Domain-independent real-time service provisioning in digital platforms: Featuring bundling and customer time-preference

2023 | Decision Support Systems | Citations: 0

Authors: Mukherjee, Anik; Rangaraja P., Sundarraj; Vander Meer, Debra; Dutta, Kaushik

Abstract: Digital platforms have emerged as an important technology underpinning the new e ... Expand

Abstract: Digital platforms have emerged as an important technology underpinning the new economy. A key problem in such platforms concerns provisioning decisions for customer-service requests, in order to maximize the pro­ vider’s revenue subject to resource availability. Provisioning is important to meet customer needs, and in turn, for customer retention. The service provisioning problem, in addition to being NP-hard, is further compounded by the fact that the timeframe to make provisioning decisions is short, and more importantly, varies considerably across different platform domains. Digital-platform customers often request bundled services and are sensitive to when tasks get completed. We describe a behavioral-economic model of this problem and develop a domainindependent solution featuring a judicious combination of optimization-backed rule-based approach. Through extensive computational results, we show how our approach has the potential for generalizability and tailor­ ability across different domains in a seamless manner, thus offering the advantages proposed in the softwarereusability literature. Implications to platform sustainability, reusable platform solutions and to practical ap­ proaches to real-time platforms are also provided. Collapse

Topics: digital platform decision making cloud computing ride sharing platform smartphone

Methods: experiment computational algorithm knapsack problem association rule learning simulation

Theories: behavioral economics

Clustering temporal disease networks to assist clinical decision support systems in visual analytics of comorbidity progression

2021 | Decision Support Systems | Citations: 3

Authors: Lu, Yajun; Chen, Suhao; Miao, Zhuqi; Delen, Dursun; Gin, Andrew

Abstract: Detection and characterization of comorbidity, the presence of more than one dis ... Expand

Abstract: Detection and characterization of comorbidity, the presence of more than one distinct disorder or illness concurrently occurring among a specific cohort of patients, is an invaluable decision aid and a prominent challenge in healthcare research and practice. The aim of this paper is to design a novel visual analytics system that can support efficient pattern detection and intuitive visualization of comorbidity progression modeled via temporal disease networks (TDNs). In the underlying system, we proposed two new clustering tech­ nologies—temporal clustering and disease clustering to detect the time of notable progression changes and simplify the visualization of TDNs. Through two case studies on Clostridioides Difficile and stroke, we demon­ strate that the proposed system is able to provide evidence-based and visual insights regarding comorbidity progression effectively for clinical decision support. Collapse

Topics: health information system Python data warehouse decision support systems design

Methods: cluster analysis computational algorithm case study literature study structured literature research

A web-based group decision support system for academic term preparation

2018 | Decision Support Systems | Citations: 0

Authors: Siddiqui, Atiq W.; Raza, Syed Arshad; Tariq, Zeeshan Muhammad

Abstract: In this paper, we present a web-based group decision support system for the Acad ... Expand

Abstract: In this paper, we present a web-based group decision support system for the Academic Term Preparation problem faced at a business school of a large Middle Eastern university. This multi-stage problem involves several stakeholders that need to coordinate for the course offerings, instructor assignments, and the preparation of the relevant timetables. This coordination is facilitated through a web application that implements all stages of the workflow. It further automates the timetabling task through an optimization module using a new multi-objective mixed integer programming model. The model is based on curriculum and student sectioning requirements; academic and administrative policies; and, the present capacity situation. The new system replaces a semi-automated spread sheet tool, resulting in improved quality of timetables, efficient workflow coordination, customized reporting, significantly reduced lead times, and eradication of human errors. Collapse

Topics: group decision support system decision support system Python spreadsheet enterprise architecture

Methods: computational algorithm mixed-integer programming integer programming optimization model multi objective optimization