Customer Locations Research Articles

A novel method to handle route failure in fuzzy vehicle routing problem with hard time windows and uncertain demand

In this paper, a fuzzy vehicle routing problem with hard time windows (FVRPTW) having uncertain demand is addressed. Since customer's demand is assumed to be known only upon arrival at customer location, a planned route may fail to meet realised demand of customer. This situation is known as route failure. Instead of adopting simple recourse methods to handle route failure, this paper proposes a novel method titled 'non-restocking' to solve route failure. The problem is formulated as a two stage recourse model with an objective of minimising the total cost and at the same time maximising the obtained sale in a competitive environment. Uncertainty in demand is represented by fuzzy numbers. Route construction for the modelled problem is done by ant colony system. The computational results show that the proposed two stage model with the proposed new recourse scheme produces high quality solutions. Benefits of the proposed method are demonstrated by applying it to milk routing problem.

Failure Detection Algorithm for Testing Dynamic Web Applications

Web applications are the most widely used software in the internet. When a web application is developed and deployed in the real environment, It is very severe if any bug found by the attacker or the customer or the owner of the web application. It is the very important to do the proper pre-analysis testing before the release. It is very costly thing if the proper testing of web application is not done at the development location and any bug found at the customer location. For web application testing the existing systems such as DART, Cute and EXE are available. These tools generate test cases by executing the web application on concrete user inputs. These tools are best suitable for testing static web sites and are not suitable for dynamic web applications. The existing systems needs user inputs for generating the test cases. It is most difficult thing for the human being to provide dynamic inputs for all the possible cases. This paper presents algorithms and implementation, and an experimental evaluation that revealed HTML Failures, Execution Failures, Includes in PHP Web applications.

Development of Android Application for Courier Monitoring System

Accuracy and precision are two important factors which must be fulfilled in distribution of goods, so the business proses lifecycle of an enterprise or company would run properly. However, at the time of the distribution process performed, a problem could occur. A complicated ordering flow can be one of the problem as happened in the distribution of medicine to pharmacies or hospitals. Medicine can be ordered when distributors assign some of its salesman for visiting pharmacies or hospitals and offer medicine to be sold. Pharmacies who will order must create a letter and handed it to the salesman. All orders from some pharmacies or hospitals will be entered into the order book manually by the salesman. Afterwards, order book returned to distributor and after going through several process again, the order will be sent to the pharmacy or hospital. That issue is the topic of this research, which at the time of delivery of order, courier could be negligent. Therefore, courier needs to be monitored to determine whether the order has been shipped or not. The purpose of this research is to design and build a courier monitoring system. Application is an android based application using GPS Location to trace courier location, and Android Cloud to Device Messaging for push notification feature. There are three modules for users of this application. Application in part one will be used by the medicine distributor company. The second one are used by the salesman to make an order while at the customer premises. The last part are used by the courier for order information and customer location. With this courier monitoring system, pharmacies or hospitals could order medicine quickly and in the right amount. This application can also help distributors in order to monitor location of the courier. So that the business process of both parts would run properly.

Calculation of 20 kV Distribution Network Energy Losses and Minimizing Effort Using Network Reconfiguration in Region of PT PLN (Persero) UPJ Bantul

Power distribution system is a component of an electric power system to deliver electrical energy from substation to customer location. In power distribution system, there are some power loss was changed as heat. Power distribution losses is a natural occurrence, so one gets to be done only minimize to support global energy efficiency. The way to reduce power loss in the power distribution system is by reconfiguration the existing line. Reconfiguration means a process of operating the switch (NO and NC) and change the topology line. Then, power loss in the power distribution system is computed with “ETAP” simulation software. From computing result of distribution network losses on existing line at PT. PLN UPJ. Bantul BNL 6, BNL 7 and BNL 11 feeders are gotten energy losses as 2,669,328 kWh per year or 1.72 %. Network reconfiguration that involves BNL 6, BNL 7 and BNL 11 feeder gets energy losses decrease by 1.00 % per year.

Time-dependent vehicle routing problem with path flexibility

Conventionally, vehicle routing problems are defined on a network in which the customer locations and arcs are given. Typically, these arcs somehow represent the distances or expected travel time derived from the underlying road network. When executed, the quality of the solutions obtained from the vehicle routing problem depends largely on the quality of the road network representation. This paper explicitly considers path selection in the road network as an integrated decision in the time-dependent vehicle routing problem, denoted as path flexibility (PF). This means that any arc between two customer nodes has multiple corresponding paths in the road network (geographical graph). Hence, the decisions to make are involving not only the routing decision but also the path selection decision depending upon the departure time at the customers and the congestion levels in the relevant road network. The corresponding routing problem is a time-dependent vehicle routing problem with path flexibility (TDVRP–PF). We formulate the TDVRP–PF models under deterministic and stochastic traffic conditions. We derive important insights, relationships, and solution structures. Based on a representative testbed of instances (inspired on the road network of Beijing), significant savings are obtained in terms of cost and fuel consumption, by explicitly considering path flexibility. Having both path flexibility and time-dependent travel time seems to be a good representation of a wide range of stochasticity and dynamics in the travel time, and path flexibility serves as a natural recourse under stochastic conditions. Exploiting this observation, we employ a Route-Path approximation method generating near-optimal solutions for the TDVRP–PF under stochastic traffic conditions.

Investigation and analysis of power consumption in convenience stores in Taiwan

By using a remote energy-conserving monitoring system, this study investigated the power consumption data of 30 convenience stores in northern, central, and southern Taiwan. Data collected comprised the temperature, humidity, and power consumption data of air conditioning, freezer and refrigerator, lighting, and heating equipment. In addition, factors that affected the power consumption of the stores were examined by collecting the basic information of each store, including total floor area, location, sales revenue, and number of customers visited. The results revealed that the average annual energy use intensity in a convenience store in Taiwan was 2346kWh/m2/year, which was significantly higher than those of office buildings and hotels. The power consumption of the four types of investigated convenience stores is arranged in a descending order as follows: convenience stores in commercial areas, residential areas, suburban areas, and strategic road areas. The demand use intensity of food storage equipment (i.e., freezers and refrigerators) was the highest among the investigated types of equipment in the convenience stores, at 88.58W/m2. Therefore, the first priority for improving energy conservation is to replace old and outdated food storage equipment with efficient units. Finally, a power consumption model for convenience stores was formulated through the examined power-consuming factors, and may serve as a reference for forecasting the power consumption of planned convenience stores.

A generic framework for multi-criteria decision support in eco-friendly urban logistics systems

In this paper, we present a multi-objective shortest path evolutionary algorithm for comprehensive solutions to real-world manifestations of the classical vehicle routing problem. The shift from being a purely academic pursuit is highlighted by the introduction of a generic optimization framework which accommodates a variety of attributes that commonly occur in industrial applications. Specifically, the paper's main contribution are as follows: (1) consideration for the following real-world constraints: (a) time windows at customer locations, (b) simultaneous pickup and delivery demands, (c) a heterogeneous fleet of vehicles, and (d) the heterogeneity of traffic congestion levels in urban transportation networks; (2) assimilation of all the above attributes into a multi-objective program which aims to minimize environmental impact, while simultaneously addressing the overall operational costs of the routing solution and service quality concerns; a feat that has not been fully realized by known intelligent systems according to the authors’ best knowledge. In order to showcase the efficacy of the proposed algorithm, it is first tested on existing benchmark instances and then applied on a pair of real-world industrial examples from Singapore. These industrial examples serve as a source of new benchmarks which facilitate the study of different routing constraints and their effects on the economic and environmental viability of urban logistics systems.

3D GEOMARKETING SEGMENTATION: A HIGHER SPATIAL DIMENSION PLANNING PERSPECTIVE

Abstract. Geomarketing is a discipline which uses geographic information in the process of planning and implementation of marketing activities. It can be used in any aspect of the marketing such as price, promotion or geo targeting. The analysis of geomarketing data use a huge data pool such as location residential areas, topography, it also analyzes demographic information such as age, genre, annual income and lifestyle. This information can help users to develop successful promotional campaigns in order to achieve marketing goals. One of the common activities in geomarketing is market segmentation. The segmentation clusters the data into several groups based on its geographic criteria. To refine the search operation during analysis, we proposed an approach to cluster the data using a clustering algorithm. However, with the huge data pool, overlap among clusters may happen and leads to inefficient analysis. Moreover, geomarketing is usually active in urban areas and requires clusters to be organized in a three-dimensional (3D) way (i.e. multi-level shop lots, residential apartments). This is a constraint with the current Geographic Information System (GIS) framework. To avoid this issue, we proposed a combination of market segmentation based on geographic criteria and clustering algorithm for 3D geomarketing data management. The proposed approach is capable in minimizing the overlap region during market segmentation. In this paper, geomarketing in urban area is used as a case study. Based on the case study, several locations of customers and stores in 3D are used in the test. The experiments demonstrated in this paper substantiated that the proposed approach is capable of minimizing overlapping segmentation and reducing repetitive data entries. The structure is also tested for retrieving the spatial records from the database. For marketing purposes, certain radius of point is used to analyzing marketing targets. Based on the presented tests in this paper, we strongly believe that the structure is capable in handling and managing huge pool of geomarketing data. For future outlook, this paper also discusses the possibilities of expanding the structure.

Fuzzy random cost–profit tradeoff location model for a vehicle inspection station with regional constraints

Transportation facility and automotive service enterprise location is an interesting and important issue. In practice, such factors as customer demand, allocations, even locations of customers and facilities are usually changing, thus making facility location problematic with uncertainty. To account for it, some researchers have addressed stochastic/fuzzy models for locating an automotive service enterprise. However, probabilistic/fuzzy models are not suitable to describe all kinds of uncertainty, but only randomness or fuzziness. In fact, the uncertain environment of locating an automotive service enterprise is a mixed one with both randomness and fuzziness. To handle this issue in a practical manner, this work proposes fuzzy random tradeoff issues for it. Moreover, some regional constraints can greatly influence its location. By taking the vehicle inspection station as a typical example, this work presents new fuzzy random cost–profit tradeoff models of its location problem with regional constraints. A hybrid algorithm integrating fuzzy random simulation and genetic algorithms is adopted to solve the proposed models. Additionally, some risk factors have a great impact on decision making when faced with a location problem. This work thus conducts a risk performance analysis for locating an automotive service enterprise. Some numerical examples are given to illustrate the proposed models. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Customer Location and the International Reach of National Competition Laws

Customer Location and the International Reach of National Competition Laws

NBA Primary Market Ticket Consumers: Ex Ante Expectations and Consumer Market Origination

We add to the recent ticket market literature by using a unique, disaggregated, and proprietary data set of primary market ticket sales transactions from a National Basketball Association team that includes previously unavailable information on date of purchase, customer location, and other consumer demographics. We find that local and out-of-market fans differ in their total purchase amounts, with out-of-market fans spending more than local consumers, on average, and differential spending effects based on the home team win probability. In particular, this differential behavior has important implications for Rottenberg’s uncertainty of outcome hypothesis. We find evidence that interest in visiting team quality dominates interest in perceived contest uncertainty, fitting the reference-dependent preference model in the context of low local team quality. Further, these findings also have important implications related to market segmentation and dynamic ticket pricing in professional sport.

Household Switching Behavior at Depository Institutions: Evidence from Survey Data

This article presents descriptive findings from new survey data on households' decisions to change or remain with their providers of checking or savings accounts. The data show that the distribution of household tenure is wide, and that about a third of households have never changed depository institutions. The primary reason reported for changing banks is a household relocation; other reasons are customer service and price factors. Customer service and location are the most frequently cited reasons for remaining with a bank. The importance of location and mobility supports previous survey evidence that the local area is the appropriate market for competitive analysis in banking. The findings presented here are consistent with earlier studies showing that population migration increases competitive pressure on firms and therefore should mitigate the anticompetitive effects of bank mergers.

An integrated production, inventory, warehouse location and distribution model

This paper proposes an integrated production and distribution planning optimization model for multiple manufacturing locations, producing multiple products with deterministic demand at multiple locations. There are multiple modes of transport from plants to demand locations and warehouses. This study presents a model which allows decision makers to optimize plant production, transport and warehouse location simultaneously to fulfill the demands at customer locations within a multi-plant, multi-product, and multi-route supply chain system when the locations of the plants are already fixed. The proposed model is solved for sample problems and tested using real data from a cement manufacturing company in India. An analysis of the results suggests that this model can be used for various strategic and tactical production and planning decisions.

Multiobjective Optimization Models for Locating Vehicle Inspection Stations Subject to Stochastic Demand, Varying Velocity and Regional Constraints

Deciding an optimal location of a transportation facility and automotive service enterprise is an interesting and important issue in the area of facility location allocation (FLA). In practice, some factors, i.e., customer demands, allocations, and locations of customers and facilities, are changing, and thus, it features with uncertainty. To account for this uncertainty, some researchers have addressed the stochastic time and cost issues of FLA. A new FLA research issue arises when decision makers want to minimize the transportation time of customers and their transportation cost while ensuring customers to arrive at their desired destination within some specific time and cost. By taking the vehicle inspection station as a typical automotive service enterprise example, this paper presents a novel stochastic multiobjective optimization to address it. This work builds two practical stochastic multiobjective programs subject to stochastic demand, varying velocity, and regional constraints. A hybrid intelligent algorithm integrating stochastic simulation and multiobjective teaching-learning-based optimization algorithm is proposed to solve the proposed programs. This approach is applied to a real-world location problem of a vehicle inspection station in Fushun, China. The results show that this is able to produce satisfactory Pareto solutions for an actual vehicle inspection station location problem.

Time constrained maximal covering salesman problem with weighted demands and partial coverage

In a routing framework, it may not be viable to visit every single customer separately due to resource limitations or efficiency concerns. In such cases, utilizing the notion of coverage; i.e., satisfying the demand of multiple customers by visiting a single customer location, may be advantageous. With this motivation, we study the time constrained maximal covering salesman problem (TCMCSP) in which the aim is to find a tour visiting a subset of customers so that the amount of demand covered within a limited time is maximized. We provide flow and cut formulations and derive valid inequalities. Since the connectivity constraints and the proposed valid inequalities are exponential in the size of the problem, we devise different branch-and-cut schemes. Computational experiments performed on a set of problem instances demonstrate the effectiveness of the proposed valid inequalities in terms of strengthening the linear relaxation bounds as well as speeding up the solution procedure. Moreover, the results indicate the superiority of using a branch-and-cut methodology over a flow-based formulation. Finally, we discuss the relation between the problem parameters and the structure of optimal solutions based on the results of our experiments.

Taxonomy of Marketing Strategies Using Bank Customers’ Clustering

<p>Purpose-The goal of this study is to identify the main clusters of bank customers in order to help commercial banks to better identify their customers and design more efficient marketing strategies.</p><p>Design/methodology/approach–Data from 250 bank customers were analyzed by using two-step scalable clustering.</p><p>Findings-Five different clusters of bank customers were identified, namely, favorite customers, creditworthy customers, non-creditworthy customers, passers, and friends. The findings indicated that disparate clusters of bank customers are extremely different based on their loan amount, default risk, account balance, degree of loyalty and profitability for the bank.</p><p>Practical implications-The differences which were observed between these five clusters of bank customers accentuate the importance of customer clustering and market segmentation in the financial services industry. Customer clustering can help financial institutions to augment their competitiveness by shifting from traditional marketing strategies to target marketing and segmentation-based marketing approaches.</p><p>Originality/value-The most important contribution of this study is the incorporation of a wide range of factors that can potentially affect customer clustering in the analysis, whereas, the majority of previous studies only focused on a limited number of variables in order to determine the customer clusters. Specifically, the customer clustering in this study was performed by using demographic variables, profitability, loan amount, default risk, account balance, loyalty, account type, account closure history, customer location, and account currency. </p>

Fuzzy Multi-objective Expected Value Optimization Models for Locating an Automotive Service Enterprise

Automotive service enterprise location is an interesting and important issue in the logistic field. In practice, some factors of its facility location allocation (FLA) problem, i.e., customer demands, allocations, even locations of customers and facilities, are usually changing, and thus FLA problem features with uncertainty. To account for this uncertainty, some researchers have addressed the fuzzy time and cost issues for locating an automotive service enterprise. However, a decision-maker hopes to minimize the transportation time of customers meanwhile minimizing their transportation cost when locating a facility. Also, they prefer to arrive at the destination within the specific time and cost. To handle this issue via a more practical manner, by taking the vehicle inspection station as a typical automotive service enterprise example, this work presents a fuzzy multi-objective expected value optimization approach to address it. Moreover, some region constraints can greatly influence FLA and travel velocity is also an uncertain variable due to the influence of some unpredictable factors in the location process. To do so, this work builds two practical fuzzy multi-objectives programming models of its location with regional constraints, fuzzy inspection demand, and varying velocity. A hybrid algorithm integrating fuzzy simulation, neural networks (NN), and Genetic Algorithms (GA), namely a random weight based multi-objective NN-GA, is proposed to solve the proposed models. A numerical example is given to illustrate the proposed models and the effectiveness of the proposed algorithm.

Finding optimal tour schedules on transportation paths under extended time window constraints

This paper addresses time-critical routing on a given path under release dates and deadline restrictions, while the minimization of the total weighted completion time is pursued. Since there may be destinations with flexible picking area resources that enable a delivery of goods before the defined release date at no additional costs, the well-known Line-Traveling Repairman problem (Line-TRPTW) is extended into the Line-TRPeTW. The Line-TRPeTW has various practical applications, such as, for example, the tour planning of an inland container ship along a river or that of a vehicle along a coastline. Although the feasibility variant of the Line-TRPTW/Line-TRPeTW is known to be strongly $${\mathcal {NP}}$$NP-hard, a first practically applicable Branch&Bound procedure is generated. By making use of different dominance rules and lower bounds, a comprehensive computational study proves that instances comprising up to 200 requests and locations are solved to optimality in reasonable time. Moreover, the paper analyzes the complexity of the simpler variant with relaxed release dates at all customer locations. This relaxed variant provides tight lower bounds. Furthermore, the complexity analysis shows that the relaxed problem variant is binary $${\mathcal {NP}}$$NP-hard even if deadlines are ignored, but can be efficiently solved by a specific Dynamic Programming approach that runs in pseudo-polynomial time.

Solución del problema de ruteo de vehículos con demandas estocásticas mediante la optimización por espiral

El artículo presenta los resultados del estudio de un problema de ruteo de vehículos con demandas estocásticas (Vehicle Routing Problem with Stochastic Demands, VRPSD), en el cual la única variable estocástica es la demanda de los clientes, esta variable sigue una distribución discreta, y su valor solo es conocido cuando el vehículo llega a la ubicación del cliente. Para su solución, se implementó la metaheurística denominada Optimización por Espiral, con el enfoque a priori y la estrategia de reabastecimiento preventivo para un solo vehículo. Para mejorar el método se inicializaron las rutas mediante la heurística del vecino más cercano, y posteriormente se utilizó la mutación, un operador evolutivo, para ampliar la zona de exploración de los puntos de búsqueda. Adicionalmente, se utilizó el intercambio 2-Opt, una heurística de búsqueda local, con el fin de intensificar la búsqueda en la vecindad de soluciones óptimas encontradas. Por otra parte, se realizó un diseño de experimentos 23, con el fin de determinar la influencia de cada factor en la función objetivo. Este análisis se llevó a cabo en 8 instancias diferentes que fueron diseñadas y desarrolladas por Galván et al. [1]. Finalmente, se compararon los resultados obtenidos con los arrojados por el algoritmo híbrido EPSO, con el objetivo de probar la eficiencia y eficacia del algoritmo desarrollado. Esta comparación evidenció que el método propuesto obtiene mejores resultados en todas las instancias, con mejoras de hasta el 5,71 %.

The vehicle routing problem with drones: several worst-case results

In this paper, we introduce the vehicle routing problem with drones (VRPD). A fleet of trucks equipped with drones delivers packages to customers. Drones can be dispatched from and picked up by the trucks at the depot or any of the customer locations. The objective is to minimize the maximum duration of the routes (i.e., the completion time). The VRPD is motivated by a number of highly influential companies such as Amazon, DHL, and Federal Express, actively involved in exploring the potential use of commercial drones for package delivery. After stating our simplifying assumptions, we pose a number of questions in order to study the maximum savings that can be obtained from using drones; we then derive a number of worst-case results. The worst-case results depend on the number of drones per truck and the speed of the drones relative to the speed of the truck.