scholarly journals JANOS: An Integrated Predictive and Prescriptive Modeling Framework

2021 ◽  
Author(s):  
David Bergman ◽  
Teng Huang ◽  
Philip Brooks ◽  
Andrea Lodi ◽  
Arvind U. Raghunathan
Keyword(s):  
Predictive Modeling ◽  
Predictive Models ◽  
Optimization Model ◽  
Software Tool ◽  
Linear Transformations ◽  
Modeling Framework ◽  
Decision Variables ◽  
Prescriptive Model ◽  
The Relationship ◽  
Prescriptive Analysis

Business research practice is witnessing a surge in the integration of predictive modeling and prescriptive analysis. We describe a modeling framework JANOS that seamlessly integrates the two streams of analytics, allowing researchers and practitioners to embed machine learning models in an end-to-end optimization framework. JANOS allows for specifying a prescriptive model using standard optimization modeling elements such as constraints and variables. The key novelty lies in providing modeling constructs that enable the specification of commonly used predictive models within an optimization model, have the features of the predictive model as variables in the optimization model, and incorporate the output of the predictive models as part of the objective. The framework considers two sets of decision variables: regular and predicted. The relationship between the regular and the predicted variables is specified by the user as pretrained predictive models. JANOS currently supports linear regression, logistic regression, and neural network with rectified linear activation functions. In this paper, we demonstrate the flexibility of the framework through an example on scholarship allocation in a student enrollment problem and provide a numeric performance evaluation. Summary of Contribution. This paper describes a new software tool, JANOS, that integrates predictive modeling and discrete optimization to assist decision making. Specifically, the proposed solver takes as input user-specified pretrained predictive models and formulates optimization models directly over those predictive models by embedding them within an optimization model through linear transformations.

scholarly journals A Fuzzy Optimization Model for the Berth Allocation Problem and Quay Crane Allocation Problem (BAP + QCAP) with n Quays

2021 ◽  
Vol 9 (2) ◽  
pp. 152
Author(s):  
Edwar Lujan ◽  
Edmundo Vergara ◽  
Jose Rodriguez-Melquiades ◽  
Miguel Jiménez-Carrión ◽  
Carlos Sabino-Escobar ◽  
...  
Keyword(s):  
Optimization Model ◽  
Fuzzy Optimization ◽  
Optimal Solution ◽  
Allocation Problem ◽  
Berth Allocation ◽  
Triangular Fuzzy Numbers ◽  
Berth Allocation Problem ◽  
Arrival Times ◽  
Decision Variables ◽  
Short Time

This work introduces a fuzzy optimization model, which solves in an integrated way the berth allocation problem (BAP) and the quay crane allocation problem (QCAP). The problem is solved for multiple quays, considering vessels’ imprecise arrival times. The model optimizes the use of the quays. The BAP + QCAP, is a NP-hard (Non-deterministic polynomial-time hardness) combinatorial optimization problem, where the decision to assign available quays for each vessel adds more complexity. The imprecise vessel arrival times and the decision variables—berth and departure times—are represented by triangular fuzzy numbers. The model obtains a robust berthing plan that supports early and late arrivals and also assigns cranes to each berth vessel. The model was implemented in the CPLEX solver (IBM ILOG CPLEX Optimization Studio); obtaining in a short time an optimal solution for very small instances. For medium instances, an undefined behavior was found, where a solution (optimal or not) may be found. For large instances, no solutions were found during the assigned processing time (60 min). Although the model was applied for n = 2 quays, it can be adapted to “n” quays. For medium and large instances, the model must be solved with metaheuristics.

scholarly journals High coverage COVID-19 mRNA vaccination rapidly controls SARS-CoV-2 transmission in long-term care facilities

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Pablo M. De Salazar ◽  
Nicholas B. Link ◽  
Karuna Lamarca ◽  
Mauricio Santillana
Keyword(s):  
Long Term Care ◽  
Mitigation Strategies ◽  
Key Factors ◽  
Modeling Framework ◽  
High Coverage ◽  
Term Care ◽  
Early Effect ◽  
Care Facilities ◽  
The Relationship

Abstract Background Residents of Long-Term Care Facilities (LTCFs) represent a major share of COVID-19 deaths worldwide. Measuring the vaccine effectiveness among the most vulnerable in these settings is essential to monitor and improve mitigation strategies. Methods We evaluate the early effect of the administration of BNT162b2-mRNA vaccine to individuals older than 64 years residing in LTCFs in Catalonia, Spain. We monitor all the SARS-CoV-2 documented infections and deaths among LTCFs residents once more than 70% of them were fully vaccinated (February–March 2021). We develop a modeling framework based on the relationship between community and LTCFs transmission during the pre-vaccination period (July–December 2020). We compute the total reduction in SARS-CoV-2 documented infections and deaths among residents of LTCFs over time, as well as the reduction in the detected transmission for all the LTCFs. We compare the true observations with the counterfactual predictions. Results We estimate that once more than 70% of the LTCFs population are fully vaccinated, 74% (58–81%, 90% CI) of COVID-19 deaths and 75% (36–86%, 90% CI) of all expected documented infections among LTCFs residents are prevented. Further, detectable transmission among LTCFs residents is reduced up to 90% (76–93%, 90% CI) relative to that expected given transmission in the community. Conclusions Our findings provide evidence that high-coverage vaccination is the most effective intervention to prevent SARS-CoV-2 transmission and death among LTCFs residents. Widespread vaccination could be a feasible avenue to control the COVID-19 pandemic conditional on key factors such as vaccine escape, roll out and coverage.

Shore-Based Air-Defence Unit Shooting Mode Optimization Model Research

2014 ◽  
Vol 543-547 ◽  
pp. 1786-1789
Author(s):  
Da Wei Yang ◽  
Jian Chong Chu ◽  
Zi Ming Wang ◽  
Wei Bo Li
Keyword(s):  
Optimization Model ◽  
Air Defense ◽  
Model Research ◽  
Mutual Relations ◽  
Firing Efficiency ◽  
The Relationship

This paper analyzes the relationship between firing way and firing command way. Using the theory of probability and air defense combat strategy, it describes the concept, characteristics, and its mutual relations between concentration, dispersion, and mixing command in shooting mode, analyzes the influence of different ways of command on group firing efficiency.

Predictive Modeling for Adverse Events and Risk Stratification Programs for People Receiving Cancer Treatment

2021 ◽  
pp. OP.21.00198
Author(s):  
Chelsea K. Osterman ◽  
Hanna K. Sanoff ◽  
William A. Wood ◽  
Megan Fasold ◽  
Jennifer Elston Lafata
Keyword(s):  
Clinical Practice ◽  
Risk Stratification ◽  
Cancer Treatment ◽  
Acute Care ◽  
Predictive Modeling ◽  
Predictive Models ◽  
Emergency Department Visits ◽  
Targeted Interventions ◽  
Risk Algorithm ◽  
Oncology Care

Emergency department visits and hospitalizations are common among people receiving cancer treatment, accounting for a large proportion of spending in oncology care and negatively affecting quality of life. As oncology care shifts toward value- and quality-based payment models, there is a need to develop interventions that can prevent these costly and low-value events among people receiving cancer treatment. Risk stratification programs have the potential to address this need and optimally would consist of three components: (1) a risk stratification algorithm that accurately identifies patients with modifiable risk(s), (2) intervention(s) that successfully reduce this risk, and (3) the ability to implement the risk algorithm and intervention(s) in an adaptable and sustainable way. Predictive modeling is a common method of risk stratification, and although a number of predictive models have been developed for use in oncology care, they have rarely been tested alongside corresponding interventions or developed with implementation in clinical practice as an explicit consideration. In this article, we review the available published predictive models for treatment-related toxicity or acute care events among people receiving cancer treatment and highlight challenges faced when attempting to use these models in practice. To move the field of risk-stratified oncology care forward, we argue that it is critical to evaluate predictive models alongside targeted interventions that address modifiable risks and to demonstrate that these two key components can be implemented within clinical practice to avoid unplanned acute care events among people receiving cancer treatment.

Consumer evaluation of ingredient branding strategy

2017 ◽  
Vol 40 (7) ◽  
pp. 768-782 ◽  
Author(s):  
M. Deniz Dalman ◽  
Kartikeya Puranam
Keyword(s):  
Exploratory Research ◽  
Relative Importance ◽  
High Involvement ◽  
Content Type ◽  
Product Categories ◽  
Ingredient Branding ◽  
Decision Variables ◽  
Marketing Activities ◽  
The Relationship ◽  
Practical Implications

Purpose Prior research in ingredient branding (IB) has identified several important decision variables consumers use when evaluating IB alliances. This exploratory research aims to investigate the relationship between these variables and consumers’ buying likelihood of the IB alliance and the relative importance of these variables for low- vs high-involvement product categories. Design/methodology/approach A study with the participation of 458 mTurkers was conducted and the data were analyzed using random forests. Findings Findings reveal relative importance of different variables for an IB alliance and that these differ for low- vs high-involvement categories. Research limitations/implications Being exploratory in nature, this research has several limitations, such as using only one high- and one low-involvement categories. Practical implications Results of this research will help brand managers as they make decisions entering an IB alliance as well as with investing their budget on different aspects of their brand, and tailoring their marketing activities for low- vs high-involvement product categories. Originality/value To the best of authors’ knowledge, this paper is the first to discuss the relative importance of different decision variables in an IB context empirically.

scholarly journals The use of gamification in xMOOCs about energy: Effects and predictive models for participants’ learning

2019 ◽  
pp. 43-59 ◽  
Author(s):  
Elvira G Rincon Flores ◽  
Juanjo Mena ◽  
María Soledad Ramírez Montoya ◽  
Raul Ramirez Velarde
Keyword(s):  
Predictive Models ◽  
Online Courses ◽  
National Laboratory ◽  
Analytical Data ◽  
The United States ◽  
Massive Open Online Courses ◽  
Completion Rates ◽  
Academic Degree ◽  
Added Benefit ◽  
The Relationship

Open access education has significantly grown in strength as a new way of fostering innovation in schools. Such is the case of massive open online courses (MOOCs), which have the added benefit of encouraging the democratisation of learning. In this sense, the Bi-National Laboratory on Smart Sustainable Energy Management and Technology Training between Mexico and the United States of America was launched with the purpose of trying MOOC technology and measuring its impact on the academic, business, and social sectors. Under this scenario, this study aimed to show the relationship between using gamification and level of performance in a MOOC on energy topics. The methodology was quantitative, using the course analytical data for socio-demographic information and predictive models. A total of 6246 participants enrolled in the MOOC and 1060 finished it. The results showed that participants aged between 20 and 50 had the highest completion rates in the gamified challenge; the higher academic degree, the more inclined participants were to solve the gamified challenge; and no such distinction exists by gender.

scholarly journals A Cost Optimization Model for Multiresource Leveling Problem without Project Duration Constraint

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Qingyou Yan ◽  
Qian Zhang ◽  
Xin Zou
Keyword(s):  
Optimization Model ◽  
Cost Optimization ◽  
Resource Usage ◽  
Project Duration ◽  
Resource Demand ◽  
Resource Leveling ◽  
Proposed Model ◽  
Liquidated Damages ◽  
The Relationship

The study of traditional resource leveling problem aims at minimizing the resource usage fluctuations and obtaining sustainable resource supplement, which is accomplished by adjusting noncritical activities within their start and finish time. However, there exist limitations in terms of the traditional resource leveling problem based on the fixed project duration. This paper assumes that the duration can be changed in a certain range and then analyzes the relationship between the scarce resource usage fluctuations and project cost. This paper proposes an optimization model for the multiresource leveling problem. We take into consideration five kinds of cost: the extra hire cost when the resource demand is greater than the resource available amount, the idle cost of resource when the resource available amount is greater than the resource demand, the indirect cost related to the duration, the liquidated damages when the project duration is extended, and the incentive fee when the project duration is reduced. The optimal objective of this model is to minimize the sum of the aforementioned five kinds of cost. Finally, a case study is examined to highlight the characteristic of the proposed model at the end of this paper.

From Opacity to Transparency

2021 ◽  
pp. 240-271
Author(s):  
Sarosh Kuruvilla
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Data Sharing ◽  
Predictive Modeling ◽  
Predictive Models ◽  
Trade Unions ◽  
Evidence Based ◽  
Private Regulation ◽  
Worker Participation ◽  
Evidence Based Decision Making

This chapter studies specific ways in which opacity can be reduced — through the use of niche institutions, by stimulating the internalization goals of private regulation, and through fostering a critical mindset. It draws attention to the varieties of transparency required and specifically to the integration and inclusion of workers in private regulation programs to stimulate internalization of goals, especially through worker participation in compliance auditing and through methods such as surveys by which workers' perspectives are heard. The chapter then highlights the need for more data sharing, data analysis, and predictive modeling and concludes with specific recommendations for the variety of actors in private regulation to move the institutional field from opacity to transparency. Only through data analysis can we generate the predictive models that allow for evidence-based decision making and identification of other means by which the coupling of private regulation programs with worker outcomes can be increased. Ultimately, workers and trade unions, in what has been called contingent coupling, can help “shrink the gap between practices and outcomes” for workers by leveraging the private regulation policies of brands.

Managing Uncertainty in Geospatial Predictive Models

2010 ◽  
pp. 332-339
Author(s):  
Iftikhar U. Sikder
Keyword(s):  
Predictive Modeling ◽  
Predictive Models ◽  
Conditioning Factors ◽  
Classification Uncertainty

Geospatial predictive models often require mapping of predefined concepts or categories with various conditioning factors in a given space. This chapter discusses various aspects of uncertainty in predictive modeling by characterizing different typologies of classification uncertainty. It argues that understanding uncertainty semantics is a perquisite for efficient handling and management of predictive models.

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