Customer Locations Research Articles

Real-Time Optimization of Personalized Assortments

Motivated by the availability of real-time data on customer characteristics, we consider the problem of personalizing the assortment of products for each arriving customer. Using actual sales data from an online retailer, we demonstrate that personalization based on each customer's location can lead to over 10% improvements in revenue compared to a policy that treats all customers the same. We propose a family of index-based policies that effectively coordinate the real-time assortment decisions with the back-end supply chain constraints. We allow the demand process to be arbitrary and prove that our algorithms achieve an optimal competitive ratio. In addition, we show that our algorithms perform even better if the demand is known to be stationary. Our approach is also flexible and can be combined with existing methods in the literature, resulting in a hybrid algorithm that brings out the advantages of other methods while maintaining the worst-case performance guarantees.This paper was accepted by Dimitris Bertsimas, special issue on business analytics.

Call Duration Characteristics based on Customers Location

Abstract Nowadays a lot of different researches are performed based on call duration distributions (CDD) analysis. However, the majority of studies are linked with social relationships between the people. Therefore the scarcity of information, how the call duration is associated with a user's location, is appreciable. The goal of this paper is to reveal the ties between user's voice call duration and the location of call. For this reason we analyzed more than 5 million calls from real mobile network, which were made over the base stations located in rural areas, roads, small towns, business and entertainment centers, residential districts. According to these site types CDD’s and characteristic features for call durations are given and discussed. Submitted analysis presents the users habits and behavior as a group (not an individual). The research showed that CDD’s of customers being them in different locations are not equal. It has been found that users at entertainment, business centers are tend to talk much shortly, than people being at home. Even more CDD can be distorted strongly, when machinery calls are evaluated. Hence to apply a common CDD for a whole network it is not recommended. The study also deals with specific parameters of call duration for distinguished user groups, the influence of network technology for call duration is considered.

A new fuzzy logic approach to capacitated dynamic Dial-a-Ride problem

Almost all Dial-a-Ride problems (DARP) described in the literature pertain to the design of optimal routes and schedules for n customers who specify pick-up and drop-off times. In this article we assume that the customer is mainly concerned with the drop-off time because it is the most important to the customer. Based on the drop-off time specified by the customer and the customer's location, a pick-up time is calculated and given to the customer by the dispatching office. We base our formulation on a dynamic fuzzy logic approach in which a new request is assigned to a vehicle. The fuzzy logic algorithm chooses the vehicle to transport the customer by seeking to satisfy two objectives. The first reflects the customer's preference and minimizes the time a customer spends in the vehicle, and the second reflects the company's preference and minimizes the distance a vehicle needs to travel to transport the customer. The proposed heuristic algorithm is relatively simple and computationally efficient in comparison with most deterministic algorithms for solving both small and large sized problems.

Influence of resources on maintenance operations with different asset monitoring levels

Purpose – The demand for contracts on assets availability has increased. Recently published papers show that the use of asset health monitoring technologies is being encouraged to improve the asset performance. This is based on reason rather than analysis. This paper aims to understand and assess the effect of different types of business processes for maintenance resource levels on the behaviour of the maintenance operations and asset availability located at different customer locations using different asset monitoring levels. Design/methodology/approach – A discrete event simulation (DES) model was developed to mimic complex maintenance operations with different monitoring levels (reactive, diagnostics, and prognostics). The model was created to understand and assess the influence of resources (labour and spare parts) on a particular maintenance operation. The model was created to represent different levels of asset monitoring to be applied in a case study. Subsequently, different levels of spare parts (ranging from deficient inventory to a plentiful spares inventory) and labour were applied to show the effects of those resources on the asset availability. Findings – This research has found that the DES was able to discern different processes for asset monitoring levels in complex maintenance operations. It also provided numerical evidence about applying such asset monitoring levels and proved that the higher asset monitoring level does not always guarantee higher asset availability. Practical implications – The developed model is a unique model that can provide the decision makers of maintenance operations with numerical evidence to select an appropriate asset monitoring level based on their particular maintenance operations. Originality/value – A novel DES model was developed to support maintenance operations decision makers in selecting the appropriate asset monitoring level for their particular operations. This unique approach provides numerical evidence rather than reasoning, and also proves that the higher asset monitoring level does not always guarantee higher asset availability.

Hybrid Heuristic Algorithm for solving Capacitated Vehicle Routing problem

The Capacitated Vehicle Routing Problem is the most common and basic variant of the vehicle routing problem, where it represents an important problem in the fields of transportation, distribution and logistics. It involves finding a set of optimal routes that achieve the minimum cost and serve scattered customer locations under several constraints such as the distance between customers’ locations, available vehicles, vehicle capacity and customer demands. The Cluster first – Route second is the proposed approach used to solve capacitated vehicle routing problem which applied in a real case study used in that research, it consists of two main phases. In the first phase, the objective is to group the closest geographical customer locations together into clusters based on their locations, vehicle capacity and demands by using Sweep algorithm. In the second phase, the objective is to generate the minimum cost route for each cluster by using the Nearest Neighbor algorithm. The hybrid approach is evaluated by Augerat’s Euclidean benchmark datasets.

Global competitive conditions driving the manufacturing location decision

Given today's rapidly shifting global competitive conditions—including customer location, natural disasters, currency valuation, labor and transportation costs and availability—many U.S. companies are revisiting decisions about their preferred manufacturing location(s). The purpose of this research is to understand some of the trends that affect whether U.S.-based companies bring their production back to the United States or relocate it to different geographical locations (reshore). The focus is on the key factors that affect companies’ manufacturing location decisions, the importance of these factors, and how the importance has changed over time. Because of the complexity involved in the manufacturing location decision, key risk factors inherent in the manufacturing decision are also assessed. Survey responses from 319 companies that currently manage offshore manufacturing plants are analyzed. Among other insights, this study found that 40% of these companies perceived a trend toward reshoring to the U.S. in their industries. The companies involved in this study also place an increasing importance on where their customers want them to locate, as well as how the location could help expand into new customer markets. These and further results and implications for U.S. manufacturing companies are presented herein.

A bi-objective stochastic single facility location model for a supermarket

Selecting a new location is vital for retail stores such as chain supermarkets and can be considered as a huge competitive advantage which may result in their failure or success. Location models are hard to implement in the real world, firstly because of the uncertainties of the input parameters and secondly, due to the intensive computations involved when the solution space is large such as a city. In this study, a stochastic bi-objective model is developed for point and area destinations with the purpose of finding a single new location for a chain supermarket that aims to be close to more customers and have the minimum number of competitors near to the new location. Customer locations are considered to be regional with a uniform probability distribution. A reduced gradient solution procedure is used as an algorithm for solving the model. The problem is solved with the help of the MATLAB software due to the high computations involved.

Probabilistic Formulation for Emergency Facility Location in a Divided Area

Capacitated minimax facility location-allocation (LA) is a type of facility problem which is of prime importance for emergency situation, since it directly affects the service response time to customers. Capacitated minimax LA problem is concerned with locating some new facilities and allocating their capacity to customers when the maximum travelled distance from customers to facilities is minimized. This study involves a fixed line barrier in a region with some border crossings along it which divides the area into two subregions. Although several studies have recently been done on this problem in which the customer locations are known with certainty, much less attention has been devoted to developing a comprehensive mathematical model for the probabilistic extension of customer locations when there are some restrictions in the region. In order to fill this gap, a Mixed Integer Nonlinear Programming (MINLP) model is proposed for facility LA when customer locations are randomly distributed according to a bivariate normal probability distribution. Finally, the BARON solver in the GAMS software is used to solve the model, and a numerical example is provided to demonstrate its efficiency.

Relative kW Response to Residential Time-Varying Pricing in British Columbia

We apply graphical exploration and regression analysis to estimate 1717 participants' relative kW responses in BC Hydro's residential TOU/CPP pilot study. We define a customer's relative kW response as the percentage change in the customer's hourly kW demand due to exposure to time-varying pricing. Compared to the control group of customers facing non-TOU rates, we find that TOU pricing yields a statistically-significant evening peak kW decrease of 4-11%, after controlling for the effects of day of the week, month of the year, weather, customer location, and customer size. CPP produces an additional peak kW reduction of about 9%, which can be further increased to about 33% through remotely-activated load control of space and water heaters. Hence, a scheme of TOU pricing augmented with CPP and load control on system peak days can be a highly effective demand-response strategy for winter-peaking utilities.

Real-Time Fleet Management and Rerouting in City Logistics

The urban freight distribution is highly susceptible to unexpected events that often occur during delivery, such as delays at customer locations or due to traffic conditions. Such events may lead to inferior customer service, or higher costs, areas in which intelligent real-time fleet management may prove beneficial. In this paper, the authors present such a system that incorporates methods to estimate the expected travel time of a delivery vehicle, combining AVL-based real-time and historical data, with algorithms for efficient vehicle re-routings. The system continuously monitors the delivery process, detects possible delays in real-time, and adjusts the delivery schedule accordingly by suggesting effective re-routing strategies. The authors report results from testing the system via simulation and in a case study, and illustrate the extent of delivery performance improvements that may be achieved through such an approach.

The capacitated multi-facility location–allocation problem with probabilistic customer location and demand: two hybrid meta-heuristic algorithms

A new mathematical model for the capacitated multi-facility location–allocation problem with probabilistic customers' locations and demands is developed in this article. The model is formulated into the frameworks of the expected value model (EVM) and the chance-constrained programming (CCP) based on two different distance measures. In order to solve the model, two hybrid intelligent algorithms are proposed, where the simplex algorithm and stochastic simulation are the bases for both algorithms. However, in the first algorithm, named SSGA, a special type of genetic algorithm is combined and in the second, SSVDO, a vibration-damping optimisation (VDO) algorithm is united. The Taguchi method is employed to tune the parameters of the two proposed algorithms. Finally, some numerical examples are given to illustrate the applications of the proposed methodologies and to compare their performances.

Tackling complexities of cyclic inventory routing under conditions of limited modelling and computing capacity

This paper presents a tractable approximation method for reliably finding near-optimal solutions to the cyclic inventory routing problem (IRP) without requiring the specialised modelling skills and computing technology to deploy more exact solution approaches. Together with simultaneously attaining high solution quality and efficiency, the method's contribution to the IRP literature stems from modelling transportation cost in a way that parsimoniously captures (a) complexities of estimating vehicle requirements and (b) the heretofore ignored phenomenon of possible correlation between customer locations and demands. An extensive set of computational settings to test the method's performance indicated that vis-à-vis a well-founded benchmark from the literature, the average gap in cost is just slightly above 1%, with a worst-case gap of 4.8% across all settings.

Beam search heuristics for the single and multi-commodity capacitated Multi-facility Weber Problems

The Multi-facility Weber Problem (MWP) is concerned with locating I uncapacitated facilities in the plane to satisfy the demand of J customers with the minimum total transportation cost of a single commodity. It is a non-convex optimization problem and difficult to solve. In this work, we focus on the capacitated extensions of the MWP which are Capacitated MWP (CMWP) and multi-commodity CMWP (MCMWP). Both the CMWP and MCMWP impose capacity restrictions on facilities. Indeed, the MCMWP is a natural extension of the CMWP and considers the situation where K distinct commodities are shipped subject to limitations on the total amount of goods sent from facilities to the customers. Customer locations, demands and capacities for each commodity are known a priori. The transportation costs, which depend on the commodity type, are proportional to the distance between customers and facilities. We first introduce branch and bound algorithms for both the CMWP and the MCMWP then we propose beam search heuristics for these problems. According to our computational experiments on standard and randomly generated test instances, we can say that the new heuristics perform very well.

A Stochastic Vehicle Routing Problem with Travel Time Uncertainty: Trade-Off Between Cost and Customer Service

On-time shipment delivery is critical for just-in-time production and quick response logistics. Due to uncertainties in travel and service times, on-time arrival probability of vehicles at customer locations can not be ensured. Therefore, on-time shipment delivery is a challenging job for carriers in congested road networks. In this paper, such on-time shipment delivery problems are formulated as a stochastic vehicle routing problem with soft time windows under travel and service time uncertainties. A new stochastic programming model is proposed to minimize carrier’s total cost, while guaranteeing a minimum on-time arrival probability at each customer location. The aim of this model is to find a good trade-off between carrier’s total cost and customer service level. To solve the proposed model, an iterated tabu search heuristic algorithm was developed, incorporating a route reduction mechanism. A discrete approximation method is proposed for generating arrival time distributions of vehicles in the presence of time windows. Several numerical examples were conducted to demonstrate the applicability of the proposed model and solution algorithm.

An ant colony optimization technique for solving min–max Multi-Depot Vehicle Routing Problem

The Multi-Depot Vehicle Routing Problem (MDVRP) involves minimizing the total distance traveled by vehicles originating from multiple depots so that the vehicles together visit the specified customer locations (or cities) exactly once. This problem belongs to a class of Nondeterministic Polynomial Hard (NP Hard) problems and has been used in literature as a benchmark for development of optimization schemes. This article deals with a variant of MDVRP, called min–max MDVRP, where the objective is to minimize the tour-length of the vehicle traveling the longest distance in MDVRP. Markedly different from the traditional MDVRP, min–max MDVRP is of specific significance for time-critical applications such as emergency response, where one wants to minimize the time taken to attend any customer. This article presents an extension of an existing ant-colony technique for solving the Single Depot Vehicle Routing Problem (SDVRP) to solve the multiple depots and min–max variants of the problem. First, the article presents the algorithm that solves the min–max version of SDVRP. Then, the article extends the algorithm for min–max MDVRP using an equitable region partitioning approach aimed at assigning customer locations to depots so that MDVRP is reduced to multiple SDVRPs. The proposed method has been implemented in MATLAB for obtaining the solution for the min–max MDVRP with any number of vehicles and customer locations. A comparative study is carried out to evaluate the proposed algorithm's performance with respect to a currently available Linear Programming (LP) based algorithm in literature in terms of the optimality of solution. Based on simulation studies and statistical evaluations, it has been demonstrated that the ant colony optimization technique proposed in this article leads to more optimal results as compared to the existing LP based method.

Study on Coordination Issues for Flexibility in Supply Chain of SMEs: A Case Study

As a result of globalization and liberalization, Indian small and medium enterprises (SMEs) have been passing through a transitional period. With slowing down of economy in India and abroad, countries like China is giving tough competition by offering quality products at lower cost. To face this tough competition of open global markets, SMEs must have coordinated and flexible supply chain (SC). Supply chain management (SCM) can help any organization when implemented effectively. A coordinated and flexible SC increases the SC profitability. SMEs in India lack resources and guidance for SCM implementation. In this paper authors have tried to discuss SCM implementation in an illumination sources manufacturing Indian SME. Situation-actor-process (SAP)–learning-action-performance (LAP) model has been applied for this case study. The situation represents the present scenario of the organization. Actors are the participants, influencing the situation to evolve different business processes. Based on SAP, various learning issues have been analyzed which will help in identifying suitable action for improving the performance. From case study it is observed that on time delivery, innovativeness, flexibility of manufacturing system, location of suppliers and customers, management of transportation system are main priorities for case organization while forming SC strategy.

Exact Solutions to the Symmetric and Asymmetric Vehicle Routing Problem with Simultaneous Delivery and Pick-Up

AbstractIn reverse logistics networks, products (e.g., bottles or containers) have to be transported from a depot to customer locations and, after use, from customer locations back to the depot. In order to operate economically beneficial, companies prefer a simultaneous delivery and pick-up service. The resulting Vehicle Routing Problem with Simultaneous Delivery and Pick-up (VRPSDP) is an operational problem, which has to be solved daily by many companies. We present two mixed-integer linear model formulations for the VRPSDP, namely a vehicle-flow and a commodity-flow model. In order to strengthen the models, domain-reducing preprocessing techniques, and effective cutting planes are outlined. Symmetric benchmark instances known from the literature as well as new asymmetric instances derived from real-world problems are solved to optimality using CPLEX 12.1.

VRP12 (vehicle routing problem with distances one and two) with side constraints

The problem undertaken in this study is inspired from a real life application. Consider a vehicle routing problem where the distances between the customer locations are either one or two. We name this problem as VRP12 in an analogy for the name TSP12 used for the traveling salesman problem in the literature. Additionally, assume that, the time to visit each customer is not constant and the visiting time together with the travel time constitutes the capacity of the vehicle. Furthermore, each customer has two characteristics and any two customers having a common characteristic should not be visited at the same time. If visited, a penalty fee incurs. In this study, we give the formulation of this problem and suggest some simple but effective algorithms that can be used to solve it. The algorithms are built with the relaxation of the side constraints but their performances are evaluated with their success in satisfying them. Information on our case study is also provided.

A hybrid metaheuristic approach for the rollon–rolloff vehicle routing problem

This paper discusses the rollon–rolloff vehicle routing problem, a sanitation routing problem in which large containers are left at customer locations such as construction sites and shopping centers. Customers dump their garbage into large waste containers and request for waste treatment services. Tractors then transport a container at a time between customer locations, disposal facility, and depot. The objective of the problem is to determine routes that minimize the number of required tractors and their deadhead time to serve all given customer demands. We propose a hybrid metaheuristic approach that consists of a large neighborhood search and various improvement methods to solve the problem. The effectiveness of the proposed approach is demonstrated by computational experiments using benchmark data. New best-known solutions are found for 17 problems out of 20 benchmark instances.

International Tax Reform Comment Letter to House Ways and Means Committee - Why a Worldwide Full-Inclusion System is Far Better for Our Country and Society than a Territorial System

I am commenting on the form that International Tax Reform should take. In brief, the Territorial System strongly lobbied for by the U.S.-based multinational corporations (MNCs) standing to benefit from such a system is not what’s best for our country or our society. It is bad tax policy in today’s environment where it is so easy for MNCs with their extensive resources to shift profits from the U.S. and into low-tax countries. Rather than implementing a Territorial System such as that proposed in the October 26, 2011 Ways and Means Discussion Draft (W&MDD) or continuing our current Deferral System, our country, its people, and yes even our MNCs would best be served by implementing a Worldwide Full-Inclusion System (WFI System). Such a system would actually eliminate or severely curtail the strong motivation (even stronger under a Territorial System) that MNCs now have to shift profits out of the U.S. and into low-taxed countries, thereby achieving non-taxation. Profit shifting motivation disappears when achieving double non-taxation is no longer possible because there’s a current federal tax on all earnings and that tax cannot be eliminated through any tax schemes and creative avoidance. So, this is what is needed: A taxation system that eliminates the present strong motivation to achieve double non-taxation through operating, owning assets, and bearing risks outside the U.S. These efforts, which shift profits and jobs out of the U.S., distort the business and investment decisions of our MNCs. International Tax Reform must put in place a new taxation system that will leave our MNCs free to make their business decisions based solely on business and investment factors such as location of raw materials and customers, employee wage rates, transportation costs, availability of qualified personnel, etc. Only the WFI System accomplishes this.