Smart Mobility Meets Industry: Enhancing Energy Flexibility Potentials by Combining Industrial Production & Electric Vehicle Charging

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Bayer, David; Bollenbach, Jessica; Lersch, Jonathan; Rusche, Simon; Weibelzahl, Martin

Abstract: The increasing share of renewable energy sources poses the challenge of volatil ... Expand

Abstract: The increasing share of renewable energy sources poses the challenge of volatile energy generation, requiring demand side management (DSM) to manage such volatility. In view of the high industrial electricity demand and the increasing charging demand of electric vehicles (EVs), both industry and mobility represent relevant areas of DSM. However, the combination of EV charging and energy-intensive industrial processes still contains untapped synergy potentials. Thus, we present a mixed-integer linear program and quantify the economic and ecologic potential of combining energy flexibilities of industry and electric mobility within a case study. For our evaluation, we compare the results of our model to a benchmark case that separately manages industry and EV flexibilities. Our findings suggest that implementing our approach would yield both economic and ecological benefits, resulting in a reduction in anticipated costs and emissions, as well as a decrease in associated uncertainty. KeywordsEnergy-flexible Factory, Vehicle-to-Factory, Electric Vehicle Charging, Mixed-Integer Linear Programming Additionally, the increasing use of electric vehicles (EVs) raises concerns that the existing distribution networks may become strained beyond their capacity (Godina et al., 2016). These concerns are due to Smart Mobility Meets Industry Collapse

Topics: decision making manufacturing information system

Methods: simulation case study optimization model integer linear programming dynamic programming

How to Maximize Clicks for Display Advertisement in Digital Marketing? A Reinforcement Learning Approach

2023 | Information Systems Frontiers | Citations: 0

Authors: Singh, Vinay; Nanavati, Brijesh; Kar, Arpan Kumar; Gupta, Agam

Abstract: One of the core challenges in digital marketing is that the business conditions ... Expand

Abstract: One of the core challenges in digital marketing is that the business conditions continuously change, which impacts the reception of campaigns. A winning campaign strategy can become unfavored over time, while an old strategy can gain new traction. In data driven digital marketing and web analytics, A/B testing is the prevalent method of comparing digital campaigns, choosing the winning ad, and deciding targeting strategy. A/B testing is suitable when testing variations on similar solutions and having one or more metrics that are clear indicators of success or failure. However, when faced with a complex problem or working on future topics, A/B testing fails to deliver and achieving long-term impact from experimentation is demanding and resource intensive. This study proposes a reinforcement learning based model and demonstrates its application to digital marketing campaigns. We argue and validate with actual-world data that reinforcement learning can help overcome some of the critical challenges that A/B testing, and popular Machine Learning methods currently used in digital marketing campaigns face. We demonstrate the effectiveness of the proposed technique on real actual data for a digital marketing campaign collected from a firm. Collapse

Topics: marketing management artificial intelligence privacy big data web page

Methods: computational algorithm simulation experiment reinforcement learning descriptive statistic

Bidder Support in Multi-item Multi-unit Continuous Combinatorial Auctions: A Unifying Theoretical Framework

2022 | Information Systems Research | Citations: 0

Authors: Adomavicius, Gediminas; Gupta, Alok; Yang, Mochen

Abstract: Despite known advantages of combinatorial auctions, wide adoption of this alloca ... Expand

Abstract: Despite known advantages of combinatorial auctions, wide adoption of this allocation mechanism, especially in consumer-oriented marketplaces, is limited partially by the lack of effective bidder support information that can assist bidders to make bidding decisions. In this paper, we study the bidder support problem for general multi-item multi-unit (MIMU) combinatorial auctions, where multiple heterogeneous items are being auctioned and multiple homogeneous units are available for each item. Specifically, we consider continuous MIMU auctions, which impose minimal restrictions on bidding activities, thereby reducing the complexity of participation. Two prevalent bidding languages: OR bidding and XOR bidding, are discussed separately. For MIMU auctions with XOR bids, we derive theoretical results to calculate important bidder support metrics. We further demonstrate that bidder support results for MIMU auctions with OR bids can be derived directly from those with XOR bids, by viewing OR bids as XOR bids with each bid submitted by a unique bidder. Consequently, we establish MIMU auctions with XOR bids as the most general case, and unify the theoretical insights on bidder support problem for different bidding languages as well as different special cases of general MIMU auctions, namely single-item multi-unit (SIMU) auctions and multi-item single-unit (MISU) auctions. The derived theoretical results lead to algorithmic procedures that are capable of providing bidder support information efficiently in practice, and that outperform the commonly used integer programming approach. Theoretical insights of the general MIMU auctions also extend to auctions with additional bidding constraints, including batch-based combinatorial auctions, hierarchical combinatorial auctions, and combinatorial reverse auctions. History: This paper has been accepted for the Information Systems Research Special Section on Market Design and Analytics. Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2021.1068. Collapse

Topics: procurement management Linux computer hardware programming language accounting

Methods: simulation computational algorithm descriptive statistic integer programming dynamic programming

Theories: game theory

An Abortion Based Search Method for Optimal Coalition Structure Generation

2022 | Group Decision and Negotiation | Citations: 0

Authors: Narayan, Changder; Samir, Aknine; Animesh, Dutta

Abstract: The Coalition Structure Generation (CSG) problem is a partitioning of a set of a ... Expand

Abstract: The Coalition Structure Generation (CSG) problem is a partitioning of a set of agents into exhaustive and disjoint subsets to maximize social welfare. This NP-complete problem arises in many practical scenarios. Prominent examples are included in the field of transportation, e-Commerce, distributed sensor networks, and others. The fastest exact algorithm to solve the CSG problem is ODP-IP, which is a hybrid version of two previously established algorithms, namely Improved Dynamic Programming (IDP) and IP. In this paper, we show that the ODP-IP algorithm performs many redundant operations. To improve ODP-IP, we propose a faster abortion mechanism to speed up IP’s search. Our abortion mechanism decides at runtime which of the IP’s operations are redundant to skip them. Then, we propose a modified version of IDP (named MIDP) and an improved version of IP (named IIP). Based on these two improved algorithms, we develop a hybrid version (MIDP-IIP) to solve the CSG problem. After a detailed description of the new algorithm MIDP-IIP, an experimental comparison is conducted against ODP-IP. Our analysis shows that MIDP-IIP performs fewer operations than ODP-IP. In addition, MIDP-IIP reduced significantly many problem instances running times (11–37%). Collapse

Topics: Java

Methods: computational algorithm experiment literature study dynamic programming

Theories: cooperative game theory

Designing Personalized Treatment Plans for Breast Cancer

2021 | Information Systems Research | Citations: 6

Authors: Chen, Wei; Lu, Yixin; Qiu, Liangfei; Kumar, Subodha

Abstract: Breast cancer remains the leading cause of cancer deaths among women around the ... Expand

Abstract: Breast cancer remains the leading cause of cancer deaths among women around the world. Contemporary treatment for breast cancer is complex and involves highly specialized medical professionals collaborating in a series of information-intensive processes. This poses significant challenges to personalization and customization of treatment plans for individual patients. In this research, we follow the information systems design science paradigm and propose a novel framework for decision support of treatment planning for early stage breast cancer patients undergoing radiotherapy. The core of our framework consists of a predictive model that predicts patient outcome of a treatment plan based on clinical and patient characteristics, and an optimization model that optimizes the treatment plan based on predicted outcomes of different plans. Using a series of simulation experiments, we show that the treatment plans generated from our framework consistently outperform those from the existing practices in balancing the risk of local tumor recurrence and radiation-induced adverse effects, thereby reducing the treatment cost associated with these adverse effects. Our research contributes to the growing literature that examines the potential of healthcare information technologies in delivering cost-effective care. Further, we also contribute to healthcare practices by providing models and tools that have pragmatic value as part of the clinical care delivery system. Collapse

Topics: healthcare data usability personalization decision support decision support system

Methods: simulation experiment optimization model computational algorithm machine learning

Theories: control theory

A novel decision support system for optimizing aircraft maintenance check schedule and task allocation

2021 | Decision Support Systems | Citations: 0

Authors: Deng, Qichen; Santos, Bruno F.; Verhagen, Wim J.C.

Abstract: Modern aircraft have thousands of parts, systems, and components that need to be ... Expand

Abstract: Modern aircraft have thousands of parts, systems, and components that need to be recurrently inspected or replaced. To keep the fleet airworthy, maintenance planners have to schedule the maintenance checks for each aircraft and the associated tasks. In practice, these two complex problems are solved following the experience of planners, resulting in sub-efficient solutions. This paper presents the first decision support system (DSS) devel­ oped for optimizing both aircraft maintenance check schedule and task allocation. The DSS integrates aircraft maintenance check scheduling, task allocation to each maintenance check, and shift planning in the same framework. The practical relevance of the DSS is illustrated through three test cases. The results show that the DSS can be used not only to optimize maintenance plans but also to study future maintenance policies. The results reveal substantial improvements in all key performance indicators compared with the planning approach followed by a partner airline. Collapse

Topics: decision support system human in the loop human computer interaction decision making database system

Methods: computational algorithm case study dynamic programming 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

Text-based Causality Modeling with a Conceptual Label in a Hierarchical Topic Structure Using Bayesian Rose Trees

2021 | HICSS | Citations: 0

Authors: Ogawa, Takuro; Saga, Ryosuke

Abstract: This paper describes a method for constructing a causality model from review tex ... Expand

Abstract: This paper describes a method for constructing a causality model from review text data. Review text data include the evaluation factors of rating, and causality model extraction from text data is important for understanding the evaluation factors and their relationships. Several methods are available for extracting causality models by using a topic model. In particular, the method based on hierarchical latent Dirichlet allocation is useful for hierarchically comprehending causality structure. However, the depth of each topic in a hierarchical structure is forcefully pruned even if granularities differ for each topic. Thus, interpreting a hierarchical topic structure is difficult. To solve these problems, we construct a hierarchical topic structure with different depths by using Bayesian rose trees. Furthermore, we use conceptual labeling to add explicit semantics for each topic for interpretation. An experiment confirms that this model is accurate and interpretable using actual data. Collapse

Topics: electronic commerce Python

Methods: experiment causality analysis structural equation modeling topic model computational algorithm

Platform entry and homing as competitive strategies under cross-sided network effects

2021 | Decision Support Systems | Citations: 0

Authors: Wu, Cheng-Han; Chamnisampan, Netnapha

Abstract: In a competitive environment, determining when to enter the market and what homi ... Expand

Abstract: In a competitive environment, determining when to enter the market and what homing policies should be adopted are crucial strategic decisions for platform-based businesses. In this study, we investigate the in­ teractions between two platforms competing in two-sided markets with cross-sided network effects and deter­ mine their prices under different entry and homing strategies. We derive the equilibrium demands of two-sided markets for platforms and the equilibrium prices for two competing platforms. We further conduct a comparative analysis among scenarios with different combinations of homing and entry strategies. We find that early-entry platforms will preserve high margins in large early markets, where fierce price competition occurs once their rivals enter these markets. However, early entry is not always beneficial for platforms because the marketcannibalization effect emerges in the subsequent period. Furthermore, this effect will be amplified by the plat­ forms’ homing and entry strategies. In addition, cross-sided network effects lead buyers, sellers, and platforms to prefer a multi-homing policy, which is beneficial for society as a whole. Managerial insights are also provided for platform-based businesses. Collapse

Topics: network effect price management digital platform anonymity business-to-consumer

Methods: dynamic programming lasso regression cross sectional research

Theories: game theory

Designing information feedback for bidders in multi-item multi-unit combinatorial auctions

2020 | Decision Support Systems | Citations: 0

Authors: Sen, Anup K.; Bagchi, Amitava; Chakraborty, Soumyakanti

Abstract: Combinatorial auctions (CAs) promote allocative efficiency and are important mar ... Expand

Abstract: Combinatorial auctions (CAs) promote allocative efficiency and are important market mechanisms for a wide variety of specialized domains where bidders are allowed to place bids on packages of items. However, the adoption of combinatorial auctions in real-life business scenarios has been fairly limited, perhaps because bidders find it difficult to construct their bids without extensive knowledge of the current state of the auction. In this paper, we develop decision support tools for bidders to provide information feedback at runtime for the general class of combinatorial auctions namely, online (continuous) multi-item multi-unit combinatorial auctions (MUCAs), an area that has witnessed a rapid growth of interest in recent years. In online MUCAs the number of packages may be large, and bidders need real time decision support to construct their bids, such as information on the ask prices of packages. The deadness level of a package serves as the ask price, and indicates the minimum bid on the package that keeps it in contention for inclusion in winning combinations in future. It has proved a challenge to find a satisfactory method for computing the deadness levels of packages in MUCAs. Here we present exact methods for determining package deadness levels in such auctions. Both the OR and the XOR formulations are considered. Experiments on simulated data as well as on live data show that the time and memory requirements are not excessive, so it appears possible to adopt the methods for the procurement and sale of commodities in B2B and B2C markets. Collapse

Topics: decision support system procurement management business-to-business online auction business-to-consumer

Methods: computational algorithm experiment simulation dynamic programming survey