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Business case analysis with R : simulation tutorials to support complex business decisions
This tutorial teaches you how to use the statistical programming language R to develop a business case simulation and analysis. It presents a methodology for conducting business case analysis that minimizes decision delay by focusing stakeholders on what matters most and suggests pathways for minimizing the risk in strategic and capital allocation decisions. Business case analysis, often conducted in spreadsheets, exposes decision makers to additional risks that arise just from the use of the spreadsheet environment.
Book
Development and validation of the Michigan Chronic Disease Simulation Model (MICROSIM)
Strategies to prevent or delay Alzheimer’s disease and related dementias (AD/ADRD) are urgently needed, and blood pressure (BP) management is a promising strategy. Yet the effects of different BP control strategies across the life course on AD/ADRD are unknown. Randomized trials may be infeasible due to prolonged follow-up and large sample sizes. Simulation analysis is a practical approach to estimating these effects using the best available existing data. However, existing simulation frameworks cannot estimate the effects of BP control on both dementia and cardiovascular disease. This manuscript describes the design principles, implementation details, and population-level validation of a novel population-health microsimulation framework, the MIchigan ChROnic Disease SIMulation (MICROSIM), for The Effect of Lower Blood Pressure over the Life Course on Late-life Cognition in Blacks, Hispanics, and Whites (BP-COG) study of the effect of BP levels over the life course on dementia and cardiovascular disease. MICROSIM is an agent-based Monte Carlo simulation designed using computer programming best practices. MICROSIM estimates annual vascular risk factor levels and transition probabilities in all-cause dementia, stroke, myocardial infarction, and mortality in a nationally representative sample of US adults 18+ using the National Health and Nutrition Examination Survey (NHANES). MICROSIM models changes in risk factors over time, cognition and dementia using changes from a pooled dataset of individual participant data from 6 US prospective cardiovascular cohort studies. Cardiovascular risks were estimated using a widely used risk model and BP treatment effects were derived from meta-analyses of randomized trials. MICROSIM is an extensible, open-source framework designed to estimate the population-level impact of different BP management strategies and reproduces US population-level estimates of BP and other vascular risk factors levels, their change over time, and incident all-cause dementia, stroke, myocardial infarction, and mortality.
Journal Article
Design and Development of Digital Twins: a Case Study in Supply Chains
Digital twin technology consists of creating virtual replicas of objects or processes that simulate the behavior of their real counterparts. The objective is to analyze its effectiveness or behavior in certain cases to improve its effectiveness. Applied to products, machines and even complete business ecosystems, the digital twin model can reveal information from the past, optimize the present and even predict the future performance of the different areas analyzed. In the context of supply chains, digital twins are changing the way they do business, providing a range of options to facilitate collaborative environments and data-based decision making and making business processes more robust. This paper proposes the design and development of a digital twin for a case study of a pharmaceutical company. The technology used is based on simulators, solvers and data analytic tools that allow these functions to be connected in an integral interface for the company.
Journal Article
Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications
Energy is vital parameter for communication in Internet of Things (IoT) applications via Wireless Sensor Networks (WSN). Genetic algorithms with dynamic clustering approach are supposed to be very effective technique in conserving energy during the process of network planning and designing for IoT. Dynamic clustering recognizes the cluster head (CH) with higher energy for the data transmission in the network. In this paper, various applications, like smart transportation, smart grid, and smart cities, are discussed to establish that implementation of dynamic clustering computing-based IoT can support real-world applications in an efficient way. In the proposed approach, the dynamic clustering-based methodology and frame relay nodes (RN) are improved to elect the most preferred sensor node (SN) amidst the nodes in cluster. For this purpose, a Genetic Analysis approach is used. The simulations demonstrate that the proposed technique overcomes the dynamic clustering relay node (DCRN) clustering algorithm in terms of slot utilization, throughput and standard deviation in data transmission.
Journal Article
A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives
With the rapid advancement of cyber-physical systems, Digital Twin (DT) is gaining ever-increasing attention owing to its great capabilities to realize Industry 4.0. Enterprises from different fields are taking advantage of its ability to simulate real-time working conditions and perform intelligent decision-making, where a cost-effective solution can be readily delivered to meet individual stakeholder demands. As a hot topic, many approaches have been designed and implemented to date. However, most approaches today lack a comprehensive review to examine DT benefits by considering both engineering product lifecycle management and business innovation as a whole. To fill this gap, this work conducts a state-of-the art survey of DT by selecting 123 representative items together with 22 supplementary works to address those two perspectives, while considering technical aspects as a fundamental. The systematic review further identifies eight future perspectives for DT, including modular DT, modeling consistency and accuracy, incorporation of Big Data analytics in DT models, DT simulation improvements, VR integration into DT, expansion of DT domains, efficient mapping of cyber-physical data and cloud/edge computing integration. This work sets out to be a guide to the status of DT development and application in today’s academic and industrial environment.
Journal Article
Mental representation and the discovery of new strategies
Research summary: Managers' mental representations affect the perceived payoffs and alternatives that managers consider. Thus, mental representations affect how managers search for profitable strategies as well as the quality of strategies they discover. To study how mental representation and search interact, we formally model the dual search over possible representations and over policy choices of a strategy \"landscape.\" We analyze when it is preferable to emphasize searching for the best policies rather than the best mental representation, and vice versa. We show that, in the long run, a balance between the two search modes not only results in better expected performance, but also reduces the variation in performance. Additionally, the article describes conditions under which increased accuracy of mental representations can actually worsen firm performance. Managerial summary: Managers' mental representations affect the perceived payoffs and alternatives that managers consider. Thus, mental representations affect the quality of strategies managers can discover. We analyze a computer simulation of how managers use mental representations to search for strategies. This sheds light on how managers should deal with the trade-off between searching for policies and searching for representations; that is, whether managers should think creatively about how to represent a strategy problem or whether they should just stick to the current problem understanding, and try to find ways to improve performance as suggested by the current representation. We provide insight regarding the balance between the two search modes and describe conditions under which increasingly accurate mental representations can worsen firm performance.
Journal Article
Online Vehicle Routing: The Edge of Optimization in Large-Scale Applications
With the emergence of ride-sharing companies that offer transportation on demand at a large scale and the increasing availability of corresponding demand data sets, new challenges arise to develop routing optimization algorithms that can solve massive problems in real time. In “Online Vehicle Routing: The Edge of Optimization in Large-Scale Applications,” D. Bertsimas, P. Jaillet, and S. Martin present a novel and generalizable backbone algorithm that uses integer optimization to find high-quality solutions to large routing optimization problems. The algorithm, together with the real-time routing optimization software framework developed and shared by the authors, can dispatch thousands of taxis serving more than 25,000 customers per hour. An extensive study with historical simulations of Yellow Cabs in New York City is used to both show that the algorithm improves on the performance of existing heuristics and to provide insights on the optimization opportunities of a large ride-sharing fleet.
With the emergence of ride-sharing companies that offer transportation on demand at a large scale and the increasing availability of corresponding demand data sets, new challenges arise to develop routing optimization algorithms that can solve massive problems in real time. In this paper, we develop an optimization framework, coupled with a novel and generalizable backbone algorithm, that allows us to dispatch in real time thousands of taxis serving more than 25,000 customers per hour. We provide evidence from historical simulations using New York City routing network and yellow cab data to show that our algorithms improve upon the performance of existing heuristics in such real-world settings.
The online supplement is available at
https://doi.org/10.1287/opre.2018.1763
.
Journal Article
Digital Twin Framework for Large-Scale Optimization Problems in Supply Chains: A Case of Packing Problem
The development of new information technologies at the beginning of the 21st century allows the integration between the physical and the virtual world. In Engineering, an emerging technology called digital twins is presented as the mechanism to virtualize the operation of devices, machines and processes. In industrial engineering and specifically in supply chains there is a growing interest in the development of digital twins. For this reason, this paper proposes the integration of large-scale optimization problems in a digital platform that allows the solution of these problems for decision-making in real time. Bin-Packing and Vehicle Routing problems are addressed through the interface of a commercial supply chain management platform and heuristic optimization algorithms. We use technology based on simulation of discrete events to achieve the periodic decisions that make up the Digital Supply ChainTwin engine. A hypothetical case solution is presented to verify the performance of the proposed development.
Journal Article