bargaining game model.     

The asymptotic validity of theF-test in a two-stage least squares model

The usualF-test has been used to test a general linear hypothesis for a two-stage least squares method in a system of economic equations. However, we find that thisF-test is actually asymptotically invalid. Some suggestions are given for testing a general linear hypothesis in this situation.     

A new pricing scheme for a multi-period pool-based electricity auction

A new pricing scheme is proposed for determining the social welfare distribution in a centralized pool-based auction in the context of solving the unit commitment problems under competition. A significant contribution of this paper over previous publications on this subject is the inclusion of the price-responsive demand side for the multi-period auctions with dynamic commitment characteristics. The model allows every thermal unit and every consumer to obtain individual maximum profits, and at the same time it gives the market coordinator an adequate tool for solving the ensuing technologically constrained unit commitment problem with fair market clearing. The pricing model is in the form of a mixed linear programming model that minimizes the sum of the compensation costs. The accompanying case study illustrates the approach proposed.     

A sequential auction involving asymmetrically-informed bidders

An example is given of a sequential auction in which, at equilibrium, the expected profit of an informed bidder may be strictly less than the expected profit of an uninformed bidder. This phenomenon is interpreted in terms of the internal game between a player's “types” which arises in a setting of incomplete information.     

A model of distributionally robust two-stage stochastic convex programming with linear recourse

We consider distributionally robust two-stage stochastic convex programming problems, in which the recourse problem is linear. Other than analyzing these new models case by case for different ambiguity sets, we adopt a unified form of ambiguity sets proposed by Wiesemann, Kuhn and Sim, and extend their analysis from a single stochastic constraint to the two-stage stochastic programming setting. It is shown that under a standard set of regularity conditions, this class of problems can be converted to a conic optimization problem. Numerical results are presented to show the efficiency of the distributionally robust approach.     

An auction model arising from an Internet search service provider

Most search service providers such as Lycos and Google either produce irrelevant search results or unstructured company listings to the consumers. To overcome these two shortcomings, search service providers such as GoTo.com have developed mechanisms for firms to advertise their services and for consumers to search for the right services. To provide relevant search results, each firm who wishes to advertise at the GoTo site must specify a set of keywords. To develop structured company listings, each firm bids for priority listing in the search results that appear on the GoTo site. Since the search results appear in descending order of bid price, each firm has some control over the order in which the firm appears on the list resulting from the search. In this paper, we present a one-stage game for two firms that captures the advertising mechanism of a search service provider (such as GoTo). This model enables us to examine the firm’s optimal bidding strategy and evaluate the impact of various parameters on the firm’s strategy. Moreover, we analyze the conditions under which all firms would increase their bids at the equilibrium. These conditions could be helpful to the service provider when developing mechanisms to entice firms to submit higher bids.     

A two-stage stochastic programming model for scheduling replacements in sow farms

This paper presents a formulation and resolution of a two-stage stochastic linear programming model with recourse for sow farms producing piglets. The proposed model considers a medium-term planning horizon and specifically allows optimal replacement and schedule of purchases to be obtained for the first stage. This model takes into account sow herd dynamics, housing facilities, reproduction management, herd size with initial and final inventory of sows and uncertain parameters such as litter size, mortality and fertility rates. These last parameters are explicitly incorporated via a finite set of scenarios. The proposed model is solved by using the algebraic modelling software OPL Studio from ILOG, in combination with the solver CPLEX to solve the linear models resulting from different instances considered. The article also presents results obtained with previous deterministic models assessing the suitability of the stochastic approach. Finally, the conclusions drawn from the study including an outlook are presented.      

Comparing the Spanish and the discriminatory auction formats: A discrete model with private information

The Spanish Treasury is the only Treasury in the world that uses a hybrid system of discriminatory and uniform price auctions to sell government debt: winning bidders pay their bid price for each unit if this is lower than the weighted average price of winning bids (WAP), and pay the WAP otherwise. Following Gordy [Gordy, M., 1996. Multiple bids in a multiple-unit common-value auction. Board of Governors of the Federal Reserve System], we model the Spanish auction as a common value auction of multiple units with private information, allowing for multiple bids. Numerical analysis shows that bidders spread their bids more in the Spanish than in the discriminatory auction and bid higher for the first unit, and that the expected seller’s revenue is higher in the Spanish than in the discriminatory auction within a reasonable set of parameter values.     

A robust optimization model for multi-product two-stage capacitated production planning under uncertainty

Production planning (PP) is one of the most important issues carried out in manufacturing environments which seeks efficient planning, scheduling and coordination of all production activities that optimizes the company’s objectives. In this paper, we studied a two-stage real world capacitated production system with lead time and setup decisions in which some parameters such as production costs and customer demand are uncertain. A robust optimization model is developed to formulate the problem in which minimization of the total costs including the setup costs, production costs, labor costs, inventory costs, and workforce changing costs is considered as performance measure. The robust approach is used to reduce the effects of fluctuations of the uncertain parameters with regards to all the possible future scenarios. A mixed-integer programming (MIP) model is developed to formulate the related robust production planning problem. In fact the robust proposed model is presented to generate an initial robust schedule. The performance of this schedule could be improved against of any possible occurrences of uncertain parameters. A case from an Iran refrigerator factory is studied and the characteristics of factory and its products are discussed. The computational results display the robustness and effectiveness of the model and highlight the importance of using robust optimization approach in generating more robust production plans in the uncertain environments. The tradeoff between solution robustness and model robustness is also analyzed.     

The EU regulation on cross-border trade of electricity: A two-stage equilibrium model

The regulation on cross-border exchanges of electricity in the European Union is meant to enhance the trade of electricity between Member States, by facilitating access to the network and improving the management of congestion at the interconnections. This paper presents a computational model that embeds these two features. The problem is cast in the form of a two-stage equilibrium between regional Regulators. In the first stage, they decide on the allocation of their regional network costs between generators and customers. Either they maximise their regional welfare non-cooperatively (Nash equilibrium), or they centralise the decision as a super-regulator (leading to a cooperative equilibrium). In the second-stage equilibrium, the consequences of first-stage’s decisions are assessed by modelling the energy market as the result of imperfect competition equilibrium on competitive market, coupled with regulated pricing on the domestic less competitive markets. The “rules” that come out of the first-stage game largely influence the final equilibrium. We illustrate this on an extensive numerical example, showing that the model behaves properly and identifying policy issues worth of further investigations.     

A multivariate adaptive regression splines cutting plane approach for solving a two-stage stochastic programming fleet assignment model

The fleet assignment model assigns a fleet of aircraft types to the scheduled flight legs in an airline timetable published six to twelve weeks prior to the departure of the aircraft. The objective is to maximize profit. While costs associated with assigning a particular fleet type to a leg are easy to estimate, the revenues are based upon demand, which is realized close to departure. The uncertainty in demand makes it challenging to assign the right type of aircraft to each flight leg based on forecasts taken six to twelve weeks prior to departure. Therefore, in this paper, a two-stage stochastic programming framework has been developed to model the uncertainty in demand, along with the Boeing concept of demand driven dispatch to reallocate aircraft closer to the departure of the aircraft. Traditionally, two-stage stochastic programming problems are solved using the L-shaped method. Due to the slow convergence of the L-shaped method, a novel multivariate adaptive regression splines cutting plane method has been developed. The results obtained from our approach are compared to that of the L-shaped method, and the value of demand-driven dispatch is estimated.