Correlation between safety assessments in the driver-car interaction design process

With the functional revolution in modern cars, evaluation methods to be used in all phases of driver–car interaction design have gained importance. It is crucial for car manufacturers to discover and solve safety issues early in the interaction design process. A current problem is thus to find a correlation between the formative methods that are used during development and the summative methods that are used when the product has reached the customer. This paper investigates the correlation between efficiency metrics from summative and formative evaluations, where the results of two studies on sound and navigation system tasks are compared. The first, an analysis of the J.D. Power and Associates APEAL survey, consists of answers given by about two thousand customers. The second, an expert evaluation study, was done by six evaluators who assessed the layouts by task completion time, TLX and Nielsen heuristics. The results show a high degree of correlation between the studies in terms of task efficiency, i.e. between customer ratings and task completion time, and customer ratings and TLX. However, no correlation was observed between Nielsen heuristics and customer ratings, task completion time or TLX. The results of the studies introduce a possibility to develop a usability evaluation framework that includes both formative and summative approaches, as the results show a high degree of consistency between the different methodologies. Hence, combining a quantitative approach with the expert evaluation method, such as task completion time, should be more useful for driver–car interaction design.     

A class of multi-objective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor

An important issue, when shipping cost and customers demand are random fuzzy variables in supply chain network (SCN) design problem, is to find the network strategy that can simultaneously achieve the objectives of minimization total cost comprised of fixed costs of plants and distribution centers (DCs), inbound and outbound distribution costs, and maximization customer services that can be rendered to customers in terms of acceptable delivery time. In this paper, we propose a random fuzzy multi-objective mixed-integer non-linear programming model for the SCN design problem of Luzhou Co., Ltd. which is representative in the industry of Chinese liquor. By the expected value operator and chance constraint operator, the model has been transformed into a deterministic multi-objective mixed-integer non-linear programming model. Then, we use spanning tree-based genetic algorithms (st-GA) by the Prüfer number representation to find the SCN to satisfy the demand imposed by customers with minimum total cost and maximum customer services for multi-objective SCN design problem of this company under condition of random fuzzy customers demand and transportation cost between facilities. Furthermore, the efficacy and the efficiency of this method are demonstrated by the comparison between its numerical experiment results and those of tradition matrix-based genetic algorithm.     

Less Expert Workers and Customer Complaints: Automotive Case Study

Many researchers consider absenteeism to be a significant problem for employers. Absenteeism lowers the overall level of worker expertise, which may affect product quality and trigger an increase in customer complaints. This research was carried out in a prominent automobile manufacturing company, with an assembly line–driven rigid production process. This article gathered customer quality complaints registered during the 17 months after cars were delivered to customers, from one year of car production, to analyze how employee absenteeism affects customer quality complaints. The empirical evidence presented by this research in the automotive industry confirms that absenteeism does not lead, necessarily, to a decrease in quality. This evidence must make organization redefine the value of their expert workers. Moreover, these results could have an impact on assembly‐line design and the use of worker job‐rotation programs for technologically sophisticated assembly lines. © 2012 Wiley Periodicals, Inc.     

An Inventory Management Problem

Inventory management is a key problem in several industries, (car renting, storehouse space renting, etc.). It consists of managing a given fleet of equipment in order to satisfy requests to use it. When requests exceed the stock of available equipment, a decision has to be made, either to subcontract some requests to another provider or to purchase new pieces of equipment. The main difficulty lies in the fact that a subcontracted request must be subcontracted for all the duration of the request. For example, if a subcontracted car is rented to a given customer, this customer will keep the subcontracted car for all the duration of the rental. In this paper, we propose a set of benchmark problem instances, derived from real-world inventory management problems.     

An efficient hybrid algorithm for the Traveling Car Renter Problem

This paper tackles a Traveling Salesman Problem variant called Traveling Car Renter Problem, where one car renter desires to travel among cities using a rented vehicle. Basically, the car renter has two options when he/she arrives in a city: to return the vehicle and rent another one or to keep the same car until the next city. Every time a car is delivered in a city, a return fee must be paid. Travel cost between any pair of cities also depends on the chosen car. The objective is to establish a Hamiltonian cycle minimizing the travel costs and returning fees. An evolutionary algorithm (EA) and a hybrid method called Adaptive Local Search Procedure (ALSP) are proposed for this problem. Both were compared to the best known algorithm in literature and obtained better results for non-Euclidean instances. Such algorithms compose an efficient model for a better exploration of the problem solutions space. From the expert system point-of-view, we propose a novel inference engine with minimized results error.     

A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings

Nowadays is very common the presence of tall buildings in the business centres of the main cities of the world. Such buildings require the installation of numerous lifts that are coordinated and managed under a unique control system. Population working in the buildings follows a similar traffic pattern generating situations of traffic congestion. The problem arises when a passenger makes a hall call wishing to travel to another floor of the building. The dispatching of the most suitable car is the optimization problem we are tackling in this paper. We develop a viral system algorithm which is based on a bio-inspired virus infection analogy to deal with it. The viral system algorithm is compared to genetic algorithms, and tabu search approaches that have proven efficiency in the vertical transportation literature. The experiments undertaken in tall buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, provide valuable results and show how viral system outperforms such soft computing algorithms.     

面向中小型旅行社的客服系统构建

通过分析中小旅行社的特点,以及客户服务中存在的问题,本文针对这些存在的问题与特点,采用．NET2．0技术,三层架构体系来构建适合中小型旅行社的客服系统,优化投诉流程,协同化解决问题,并建立起相应的客户信息库,投诉处理规则库,监控工作业务流程,用以改善旅行社的客服质量水平,应用规则处理,提高客户投诉处理的效率,利用客户信息库对客户进行分析,从而形成相关的知识库,最终实现较为初级的客户关系管理。     

Applying conjoint analysis to evaluate consumer preferences toward subcompact cars

The purpose of this study is to find the important attributes of subcompact cars that effect customer choice, using conjoint analysis as an analytical tool. Subcompact cars currently make up the biggest segment of the car industry, but before such products are launched, firms should know customer needs in order to determine the most suitable product specifications. However, consumers tend to evaluate the key attributes of a product as a whole, rather than independently, and thus conjoint analysis can be very useful in this context as a result of decomposing such overall evaluations into their individual components. This study thus adopts conjoint analysis to uncover the hidden motivations that may not even be apparent to the respondents themselves when considering the appeal of subcompact cars. As a result, the following six attributes of subcompact cars were identified: power, appearance, safety, fuel efficiency, price and gadgets. Using subjects from Thailand, 201 respondents were asked to rank their preferences for eight subcompact car models with different combinations of these six attributes. The results indicate that a subcompact car’s appearance has the greatest influence on the respondents’ buying intentions, followed by fuel efficiency, price, and safety, which all had similar effects. Power and gadgets are the two least important attributes with regard to purchase intention. Based on the current findings, it is concluded that conjoint analysis is an appropriate tool to identify the key attributes of subcompact cars. Using the results presented in this work, manufacturers can better tailor their subcompact cars to meet the needs of customers, and thus increase sales.     

基于时变间距和相对角度的无人车跟随控制方法研究

本文考虑实际道路上的车辆跟随运行模式,研究了无人车以时变的相对距离和相对角度跟随行驶的控制问题.本文首先采用领航跟随模式建立了领航车与跟随车之间的误差模型,将无人车之间的相对距离和相对角度作为时变量输入.接着使用反馈控制法设计了跟随车速度控制器和角速度控制器.用李雅普诺夫方法证明了控制器的稳定性,用Barbalat引理从理论上证明了跟踪误差渐近收敛.最后用Matlab/Simulink对无人车的跟随控制进行仿真,仿真结果表明在无人车之间的相对距离和相对角度是时变量的条件下,跟随车可以很好地沿着领航车的前进轨迹跟随行驶.     

Traveling salesman problems with profits and stochastic customers

In this paper, we introduce a class of new selection and routing problems, and name it as the traveling salesman problem with profits and stochastic customers (TSPPSC), which is an extension of the traveling salesman problem with profits (TSPP). The class of new problems is put forward to address how to deal with stochastic customer presence under the environment in which an associated profit is obtained once a customer is visited. It is defined on a complete graph in which profits are associated with the vertices and travel costs are associated with the edges. Each vertex (customer) has a probability of requiring a visit. The objective is the simultaneous optimization of the expected collected profits and expected travel costs. According to the way the two objectives (profits and travel costs) are addressed, TSPPSC is categorized into three subproblems. Mathematical formulations are provided for these problems and a genetic algorithm is proposed to solve one of these subproblems. Computational experiments conducted on several sets of instances show a good performance of the proposed algorithm.     

Multicriteria pickup and delivery problem with transfer opportunity

In this research, we develop a multiobjective vehicle routing and scheduling heuristic for a pickup and delivery problem. The problem contains time window, advanced request, multi-vehicle and many-to-many transport. In addition, the fleet size is not predetermined, and customers are allowed to transfer between vehicles. The objectives of scheduling are to minimize vehicle expense, tardiness and travel time. We propose a concurrent scheduling approach, which allocates customers to more than one vehicle and assigns more than one customer to a vehicle at a time. It differs from the usual concurrent approach in three aspects: (i) it uses the look-ahead strategy to construct miniroute; (ii) it adopts the head/tail, head, and tail integration techniques; and (iii) it allows interactivity. The procedure takes full advantage of due time and travel time information and is implemented through a computer program. It is a one-phase heuristic that can be reiterated when necessary. We provide detailed programming procedures and present the computational results of the proposed algorithm through the real data.     

The design of entrances of taxis for elderly and disabled passengers: an experimental study

This paper presents an experimental study with the aim of finding suitable dimensions of the entrances of cars used in taxi service for disabled people. Ambulant disabled people and wheelchair users who were entitled to travel with the special transportation service and were used to travel by taxi participated in the trials. An investigation of the taxi service showed that taxis transport both ambulant disabled people and people using wheelchairs, who can move from the wheelchair to a car seat. The disabled travellers found it difficult to enter and leave the cars due to the narrow doorways. The most difficult action was the lifting of their feet into and out of the cars. In the experimental study the subjects had to get into and out of a mock-up car where the entrance could be altered by changes to the width and height of the doorway, the height of the sill, the location of the seat and the angle to which the door opens. The registration was made by video-filming, observation and interviews. The actions and duration of the different phases during the entering and exiting were analysed from the video-films. The results show that much the same entrance dimensions are required both by people confined to wheelchairs and ambulant disabled people. The paper presents suitable dimensions for the front entrances of cars in order to suit people with mobility impairments.     

A framework for analysis of the effect of time on shopping behavior

Due to technological developments, data about how many items a customer buys and how long the customer spends in a supermarket are available. A major problem with the data, however, is that there is no framework that considers the heterogeneity hidden in the data. In this article, we propose a framework that considers heterogeneity in the number of items a customer buys. The first step of our framework is based on the Poisson mixture regression model using a stationary time in the department where the items are sold as its independent variable. This model finds latent homogeneous groups of customers and gives the regression models within each group. It simultaneously classifies the customers into the homogeneous groups. In the second step of our framework, a method to investigate whether another factor (variable) influences the classification into homogeneous groups is presented. This proposed framework is applied to real data collected from the customers, and the effectiveness of the framework is shown. The managerial implications are drawn from the result of the analysis.     

考虑客户分类的随机时间车辆路径优化模型与算法

针对一类考虑客户分类、随机旅行时间、随机服务时间及时间窗约束的车辆路径问题构建了机会约束规划模型,该模型考虑两类客户（普通客户与优质客户）并通过添加机会约束条件确保优质客户获得准时服务的概率。同时,设计了变邻域迭代局部搜索算法,并给出了一种基于最小等待时间的初始解生成启发式规则。基于Solomon算例进行了多组仿真实验。仿真实验结果表明,所设计生成初始解的启发式规则是有效的;所给算法能够在短时间内找到确定问题和随机问题的近似最优解;客户比与车辆使用数目呈正相关关系。研究结果对解决资源有限条件下克服随机不确定性因素带来的不利影响、保证客户服务水平等问题有一定的参考意义。     

Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer

Most marketers have difficulty in identifying the right customers to engage in successful campaigns. So far, customer segmentation is a popular method that is used for selecting appropriate customers for a launch campaign. Unfortunately, the link between customer segmentation and marketing campaign is missing. Another problem is that database marketers generally use different models to conduct customer segmentation and customer targeting. This study presents a novel approach that combines customer targeting and customer segmentation for campaign strategies. This investigation identifies customer behavior using a recency, frequency and monetary (RFM) model and then uses a customer life time value (LTV) model to evaluate proposed segmented customers. Additionally, this work proposes using generic algorithm (GA) to select more appropriate customers for each campaign strategy. To demonstrate the efficiency of the proposed method, this work performs an empirical study of a Nissan automobile retailer to segment over 4000 customers. The experimental results demonstrate that the proposed method can more effectively target valuable customers than random selection.     

An integer programming approach for the time-dependent traveling salesman problem with time windows

Congestion in large cities and populated areas is one of the major challenges in urban logistics, and should be addressed at different planning and operational levels. The Time Dependent Travelling Salesman Problem (TDTSP) is a generalization of the well known Traveling Salesman Problem (TSP) where the travel times are not assumed to be constant along the day. The motivation to consider the time dependency factor is that it enables to have better approximations to many problems arising from practice. In this paper, we consider the Time-Dependent Traveling Salesman Problem with Time Windows (TDTSP-TW), where the time dependence is captured by considering variable average travel speeds. We propose an Integer Linear Programming model for the problem and develop an exact algorithm, which is compared on benchmark instances with another approach from the related literature. The results show that the approach is able to solve instances with up to 40 customers.     

QBD approximations of a call center queueing model with general patience distribution

This paper investigates a computationally practical way for analyzing a call center queueing model, i.e., a finite-capacity, multi-server queueing model, where each server goes on a single vacation. Poisson arrival process and exponential service and vacation times are assumed. We also assume that each customer may leave the queue due to impatience. Customers’ patience times are i.i.d. random variables with a general distribution. Level-dependent finite QBD (quasi-birth–death) processes are employed to approximate such a queueing model. Two approaches are considered. The first one uses the phase-type (PH) distribution to approximate the general patience distribution, whereas the second one is based on the idea of replacing the eventual reneging of customers with balking. We find that the first approach is almost impossible to compute numerically due to the exponential growth of the size of the block matrices in a level-dependent finite QBD. We examine the validity and applicability of the approximation based on the second approach and show that it gives us a practical way to obtain performance measures of call center systems in practical scale with sufficiently reasonable accuracy.     

A discrete-time retrial queue with negative customers and unreliable server

This paper treats a discrete-time single-server retrial queue with geometrical arrivals of both positive and negative customers in which the server is subject to breakdowns and repairs. Positive customers who find sever busy or down are obliged to leave the service area and join the retrial orbit. They request service again after some random time. If the server is found idle or busy, the arrival of a negative customer will break the server down and simultaneously kill the positive customer under service if any. But the negative customer has no effect on the system if the server is down. The failed server is sent to repair immediately and after repair it is assumed as good as new. We analyze the Markov chain underlying the queueing system and obtain its ergodicity condition. The generating functions of the number of customers in the orbit and in the system are also obtained along with the marginal distributions of the orbit size when the server is idle, busy or down. Finally, some numerical examples show the influence of the parameters on some crucial performance characteristics of the system.     

Time Petri nets as an evaluation tool for handling travel time uncertainty in vehicle routing solutions

Congestion on roads leads to uncertainty in travel times, which is important in delivery of goods, especially in a business environment where high levels of customer service are expected. Delivery periods to customers might be constrained by time windows, which makes the scheduling and routing of vehicles from the supplier’s side more difficult. Operations Research methods turn into heuristics for this type of application. But when, on top, uncertainty on travel times are the case, any hope of a simple and well-performing heuristic is lost. This study applies a methodology in which a heuristic is used to find a solution for scheduling and routing under deterministic travel times and, by means of simulation, though the use of Time Petri nets evaluates the sensitivity of the solution to uncertainties in travel times from one customer to the next in a route.     

Improving Personalization Solutions through Optimal Segmentation of Customer Bases

On the Web, where the search costs are low and the competition is just a mouse click away, it is crucial to segment the customers intelligently in order to offer more targeted and personalized products and services to them. Traditionally, customer segmentation is achieved using statistics-based methods that compute a set of statistics from the customer data and group customers into segments by applying distance-based clustering algorithms in the space of these statistics. In this paper, we present a direct grouping-based approach to computing customer segments that groups customers not based on computed statistics, but in terms of optimally combining transactional data of several customers to build a data mining model of customer behavior for each group. Then, building customer segments becomes a combinatorial optimization problem of finding the best partitioning of the customer base into disjoint groups. This paper shows that finding an optimal customer partition is NP-hard, proposes several suboptimal direct grouping segmentation methods, and empirically compares them among themselves, traditional statistics-based hierarchical and affinity propagation-based segmentation, and one-to-one methods across multiple experimental conditions. It is shown that the best direct grouping method significantly dominates the statistics-based and one-to-one approaches across most of the experimental conditions, while still being computationally tractable. It is also shown that the distribution of the sizes of customer segments generated by the best direct grouping method follows a power law distribution and that microsegmentation provides the best approach to personalization.