Customer Locations Research Articles

Splitting Shopping and Delivery Tasks in an On-Demand Personal Shopper Service

Inspired by several recent startups, we study an on-demand delivery service that lets cus- tomers shop online for products from a number of brick and mortar stores. The customer orders are fulfilled by a fleet of personal shoppers who are responsible for both the shopping of orders at the stores and the delivery of these to customer locations. The operation of such a service requires to dynamically manage new requests, coordinate a fleet of shoppers, schedule shopping operations at stores, and execute deliveries to customers on time. Our work presents three operational strategies, each requiring different levels of shopper flexibility and implementation complexity. We quantify the performance of each strategy in a vast family of computational experiments. Also, the performance of this on-demand shopping service is compared to a setting in which customers travel to stores to shop themselves. Our numerical experiments show that there are significant savings in resources spent in shopping (up to 55.2%) when this activity is outsourced.

A Benders decomposition-based approach for logistics service network design

We propose an exact solution method for a Logistics Service Network Design Problem (LSNDP) inspired by the management of restaurant supply chains. In this problem, a distributor seeks to source and fulfill customer orders of products (fruits, meat, napkins, etc.) through a multi-echelon distribution network consisting of supplier locations, warehouses, and customer locations in a cost-effective manner. As these products are small relative to vehicle capacity, an effective strategy for achieving low transportation costs is consolidation. Specifically, routing products so that vehicles transport multiple products at a time, with each product potentially sourced by a different supplier and destined for a different customer. As instances of this problem of sizes relevant to the operations of an industrial partner are too large for off-the-shelf optimization solvers, we propose a suite of techniques for enhancing a Benders decomposition-based algorithm, including a strengthened master problem, valid inequalities, and a heuristic. Together, these enhancements enable the resulting method to produce provably high-quality solutions to multiple variants of the problem in reasonable run-times.

An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows

This work proposes an enhanced artificial bee colony algorithm (ABC) to solve the vehicle routing problem with time windows (VRPTW). In this work, the fuzzy technique, scatter search method, and SD-based selection method are combined into the artificial bee colony algorithm. Instead of randomly producing the new solution, the scout randomly chooses the replacement solution from the abandoned solutions from the onlooker bee stage. Effective customer location networks are constructed in order to minimize the overall distance. The proposed algorithm is tested on the Solomon benchmark dataset where customers live in different geographical locations. The results from the proposed algorithm are shown in comparison with other algorithms in the literature. The findings from the computational results are very encouraging. Compared to other algorithms, the proposed algorithm produces the best result for all testing problem sets. More significantly, the proposed algorithm obtains better quality than the other algorithms for 39 of the 56 problem instances in terms of vehicle numbers. The proposed algorithm obtains a better number of vehicles and shorter distances than the other algorithm for 20 of the 39 problem instances.

Joint optimization of customer location clustering and drone-based routing for last-mile deliveries

With growing consumer demand and expectations, companies are attempting to achieve cost-efficient and faster delivery operations. The integration of autonomous vehicles, such as drones, in the last-mile network design can curtail many operational challenges and provide a competitive advantage. This paper deals with the problem of delivering orders to a set of customer locations using multiple drones that operate in conjunction with a single truck. To take advantage of the drone fleet, the delivery tasks are parallelized by concurrently dispatching the drones from a truck parked at a focal point (ideal drone launch location) to the nearby customer locations. Hence, the key decisions to be optimized are the partitioning of delivery locations into small clusters, identifying a focal point per cluster, and routing the truck through all focal points such that the customer orders in each cluster are fulfilled either by a drone or truck. In contrast to prior studies that tackle this problem using multi-phase sequential procedures, this paper presents mathematical programming models to jointly optimize all the decisions involved. We also consider two polices for choosing a cluster focal point - (i) restricting it to one of the customer locations, and (ii) allowing it to be anywhere in the delivery area (i.e., a customer or non-customer location). Since the models considering unrestricted focal points are computationally expensive, an unsupervised machine learning-based heuristic algorithm is proposed to accelerate the solution time. Initially, we treat the problem as a single objective by independently minimizing either the total cost or delivery completion time. Subsequently, the two conflicting objectives are considered together for obtaining the set of best trade-off solutions. An extensive computational study is conducted to investigate the impacts of restricting the focal points, and the influence of adopting a joint optimization method instead of a sequential approach. Finally, several key insights are obtained to aid the logistics practitioners in decision making.

Design and evaluation of a multi-trip delivery model with truck and drones

We extend the Traveling Repairman Problem (TRP) by assuming a single truck which can stop at customer locations and launch drones multiple times from each stop location to serve customers. The problem is mathematically modeled, several bound analyses are conducted to determine the maximum possible improvements in customer waiting times, and an efficient hybrid Tabu Search-Simulated Annealing algorithm is developed to solve the problem. The results of evaluations on several problem instances suggest that the system can yield considerable reductions in customer waiting time for a wide range of model parameters compared to the conventional delivery models.

Metaheuristic algorithm for solving the multi-objective vehicle routing problem with time window and drones

In some special rescue scenarios, the needed goods should be transported by drones because of the landform. Therefore, in this study, we investigate a multi-objective vehicle routing problem with time window and drone transportation constraints. The vehicles are used to transport the goods and drones to customer locations, while the drones are used to transport goods vertically and timely to the customer. Three types of objectives are considered simultaneously, including minimization of the total energy consumption of the trucks, total energy consumption of the drones, and the total number of trucks. An improved artificial bee colony algorithm is designed to solve the problem. In the proposed algorithm, each solution is represented by a two-dimensional vector, and the initialization method based on the Push-Forward Insertion Heuristic is embedded. To enhance the exploitation abilities, an improved employed heuristic is developed to perform detailed local search. Meanwhile, a novel scout bee strategy is presented to improve the global search abilities of the proposed algorithm. Several instances extended from the Solomon instances are used to test the performance of the proposed improved artificial bee colony algorithm. Experimental comparisons with the other efficient algorithms in the literature verify the competitive performance of the proposed algorithm.

A Decision Support System for Attended Home Services

The authors describe a decision support system developed to solve a practical attended home services problem faced by Iren Group, an Italian multiutility company operating in the distribution of electricity, gas, and water. The company operates in several regions across Italy and aims to optimize the dispatching of technicians to customer locations where they perform installations, closures, or maintenance activities within time slots chosen by the customers.

A column generation-based decomposition and aggregation approach for combining orders in inland transportation of containers

A significant portion of the total cost of the intermodal transportation is generated from the inland transportation of containers. In this paper, we design a mixed integer linear programming (MILP) model for combining orders in the inland, haulage transportation of containers. The pickup and delivery process of both 20 and 40 foot containers from the terminals to the customer locations and vice versa are optimized using heterogeneous fleet consisting of both 20 ft and 40 ft trucks/chasses. Important operational constraints such as the time window at order receivers, the payload weight of containers and the regulation of the working hours are considered. Based on an assignment problem structure, this MILP solves efficiently to optimality for problems with up to 120 orders. To deal with larger instances, a decomposition and aggregation heuristic is designed. The basic idea of this approach is to decompose order locations geographically into fan-shaped subareas based on the angle of the order location to the port baseline, and solve the sub problems using the proposed MILP model. To balance the fleet size amongst all subgroups, column generation is used to iteratively adjust the number of allocated trucks according to the shadow-price of each truck type. Based on decomposed solutions, orders that are “fully” combined with others are removed and an aggregation phase follows to enable wider combination choices across subgroups. The decomposition and aggregation solution process is tested to be both efficient and cost-saving.

A Clustering Based Routing Heuristic for Last-Mile Logistics in Fresh Food E-Commerce

This study considers the fresh food city logistics that involves the last-mile distribution of commodities to the customer locations from the local distribution centres (LDCs) established by the e-commerce firms. In this scenario, the last-mile logistics is crucial for its speed of response and the effectiveness in distribution of packages to the target destinations. We propose a clustering-based routing heuristic (CRH) to manage the vehicle routing for the last-mile logistic operations of fresh food in e-commerce. CRH is a clustering algorithm that performs repetitive clustering of demand nodes until the nodes within each cluster become serviceable by a single vehicle. The computational complexity of the algorithm is reduced due to the downsizing of the network through clustering and, hence, produces an optimum feasible solution in less computational time. The algorithm performance was analysed using various operating scenarios and satisfactory results were obtained.

Optimal Battery Sizing for Electric Truck Delivery

Finding the cost-optimal battery size in the context of parcel delivery with Electric Vehicles (EVs) requires solving a tradeoff between using the largest possible battery (so as to maximize the number of deliveries over a given time) and the relative costs (initial investment plus the unnecessary increase of the truck weight during delivery). In this paper, we propose a framework for the optimal battery sizing for parcel delivery with an electric truck; we implement an electric truck simulator including a nonlinear battery model to evaluate revenue, battery cost, charging cost, and overall profit for annual delivery. Our framework finds the cost-optimal battery size for different parcel weight distributions and customer location distributions. We analyze the effect of battery sizing on the profit, which is up to 56%.

Vehicle Routing with Shipment Consolidation

The Vehicle Routing Problem with Split Deliveries (VRPSD) is a decision model that is increasingly used in practice to enhance the efficiency of last-mile distribution, an important task which all manufacturing, distribution, and retail firms must perform. In its current form, however, the VRPSD is missing an important cost saving opportunity by not allowing interactions among multiple vehicles that visit the same customer. In this paper, we introduce an extended version of the VRPSD, called the Vehicle Routing Problem with Shipment Consolidation (VRPC), a model that considers not only split deliveries, but also shipment exchanges (transfers) between different vehicles at certain customer locations (a process referred to as mid-route shipment consolidation). We conduct a series of numerical experiments, as well as a case study, to show that VRPC solutions match or outperform those of the VRPSD. Results indicate that up to 10 percent savings may be attained through the VRPC in lieu of the VRPSD, which implies that the efficiency of last-mile delivery may be improved by allowing mid-route shipment consolidation at select customer locations.

Rating Analysis of Service Quality, Comfort, Cleanliness, Food and Location of Hotels in DIY to Customer Satisfaction in Hotel Customers in Traveloka

The objective of this research is to determine the quality of hotel service, comfort, cleanliness, food and location ratings in Yogyakarta City on customer satisfaction in hotel consumers in Traveloka. This study applies a quantitative cross-sectional method. The research population is 1,090 hotels that have been rated on the Traveloka website or application and 1-5-star hotels located in Yogyakarta City. The results of this research found that: 1) out of 5 research variables which include service, cleanliness, food, location, and security, only 4 variables have significant effect on customer satisfaction, namely service, cleanliness, location, and comfort, 2) the 3 variables only give effect of 60% while the remaining 40% is contributed by other factors not observed in this research, 3) the Fcount value is 223.561 with probability value (Sig.) = 0,000. The Fcount value (223.561) > Ftable (0.89) which means that Ho is rejected and Ha is accepted. The most dominant variable of this research is service proved by β value which is higher than the 3 variables that has effect of β = 0.312. It means that increasing consumer satisfaction at star hotels should improve hotel services as well.

Solution Approaches for Multi-workday Vehicle Routing Problem with Manual Unloading

The daily delivery routes and carried loads of vehicles are determined for a multi-workday planning period. Upon arrival at customer locations, workers, who are pre-assigned to vehicles, must perform manual unloading of goods. The multi-workday vehicle routing problem (MW-VRP) with manual unloading is solved using three solution approaches, namely, minimum-distance fixed-route, minimum-distance varying-route and workload-balanced varying-route. The total travel distances of vehicles and average percent residual energies among workers are determined and compared among the three approaches. The results show that the MW-VRP which considers the physical workloads that workers have to endure each workday throughout the planning period can help to distribute daily manual handling tasks more evenly among workers.

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

The “factory-in-a-box” concept involves assembling production modules (i.e., factories) in containers and transporting the containers to different customer locations. Such a concept could be highly effective during emergencies, when there is an urgent demand for products (e.g., the COVID-19 pandemic). The “factory-in-a-box” planning problem can be divided into two sub-problems. The first sub-problem deals with the assignment of raw materials to suppliers, sub-assembly decomposition, assignment of sub-assembly modules to manufacturers, and assignment of tasks to manufacturers. The second sub-problem focuses on the transport of sub-assembly modules between suppliers and manufacturers by assigning vehicles to locations, deciding the order of visits for suppliers, manufacturers, and customers, and selecting the appropriate routes within the transportation network. This study addresses the second sub-problem, which resembles the vehicle routing problem, by developing an optimization model and solution algorithms in order to optimize the “factory-in-a-box” supply chain. A mixed-integer linear programming model, which aims to minimize the total cost of the “factory-in-a-box” supply chain, is presented in this study. CPLEX is used to solve the model to the global optimality, while four metaheuristic algorithms, including the Evolutionary Algorithm, Variable Neighborhood Search, Tabu Search, and Simulated Annealing, are employed to solve the model for large-scale problem instances. A set of numerical experiments, conducted for a case study of “factory-in-a-box”, demonstrate that the Evolutionary Algorithm outperforms the other metaheuristic algorithms developed for the model. Some managerial insights are outlined in the numerical experiments as well.

Synchronizing Travelling and Waiting Processes: Customer Strategy with an Online Reservation System

Online reservation system allows customers to join a queue and virtually wait for service before arriving on site. For example, some platforms have been designed to collect the information of restaurants and show the real-time congestion levels (e.g., Nowait). We consider a model in which customers must travel from their location to the service site incurring a travelling cost. Upon arrival to the service site the customer may have to wait until the customers ahead finish their service. When customers intend to book service online, they are informed about their positions in the queue at the time of booking, so that they make their decision whether or not to join the queue taking into account both their travelling time and expected waiting time. Intuitively, the more time customers spend on travelling to the site, the less time they have to wait in line. Therefore, there exists a tradeoff between the travelling and waiting cost. We show that if the travelling cost is sufficiently high, the attraction of the service is decreasing in the distance between the customer and service location; and if the travelling cost is negligible, the attraction of the service is increasing in the distance. With such customers behavior, the optimal location strategy of a service site heavily relies on the conditions of the travelling and benefit of the service.

Balanced Scorecard-Based Evaluation of Sustainable Energy Investment Projects with IT2 Fuzzy Hybrid Decision Making Approach

The purpose of this study is to determine the issues that financial institutions should pay attention to in their decision to provide financing to large scale energy projects. Within this framework, taking into account the Balanced Scorecard (BSC) approach, 4 dimensions and 8 criteria that can be effective in these decisions of financial institutions were determined. After that, the importance weights of these dimensions and criteria were determined by interval type-2 (IT2) fuzzy DEMATEL method. In addition, 3 different types of banks (public, private, foreign) are listed for their performance in financing energy projects. According to the results of the analysis, the technological and financial adequacy of the company that will invest in energy is the issue that financial institutions should pay the most attention to in their credit decision. Therefore, it is important for financial institutions to visit the customer's location and pay attention to the technological adequacy in the process of evaluating the customer's demand for credit for a large-scale energy project. In addition, it would be appropriate not to provide this fund to the energy companies with high indebtedness and insufficient liquidity. Another result of the study is that state banks have the lowest performance in financing these large-scale energy projects. When this result is taken into consideration, state banks need to pay more attention in evaluating large-scale energy projects in order to avoid major losses.

Tackling a VRP challenge to redistribute scarce equipment within time windows using metaheuristic algorithms

This paper reports on the results of the VeRoLog Solver Challenge 2016–2017: the third solver challenge facilitated by VeRoLog, the EURO Working Group on Vehicle Routing and Logistics Optimization. The authors are the winners of second and third places, combined with members of the challenge organizing committee. The problem central to the challenge was a rich VRP: expensive and, therefore, scarce equipment was to be redistributed over customer locations within time windows. The difficulty was in creating combinations of pickups and deliveries that reduce the amount of equipment needed to execute the schedule, as well as the lengths of the routes and the number of vehicles used. This paper gives a description of the solution methods of the above-mentioned participants. The second place method involves sequences of 22 low level heuristics: each of these heuristics is associated with a transition probability to move to another low level heuristic. A randomly drawn sequence of these heuristics is applied to an initial solution, after which the probabilities are updated depending on whether or not this sequence improved the objective value, hence increasing the chance of selecting the sequences that generate improved solutions. The third place method decomposes the problem into two independent parts: first, it schedules the delivery days for all requests using a genetic algorithm. Each schedule in the genetic algorithm is evaluated by estimating its cost using a deterministic routing algorithm that constructs feasible routes for each day. After spending 80 percent of time in this phase, the last 20 percent of the computation time is spent on Variable Neighborhood Descent to further improve the routes found by the deterministic routing algorithm. This article finishes with an in-depth comparison of the results of the two approaches.

Multi-cloud provisioning of business processes

The Cloud offers enhanced flexibility in the management of resources for any kind of application while it promises the reduction of its cost as well as its infinite scalability. In this way, due to these advantages, there is a recent move towards migrating business processes (BPs) in the Cloud. Such a move is currently performed in a manual manner and only in the context of one Cloud. However, a multi- & cross-Cloud configuration of a BP can be beneficial as it can allow exploiting the best possible offers from multiple Clouds and enable to avoid the lock-in effect by also having the ability to deploy different instances of the BP in different Clouds close to the locations of BP customers. In this respect, this article presents a novel architecture of an environment which realises the vision of multi-Cloud BP provisioning. This environment involves innovative components which support the cross-level orchestration of cloud services as well as the cross-level monitoring and adaptation of BPs. It also relies on a certain language called CAMEL which has been extended to support the adaptive provisioning of multi-Cloud BPs.

A hotel recommendation system based on customer location: a link prediction approach

Hotel recommendation is one of the most used application areas in recommendation systems. So far, many hotel recommendation systems have been proposed. Most of these systems are based collaborative filtering, content-based filtering, and association rule methods and employ the features of hotel, the ratings given by user, online reviews and comments in social network about the related hotel as data. However, due to the difficulty of processing the data used, the performance rates and speeds of these methods are relatively slow. As a solution to these problems in this paper, we propose a novel hotel recommendation system based on link prediction method. For this purpose, a customer-hotel bipartite network was first constructed and the relationship information in this network was used as data. Then, a supervised link prediction method that consider customers’ location was presented. To the best of our knowledge, this is the first study that recommends hotel by using link prediction method. The experimental results conducted on data crawled from TripAdvisor.com demonstrate that the proposed method captures an accuracy of 89.5% and outperforms the other recent related algorithms.

Stokastik Talepli Araç Rotalama Problemi İçin Literatür Taraması

Vehicle Routing Problem (VRP) is the problem of identifying suitable routes in a way that minimizes the cost in order to serve customers in different locations from one or more depots by one or more vehicles. However, in real life problems, Stochastic Vehicle Routing Problem (SVRP) with stochastic information appears more than deterministic problems in which all parameters are known in advance. When the studies on SVRP in the literature are examined, it is found that the researchers study the most about vehicle routing problem with stochastic demand (VRPSD) in which stochastic demand takes place. In this study, a situation was examined in such a way that customer demands are not known exactly until the vehicle reaches the customer location. In VRPSD, it is accepted that the demands from customers consist of random variables with a certain probability distribution. Studies on VRPSD in the literature have been examined in detail and a classification has been made under the specified constraints. Studies on VRPSD are evaluated according to this classification, mathematical models developed for VRPSD and the proposed solution approaches for solving the problem are laid out and an effort is made to determine which problem the researchers concentrated on the most.