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

STAY FLEXIBLE: A PRESCRIPTIVE PROCESS MONITORING APPROACH FOR ENERGY FLEXIBILITY-ORIENTED PROCESS SCHEDULES

2023 | European Conference On Information Systems | Citations: 0

Authors: Hermann, Julia; Rusche, Simon; Moder, Linda; Weibelzahl, Martin

Abstract: The transition from fossil fuels to renewable energy sources poses major challe ... Expand

Abstract: The transition from fossil fuels to renewable energy sources poses major challenges for balancing increasingly weather-dependent energy supply and demand. Demand-side energy flexibility, offered particularly by companies, is seen as a promising and necessary approach to address these challenges. Process mining provides significant potential to prevent a deterioration of product quality or process flows due to flexibilization and allows for exploiting monetary benefits associated with flexible process operation. Hence, we follow the design science research paradigm to develop PM4Flex, a prescriptive process monitoring approach, that generates recommendations for pending process flows optimized under fluctuating power prices by implementing established energy flexibility measures. Thereby, we consider company-and process-specific constraints as well as historic event logs. We demonstrate and evaluate PM4Flex by implementing it as a software prototype and applying it to exemplary data from a heating and air conditioning company, observing considerable cost savings of 1.56ct per kWh or 34.35%. Collapse

Topics: process mining procurement management database system virtualization technology energy information system

Methods: event log design artifact optimization model literature study design science

Will they take this offer? A machine learning price elasticity model for predicting upselling acceptance of premium airline seating

2023 | Information & Management | Citations: 0

Authors: Thirumuruganathan, Saravanan; Emadi, Noora Al; Jung, Soon-gyo; Salminen, Joni; Robillos, Dianne Ramirez; Jansen, Bernard J.

Abstract: Employing customer information from one of the world’s largest airline companies ... Expand

Abstract: Employing customer information from one of the world’s largest airline companies, we develop a price elasticity model (PREM) using machine learning to identify customers likely to purchase an upgrade offer from economy to premium class and predict a customer’s acceptable price range. A simulation of 64.3 million flight bookings and 14.1 million email offers over three years mirroring actual data indicates that PREM implementation results in approximately 1.12 million (7.94%) fewer non-relevant customer email messages, a predicted increase of 72,200 (37.2%) offers accepted, and an estimated $72.2 million (37.2%) of increased revenue. Our results illustrate the potential of automated pricing information and targeting marketing messages for upselling acceptance. We also identified three customer segments: (1) Never Upgrades are those who never take the upgrade offer, (2) Upgrade Lovers are those who generally upgrade, and (3) Upgrade Lover Lookalikes have no historical record but fit the profile of those that tend to upgrade. We discuss the implications for airline companies and related travel and tourism industries. Collapse

Topics: electronic mail marketing management price management customer satisfaction application programming interface

Methods: autoencoder data transformation machine learning experiment principal component analysis

Theories: optimization theory decision theory

Maximizing student opportunities for in-person classes under pandemic capacity reductions

2022 | Decision Support Systems | Citations: 0

Authors: Cardonha, Carlos; Bergman, David; Day, Robert

Abstract: In this article, we describe the decision support system that was developed for ... Expand

Abstract: In this article, we describe the decision support system that was developed for the assignment of courses to teaching modalities and rooms for the Fall semester of 2020 at the University of Connecticut (UConn). With the adoption of safety/mitigation standards imposed by the COVID-19 pandemic, the seating capacities of rooms were reduced by more than 70%, thus making virtually every existing room assignment for Fall 2020 infeasible. The demand for in-person instruction required the reassignment of a large number of courses to rooms, where not all requests for physical space could be accommodated. In order to maximize opportunities for in-person in­ struction, UConn introduced a teaching modality in which class meetings are attended on campus by only 50% of the enrolled students. As decision makers were given partial flexibility to assign teaching modalities to classes, the complexity of the assignment problem increased considerably, especially because the real-world instances involved hundreds of rooms and thousands of classes and required a quick solution turnaround in practice. In this article, we introduce this flexible assignment problem and describe the two mixed-integer programming for­ mulations that were used to solve the real-world instances of the problem; in particular, one of the formulations leverages structural properties presented in this work in order to represent the problem in a more compact way. We explain how we tailored our algorithms to solve the real-world problem, describe the dynamics of the interactive decision support system created in this initiative, and present insights derived from our study. Collapse

Topics: decision support system digital dashboard decision support covid 19 pandemic

Methods: computational algorithm optimization model experiment mixed-integer programming integer linear programming

An interactive decision support system for real-time ambulance relocation with priority guidelines

2022 | Decision Support Systems | Citations: 0

Authors: Hajiali, Mahdi; Teimoury, Ebrahim; Rabiee, Meysam; Delen, Dursun

Abstract: Changes in demand patterns and unexpected events are the two primary sources of ... Expand

Abstract: Changes in demand patterns and unexpected events are the two primary sources of delays in healthcare emer­ gency operations. To mitigate such delays, researchers proposed the movement of idle ambulances between emergency bases as one of the effective ways to improve the areal coverage of future demands. In this study, we have developed a model-driven decision support system that simultaneously seeks to maximize demand coverage while minimizing travel time by optimally relocating emergency response vehicles. The developed mathematical model partitions and prioritizes demand into four categories and continuously updates them over time. Furthermore, it dynamically calculates the number of coverages in different regions based on the current location of idle ambulances. Also, we developed a real-time risk assessment DSS for recommended relocations, which could be utilized as a reference by the EMS user while implementing suggested relocation decisions. A real case study is used to validate the proposed DSS, and its final output is compared to the existing operational policy. The findings show that the average workload added to each ambulance due to relocations has significantly improved the response time and coverage ratio. Compared to the existing operational policy, the developed decision support system decreased the time to respond to calls, which was deemed to be more than to offsets the increase in travel time due to relocation. Furthermore, the system also reduced the total working time of all ambulances by about 9% per shift. Collapse

Topics: decision support system user interface usability laptop computer programming language

Methods: mathematical model case study trade-off analysis method analytic hierarchy process simulation

An Integer Programming Approach to Solving the Inverse Graph Model for Conflict Resolution with Two Decision Makers

2022 | Group Decision and Negotiation | Citations: 0

Authors: Han, Yu; Xu, Haiyan; Fang, Liping; Hipel, Keith W.

Abstract: Operational algorithms for solving the inverse problem for the graph model for c ... Expand

Abstract: Operational algorithms for solving the inverse problem for the graph model for conflict resolution are presented for the case of two decision makers (DMs) under a variety of solution concepts, including Nash stability (Nash), general metarationality (GMR), symmetric metarationality (SMR), and sequential stability (SEQ). The algorithms based on integer programming enable a DM, an analyst, or a mediator to obtain all of the preferences required to make a specified state to be an equilibrium or resolution. For the cases of Nash, GMR, and SMR, the respective inverse algorithm for the focal DM is formulated as a 0–1 integer linear programming problem even when both DMs’ preferences are unknown. For the situation of SEQ, when both DMs’ preferences are unknown, the focal DM’s algorithm is a 0–1 integer nonlinear programming problem while, under the condition that the opponent’s preferences are known, the focal DM’s 0–1 integer programming problem is linear. The usefulness of the algorithms developed is demonstrated by applying them to an illustrative dispute. Collapse

Methods: integer programming computational algorithm linear optimization integer linear programming

Theories: prisoner's dilemma

Decisions on train rescheduling and locomotive assignment during the COVID-19 outbreak: A case of the Beijing-Tianjin intercity railway

2022 | Decision Support Systems | Citations: 0

Authors: Kang, Liujiang; Xiao, Yue; Sun, Huijun; Wu, Jianjun; Luo, Sida; Buhigiro, Nsabimana

Abstract: Travel restriction measures have been widely implemented to curb the continued s ... Expand

Abstract: Travel restriction measures have been widely implemented to curb the continued spread of COVID-19 during the Chinese Lunar New Year celebrations. Many operation lines and train schedules of China’s railway were either heavily adjusted or canceled. In this study, a mixed-integer linear programming model and a two-step solution algorithm were developed to handle such large-scale adjustments. The formulation considers a flexible time window for each operation line and locomotive traction operations, and minimizes the number of locomotives utilized with their total idle time for train rescheduling and locomotive assignment, respectively. The solution algorithm determines the minimum locomotive fleet size based on the optimal train rescheduling results; it then reduces the traction idle time of locomotives. In response to the uncertainty of COVID-19, two tailored ap­ proaches were also designed to recover and remove operation lines, which can insert and cut operation lines based on the results of locomotive assignment. Finally, we conducted a case study of the Beijing-Tianjin intercity railway from the start of the COVID-19 outbreak to the recovery of operations. Collapse

Topics: pandemic workstation decision making

Methods: computational algorithm case study optimization model integer linear programming

Consensus Building for Uncertain Large-Scale Group Decision-Making Based on the Clustering Algorithm and Robust Discrete Optimization

2022 | Group Decision and Negotiation | Citations: 0

Authors: Li, Yuanming; Ji, Ying; Qu, Shaojian

Abstract: Consensus reaching processes (CRPs) including the feedback adjustment mechanism ... Expand

Abstract: Consensus reaching processes (CRPs) including the feedback adjustment mechanism generally require extended periods of time to bridge the opinion gap among decision makers. Therefore, minimum cost consensus (MCC) problems with known adjustment costs have been widely reported. However, the exact unit adjustment costs are difficult to obtain through practical CRPs. To solve these problems, this paper proposes a novel CRP framework for uncertain large-scale group decision-making based on robust discrete optimization. First, an enhanced iterative self-organizing data analysis technique algorithm is provided to dynamically cluster decision makers together in small subgroups under interval opinions. Second, to establish the optimization-based consensus rules in the feedback process, an MCC integer optimization model is established to minimize the total consensus costs in consensus reaching. Furthermore, with the indeterminate unit adjusting costs, a robust discrete MCC optimization model is constructed, which can control the degree of conservatism of the optimal consensus opinion and compute the optimal modified opinions of decision makers. Finally, a case study and comparative analysis indicate the effectiveness and superiority of the proposed CRP method and that the robust discrete MCC model has stronger robustness in the uncertain decision environment. Collapse

Topics: decision making innovation management

Methods: cluster analysis computational algorithm discrete optimization case study k-means clustering

Using Word Embeddings to Deter Intellectual Property Theft through Automated Generation of Fake Documents

2021 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Abdibayev, Almas; Chen, Dongkai; Chen, Haipeng; Poluru, Deepti; Subrahmanian, V. S.

Abstract: Theft of intellectual property is a growing problem—one that is exacerbated by t ... Expand

Abstract: Theft of intellectual property is a growing problem—one that is exacerbated by the fact that a successful compromise of an enterprise might only become known months after the hack. A recent solution called FORGE addresses this problem by automatically generating N “fake” versions of any real document so that the attacker has to determine which of the N + 1 documents that they have exfiltrated from a compromised network is real. In this article, we remove two major drawbacks in FORGE: (i) FORGE requires ontologies in order to generate fake documents—however, in the real world, ontologies, especially good ontologies, are infrequently available. The WE-FORGE system proposed in this article completely eliminates the need for ontologies by using distance metrics on word embeddings instead. (ii) FORGE generates fake documents by first identifying “target” concepts in the original document and then substituting “replacement” concepts for them. However, we will show that this can lead to sub-optimal results (e.g., as target concepts are selected without knowing the availability and/or quality of the replacement concepts, they can sometimes lead to poor results). Our WE-FORGE system addresses this problem in two possible ways by performing a joint optimization to select concepts and replacements simultaneously. We conduct a human study involving both computer science and chemistry documents and show that WE-FORGE successfully deceives adversaries. Collapse

Topics: information retrieval knowledge representation IT security Google+ innovation management

Methods: optimization model word embedding term frequency–inverse document frequency experiment ontological modelling

On selecting a probabilistic classifier for appointment no-show prediction

2021 | Decision Support Systems | Citations: 0

Authors: Harris, Shannon L.; Samorani, Michele

Abstract: Appointment no-shows are disruptive to healthcare clinics, and may increase pati ... Expand

Abstract: Appointment no-shows are disruptive to healthcare clinics, and may increase patient waiting time and clinic overtime, resulting in increased clinic costs. Appointment scheduling models typically mitigate the negative effects of no-shows through appointment overbooking. Recent work has proposed a predictive overbooking framework, where a probabilisitic classifier predicts the no-show probability of individual appointment requests, and a scheduling algorithm uses those predictions to optimally schedule appointments. Because predicting noshows is typically an imbalanced classification problem, the preferred classifier is often chosen based upon the area under the receiver operator characteristic curve (AUC), which is a commonly used metric for many other imbalanced classification problems. Contrary to intuition, in this paper we show that employing the AUC to select a classifier results in significantly lower schedule efficiency than using other metrics such as Log Loss or Brier Score. Our computational experiments, validated on large real-world appointment data, suggest that by using Log Loss or Brier Score instead of AUC, practitioners can improve the schedule quality by 3–7%. Collapse

Topics: Python logistics management

Methods: machine learning experiment parametric test ANOVA logistic regression