Subset Of Customers Research Articles

Applying queueing theory to evaluate wait-time-savings of triage algorithms

In the past decade, artificial intelligence (AI) algorithms have made promising impacts in many areas of healthcare. One application is AI-enabled prioritization software known as computer-aided triage and notification (CADt). This type of software as a medical device is intended to prioritize reviews of radiological images with time-sensitive findings, thus shortening the waiting time for patients with these findings. While many CADt devices have been deployed into clinical workflows and have been shown to improve patient treatment and clinical outcomes, quantitative methods to evaluate the wait-time-savings from their deployment are not yet available. In this paper, we apply queueing theory methods to evaluate the wait-time-savings of a CADt by calculating the average waiting time per patient image without and with a CADt device being deployed. We study two workflow models with one or multiple radiologists (servers) for a range of AI diagnostic performances, radiologist’s reading rates, and patient image (customer) arrival rates. To evaluate the time-saving performance of a CADt, we use the difference in the mean waiting time between the diseased patient images in the with-CADt scenario and that in the without-CADt scenario as our performance metric. As part of this effort, we have developed and also share a software tool to simulate the radiology workflow around medical image interpretation, to verify theoretical results, and to provide confidence intervals for the performance metric we defined. We show quantitatively that a CADt triage device is more effective in a busy, short-staffed reading setting, which is consistent with our clinical intuition and simulation results. Although this work is motivated by the need for evaluating CADt devices, the evaluation methodology presented in this paper can be applied to assess the time-saving performance of other types of algorithms that prioritize a subset of customers based on binary outputs.

Prize-collecting Electric Vehicle routing model for parcel delivery problem

Electric Vehicle (EV)-based parcel delivery is getting much more popular these days due to EVs being eco-friendly and sustainable. One well-known problem in the literature to study last-mile delivery via EVs is the Electric Vehicle Routing Problem (EVRP). One limitation of EVRP is that it assumes all customers must be visited directly by EVs. This forces companies to purchase or utilize additional EVs, increasing operational costs for EV utilization and routing. To overcome this limitation, the present work introduces the Prize-collecting EVRP with Time Windows (PC-EVRP-TW), utilizing multiple EVs for the home-delivery service of high-priority customers (customers with high loyalty or premium memberships), leaving the rest for the next day or outsourcing to a third-party shipper. It also ensures a minimum number of served demands to maintain service quality. PC-EVRP-TW is modeled by mixed-integer linear programming and solved by a problem-specific hybrid metaheuristic aiming to minimize EVs’ route and usage cost and maximize collected profits (prizes) while satisfying EVs’ load and battery capacity, task completion, and time window constraints. Analyses were performed on the optimal solution of PC-EVRP-TW. The results show that the company could achieve significant route cost savings, up to 22% compared to EVRP-TW. Additionally, remarkable reductions in EV usage costs, 6%, 16%, and 18%, were observed for various-size instances of PC-EVRP-TW. These findings highlight the potential for practitioners and managers to realize substantial cost savings and optimize EV usage by selectively serving a prioritized subset of customers daily, particularly when facing high utilization costs and the inability to serve all customers directly.

Effectiveness of real-world marketing of organic foods and beverages: A systematic review of recent evidence

Increasing consumer demand for organic foods and beverages is critical to boost sustainable agricultural production. Marketing interventions can be an effective way of influencing consumer preferences, but little is known about the effectiveness of these interventions at increasing demand for organic foods and beverages. This review synthesises recent evidence from studies evaluating real-world marketing interventions that aim to increase consumer demand for organic foods and beverages. Five databases were systematically searched and 18,743 titles and abstracts were screened against eligibility criteria. 21 studies covering 29 marketing interventions were included and assessed for risk of bias using the Evidence Project risk of bias tool. We found that price premiums had negative or no effects on overall consumer demand, although a subset of customers were willing to pay higher prices. Price promotions had mixed effects depending on product and customer type. Some place-based interventions–such as arranging organic products in a cluster and ensuring ready availability of these products–had positive and statistically significant impacts on consumer demand. Some promotional interventions–such as including organic messaging near product displays and securing newspaper coverage–were generally effective at increasing consumer demand. Ultimately, the effectiveness of these interventions differed across product categories, message framing, and the type of store and customer, necessitating strategic targeting of organic marketing interventions to promote more sustainable consumption.

Information Transparency With Targeting Technology for Online Service Operations Platform

Social technologies have enabled the emergence of online platforms that provide offline service consultations and recommendations. In this environment, economic inefficiency arises when customers are not fully aware of their horizontally differentiated preferences. With its expertise or data dominance, a platform can be more informed about customers’ hidden preferences. We focus on an instrumental social technology, that is, targeting, which is a type of data-driven personalized information provision to manipulate customers’ beliefs about service quality. We propose a Hotelling model wherein customers are sensitive to the delays for service while making Bayesian belief updates based on a platform’s recommendations. When customers self-select their favorite service, their choices impose negative externalities through congestion and welfare loss. Our results indicate that service recommendations allow customers to navigate toward the more appropriate service, thus improving matching efficiency, reducing congestion costs, and enhancing aggregate customer welfare. We further identify the role of “information transparency” and study how the platform should strategically release information by making personalized service recommendations to customers. Interestingly, when a customer-centric platform maximizes aggregate customer welfare, we identify the “value of opaqueness” by strategically withholding service recommendations from a subset of customers and notice that this effect is more pronounced for a profit-seeking platform. Our results offer a better understanding of information transparency policies in the joint design of service recommendation systems and pricing mechanisms.

A competitive heuristic algorithm for vehicle routing problems with drones

We propose a heuristic algorithm capable of handling multiple variants of the vehicle routing problem with drones (VRPD). Assuming that the drone may be launched from a node and recovered at another, these variants are characterized by three axes, (1) minimizing the transportation cost or minimizing the makespan, (2) the drone is either allowed or not allowed to land while awaiting recovery, and (3) single or multiple trucks each equipped with a drone. In our algorithm, we represent a VRPD solution as a set of customer sequences and evaluate it via local search procedures solving for each sequence a problem that we refer to as the fixed route drone dispatch problem (FRDDP). Given a sequence of customers to be served by a single truck and its drone, the FRDDP selects a subset of customers to be served by the drone and determines drone launch and recovery nodes, while ensuring that each such customer is positioned between two nodes in the initial sequence. We introduce a heuristic dynamic program (HDP) to solve the FRDDP with reduced computational complexity compared to an exact solution algorithm for the problem. We reinforce our algorithm by developing filtering strategies based on the HDP. We benchmark the performance of our algorithm on nine benchmark sets pertaining to four VRPD variants resulting in 932 instances. Our algorithm computes 651 of 680 optimal solutions and identifies 189 new best-known solutions.

A Weighted and Epsilon-Constraint Biased-Randomized Algorithm for the Biobjective TOP with Prioritized Nodes

This paper addresses a multiobjective version of the Team Orienteering Problem (TOP). The TOP focuses on selecting a subset of customers for maximum rewards while considering time and fleet size constraints. This study extends the TOP by considering two objectives: maximizing total rewards from customer visits and maximizing visits to prioritized nodes. The MultiObjective TOP (MO-TOP) is formulated mathematically to concurrently tackle these objectives. A multistart biased-randomized algorithm is proposed to solve MO-TOP, integrating exploration and exploitation techniques. The algorithm employs a constructive heuristic defining biefficiency to select edges for routing plans. Through iterative exploration from various starting points, the algorithm converges to high-quality solutions. The Pareto frontier for the MO-TOP is generated using the weighted method, epsilon-constraint method, and Epsilon-Modified Method. Computational experiments validate the proposed approach’s effectiveness, illustrating its ability to generate diverse and high-quality solutions on the Pareto frontier. The algorithms demonstrate the ability to optimize rewards and prioritize node visits, offering valuable insights for real-world decision making in team orienteering applications.

Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart Meters

In the context of increasingly necessary energy transition, the precise modeling of profiles for low-voltage (LV) network consumers is crucial to enhance hosting capacity. Typically, load curves for these consumers are estimated through measurement campaigns conducted by Distribution System Operators (DSOs) for a representative subset of customers or through the aggregation of load curves from household appliances within a residence. With the instrumentation of smart meters becoming more common, a new approach to modeling profiles for residential customers is proposed to make the most of the measurements from these meters. The disaggregation model estimates the load profile of customers on a low-voltage network by disaggregating the load curve measured at the secondary substation level. By utilizing only the maximum power measured by Linky smart meters, along with the load curve of the secondary substation, this model can estimate the daily profile of customers. For 48 secondary substations in our dataset, the model obtained an average symmetric mean average percentage error (SMAPE) error of 4.91% in reconstructing the load curve of the secondary substation from the curves disaggregated by the model. This methodology can allow for an estimation of the daily consumption behaviors of the low-voltage customers. In this way, we can safely envision solutions that enhance the grid hosting capacity.

An effective hybrid evolutionary algorithm for the set orienteering problem

The Set Orienteering Problem (SOP) is a variant of the popular Orienteering Problem (OP) arising from a number of real-life applications. The aim is to find a tour across a subset of customers, while maximizing the collected profit within a given travel time limit. In SOP, vertices (customers) are partitioned into clusters, where a profit is associated to each cluster. The profit of a cluster is collected only if at least one vertex belonging to the cluster is contained in the tour. For this NP-hard problem, we present a highly effective hybrid evolutionary algorithm that integrates a cluster-based crossover operator, a randomized mutation operator to generate multiple distinct promising offspring solutions, and a two-phase local refinement procedure that explores both feasible and infeasible solutions in search of high-quality local optima. Extensive experiments on 192 large benchmark instances show that the proposed algorithm significantly outperforms all the existing approaches from the SOP literature. In particular, it reports improved best-known solutions (new lower bounds) for 54 instances, while matching the existing best-known result for 133 instances. We further investigate the contribution of the key algorithmic elements to the success of the proposed approach.

A New Perspective for Mining COCO Dataset

Microsoft Common Objects in Context (COCO) is a huge image dataset that has over 300 k images belonging to more than ninety-one classes. COCO has valuable information in the field of detection, segmentation, classification, and tagging; but the COCO dataset suffers from being unorganized, and classes in COCO interfere with each other. Dealing with it gives very low and unsatisfying results whether when calculating accuracy or intersection over the union in classification and segmentation algorithms. A simple method is proposed to create a customized subset from the COCO dataset by determining the class or class numbers. The suggested method is very useful as preprocessing step for any detection or segmentation algorithms such as YOLO, SSPNET, RCNN, etc. The proposed method was validated using the link net architecture for semantic segmentation. The results after applying the preprocessing were presented and compared to the state of art methods. The comparison demonstrates the exceptional effectiveness of transfer learning with our preprocessing model. Index Terms— COCO JSON files, Object detection, object tracking, semantic segmentation.

Robust learning-based real-time load estimation using sparsely deployed smart meters with high reporting rates

With increasing renewable generation and demand response, the load profiles of distribution feeders become more fluctuating and uncertain, requiring real-time load estimation (RTLE) with high temporal granularity. Smart meters (SM) provide new data sources that have the potential to enable RTLE. However, it is cost prohibitive to communicate and process real-time high-resolution data from a massive number of SMs. To address the challenge, this paper proposes a novel solution to RTLE using High-Reporting-Rate SMs (HRRSMs) installed at a sparsely selected subset of customers in the feeder. The first step is to select customers for installing HRRSMs based on clustering, such that load profiles can best represent those of the others and the whole feeder. Then, a state-of-the-art Deep Learning (DL) model is trained to capture the relation between the historical load profiles of the selected customers and that of the feeder. Finally, real-time HRRSM data from the selective customers is fed to the trained model to perform RTLE with high resolution. The method is also robustified to address anomalies in real-time HRRSM data streams. The proposed method is validated on a large real-world SM dataset. Simulation results show that even with a small number of HRRSM installation, the proposed method can track feeder loads with much improved accuracy and temporal granularity compared with conventional methods based on historical data of regular SMs, providing a cost-effective solution to the monitoring of distribution feeder loads.

Vehicle routing for connected service areas - a versatile approach covering single, hierarchical, and bi-criteria objectives

Vehicle routing in urban areas or in-house tour planning is characterized by the fact that tours have to service orders in areas with limited road or aisle access. During last mile delivery, courier service providers often face the situation that subsets of customers in urban areas are located along a single street that can be accessed from only one or two directions. This also applies to warehouses, where, due to the given grid layout, pickers locate products on shelves only in specific areas positioned along an aisle between two neighboring cross aisles. By being confronted with a substantial time and/or cost pressure, those tour planning applications have to deal with the trade-off between service orientation and cost minimization. In order to cover many of those applications, this study proposes a general model that integrates the definition of customizable service areas with limited access and soft due dates of urgent orders, whereas the objective function can be defined in a versatile way covering to choose between a single objective, a hierarchical, and a bi-criteria objective system. The model is dubbed the Vehicle Routing Problem of Service Areas (VRPSA). A comprehensive complexity analysis of the VRPSA for different objective systems shows that a pure travel cost minimization variant can be solved to optimality in polynomial time, whereas the bi-criteria variant simultaneously pursuing travel cost and tardiness cost minimization is proven to be intractable. In order to generate tour plans, a customizable best-first branch-and-bound algorithm is developed and assessed through a computational study.

A Cascading Ensemble with Custom Subset Generation and Multi-level Fusion for Enhanced Breast Cancer Detection

The ensemble learning technique is a powerful method that combines multiple machine learning models to address their individual limitations and create an optimized predictive model. By harnessing the strengths of each model, ensemble learning significantly enhances overall performance, making it particularly valuable for classification tasks across diverse domains. In this study, an efficient three-level stacking ensemble approach is proposed for diagnosing breast cancer. At the first level, a diverse set of base learners is employed, encompassing decision trees, logistic regression, k-nearest neighbors, support vector machines, and Gaussian naive Bayes. Each first-level learner is trained on distinct subsets of the training data with 10-fold cross-validation, thereby ensuring model robustness and mitigating the risk of overfitting. Transitioning to the second level, sophisticated ensemble models including Adaboost, GBM, and Random forest are introduced. These second-level learners undergo training using the validation predictions generated by the first-level models, while incorporating the top six informative features extracted from the dataset. To enhance their performance and mitigate overfitting, the models are optimized through the application of 10-fold cross-validation. In order to achieve further refinement of the ensemble, a third-level learner, represented by a deep neural network (DNN), is introduced. This DNN is trained on the validation predictions obtained from both the first and second levels, facilitating the capturing and synthesis of the collective knowledge of the ensemble. By leveraging the inherent capabilities of deep learning, the DNN maximizes the amalgamated benefits derived from the preceding levels, resulting in a substantial enhancement of the overall predictive power. The effectiveness of the ensemble approach is evaluated using well-established performance metrics, including accuracy, sensitivity, and specificity, which are derived from the analysis of the confusion matrix. The results incontrovertibly demonstrate that the ensemble models surpass individual machine learning models in accurately detecting breast cancer.

The cost of carrier consistency: Last-mile delivery by vehicle and drone for subscription-based orders

We investigate a deterministic last-mile delivery problem in which a firm delivering scheduled orders determines a subset of customers to be prioritized for drone delivery, relegating the rest to vehicle delivery. The vehicle and drone deliver in tandem over a planning horizon. The problem is amenable to a multi-day Traveling Salesman Problem with Drone, with consistency in the assignment of customers to each carrier across all days. We develop a mixed-integer program (MIP) that yields provably optimal solutions for instances having up to 40 customers over a 6-day horizon. We also devise a heuristic underpinned by the MIP to effectively and accurately prioritize customers for drone delivery. A computational study involving instances with up to 150 customers ordering over 6 days shows the matheuristic often matching the results of the proposed MIP, and also reveals a logistical burden of less than 0.5% on average over a solution lacking carrier consistency..

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Be positive: customized reference databases and new, local barcodes balance false taxonomic assignments in metabarcoding studies.

In metabarcoding analyses, the taxonomic assignment is crucial to place sequencing data in biological and ecological contexts. This fundamental step depends on a reference database, which should have a good taxonomic coverage to avoid unassigned sequences. However, this goal is rarely achieved in many geographic regions and for several taxonomic groups. On the other hand, more is not necessarily better, as sequences in reference databases belonging to taxonomic groups out of the studied region/environment context might lead to false assignments. We investigated the effect of using several subsets of a cytochrome c oxidase subunit I (COI) reference database on taxonomic assignment. Published metabarcoding sequences from the Mediterranean Sea were assigned to taxa using COInr, which is a comprehensive, non-redundant and recent database of COI sequences obtained both from BOLD and NCBI, and two of its subsets: (i) all sequences except insects (COInr-WO-Insecta), which represent the overwhelming majority of COInr database, but are irrelevant for marine samples, and (ii) all sequences from taxonomic families present in the Mediterranean Sea (COInr-Med). Four different algorithms for taxonomic assignment were employed in parallel to evaluate differences in their output and data consistency. The reduction of the database to more specific custom subsets increased the number of unassigned sequences. Nevertheless, since most of them were incorrectly assigned by the less specific databases, this is a positive outcome. Moreover, the taxonomic resolution (the lowest taxonomic level to which a sequence is attributed) of several sequences tended to increase when using customized databases. These findings clearly indicated the need for customized databases adapted to each study. However, the very high proportion of unassigned sequences points to the need to enrich the local database with new barcodes specifically obtained from the studied region and/or taxonomic group. Including novel local barcodes to the COI database proved to be very profitable: by adding only 116 new barcodes sequenced in our laboratory, thus increasing the reference database by only 0.04%, we were able to improve the resolution for ca. 0.6-1% of the Amplicon Sequence Variants (ASVs).

Optimization approaches for the urban delivery problem with trucks and drones

In this paper, we introduce an urban delivery problem with trucks and drones. The problem is to construct truck routings over a subset of parking points and customers, and drone deliveries from parking points to customers. The goal is to minimize the total costs, including the truck routing, drone delivery, and parking costs. To formulate the problem, three mathematical formulations are proposed: the mixed-integer, bi-level (BP), and three-level (TP) programming formulations. Two heuristics: bi-level (BH) and three-level heuristics (TH) are developed, inspired by the proposed BP and TP formulations. Finally, two classes of problems derived from TSPLIB are generated to test the formulations and heuristics. Results indicate that both heuristics take significantly less computational time than CPLEX, and BH found more best solutions. Thus, BH has the best performance in addressing the problem, followed by TH.

Equity-Aware Decarbonization of Residential Heating Systems

Most buildings still rely on fossil energy --- such as oil, coal and natural gas --- for heating. This is because they are readily available and have higher heat value than their cleaner counterparts. However, these primary sources of energy are also high pollutants. As the grid moves towards eliminating CO 2 emission, replacing these sources of energy with cleaner alternatives is imperative. Electric heat pumps --- an alternative and cleaner heating technology --- have been proposed as a viable replacement. In this paper, we conduct a data-driven optimization study to analyze the potential of reducing carbon emission by replacing gas-based heating with electric heat pumps 1 . We do so while enforcing equity in such transition. We begin by conducting an in-depth analysis into the energy patterns and demographic profiles of buildings. Our analysis reveals a huge disparity between lower and higher income households. We show that the energy usage intensity for lower income homes is 24% higher than higher income homes. Next, we analyze the potential for carbon emission reduction by transitioning gas-based heating systems to electric heat pumps for an entire city. We then propose equity-aware transition strategies for selecting a subset of customers for heat pump-based retrofits which embed various equity metrics and balances the need to maximize carbon reduction with ensuring equitable outcomes for households. We evaluate their effect on CO 2 emission reduction, showing that such equity-aware carbon emission reduction strategies achieve significant emission reduction while also reducing the disparity in the value of selected homes by 5X compared to a carbon-first approach.

Predictors of Success following Microvascular Replantation Surgery of the Upper Extremity in Adult Patients.

Data gathered for this retrospective study were obtained from a custom subset of the 2015-2020 IBM Truven MarketScan Commercial and IBM Truven MarketScan Medicare Supplemental databases. Data were sorted using Current Procedural Terminology codes and International Classification of Disease, Ninth and Tenth Revision, diagnosis codes. Increasing age was significantly associated with undergoing initial amputation compared with replantation (P < 0.001; 95% CI, 47.146-50.654). Among comorbid conditions, patients with diabetes mellitus type II (2.4% versus 24.3%; P < 0.001), hypertension (11.9% versus 28.0%; P = 0.03), end-stage renal disease (0% versus 10.5%; P = 0.03), and hypertensive chronic kidney disease (0% versus 8.7%; P = 0.04) more commonly underwent an initial amputation procedure. When evaluating the need for secondary procedure after replantation, there was no statistical significance between groups when comparing age, gender, insurance type, general comorbidities, connective tissue disorders, mental health disorders, or geographic location. In conclusion, this study demonstrates that age and the presence of comorbidities are important factors in the differentiation of which patients undergo initial reimplantation versus initial amputation. Additionally, no specific factors were identified that were associated with secondary procedures after replantation.

On Optimizing a Multi-Mode Last-Mile Parcel Delivery System with Vans, Truck and Drone

This work focuses on the optimization of a last-mile delivery system with multiple transportation modes. In this scenario, parcels need to be delivered to each customer point. The major feature of the problem is the combination of a fleet of road vehicles (vans) with a drone. Each van visits a subset of demand nodes to be determined according to the route of the van. The drone serves the customers not served by vans. At the same time, considering the safety, policy and terrain as well as the need to replace the battery, the drone needs to be transported by truck to the identified station along with the parcel. From each such station, the drone serves a subset of customers according to a direct assignment pattern, i.e., every time the drone is launched, it serves one demand node and returns to the station to collect another parcel. Similarly, the truck is used to transport the drone and cargo between stations. This is somewhat different from the research of other scholars. In terms of the joint distribution of the drone and road vehicle, most scholars will choose the combination of two transportation tools, while we use three. The drone and vans are responsible for distribution services, and the trucks are responsible for transporting the goods and drone to the station. The goal is to optimize the total delivery cost which includes the transportation costs for the vans and the delivery cost for the drone. A fixed cost is also considered for each drone parking site corresponding to the cost of positioning the drone and using the drone station. A discrete optimization model is presented for the problem in addition to a two-phase heuristic algorithm. The results of a series of computational tests performed to assess the applicability of the model and the efficiency of the heuristic are reported. The results obtained show that nearly 10% of the cost can be saved by combining the traditional delivery mode with the use of a drone and drone stations.

Consistent vehicle routing with pickup decisions - Insights from sport academy training transfers

In professional team sports, clubs compete in a highly contested market for young talents. Especially in rural areas training transfer services are common to recruit promising underage players. If the demand for training transfers exceeds the available pickup capacities, a prioritization of pickups is necessary. Additionally, the transport of underage children has the special premises of consistent driver-player assignments. To the best of our knowledge, we are the first to present an algorithmic approach to passenger transportation problems with priorities under limited capacity and consistency requirements. We propose an iterative template-based procedure to handle the trade-off between priority maximizing pickups and assignment consistency. So far, the literature has focused on the consistent service of a predefined subset of customers. With our work we focus on the consistent service of all requesting customers. Consequently, the identification of promising template players is endogenous to our approach. We discuss the effect of template players on the service of non-template players and identify drivers of consistency. In addition, we provide a new Tabu Search neighborhood structure for heterogeneous vehicle routing under consistency, ensuring efficient vehicle deployment. We highlight the benefits of the approach by comparing our results with the routing solutions of a Bundesliga soccer club.