The VESP Model: A Conceptual Model of Supply Chain Vulnerability

International audienceDuring the last decade, researchers and practitioners became more interested in the domain of vulnerability analysis. It is considered as a key element in defining and managing supply chain risks. The great complexity of a global supply chain and of its environment, coupled with managerial trends, makes such a chain more vulnerable to disruptive events. A clear understanding of the possible consequences generated of this combination is a fundamental step to build an effective risk management plan and strategies. However, more studies are needed in order to develop the understanding of supply chain vulnerability. This article provides an explorative framework in order to analyze and quantify vulnerability within supply chains. Based on the existent literature, this article explores the factors that affect the level of Supply Chain Vulnerability (SCV). Four key components of SCV are identified (i.e. Exposure, Sensitivity, Susceptibility and Preparedness level). Based on these four categories of SCV, a conceptual model is developed. Such a model enables the definition of clear metrics and can further be used by researchers and practitioners to build consistent quantification methodologies

AN ARTIFICIAL IMMUNE SYSTEM FOR PUBLIC TRANSPORT REGULATION

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Building a binary outranking relation in uncertain, imprecise and multi-experts contexts: The application of evidence theory

AbstractWe consider multicriteria decision problems where the actions are evaluated on a set of ordinal criteria. The evaluation of each alternative with respect to each criterion may be uncertain and/or imprecise and is provided by one or several experts. We model this evaluation as a basic belief assignment (BBA). In order to compare the different pairs of alternatives according to each criterion, the concept of first belief dominance is proposed. Additionally, criteria weights are also expressed by means of a BBA. A model inspired by ELECTRE I is developed and illustrated by a pedagogical example

Profitable Vehicle Routing Problem with Multiple Trips: Modeling and Variable Neighborhood Descent Algorithm

Abstract In this paper, we tackle a new variant of the Veh icle Routing Problem (VRP) which comb ines two known variants namely the Profitable VRP and the VRP with Mult iple Trips. The resulting problem may be called the Profitable Vehicle Routing Problem with Multiple Trips. The main purpose is to cover and solve a more co mplex realistic situation of the distribution transportation. The profitability concept arises when only a subset of customers can be served due to the lack of means or for insufficiency of the offer. In this case, each customer is associated to an economical profit wh ich will be integrated to the objective function. The latter contains at hand the total collected profit minus the transportation costs. Each vehicle is allowed to perform several routes under a strict workday duration limit. This problem has a very practical interest especially for daily distribution schedules with limited vehicle fleets and short course transportation networks. We point out a new discursive approach for p rofits quantificat ion wh ich is mo re significant than those existing in the literature. We propose four equivalent mathematical formu lations for the problem which are tested and compared using CPLEX solver on small-size instances. Optimal solutions are identified. For large-size instance, two constructive heuristics are proposed and enhanced using Hill Climbing and Variable Neighborhood Descent algorithm based on a specific three-arrays-based coding structure. Finally, extensive co mputational experiments are performed including randomly generated instances and an extended and adapted benchmark fro m literature showing very satisfactory results

Zero-inflated and over-dispersed data models: Empirical evidence from insurance claim frequencies

The main objective of this paper is to model automobile claim frequency by using standard count regression and zero-inflated regression models. The use of the latter model is mainly motivated by its ability to handle the over dispersion and zero-inflation phenomenon. The sample data consist of claims data obtained from one randomly selected automobile insurance company in Tunisia for a single year, 2009, containing beginning drivers and drivers who have had a license for less than three years. Our estimation results show that many exogenous variables can explain the frequency of claims; they are not taken into account in calculating the basic insurance premium. Moreover, the ZI binomial negative regression outperforms the standard count models and the ZI Poisson model in handling zero-inflated and additional over dispersed claim count data

Zero-inflated and over-dispersed data models: Empirical evidence from insurance claim frequencies

The main objective of this paper is to model automobile claim frequency by using standard count regression and zero-inflated regression models. The use of the latter model is mainly motivated by its ability to handle the over dispersion and zero-inflation phenomenon. The sample data consist of claims data obtained from one randomly selected automobile insurance company in Tunisia for a single year, 2009, containing beginning drivers and drivers who have had a license for less than three years. Our estimation results show that many exogenous variables can explain the frequency of claims; they are not taken into account in calculating the basic insurance premium. Moreover, the ZI binomial negative regression outperforms the standard count models and the ZI Poisson model in handling zero-inflated and additional over dispersed claim count data