Fleet management in free-floating bike sharing systems using predictive modelling and explorative tools

For redistribution and operating bikes in a free-floating systems, two measures are of highest priority. First, the information about the expected number of rentals on a day is an important measure for service providers for management and service of their fleet. The estimation of the expected number of bookings is carried out with a simple model and a more complex model based on meterological information, as the number of loans depends strongly on the current and forecasted weather. Secondly, the knowledge of a service level violation in future on a fine spatial resolution is important for redistribution of bikes. With this information, the service provider can set reward zones where service level violations will occur in the near future. To forecast a service level violation on a fine geographical resolution the current distribution of bikes as well as the time and space information of past rentals has to be taken into account. A Markov Chain Model is formulated to integrate this information. We develop a management tool that describes in an explorative way important information about past, present and predicted future counts on rentals in time and space. It integrates all estimation procedures. The management tool is running in the browser and continuously updates the information and predictions since the bike distribution over the observed area is in continous flow as well as new data are generated continuously

Inventory management based on simulation of ordering process

Inventory management is one of the key operational functions of a company which, in the context of modern Supply Chain Management schemes, plays an important role both for the company itself and the coordination with the SC partners. Especially the aspect of service level and stockout probability has become critical. Classical methods of inventory management are based on simple analytical formulae which, however, only treat special cases. In this contribution we present a tool for the optimization of inventory management which is based on a simulation of the ordering processes. The full stochastic properties of the ordering process are incorporated which allows an accurate determination of performance measures like service level. With this tool, the determination of cost-minimal inventory parameters (reorder level, reorder quantity) for given stockout probability is easily possible

Modal vector fitting : a tool for generating rational models of high accuracy with arbitrary terminal conditions

This paper introduces a new approach for rational macromodeling of multiport devices that ensures high accuracy with arbitrary terminal conditions. This is achieved by reformulating the vector fitting (VF) technique to focus on eigenpairs rather than matrix elements. By choosing the least squares (LS) weighting equal to the inverse of the eigenvalue magnitude, the modal components are fitted with a relative accuracy criterion. The resulting modal vector fitting (MVF) method is shown to give a major improvement in accuracy for cases with a high ratio between the largest and smallest eigenvalue, although it is computationally more costly than VF. It is also shown how to utilize the impedance characteristics of the adjacent network in the fitting process. The application of MVF is demonstrated for a two-conductor stripline, a coaxial cable, and a transformer measurement. We also show a simplified procedure which achieves similar results as MVF if the admittance matrix can be diagonalized by a constant transformation matrix. The extracted model is finally subjected to passivity enforcement by the modal perturbation method, which makes use of a similar LS formulation as MVF for the constrained optimization problem

Process performance analysis and layout optimization for a medium-sized manufacturing enterprise

For estimating the potential for process improvement for a medium-sized mechanical manufacturing enterprise, a study was performed. The goal was to identify potential for throughput time reduction and for improvement of the material handling by layout optimization of the factory. The study is based on statistical data analysis of historical order processing data.From these data, the flow of the orders through the production system as well as dynamical properties of the order flow like, e.g., waiting times at the working stations, was derived. The results show that there is a large potential for reduction of throughput time. The ratio of waiting times to processing times is quite large compared with reference values from analytical queuing systems and benchmarks. Based on the quantitative data of the order flow, both a manual and a numerical layout optimization was performed. With both methods, a significant reduction of the total transport way compared with the actual situation could be achieved. However, the numerical approach was significantly better than the manual approach

Flow-time estimation in dynamic job shops with priority scheduling using a hybrid modelling approach

A new approach for due date assignment in dynamic job shops with priority scheduling is presented. The future temporal development of the production system, eventually determining the flow-time of a job, is governed by both the processing of the jobs already present in the system as well as the processing of future arriving jobs. We combine a simulation-like approach for the already known jobs with a stochastic model describing the influence of future arriving jobs. The resulting model is a hybrid system dynamics model that can be solved numerically, leading to estimates for the flow-time of all available jobs. In a simulation study, we compare the new approach with other popular methods known in literature. Our results indicate that the new method significantly outperforms all other studied methods in terms of accuracy of the estimates, in most cases by at least a factor of two. Furthermore, the effect of priority scheduling can be modelled correctly, yielding good estimates for jobs of different priorities

Group Fairness in Prediction-Based Decision Making: From Moral Assessment to Implementation

Ensuring fairness of prediction-based decision making is based on statistical group fairness criteria. Which one of these criteria is the morally most appropriate one depends on the context, and its choice requires an ethical analysis. In this paper, we present a step-by-step procedure integrating three elements: (a) a framework for the moral assessment of what fairness means in a given context, based on the recently proposed general principle of "Fair equality of chances" (FEC) (b) a mapping of the assessment's results to established statistical group fairness criteria, and (c) a method for integrating the thus-defined fairness into optimal decision making. As a second contribution, we show new applications of the FEC principle and show that, with this extension, the FEC framework covers all types of group fairness criteria: independence, separation, and sufficiency. Third, we introduce an extended version of the FEC principle, which additionally allows accounting for morally irrelevant elements of the fairness assessment and links to well-known relaxations of the fairness criteria. This paper presents a framework to develop fair decision systems in a conceptually sound way, combining the moral and the computational elements of fair prediction-based decision-making in an integrated approach. Data and code to reproduce our results are available at this https UR

On the economics of asset management

Asset Management is about realizing value from physical assets. To do this, money has to be invested in physical assets (purchase, maintenance, consumables, etc.) thus producing a specific technical performance for each asset over its lifecycle. The technical performance then allows to realize value for the owner. This can be either a monetary value (e.g. for a production firm that can sell products) or a non-monetary value (e.g. for a utility that can provide a reliable electricity supply). We examine the nature of physical assets as investment objects and derive some conclusions on optimal investment strategies. We develop a general model for physical assets as investment objects, simultaneously describing both the life cycle cost structure and the value realization under different operational policies. We show that physical assets are investments that have properties which distinguish them from classical financial investments such as bonds, stocks, or the like. In particular, the non-proportional relation of investment and value creation has important implications for the derivation of optimal investment strategies. We apply the framework to the problem of budget allocation in a portfolio of physical assets. The model allows the calculation of the optimal allocation such that the total value creation is maximized. It turns out that the solution is similar to the well-known Equimarginal Principle. We also re-examine a classical optimization problem from the maintenance literature and show that the classical solution may lead to wrong results because assets are regarded in isolation instead as part of a larger system of investment options. Since our approach combines both the cost and the value generation aspect of physical assets, and includes operational lifecycle policy decisions, it could form the conceptual basis for a new approach to asset management

Modelling customer lifetime value in contractual settings

Service provision is often governed by a contract (e.g., newspaper subscriptions, phone contracts, and credit agreements). Typically, such a contract includes rules that influence the dynamics of the customer in the marketplace. Typical examples are minimum contract durations, or fixed time instants for contract termination. The goal of these rules is to increase the future total profit gained from the customer, which is usually denoted with the term customer lifetime value (CLV). We analyse the problem of calculating the CLV under general contract structures. We show that classical Markov models for describing the customer dynamics are not appropriate and may lead to huge errors in the CLV. We propose a semi-Markov formulation which leads to substantially better results. We apply the framework to data of newspaper subscription

The concept of shadow price to monetarize the intangible value of expertise

The pricing of knowledge based services should be based on the three following components: the cost structure, the competition and the perceived value by the client. Practically, it is mainly based on the cost structure which does not account for the real value provided to the client. Based on an integrated optimization model combining an aggregate planning model with a share of choice model, we produce implicit values of expertise. Preliminary results will be presented about a travel agency

Churn prediction based on text mining and CRM data analysis

Within quantitative marketing, churn prediction on a single customer level has become a major issue. An extensive body of literature shows that, today, churn prediction is mainly based on structured CRM data. However, in the past years, more and more digitized customer text data has become available, originating from emails, surveys or scripts of phone calls. To date, this data source remains vastly untapped for churn prediction, and corresponding methods are rarely described in literature. Filling this gap, we present a method for estimating churn probabilities directly from text data, by adopting classical text mining methods and combining them with state-of-the-art statistical prediction modelling. We transform every customer text document into a vector in a high-dimensional word space, after applying text mining pre-processing steps such as removal of stop words, stemming and word selection. The churn probability is then estimated by statistical modelling, using random forest models. We applied these methods to customer text data of a major Swiss telecommunication provider, with data originating from transcripts of phone calls between customers and call-centre agents. In addition to the analysis of the text data, a similar churn prediction was performed for the same customers, based on structured CRM data. This second approach serves as a benchmark for the text data churn prediction, and is performed by using random forest on the structured CRM data which contains more than 300 variables. Comparing the churn prediction based on text data to classical churn prediction based on structured CRM data, we found that the churn prediction based on text data performs as well as the prediction using structured CRM data. Furthermore we found that by combining both structured and text data, the prediction accuracy can be increased up to 10%. These results show clearly that text data contains valuable information and should be considered for churn estimation