Customer Locations Research Articles

Geospatial data in AML risk management: A review of applications and best practices

This review explores the role of geospatial data in enhancing Anti-Money Laundering (AML) risk management, focusing on its applications, best practices, and strategic implications for organizations. Geospatial data, combined with advanced analytical tools, provides critical insights into transaction patterns, customer locations, and geographic risk factors, enabling financial institutions to detect suspicious activities more effectively. The paper examines how geospatial data integration supports compliance with regulatory frameworks, bolsters transaction monitoring systems, and facilitates enhanced due diligence processes. Through a systematic review of existing literature and case studies, this paper identifies key applications such as identifying high-risk jurisdictions, mapping transaction flows, and detecting cross-border money laundering schemes. Best practices are highlighted, including leveraging geospatial analytics in conjunction with machine learning algorithms, optimizing data governance, and ensuring interoperability across systems. The findings suggest that organizations adopting these practices are better equipped to combat money laundering by improving risk assessment, reducing false positives, and enhancing investigative efficiency. Additionally, the review discusses the challenges of data privacy, regulatory compliance, and the technical complexities associated with geospatial data integration. The paper concludes by outlining future prospects, recommending further advancements in real-time geospatial analytics, cross-sector collaborations, and the adoption of emerging technologies to strengthen AML frameworks. This review serves as a comprehensive guide for financial institutions seeking to incorporate geospatial data into their AML strategies, driving both compliance and operational efficiency.

Sustainable last mile logistics employing drones and e-bikes

Last mile delivery is an important and growing part of the supply chain that has a sizeable negative environmental impact. This paper considers more sustainable approaches to home-delivery that are pragmatic for many non-rural environments. We address the two-echelon, multi-trip, capacitated vehicle routing problem with home-delivery and optional self-pickup services using different combinations of drones, trucks, and electric-assisted bikes (i.e. e-bikes). In the proposed approach, parcels are transported from a depot to parcel lockers by either drones or trucks and are then delivered to customer locations by either e-bikes or trucks. The four approaches range from the most sustainable (drones then e-bikes) to partly green (drone then truck or truck then e-bike) to finally the traditional (truck then truck). We formulate a mathematical model that determines the vehicle routes to minimize the total cost, which consists of vehicle operational cost and operator wages. The four types of delivery networks are assessed and compared for both costs and emissions. Experimental results suggest that with a modest increase in total cost (as little as 13%), emission reductions of up to 92%, on average, can be achieved when using the greenest delivery strategy of drones and e-bikes. The other delivery options have varying tradeoffs between costs and environmental impact. The percentage of self-pickup customers is an influencing factor to consider when choosing the delivery strategy that best meets the organization’s budget and environmental goals, especially when using e-bikes in the second echelon.

Dynamic collaborative truck-drone delivery with en-route synchronization and random requests

Coordinated truck and drone delivery is gaining popularity in logistics as it can greatly reduce operation costs. However, existing studies on related operations management problems typically ignore the following important features: (i) the random appearance of requests, which require operators to dynamically respond to the requests; and (ii) the decisions of optimal launch and retrieval locations for trucks and drones instead of fixed to customer locations, which can significantly impact the overall time costs. To tackle these challenges, this study investigates the dynamic collaborative truck-drone routing problem with randomly arriving requests and synchronization on routes. We model the problem as a Markov Decision Process (MDP) and solve the MDP via a reinforcement learning (RL) approach. The proposed RL approach determines: (i) whether each request should be serviced upon arrival, (ii) which truck or drone should be assigned for the request, and (iii) the optimal en-route take-off and landing positions for paired trucks and drones. We further employ a framework of decentralized learning and centralized dispatching in RL to increase performance. Numerical experiments are conducted to assess the proposed solution approach on instances generated based on both the Solomon dataset and real-world operational data of a logistics operator in Singapore over several benchmark algorithms under various battery endurance levels of drones and distinct transportation scenarios including node-based dynamic collaborative truck-drone routing problem, dynamic non-collaborative truck and drone routing problem, and dynamic vehicle routing problem. The results show that our RL solution outperforms the benchmark algorithm in total profit by an average of 28.03 %, and our en-route takeoff and landing scenario outperforms the benchmark scenarios in total profit by an average of 8.43 % in multi-day instances. Additionally, compared to the traditional node-based landing scenario, employing our en-route takeoff and landing strategy can save 0.9 h/(drone*day) of waiting time on average.

Cross regional online food delivery: Service quality optimization and real-time order assignment

Online food delivery (OFD) represents a rapidly evolving e-business application that leverages cloud computing data centers, playing a crucial role in meeting the demands of urban lifestyles. With diverse order fulfillment features and increasing expectations for service quality, the task of effectively assigning riders for timely long-distance, cross-regional deliveries presents a significant engineering challenge. Previous studies often relied on traditional rider allocation methods that fail to account for varying capacities, or they utilized non-intelligent systems that did not adequately address fluctuating order demands and service delays. In this study, we introduce a robust Mixed Integer Linear Programming (MILP) optimization framework designed to minimize the total service time and delivery cost for cross-regional orders. This framework divides a large OFD area into multiple regions and utilizes both transfer vehicles and riders to optimize deliveries. To enhance the predictive accuracy of our model, we incorporate advanced machine learning techniques. Specifically, we employ the Long Short-Term Memory (LSTM) model to forecast regional order demands accurately, reflecting the dynamic nature of the marketplace. Additionally, Extreme Gradient Boosting (XGBoost) is tailored to dynamically predict travel times from restaurants to customer locations, facilitating more precise scheduling and resource allocation within the MILP framework. These machine learning techniques significantly bolster the MILP framework by providing detailed, accurate predictions that improve decision-making processes and adaptability to real-time conditions. Acknowledging the complexity of this optimization problem, we further enhance our approach by integrating a meta-heuristic algorithm, Adaptive Large Neighbor Search (ALNS), which efficiently assigns orders to the appropriate transfer vehicles and riders within polynomial time. Our Cross Regional Online Food Delivery (XROFD) system is meticulously designed to optimize both customer satisfaction and rider incentives. Simulation experiments confirm that the XROFD system not only reduces service times and delivery costs but also markedly enhances customer satisfaction and provides superior incentives for riders, outperforming existing state-of-the-art methods.

Meal Delivery Routing Problem with Stochastic Meal Preparation Times and Customer Locations

AbstractWe investigate the Meal Delivery Routing Problem (MDRP), managing courier assignments between restaurants and customers. Our proposed variant considers uncertainties in meal preparation times and future order numbers with their locations, mirroring real challenges meal delivery providers face. Employing a rolling-horizon framework integrating Sample Average Approximation (SAA) and the Adaptive Large Neighborhood Search (ALNS) algorithm, we analyze modified Grubhub MDRP instances. Considering route planning uncertainties, our approach identifies routes at least 25% more profitable than deterministic methods reliant on expected values. Our study underscores the pivotal role of efficient meal preparation time management, impacting order rejections, customer satisfaction, and operational efficiency.

The set team orienteering problem

We introduce the Set Team Orienteering Problem (STOP), a generalised variant of the Set Orienteering Problem (SOP), in which customer locations are split into multiple clusters (or groups). Each cluster is associated with a profit that can be gained only if at least one customer from the cluster is visited. There is a fleet of homogeneous vehicles at a depot, and each vehicle has a limited travel time. The goal of the STOP is to find a set of feasible vehicle routes to collect the maximum profit. We first formulate the problem as a Mixed Integer Linear Programming (MILP) to mathematically describe it. A branch-and-price (B&P) algorithm is then developed to solve the problem to optimality. To deal with large instances, we propose a Large Neighbourhood Search (LNS), which relies on problem-tailored solution representation, removal, and insertion operators. Multiple experiments on newly generated instances confirm the performance of our approaches. Remarkably, when tested on the SOP using benchmarks available in the literature, our B&P method achieves optimality in 61.9% of these instances. This is the first time such a large number of SOP instances are solved to optimality. Our LNS outperforms existing algorithms proposed to solve the SOP in terms of solution quality. Out of 612 considered instances, it improves 40 best-known solutions.

Nonparametric data-driven learning algorithms for multilocation inventory systems

We study a multilocation inventory system with unknown demand distribution using a nonparametric approach. The system consists of multiple distribution centers and customer locations, where products are shipped from the distribution centers to fulfill customer demands. We propose a novel algorithm, DMLI, for adaptive inventory management. Under specific conditions, we establish that the average expected T-period regret of DMLI converges to the optimal rate of O(1/T).

Pricing a product with network effects for sale to a fixed population of customers

AbstractWe consider a deterministic dynamic pricing problem for a product that exhibits network effects and that is sold to a fixed heterogeneous population of customers. We begin by introducing a demand model wherein those customers are arrayed over two‐dimensional space according to a bivariate probability distribution. Each customer's location in space provides a description of that customer's intrinsic value for the product as well as the extent to which the customer is influenced by the network effect. In the pricing problem, as sales accumulate over time, the set of customers who have already purchased the product grows, while the set of customers who have not yet purchased the product shrinks. The total customer population remains fixed. Those who have not yet purchased constitute the remaining population of potential buyers of the product. As time moves forward, the mix of customers that remain as potential buyers evolves endogenously. The demand model yields a geometric interpretation of the remaining population of potential buyers, and gives rise to a dynamic program with states that are sets in two‐dimensional space. It is not practical to solve the dynamic pricing problem to optimality, so we present bounds and comparative statics results that help us identify tractable heuristics and obtain rigorous performance guarantees. In numerical experiments, we find that fixed‐price policies may perform poorly, especially when the network effect is strong or the time horizon is long. We also introduce a stochastic version of the problem that uses a spatial Poisson process to describe the customers, and we develop and analyze a heuristic approach for that formulation.

THE INFLUENCE OF PRODUCT QUALITY, PRICE PERCEPTION AND LOCATION ON CUSTOMER SATISFACTION AT WARMINDO BAROKAH KEDUNGMUNDU

The purpose of this research is to analyze the influence of product quality, price perception and location on customer satisfaction at Warmindo Barokah Kedungmundu. The population in this study were Warmindo Barokah Kedungmundu customers. The research method used was purposive sampling. The data used were 99 respondents using the questionnaire data collection method. Data analysis uses instrument tests, multiple linear analysis and model tests. The results from respondent data prove that product quality has a positive effect on customer satisfaction. Price perception has no effect on customer satisfaction and location has a positive effect on customer satisfaction.

Large Scale Customer Points in Genetic Algorithms

Aiming at the path optimization problem of pharmaceutical green logistics distribution, a green logistics path optimization model is designed considering constraints such as time window limitation and vehicle capacity, with the optimization objective of minimizing the sum of vehicle transportation cost, overtime cost and green cost, and the constructed model is solved by using genetic algorithm. The constructed model and designed algorithm are verified by actual distribution data. The example experiments show that the genetic algorithm has better solution quality, higher efficiency and more stable results in solving the vehicle path problem at large-scale customer locations. The research results not only expand the vehicle path problem, but also provide decision-making reference for the distribution optimization of related express logistics enterprises.

One-Stop Flair Slot Booking Platform for All Needs

Pick Your Slot (PYS) is a versatile booking and scheduling web-based application that caters to a single platform for all your needs. PYS offers services ranging from sports such as football, cricket and badminton, personal grooming services like salons, fitness facilities such as gyms, entertainment options like a dance studio, rage room, and car wash services. The platform, built using React.js and React Native for web and mobile respectively, utilizes a SQL database backend supported by Java. It is an open-source application accessible to both customers and vendors. The app facilitates slot booking according to customers’ flexible time, PYS not only accommodates flexible scheduling but also utilizes geolocation API to detect customer locations. This feature enables the app to suggest nearby services tailored to the customer's requirements, leveraging real-time availability updates and offering on-demand services. Real-time availability in our app ensures instant slot booking, adapting to users' dynamic schedules for hassle-free planning. Users can quickly secure appointments, saving time and reducing frustration. Security is paramount, with authentication mechanisms for both vendors and customers. With its blend of intuitive design and advanced functionality, PYS offers a comprehensive solution for booking needs, making it a reliable choice for users seeking convenience and peace of mind. In a world where time is precious, our app offers swift slot bookings, recognizing the impatience of modern life. With just a few taps, users can secure their slots, bypassing unnecessary delays in the nearest location. Designed for speed and convenience, we ensure no one waits longer than necessary.

Generalized Riskiness Index in Vehicle Routing Under Uncertain Travel Times: Formulations, Properties, and Exact Solution Framework

We consider a vehicle routing problem with time windows under uncertain travel times where the goal is to determine routes for a fleet of homogeneous vehicles to arrive at the locations of customers within their stipulated time windows to the maximum extent while ensuring that the total travel cost does not exceed a prescribed budget. Specifically, a novel performance measure that accounts for the riskiness associated with late arrivals at the customers, called the generalized riskiness index (GRI), is optimized. The GRI covers several existing riskiness indices as special cases and generates new ones. We demonstrate its salient managerial and computational properties to motivate it better. We propose alternative set partitioning-based models of the problem. To obtain the optimal solution, we develop an exact solution framework combining route enumeration and branch-price-and-cut algorithms, in which the GRI is dealt with in route enumeration and column generation subproblems. We mainly reduce the solution space by exploiting the GRI and budget constraints’ properties without losing optimality. The proposed method is tested on a collection of instances derived from the literature. The results show that a new instance of the GRI outperforms several existing riskiness indices in mitigating lateness. The exact method can solve instances with up to 100 nodes to optimality. It can consistently solve instances involving up to 50 nodes, outperforming state-of-the-art methods by more than doubling the manageable instance size. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72101187, 72371204, 72021002, and 71901180], the Qatar National Research Fund [Grant ARG01-0430-230029], Natural Science Foundation of Sichuan Province [24NSFSC6232], and Guanghua Talent Project of the Southwestern University of Finance and Economics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0345 .

Turkish Cashier Problem with time windows and its solution by matheuristic algorithms

Turkish Cashier Problem (TCP) is a new application area of the traveling salesman problem that was introduced to the literature recently. In this problem, the cashier can use public transportation or take a taxi where the cashier must visit multiple customer locations while minimizing the total transportation cost. In this study, we introduce a more realistic version of this problem where time has been integrated. This aspect is achieved by imposing time intervals within which the cashier must visit the customers. We name this problem as the TCP with time windows (TCPwTW). We develop several matheuristic algorithms to solve the TCPwTW: a modified version of the Simplify and Conquer (SAC) algorithm that was suggested for the TCP, simulated annealing (SA), original and modified versions of the migrating birds optimization (MBO) algorithm coupled with mathematical programming. We also tried to find the exact optimum using a Solver where for complex problems, only lower bounds were found. Numerical experimentation reveals that while for problems with loose time intervals, an exact solver can be considered. Once the time intervals tighten up, the best solutions can be obtained using matheuristics involving SA and MBO.

Combined K-Means Clustering with Neural Networks Methods for PV Short-Term Generation Load Forecasting in Electric Utilities

The power system has undergone significant growth and faced considerable challenges in recent decades, marked by the surge in energy demand and advancements in smart grid technologies, including solar and wind energies, as well as the widespread adoption of electric vehicles. These developments have introduced a level of complexity for utilities, compounded by the rapid expansion of behind-the-meter (BTM) photovoltaic (PV) systems, each with its own unique design and characteristics, thereby impacting power grid stability and reliability. In response to these intricate challenges, this research focused on the development of a robust forecasting model for load generation. This precision forecasting is crucial for optimal planning, mitigating the adverse effects of PV systems, and reducing operational and maintenance costs. By addressing these key aspects, the goal is to enhance the overall resilience and efficiency of the power grid amidst the evolving landscape of energy and technological advancements. The authors propose a solution leveraging LSTM (long short-term memory) model for a forecasting horizon up to 168 hours. This approach incorporates combinations of K-means clustering, automated meter infrastructure (AMI) real-world PV load generation, weather data, and calculated solar positions to forecast the generation load at customer locations to achieve a 5.7% mean absolute error between the actual and the predicted generation load.

Distribution and Delivery Model on E-Commerce Service for MSMEs

This research aims to develop an e-commerce service application model for Micro, Small, and Medium Enterprises (MSMEs) that facilitates the sale of MSME products online. The application model allows MSMEs to expand market reach and sell products more efficiently through the internet. In this application, data about customer locations, product locations, and information about available road networks are collected and entered into the system. Furthermore, using Dijkstra's method, the shortest route from the initial location or distribution point to the customer can be calculated automatically by considering factors such as distance, time, and transportation that may affect the speed of delivery. The MSMEs e-commerce service application model can optimize distribution and delivery with the aim of increasing the competitiveness and growth of MSMEs in the digital era. The results obtained in this study provide guidance and contributions in the development of a sustainable e-commerce system for MSMEs.

Perancangan Sistem Informasi Hino Service on Site (Studi Kasus : Dealer Hino, PT. Persada Lampung Raya)

<span lang="EN-US">Service on site is one of the after-sales services for Hino Truck vehicles. The Service on Site contract program provides customer vehicle service services at the customer's location or site by placing a mechanic at the customer's location. In its implementation, several obstacles were encountered, such as the vehicle service history was not recorded, there were no service reports so the customer did not know the vehicle's performance. So this research develops the design of the Hino Service on Site Information System using observational research methods, literature review, and documentation. To build the system, Use Case diagrams were designed and then measured using Use Case Points (UCP) to assist management in expanding the Servicee on Site Information System. UCP will assist management when making decisions regarding system development in terms of time, human resources and finances. Software measurement using UCP in the Service on Site Information System at Hino Dealers PT. Persada Lampung Raya has a Use Case Point (UCP) score of 38.448 and is categorized as a small software size project, which is smaller than 99. With the proposed design of this system, it can simplify and speed up the on site service administration process and can provide information in the form of vehicle performance reports to customers so that they can improve service on site program</span>

Automated Correction of GIS Data for Loads and Distributed Energy Resources in Secondary Distribution Networks

Currently, utilities are making significant efforts to correct their secondary models in geographic information systems (GISs) to capture the distribution system performance due to the increased penetration of distributed energy resources (DERs) such as renewable energy resources, distributed energy storage, and electric vehicles (EVs). However, their databases are often not reliable in terms of GIS coordinates of elements such as loads and DERs resulting in an approximate representation of the secondary distribution network. This paper presents an automated tool for accurate secondary network topology GIS coordinates correction. The proposed automated tool provides a more accurate feeder topology for utilities to estimate and operate distribution systems by assigning the load and DER nodes to their corresponding customer location. To simplify the complexity of the proposed new tool, only two commonly available inputs are being used as input data: municipal parcel GIS delimitation data, and utility secondary feeder topology database. A three-stage framework is proposed to develop the automated tool: the first stage reads and processes the raw input data; the second stage works automatically with no human intervention to assign the load and DER nodes to their associated location; the third stage provides the load and DER coordinates and physical address. The proposed procedure is implemented on an actual feeder from an electrical utility with high penetration of DERs.The results show that the proposed method provides a more accurate estimation of the topology of the feeder in terms of coordinates.

Impact of Celebrity Endorsement on Customer with Reference to Mobile

This paper analyze, impact of celebrity endorsement of products (Mobile) is the collaborating multichannel promotion of products or services This marketing provides customers time and location conscious of personalized information that promotes goods, services and ideas. On other side of the debate, proponents argue that celebrity endorsements play a vital role in enhancing brand visibility and consumer recall. They contend that aligning a brand with a well-known personality can effectively capture consumer attention in a competitive Advertising landscape. Additionally, celebrities are believed to lend credibility to a brand and establish emotional connections with consumers, influencing their purchasing decisions positively. Therefore, while celebrity endorsements can be a powerful tool for brand building and marketing, their success hinges on various factors such as the relevance of the celebrity to the brand, consumer perception, and the overall marketing strategy. The debate regarding the positive or negative impact of celebrity endorsements on brands underscores the complexity of this marketing approach and the need for careful consideration and strategic alignment to maximize its effectiveness.

Considering the Beacon technology, a Green and eco-friendly revolution in the Moroccan Commercial Malls for a proximity marketing strategy

Proximity marketing is becoming an important strategy that enables linking retailers and consumers to enhance the customer experience. This marketing action aims to consider the customer location to provide a significant information at the right moment and space. Beacon technology is a real advancement in this domain of study. Beacons are a small device-wireless communication that allow detecting the presence of a person in the store through the low-energy Bluetooth signals and deliver impressive offers to visitors’ mobile phone from their area or product proximity inside the store. The objective is to offer an in time individual experience and interact efficiently with visitors. This paper presents a quantitative empirical study that aims to analyze the results of embodying the beacon technology in the large Moroccan commercial malls. For this, an online questionnaire was prepared to assemble consumers’ feedback regarding the use of this proximity marketing action. The results of this empirical study will help notifying marketers in their strategies by coming up with quantitative data collection that analyzes the customer perception of this technology, consequently, Moroccan malls may consider this relevant proximity marketing strategy in the future decisions to improve the customer experience for a long-term loyalty.

The Operational Impact of Customer Location in On-Demand Services

The Operational Impact of Customer Location in On-Demand Services