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

A GPU-Accelerated Approach to Static Stability Assessments for Pallet Loading in Air Cargo

2022 | HICSS | Citations: 0

Authors: Mazur, Philipp Gabriel; Lee, No-San; Schoder, Detlef

Abstract: The static stability constraint is one of the most important constraints in pall ... Expand

Abstract: The static stability constraint is one of the most important constraints in pallet loading and plays a substantial role when assembling safe and loadable palletizing layouts. Current approaches reach their limits as soon as additional complexity is added, which is a given in the practice of air cargo logistics, or when performance becomes important. As our central objective, we explore a new approach to calculate static stability more performantly and to cover more complexity by relaxing several simplifying assumptions. The approach is implemented in a prototype and builds on the emerging technology of graphical processing unit acceleration in combination with physics engines. We propose a new artifact design and summarize the howto knowledge in the form of abstracted design principles. Our results demonstrate an improvement in terms of performance depending on the underlying hardware. We develop a conceptual model to assist future research in choosing a solution technology. Collapse

Topics: computer hardware application programming interface artificial intelligence agile software development stream processing

Methods: simulation computational algorithm design principle conceptual modelling Student's t-test

Learning Automata-based Misinformation Mitigation via Hawkes Processes

2021 | Information Systems Frontiers | Citations: 0

Authors: Abouzeid, Ahmed; Granmo, Ole-Christoffer; Webersik, Christian; Goodwin, Morten

Abstract: Mitigating misinformation on social media is an unresolved challenge, particular ... Expand

Abstract: Mitigating misinformation on social media is an unresolved challenge, particularly because of the complexity of information dissemination. To this end, Multivariate Hawkes Processes (MHP) have become a fundamental tool because they model social network dynamics, which facilitates execution and evaluation of mitigation policies. In this paper, we propose a novel light-weight intervention-based misinformation mitigation framework using decentralized Learning Automata (LA) to control the MHP. Each automaton is associated with a single user and learns to what degree that user should be involved in the mitigation strategy by interacting with a corresponding MHP, and performing a joint random walk over the state space. We use three Twitter datasets to evaluate our approach, one of them being a new COVID-19 dataset provided in this paper. Our approach shows fast convergence and increased valid information exposure. These results persisted independently of network structure, including networks with central nodes, where the latter could be the root of misinformation. Further, the LA obtained these results in a decentralized manner, facilitating distributed deployment in real-life scenarios. Collapse

Topics: misinformation Twitter social network social media fake news

Methods: simulation experiment computational algorithm reinforcement learning optimization model

Cache-Based Multi-Query Optimization for Data-Intensive Scalable Computing Frameworks

2021 | Information Systems Frontiers | Citations: 0

Authors: Michiardi, Pietro; Carra, Damiano; Migliorini, Sara

Abstract: In modern large-scale distributed systems, analytics jobs submitted by various u ... Expand

Abstract: In modern large-scale distributed systems, analytics jobs submitted by various users often share similar work, for example scanning and processing the same subset of data. Instead of optimizing jobs independently, which may result in redundant and wasteful processing, multi-query optimization techniques can be employed to save a considerable amount of cluster resources. In this work, we introduce a novel method combining in-memory cache primitives and multi-query optimization, to improve the efficiency of data-intensive, scalable computing frameworks. By careful selection and exploitation of common (sub)expressions, while satisfying memory constraints, our method transforms a batch of queries into a new, more efficient one which avoids unnecessary recomputations. To find feasible and efficient execution plans, our method uses a cost-based optimization formulation akin to the multiple-choice knapsack problem. Extensive experiments on a prototype implementation of our system show significant benefits of worksharing for both TPC-DS workloads and detailed micro-benchmarks. Collapse

Topics: Apache Spark distributed computing application programming interface database system operating system

Methods: experiment computational algorithm design artifact optimization model knapsack problem

Time-preference-based on-spot bundled cloud-service provisioning

2021 | Decision Support Systems | Citations: 0

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

Abstract: The cloud computing spot instance is one offering that vendors are leveraging to ... Expand

Abstract: The cloud computing spot instance is one offering that vendors are leveraging to provide differentiated service to an expanding pay-per-use computing market. Spot instances have cost advantages, albeit at a trade-off of in­ terruptions that can occur when the user’s bid price falls below the spot price. The interruptions are often exacerbated since customers often require resources in bundles. For these reasons, customers might have to wait for a long time before their jobs are completed. In this paper, we propose a behavioral-economic model in the form of time-preference-based bids, wherein users are willing to use and bid for services at other times if the vendor cannot provide the resources at the preferred time. Given such bids, we consider the problem of provi­ sioning for such service requests. We develop a time-preference-based optimization model. Since the optimiza­ tion model is NP-Hard, we develop rule-based genetic algorithms. We have obtained very encouraging results with respect to standard commercial solver as a benchmark. In turn, our results provide evidence for the viability of our approach for online service-provisioning problems. Collapse

Topics: cloud computing cloud computing provider packaged software virtualization technology digital platform

Methods: computational algorithm experiment optimization model literature study knapsack problem

Theories: behavioral economics

A Graph-Based Ant Algorithm for the Winner Determination Problem in Combinatorial Auctions

2021 | Information Systems Research | Citations: 0

Authors: Ray, Abhishek; Ventresca, Mario; Kannan, Karthik

Abstract: Combinatorial auctions (CAs) are used to allocate bundles of items among interes ... Expand

Abstract: Combinatorial auctions (CAs) are used to allocate bundles of items among interested bidders. However, to resolve bidder preference elicitation problems, CAs are conducted iteratively. Winner determination is a key bottleneck that restricted the widespread adoption of such iterative CAs. Time bounded winner determination is further complicated by the increased variety and velocity of bids each round, and so regular solvers such as IBM CPLEX and A Mathematical Programming Language have been demonstrably ineffective in determining winners within a short duration. In response, heuristic-based methods have enjoyed an increase in applicability. We add to the growing body of work in heuristic-based winner determination by proposing an ant colony–based anytime algorithm (traveling ants for combinatorial auctions, or TrACA) that produces optimal or near-optimal winner determination results within specified time. Experiments are performed on 94 open test instances that display a variety in bid-bundle composition. We show and compare the performance of TrACA to 20 most recent state-of-the-art heuristics and two of the best exact algorithms (CPLEX and Max W Clique). Compared with 12 of 20 heuristics that employ classic search (e.g., stochastic local search, tabu search) or genetic or memetic algorithms, TrACA does significantly better on all instances. On the other eight heuristics that employ advanced hybrid search methodologies (e.g., hyperheuristics, biased random keys), TrACA performs significantly better on the harder test instances. Additionally, we show that TrACA resolves the trade-off between time and accuracy by achieving optimal solutions on 76 out of 94 instances, in at most one-sixth the time taken by exact algorithms. Finally, we analyze the TrACA search process and highlight factors that make TrACA suitable for solving hard auction instances within a prespecified (short) duration. Collapse

Topics: source code random-access memory Microsoft Windows virtualization technology cloud computing

Methods: computational algorithm experiment nonparametric test Mann Whitney U test optimization model

A Framework for Optimal Crowdsourcing Contest Design

2021 | International Conference on Information Systems | Citations: 0

Authors: Zhu, Wangsheng; Mo, Jiahui; Menon, Syam; Sarkar, Sumit

Abstract: Crowdsourcing contests are popular mechanisms for firms to obtain solutions to ... Expand

Abstract: Crowdsourcing contests are popular mechanisms for firms to obtain solutions to tasks from external sources. Platforms allow firms to decide the attributes of their contests, such as prize and duration. Selecting an appropriate set of attributes can increase the probability of success. We develop a framework that identifies an effective prediction model, and maximizes the predicted probability of success of a crowdsourcing contest using optimization techniques. The framework considers design features of a focal task as also those of other open tasks competing with the focal task. The contest design problem is formulated as a mixed linear knapsack problem, with several novel variants. We present solution procedures for these variants, and validate the framework using a real-world dataset. Our results show that the contest designs recommended by the proposed framework can increase success probabilities significantly relative to what seekers specify. Collapse

Topics: logistics management crowdsourcing privacy information privacy concern search engine

Methods: logistic regression optimization model knapsack problem machine learning experiment

Portfolio Decision Analysis for Evaluating Stakeholder Conflicts in Land Use Planning

2020 | Group Decision and Negotiation | Citations: 0

Authors: Fasth, Tobias; Bohman, Samuel; Larsson, Aron; Ekenberg, Love; Danielson, Mats

Abstract: Urban planning typically involves multiple actors and stakeholders with conflict ... Expand

Abstract: Urban planning typically involves multiple actors and stakeholders with conflicting opinions and diverging preferences. The proposed development plans and actions greatly affect the quality of life of the local community at different spatial scales and time horizons. Consequently, it is important for decision-makers to understand and analyse the conflicting needs and priorities of the local community. This paper presents a decision analytic framework for evaluating stakeholder conflicts in urban planning. First, the stakeholders state their preferences regarding the actions in terms of a set of criteria and estimate the weight of each criterion. Then, a conflict index and overall value for each action is calculated. Next, a set of Pareto efficient portfolios of actions are generated by solving an optimization problem with different levels of conflict as a resource constraint. Finally, a sensitivity analysis of the actions is performed. The framework is demonstrated using real-world survey data collected in the municipality of Upplands Väsby, Sweden. Collapse

Topics: content management system open source social influence

Methods: survey sensitivity analysis optimization model computational algorithm knapsack problem

Minimizing Response Time of IoT-Based Traffic Information System through Decentralized Server System

2020 | HICSS | Citations: 0

Authors: Hassanlou, Maryam; Sun, Yi; Fang, Fang

Abstract: Many metropolises seek to relieve traffic congestions and reduce vehicle acciden ... Expand

Abstract: Many metropolises seek to relieve traffic congestions and reduce vehicle accidents by implementing Intelligent Traffic Information Systems. These systems manage continuous communication between vehicles, various roadside IoT devices and a central server in real time for traffic control and vehicle navigations. Short response time is critical to the success of these time-sensitive systems. For a small area, a system with centralized server architecture may just work fine. For a larger area with more IoT devices and traffic, however, the system may experience excessive response time as a result of increased network distance and constrained server processing capacity. We propose a decentralized server system to properly manage and reduce service response time. We have also developed a binary nonlinear constrained programming model with Genetic Algorithm for a heuristic solution. Collapse

Topics: internet of things mobile system

Methods: computational algorithm experiment knapsack problem natural language processing

Fast and frugal heuristics for portfolio decisions with positive project interactions

2020 | Decision Support Systems | Citations: 5

Authors: Durbach, Ian N.; Algorta, Simón; Kantu, Dieudonné Kabongo; Katsikopoulos, Konstantinos V.; Şimşek, Özgür

Abstract: We consider portfolio decision problems with positive interactions between proje ... Expand

Abstract: We consider portfolio decision problems with positive interactions between projects. Exact solutions to this problem require that all interactions are assessed, requiring time, expertise and effort that may not always be available. We develop and test a number of fast and frugal heuristics – psychologically plausible models that limit the number of assessments to be made and combine these in computationally simple ways – for portfolio decisions. The proposed “add-the-best” family of heuristics constructs a portfolio by iteratively adding a project that is best in a greedy sense, with various definitions of “best”. We present analytical results showing that information savings achievable by heuristics can be considerable; a simulation experiment showing that port­ folios selected by heuristics can be close to optimal under certain conditions; and a behavioral laboratory ex­ periment demonstrating that choices are often consistent with the use of heuristics. Add-the-best heuristics combine descriptive plausibility with effort-accuracy trade-offs that make them potentially attractive for pre­ scriptive use. Collapse

Topics: decision making information system use decision support system decision quality

Methods: simulation experiment laboratory experiment knapsack problem mixed method