Virginia M. Miori

Business Plus Intelligence Plus Technology Equals Business Intelligence

In this article the authors will show how the parallel developments of information technology at the operational business level and decision support concepts progressed through the decades of the twentieth century with only minimal success at strategic application. They will posit that the twin technological developments of the world-wide-web and very inexpensive mass storage provided the environment to facilitate the convergence of business operations and decision support into the strategic application of business intelligence.

A Multiple Objective Goal Programming Approach to the Truckload Routing Problem

Truckload (TL) routing has always been a challenge. The TL routing problem (TRP) itself is hard, but the complexity of solving the problem increases due to the stochastic nature of TL demand. It is traditionally approached using single objective solution methodologies that range from linear programming to dynamic programming techniques. This paper presents a deterministic multiple objective formulation of the TRP. A ‘route algebra’ is developed to facilitate the solution procedure, paving the way for the use of goal programming and tabu search techniques.

Forecasting Treatment Outcomes for the Futures Drug and Alcohol Rehabilitation Center

Abstract This is the second in a series of papers focused on alcohol and substance abuse rehabilitation centers. Centers face the ongoing challenge of validating outcomes to meet the burden of evidence for insurance companies. In the first paper, data mining was used to establish baseline patterns in treatment success rates, for the Futures: Palm Beach Rehabilitation Center, that have a direct impact on a client’s ability to receive insurance coverage for treatment programs. In this paper, we examine 2016 outcomes and report on facility efficacy, alumni progression and sobriety, and forecast treatment success rates (short and long term) in support of client insurability. Data collection has been standardized and includes admissions data, electronic medical records data, satisfaction survey data, post-discharge survey data, Centers for Disease Control (CDC) data, and demographic data. Clustering, partitioning, ANOVA, stepwise regression and stepwise Logistic regression are applied to the data to determine statistically significant drivers of treatment success.

FORECASTING ACTIVITY LEVELS AS A BASELINE FOR PREDICTING PAIN AND DISCOMFORT LEVELS IN CANINES

Evaluating pain and discomfort in animals is difficult at best. Veterinarians believe however, that they can establish a proxy for estimating levels of pain and discomfort in canines by observing variations in their activity levels. Sufficient research has been conducted to justify this assertion, but little has been conducted to analyze the volumes of activity data collected. We present the first of a series of analyses aimed at ultimately presenting an effective predictive tool for canine pain and discomfort levels. In this chapter, we perform analyses on a dataset of normal (control) dogs, containing almost 3 million records. The forecasting analyses incorporated multiple polynomial regression models with transcendental transformations and ARIMA models to provide effective determination and prediction of baseline normal canine activity levels.

Simulation to Forecast the Impact of Electronic Medical Records and Inventory (Patient) Postponement in Developing Efficient Heath Care Supply Chains

The authors have previously validated a design of the health-care supply chain which treats patients as inventory without loss of respect for the patients. This work continues examination of patients as inventory while addressing the dual objectives of reducing redundancy in services and creating greater efficiency in the health-care supply chain. Historical data is used to forecast health care needs in light of the increasingly specialized health-care professionals, which have resulted in much more flexible and expensive supply chains. The lack of common data storage, or electronic medical records (EMRs), has created a need for redundancy (or rework) in medical testing. The use of EMR will also enhance our ability to forecast needs in the future. We perform simulations using SigmaFlow software to address our goals relative to the resource constraints, monetary constraints, and the overall culture of the medical supply chain. The simulation outcomes lead us to recommendations for data warehousing as well as providing mechanisms, like inventory postponement strategies, to establish structures for more efficiency, and reduced flexibility in the supply chains.

The Supply Chain in Manufacturing, Distribution, and Transportation : Modeling, Optimization, and Applications

INDUSTRIAL AND SERVICE APPLICATIONS OF THE SUPPLY CHAIN Multicriteria Decision Making in Ethanol Production Problems: A Fuzzy Goal Programming Approach Kenneth D. Lawrence, Dinesh R. Pai, Ronald K. Klimberg and Sheila M. Lawrence From Push to Pull: The Automation and Heuristic Optimization of a Caseless Filler Line in the Dairy Industry Brian W. Segulin Optimization of Medical Services: The Supply Chain and Ethical Implications Daniel J. Miori and Virginia M. Miori Using Hierarchical Planning to Exploit Supply Chain Flexibility: An Example from the Norwegian Meat Industry Peter Schutz, Asgeir Tomasgard, and Kristin Tolstad Uggen Transforming U.S. Army Supply Chains: An Analytical Architecture for Management Innovation Greg H. Parlier ANALYTIC PROBABILISTIC MODELS OF SUPPLY CHAIN PROBLEMS A Determination of the Optimal Level of Collaboration between a Contractor and Its Suppliers under Demand Uncertainty Seong-Hyun Nam, John Vitton, and Hisashi Kurata Online Auction Models and Their Impact on Sourcing and Supply Management John F. Kros and Christopher M. Keller Analytical Models for Integrating Supplier Selection and Inventory Decisions Burcu B. Keskin Inventory Optimization of Small Business Supply Chains with Stochastic Demand Kathleen Campbell, Gerard Gampagna, Anthony Costanzo and Christopher Matthews OPTIMIZATION MODELS OF SUPPLY CHAIN PROBLEMS A Dynamic Programming Approach to the Stochastic Truckload Routing Problem Virginia M. Miori Modeling Data Envelopment Analysis (DEA) Efficient Location/Allocation Decisions Ronald K. Klimberg, Samuel J. Ratick, Vinay Tavva, Sasanka Vuyyuru, and Daniel Mrazik Sourcing Models for End-of-Use Products in a Closed-Loop Supply Chain Kishore K. Pochampally and Surendra M. Gupta A Bi-Objective Supply Chain Scheduling Tadeusz Sawik Applying Data Envelopment Analysis and Multiple Objective Data Envelopment Analysis to Identify Successful Pharmaceutical Companies Ronald K. Klimberg, George P. Sillup, George Webster, Harold Rahmlow, and Kenneth D. Lawrence

Application of Triplet Notation and Dynamic Programming to Single-Line, Multi-Product Dairy Production Scheduling

The application of optimal methods for production scheduling in the dairy industry has been limited. Within supply chain terminology, dairy production was generally considered a push process but with advancements in automation, the industry is slowly transforming to a pull process. In this paper, the authors present triplet notation applied to the production scheduling of a single production line used for milk, juice, and carnival drinks. Once production and cleaning cycles are characterized as triplets, the problem is formulated. Lagrange relaxation is applied and the final solution is generated using dynamic programming.