Iowa NAPIS (National Aging Program Information System) Activity Report State Fiscal Year 2004

INAPIS (National Aging Program Information System) Activity Report collects and reports service/performance data and related program management information to the federal and state government. This report shows the number of older Iowans who receive services and the number of units by service category from Title III funding of the Older Americans Act, the Administration on Aging (AoA) and limited state general fund dollars. Additionally, it shows the number of persons served by individual services and total "unduplicated" client count across all services. In other words, if you add the total number of clients from all services it is higher than the actual number of persons served across all services, because some people need and receive more than one service.

Network revenue management with product-specific no-shows

Revenue management practices often include overbooking capacity to account for customerswho make reservations but do not show up. In this paper, we consider the network revenuemanagement problem with no-shows and overbooking, where the show-up probabilities are specificto each product. No-show rates differ significantly by product (for instance, each itinerary andfare combination for an airline) as sale restrictions and the demand characteristics vary byproduct. However, models that consider no-show rates by each individual product are difficultto handle as the state-space in dynamic programming formulations (or the variable space inapproximations) increases significantly. In this paper, we propose a randomized linear program tojointly make the capacity control and overbooking decisions with product-specific no-shows. Weestablish that our formulation gives an upper bound on the optimal expected total profit andour upper bound is tighter than a deterministic linear programming upper bound that appearsin the existing literature. Furthermore, we show that our upper bound is asymptotically tightin a regime where the leg capacities and the expected demand is scaled linearly with the samerate. We also describe how the randomized linear program can be used to obtain a bid price controlpolicy. Computational experiments indicate that our approach is quite fast, able to scale to industrialproblems and can provide significant improvements over standard benchmarks.

A randomized concave programming method for choice network revenue management

Models incorporating more realistic models of customer behavior, as customers choosing froman offer set, have recently become popular in assortment optimization and revenue management.The dynamic program for these models is intractable and approximated by a deterministiclinear program called the CDLP which has an exponential number of columns. However, whenthe segment consideration sets overlap, the CDLP is difficult to solve. Column generationhas been proposed but finding an entering column has been shown to be NP-hard. In thispaper we propose a new approach called SDCP to solving CDLP based on segments and theirconsideration sets. SDCP is a relaxation of CDLP and hence forms a looser upper bound onthe dynamic program but coincides with CDLP for the case of non-overlapping segments. Ifthe number of elements in a consideration set for a segment is not very large (SDCP) can beapplied to any discrete-choice model of consumer behavior. We tighten the SDCP bound by(i) simulations, called the randomized concave programming (RCP) method, and (ii) by addingcuts to a recent compact formulation of the problem for a latent multinomial-choice model ofdemand (SBLP+). This latter approach turns out to be very effective, essentially obtainingCDLP value, and excellent revenue performance in simulations, even for overlapping segments.By formulating the problem as a separation problem, we give insight into why CDLP is easyfor the MNL with non-overlapping considerations sets and why generalizations of MNL posedifficulties. We perform numerical simulations to determine the revenue performance of all themethods on reference data sets in the literature.

Iowa's Beach Monitoring Program, 2005

This monthly report from the Iowa Department of Transportation is about the water quality management of Iowa's rivers, streams and lakes.

Iowa's Wetland Monitoring Program, 2005

This monthly report from the Iowa Department of Transportation is about the water quality management of Iowa's rivers, streams and lakes.

Iowa Beach Monitoring Program, 2007

This monthly report from the Iowa Department of Transportation is about the water quality management of Iowa's rivers, streams and lakes.

A tractable consideration set structure for network revenue management

Models incorporating more realistic models of customer behavior, as customers choosing from an offerset, have recently become popular in assortment optimization and revenue management. The dynamicprogram for these models is intractable and approximated by a deterministic linear program called theCDLP which has an exponential number of columns. When there are products that are being consideredfor purchase by more than one customer segment, CDLP is difficult to solve since column generationis known to be NP-hard. However, recent research indicates that a formulation based on segments withcuts imposing consistency (SDCP+) is tractable and approximates the CDLP value very closely. In thispaper we investigate the structure of the consideration sets that make the two formulations exactly equal.We show that if the segment consideration sets follow a tree structure, CDLP = SDCP+. We give acounterexample to show that cycles can induce a gap between the CDLP and the SDCP+ relaxation.We derive two classes of valid inequalities called flow and synchronization inequalities to further improve(SDCP+), based on cycles in the consideration set structure. We give a numeric study showing theperformance of these cycle-based cuts.

Equivalence of piecewise-linear approximation and Lagrangian relaxation for network revenue management

The network revenue management (RM) problem arises in airline, hotel, media,and other industries where the sale products use multiple resources. It can be formulatedas a stochastic dynamic program but the dynamic program is computationallyintractable because of an exponentially large state space, and a number of heuristicshave been proposed to approximate it. Notable amongst these -both for their revenueperformance, as well as their theoretically sound basis- are approximate dynamic programmingmethods that approximate the value function by basis functions (both affinefunctions as well as piecewise-linear functions have been proposed for network RM)and decomposition methods that relax the constraints of the dynamic program to solvesimpler dynamic programs (such as the Lagrangian relaxation methods). In this paperwe show that these two seemingly distinct approaches coincide for the network RMdynamic program, i.e., the piecewise-linear approximation method and the Lagrangianrelaxation method are one and the same.

A new compact linear programming formulation for choice network revenue management

The choice network revenue management model incorporates customer purchase behavioras a function of the offered products, and is the appropriate model for airline and hotel networkrevenue management, dynamic sales of bundles, and dynamic assortment optimization.The optimization problem is a stochastic dynamic program and is intractable. A certainty-equivalencerelaxation of the dynamic program, called the choice deterministic linear program(CDLP) is usually used to generate dyamic controls. Recently, a compact linear programmingformulation of this linear program was given for the multi-segment multinomial-logit (MNL)model of customer choice with non-overlapping consideration sets. Our objective is to obtaina tighter bound than this formulation while retaining the appealing properties of a compactlinear programming representation. To this end, it is natural to consider the affine relaxationof the dynamic program. We first show that the affine relaxation is NP-complete even for asingle-segment MNL model. Nevertheless, by analyzing the affine relaxation we derive a newcompact linear program that approximates the dynamic programming value function betterthan CDLP, provably between the CDLP value and the affine relaxation, and often comingclose to the latter in our numerical experiments. When the segment consideration sets overlap,we show that some strong equalities called product cuts developed for the CDLP remain validfor our new formulation. Finally we perform extensive numerical comparisons on the variousbounds to evaluate their performance.

Targeted Small Business Program Report, 2008

Targeted Small Business (TSB) compliance is authorized by Iowa Code Chapter 19B.7. Iowa Code Chapters 73.16-73.19 requires the establishment of TSB procurement provisions through the Departments of Management, Inspections and Appeals and Economic Development. This report will provide an overview of the State of Iowa’s Targeted Small Business Program and efforts to assure equal opportunity through targeted small business procurement during FY 2007.

Iowa's Beach Monitoring Program 2007, 2008

This monthly report from the Iowa Department of Natural Resources is about the water quality management of Iowa's rivers, streams and lakes.

Revenue management under general discrete choice model of consumer behavior

Customer choice behavior, such as 'buy-up' and 'buy-down', is an importantphe-nomenon in a wide range of industries. Yet there are few models ormethodologies available to exploit this phenomenon within yield managementsystems. We make some progress on filling this void. Specifically, wedevelop a model of yield management in which the buyers' behavior ismodeled explicitly using a multi-nomial logit model of demand. Thecontrol problem is to decide which subset of fare classes to offer ateach point in time. The set of open fare classes then affects the purchaseprobabilities for each class. We formulate a dynamic program todetermine the optimal control policy and show that it reduces to a dynamicnested allocation policy. Thus, the optimal choice-based policy caneasily be implemented in reservation systems that use nested allocationcontrols. We also develop an estimation procedure for our model based onthe expectation-maximization (EM) method that jointly estimates arrivalrates and choice model parameters when no-purchase outcomes areunobservable. Numerical results show that this combined optimization-estimation approach may significantly improve revenue performancerelative to traditional leg-based models that do not account for choicebehavior.

Treatment and Early Intervention in Psychosis Program (TIPP-Lausanne): implementation of an early intervention programme for psychosis in Switzerland.

AIM: In a survey conducted in the Lausanne catchment area in 2000, we could estimate on the basis of file assessment that first-episode psychosis (FEP) patients had psychotic symptoms for more than 2 years before treatment and that 50% did not attend any outpatient appointment after discharge from hospital. In this paper, we describe the implementation of a specialized programme aimed at improving engagement and quality of treatment for early psychosis patients in the Lausanne catchment area in Switzerland. METHOD: The Treatment and Early Intervention in Psychosis Program-Lausanne is a comprehensive 3-year programme composed of (i) an outpatient clinic based on assertive case management; (ii) a specialized inpatient unit; and (iii) an intensive mobile team, connected for research to the Center for Psychiatric Neuroscience. RESULTS: Eight years after implementation, the programme has included 350 patients with a disengagement rate of 9% over 3 years of treatment. All patients have been assessed prospectively and 90 participated in neurobiological research. Based on this experience, the Health Department funded the implementation of similar programmes in other parts of the state, covering a total population of 540 000 people. CONCLUSION: Programmes for early intervention in psychosis have a major impact on patients' engagement into treatment. While development of mobile teams and assertive case management with specific training are crucial, they do not necessitate massive financial support to be started. Inclusion of a research component is important as well, in terms of service planning and improvement of both quality of care and impact of early intervention strategies.

Working with Iowans building a culture of conservation, Program Summary, November 2008

Over 94% of Iowa ’ s land is held in private ownership, and the programs of the Iowa Department of Agriculture and Land Stewardship Division of Soil Conservation (IDALS-DSC) have been established to work with these landowners. Over 90% of the landscape is used for agricultural production so much of our focus is in rural communities, but we haven’t overlooked the importance of land management in urban areas. It is crucial to understanding of both conservation and hydrology issues, that what happens on the landscape has immense consequences to the environmental health of our state and the quality of life we enjoy. IDALS-DSC is striving to integrate our programs with other agencies and local concerns to improve water and local concerns to improve water and soil quality throughout the state and nation.

Programme latin de don d'organes: les intensivistes se mettent en réseau [Latin program for organ donation: the intensivists are networking]

The new Swiss federal law on organ and transplantation strengthens the responsibilities of the intensive care units. In Italian and French speaking parts of Switzerland, the Programme Latin pour le Don d'Organe (PLDO) has been launched to foster a wider collaboration between intensivists and donation coordinators. The PLDO aims at optimising knowledge and expertise in organ donation through improvements in identification, notification and management of organ donors and their next of kin. The PLDO dispenses education to all professionals involved. Such organisation should allow increasing the number of organs available, while improving healthcare professionals experience and next of kin emotion throughout the donation process.