Multi-stage Programming Research Articles

Reasoning about multi-stage programs

AbstractWe settle three basic questions that naturally arise when verifying code generators written in multi-stage functional programming languages. First, does adding staging to a language compromise any equalities that hold in the base language? Unfortunately it does, and more care is needed to reason about terms with free variables. Second, staging annotations, as the name “annotations” suggests, are often thought to be orthogonal to the behavior of a program, but when is this formally guaranteed to be true? We give termination conditions that characterize when this guarantee holds. Finally, do multi-stage languages satisfy useful, standard extensional properties, for example, that functions agreeing on all arguments are equivalent? We provide a sound and complete notion of applicative bisimulation, which establishes such properties or, in principle, any valid program equivalence. These results yield important insights into staging and allow us to prove the correctness of quite complicated multi-stage programs.

Impacts of quality and transportation on environmental costs in multi-stage multi-product green supply chain

An increase in environmental pollution and costs of carbon dioxide (CO2) emission and subsequent increase in the supply chain's general costs all have contributed to rising attention toward these issues. Transportation and the quality of the raw materials purchased are among the most important factors affecting the environmental pollution of production chains. This paper introduces a bi-objective multi-stage multi-product mixed integer nonlinear programming (MINLP) model of green supply chain which considers the effect of different vehicles and quality of raw materials on the chain's total costs and carbon dioxide emission. Finally a numerical example explained and solved through e-constraint approach.

Modeling Water Trading under Uncertainty for Supporting Water Resources Management in an Arid Region

AbstractIn this study, a joint-probabilistic interval multistage programming (JIMP) method is developed for planning water resources management under uncertainty. The JIMP method can tackle uncertainties presented in terms of interval parameters in objective function and constraints, in addition to random variables in the left and right-hand sides of constraints. It can also reflect the dynamics in terms of decisions for water resources allocation through transactions at discrete points of a complete scenario set over a multistage context. Moreover, the JIMP method can be used for analyzing various policy scenarios that are associated with different levels of economic consequences when the promised water-allocation targets are violated. The JIMP method is applied to a real case of planning water trading for supporting the regional sustainable development of the Kaidu-Qongque River basin, which is one of the most arid regions of China. Monte Carlo simulation is introduced into the JIMP framework for evalua...

Inexact Multistage Fuzzy-Stochastic Programming Model for Water Resources Management

AbstractIn this study, an inexact multistage fuzzy-stochastic programming (IMFSP) model was developed for water resources management under uncertainty. In the IMFSP model, the uncertainties existing in the objective functions, and the constraints’ left side and right side are expressed as fuzzy sets so that the uncertainties can be directly communicated into the optimization process and various water allocation schemes can be obtained under different α-cut levels at a plurality of periods regardless of the shapes of their membership functions. The IMFSP model can not only generate the most possible, lower and upper bound values for all possible decision schemes but it can also provide information about the degrees of membership of the decision variables between the lower and upper bound values. Meanwhile, the scenario-based multistage stochastic programming was incorporated in the IMFSP model to adequately reflect the dynamic variations and uncertain information of the water resources system. Therefore, t...

Process simulation and dynamic control for marine oily wastewater treatment using UV irradiation

UV irradiation and advanced oxidation processes have been recently regarded as promising solutions in removing polycyclic aromatic hydrocarbons (PAHs) from marine oily wastewater. However, such treatment methods are generally not sufficiently understood in terms of reaction mechanisms, process simulation and process control. These deficiencies can drastically hinder their application in shipping and offshore petroleum industries which produce bilge/ballast water and produced water as the main streams of marine oily wastewater. In this study, the factorial design of experiment was carried out to investigate the degradation mechanism of a typical PAH, namely naphthalene, under UV irradiation in seawater. Based on the experimental results, a three-layer feed-forward artificial neural network simulation model was developed to simulate the treatment process and to forecast the removal performance. A simulation-based dynamic mixed integer nonlinear programming (SDMINP) approach was then proposed to intelligently control the treatment process by integrating the developed simulation model, genetic algorithm and multi-stage programming. The applicability and effectiveness of the developed approach were further tested though a case study. The experimental results showed that the influences of fluence rate and temperature on the removal of naphthalene were greater than those of salinity and initial concentration. The developed simulation model could well predict the UV-induced removal process under varying conditions. The case study suggested that the SDMINP approach, with the aid of the multi-stage control strategy, was able to significantly reduce treatment cost when comparing to the traditional single-stage process optimization. The developed approach and its concept/framework have high potential of applicability in other environmental fields where a treatment process is involved and experimentation and modeling are used for process simulation and control.

Students mental health and multistage prevention programme: The Russian experience

University students meet a lot of psychosocial challenges during education. These problems can affect their mental state, physical health, social activity and quality of life. A psychoprophylactic model of students mental health care in one of the Moscow Universities is based on the programme of medical and psychological support of education from the freshmen to graduate and on the principles of multiprofessional teamwork. The main goal of such multistage programme is to provide an effective psychosocial adjustment of students and to promote their personality growth. Preventive tasks consist of timeous recognition of mental disorders and of the psychological competences improvement.

Optimal Scheduling of Logistical Support for Medical Resource with Demand Information Updating

This paper presents a discrete time-space network model for a dynamic resource allocation problem following an epidemic outbreak in a region. It couples a forecasting mechanism for dynamic demand of medical resource based on an epidemic diffusion model and a multistage programming model for optimal allocation and transport of such resource. At each stage, the linear programming solves for a cost minimizing resource allocation solution subject to a time-varying demand that is forecasted by a recursion model. The rationale that the medical resource allocated in early periods will take effect in subduing the spread of epidemic and thus impact the demand in later periods has been incorporated in such recursion model. A custom genetic algorithm is adopted to solve the proposed model, and a numerical example is presented for sensitivity analysis of the parameters. We compare the proposed medical resource allocation mode with two traditional operation modes in practice and find that our model is superior to any of them in less waste of resource and less logistic cost. The results may provide some practical guidelines for a decision-maker who is in charge of medical resource allocation in an epidemics control effort.

Meaningful Use: Participating in the Federal Incentive Program

Meaningful use legislation was first introduced in the American Recovery and Reinvestment Act of 2009 as a multistaged program to incentivize adoption of electronic health record technology. Since that time, numerous eligible providers and eligible hospitals have captured incentive payments by installing certified electronic health record technology and capturing and reporting on key elements for patients whose health records are stored in an electronic format. Although the question of whether radiologists should participate in the program was initially debated, the evidence is now clear that lack of participation leaves a significant amount of money at risk. This article provides an overview of how the program is structured, what technology needs to be installed, the necessary data elements to capture in an electronic format, and how radiologists can effectively participate in the program to capture their maximum incentive payment.

Late data layout

Values need to be represented differently when interacting with certain language features. For example, an integer has to take an object-based representation when interacting with erased generics, although, for performance reasons, the stack-based value representation is better. To abstract over these implementation details, some programming languages choose to expose a unified high-level concept (the integer) and let the compiler choose its exact representation and insert coercions where necessary. This pattern appears in multiple language features such as value classes, specialization and multi-stage programming: they all expose a unified concept which they later refine into multiple representations. Yet, the underlying compiler implementations typically entangle the core mechanism with assumptions about the alternative representations and their interaction with other language features. In this paper we present the Late Data Layout mechanism, a simple but versatile type-driven generalization that subsumes and improves the state-of-the-art representation transformations. In doing so, we make two key observations: (1) annotated types conveniently capture the semantics of using multiple representations and (2) local type inference can be used to consistently and optimally introduce coercions. We validated our approach by implementing three language features as Scala compiler extensions: value classes, specialization (using the miniboxing representation) and a simplified multi-stage programming mechanism.

Experiences of Teen Drivers and Their Advice for the Learner License Phase

Objective: Teen drivers remain at considerable risk of injury and fatality during the earliest years of independent driving. Multistage licensing programs, such as graduated driver licensing (GDL), have been implemented in numerous jurisdictions as a form of exposure control, mandating minimum practice periods and driving restrictions such as night driving and passenger limits. However, the teen driver's experiences of GDL during the learner phase, and the driving and other advice they recommend be shared with all learners, remains unknown at this time.Methods: Thirty-seven learner drivers (aged 16–18 years, mean = 16.7, mode = 16; 9 males) from 2 high schools (one private, 3 males; one public) participated in one of 2 (group 1: private school, n = 17) 45-min group discussions.Results: Two themes emerged: (1) learning to drive and (2) supervision of the learner driver. A wealth of experiences and advice pertaining to the subthemes of supervisor behavior, GDL, road environment, vehicle logistics, and interacting with other road users were shared by learners. Numerous recommendations are made pertaining to each subtheme, such as clear instruction and feedback, tips for negotiating complex infrastructure, and normalizing of outcomes like stalled vehicles when first learning to drive. Furthermore, it appears that current approaches of issuing supporting literature at the commencement of the learner phase are insufficient.Conclusions: The wealth of experiences and advice shared by the learner drivers should be considered in refining the content and process of the learner license phase. Moreover, the nonuse of learner resources suggests that alternative mechanisms of engagement and information dissemination need to be explored.

A Variables Clustering Based Differential Evolution Algorithm to Solve Multistage Goal Programming Model in Defense Projects Portfolio

The multistage goal programming model is popular to model the defense projects portfolio optimization problem in recent years. However, as its high-dimensional variables and large-scale solution space, the addressed model is hard to be solved in an acceptable time. To deal with this challenge, we propose an improved differential evolution algorithm which combines three novel strategies i.e. the variables clustering based evolution, the whole randomized parameters, and the child-individual based selection. The simulation results show that this algorithm has the fastest convergence and the best global searching capability in 6 test instances with different scales of solution space, compared with classical differential evolution algorithm (CDE), genetic algorithm (GA) and particle swarm optimization (PSO) algorithm.

An effective heuristic for multistage linear programming with a stochastic right-hand side

The multistage Stochastic Linear Programming (SLP) problem may become numerically intractable for huge instances, in which case one can solve an approximation for example the well known multistage Expected Value (EV) problem. We introduce a new approximation to the SLP problem that we call the multistage Event Linear Programming (ELP) problem. To obtain this approximation the SLP constraints are aggregated by means of the conditional expectation operator. Based on this new problem we derive the ELP heuristic that produces a lower and an upper bound for the SLP problem. We have assessed the validity of the ELP heuristic by solving large scale instances of the network revenue management problem, where the new approach has clearly outperformed the EV approach. One limitation of this paper is that it only considers randomness on the right-hand side, which is assumed to be discrete and stagewise independent.

Worst-Case-Expectation Approach to Optimization Under Uncertainty

In this paper we discuss multistage programming with the data process subject to uncertainty. We consider a situation where the data process can be naturally separated into two components: one can be modeled as a random process, with a specified probability distribution, and the other one can be treated from a robust (worst-case) point of view. We formulate this in a time consistent way and derive the corresponding dynamic programming equations. To solve the obtained multistage problem, we develop a variant of the stochastic dual dynamic programming method. We give a general description of the algorithm and present computational studies related to planning of the Brazilian interconnected power system.

Effectiveness of Wavelength/Waveband Conversion and Its Allowable Cost Bound for Hierarchical Optical Path Networks

We analyze cost reductions possible with the introduction of a waveband converter or both wavelength and waveband converters to hierarchical optical path networks. We propose network design algorithms that are based on multistage integer linear programming (ILP) or heuristics. Numerical experiments prove that by employing waveband converters or both waveband and wavelength converters, hierarchical optical path networks can be cost effective over wide traffic demand ranges and a broad converter cost range.

Association between line per se and testcross performance for eight agronomic and quality traits in winter rye

We investigated associations between line per se and testcross performance in rye and suggested that selection for per se performance is valuable for several traits in multi-stage selection programs. Genotypic correlation between line per se and testcross performance is an important quantitative-genetic parameter for optimizing hybrid breeding programs. The main goal of this survey was to study the association of line per se and testcross performance at the phenotypic level. We used experimental data from the line per se and testcross performance of two segregating winter rye populations (A, B) with each of 220 progenies tested in six environments for eight agronomic and quality traits. Genotypic variances were considerably larger for per se than for testcross performance of all investigated traits resulting in higher heritabilities of the former in most instances. Genotypic correlations (r g) between testcross and line per se performance decreased with increasing complexity of the trait as shown by the respective heritabilities. They were highest (r g≥0.7) for plant height and test weight in population B, and thousand-kernel weight, falling number and starch content in both populations. A selection of these traits for line per se performance in early generations will save field plots in further testing testcross performance and increase efficiency of hybrid breeding.

Interval multistage joint-probabilistic integer programming approach for water resources allocation and management

In this study, an interval multistage joint-probabilistic integer programming method was developed to address certain problems in water resource regulation. This method effectively deals with data in the form of intervals and probability distribution. It can also process uncertain data in the form of joint probabilities. The proposed method can also reflect the linkage and dynamic variability between particular stages in multi-stage planning. Sensitivity analysis on moderate violations and security constraints showed that the degree of constraint violation was closely linked to the final benefits of the system. The developed method was applied in the case study of the joint-operation of the Tianzhuang and Bashan Reservoirs in Huaihe River, China. In this case study, the proposed method can deal with the water shortage problems downstream and the distribution problems caused by excess water in the reservoir. It can also guarantee the optimization of long-term water usage of both Reservoirs and the river downstream.

Stochastic and semidefinite optimization for scheduling in orthogonal frequency division multiple access networks

In this paper, we propose stochastic binary quadratic programs for the scheduling resource allocation process of a wireless orthogonal frequency division multiple access network. More precisely, we formulate a two-stage stochastic model, then we further extend the two-stage model by introducing a knapsack probabilistic constrained approach, and finally we propose a multi-stage stochastic program for this problem. The models are aimed at minimizing the total power consumption of the network at each time slot of the scheduling process subject to user bit rates, sub-carrier and modulation linear constraints. In order to compute lower bounds, we derive linear and semidefinite programming relaxations for each of the proposed models. The bounds are also compared with a basic variable neighborhood search metaheuristic approach. Numerical results show tight lower bounds for the semidefinite relaxations when compared to the linear ones and with the metaheuristic. Moreover, near optimal solutions are found with the semidefinite relaxations for the two-stage model without using probabilistic constraints and for the multi-stage program as well.

Optimal Production LineLayout Model Based on Integer Programming

To control the cost and optimize the resource utilization of enterprises, production layout problem draws recently more and more attention by industries; however, a study on this problem indicates there is a lack of research on production line design, which is a major issue in the layout problem. Traditional methodology mainly depends on the designer’s experience and calls for theory analysis and quantitative study. This paper concerns the development of a multi-stage integer programming model to describe the production line layout problem, and employs a heuristic and ant-colony algorithm to solve the problem. A practical example illustrates the effectiveness of the model.

Estimating and Understanding the Jewish Population in the United States: A Program of Research

It is inherently difficult to conduct socio-demographic studies of the Jewish population in the United States. This paper describes a multi-stage program of research that addresses the methodological and substantive challenges of providing valid socio-demographic data on the contemporary American Jewish population. The premise is that no single study or approach, and no single dataset, provides sufficient empirical support to understand a complex, ever-changing population. The program of research relies on multiple ways to integrate data sources and uses them in conjunction with one another to develop estimates of the size of the population and its characteristics. It includes data synthesis, targeted surveys, use of data synthesis in weighting of targeted surveys and triangulation. Examples of the application and utility of these methods are provided. It is estimated that as of 2010, there are a total of 6.5 million Jews in the United States. This includes 4.22 million adults who identify as Jewish by religion, and 975,000 Jews who identify as Jewish but do not consider it their religion; in addition, it incorporates 1.3 million children (under 18 years of age) who are being raised exclusively as Jewish. The proposed methods help to overcome many of the limitations and threats to validity that have plagued single studies of the population and, although imperfect, enhance our understanding of the American Jewish population.

Robust Dynamic Distribution of Security Assets in Transit Systems

A robust, mixed-integer, multistage program is presented: it seeks to secure a transit system in which risk is considered to be dynamic and varies over time. A time-varying risk measure reflects the unique nature of transit systems: accumulation of passengers at transfer facilities, stations, and transit vehicles is dynamic and increases the vulnerability of the transit users and system to adverse events. The model is robust under uncertainty and matches security assets at stations better in the face of time-varying risk by redistributing them. The volume-dependent risk measure and subsequent deployment of security assets were developed for the transit system in Washington, D.C., to demonstrate the variable nature of risk and response. The value of considering a robust solution was demonstrated by a comparison of the strategies developed from a robust approach with those from an expected value approach. Five scenarios, designed on recent events on the system, replicate the operational conditions of the transit system for the morning peak hour period and show the effectiveness of the developed deployment strategies.