Ruin probabilities with pairwise quasi-asymptotically independent and dominatedly-varying tailed claims

This paper considers the nonstandard renewal risk model in which a part of surplus is invested into a Black-Scholes market whose price process is modelled by a geometric Brownian motion, claim sizes form a sequence of not necessarily identically distributed and pairwise quasi-asymptotically independent random variables with dominatedly-varying tails.The authors obtain a weakly asymptotic formula for the finite-time and infinite-time ruin probabilities.In particular,if the claims are identically distributed and consistently-varying tailed,then an asymptotic formula is presented.     

Learning multivariate time series causal graphs based on conditional mutual information

Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis.This paper provides a method that employs both mutual information and conditional mutual information to identify the causal structure of multivariate time series causal graphical models.A three-step procedure is developed to learn the contemporaneous and the lagged causal relationships of time series causal graphs.Contrary to conventional constraint-based algorithm, the proposed algorithm does not involve any special kinds of distribution and is nonparametric.These properties are especially appealing for inference of time series causal graphs when the prior knowledge about the data model is not available.Simulations and case analysis demonstrate the effectiveness of the method.     

EVOKE: A Value-Driven Concept Selection Method for Early System Design

The development of new technologically advanced products requires the contribution from a range of skills and disciplines, which are often difficult to find within a single company or organization. Requirements establishment practices in Systems Engineering (SE), while ensuring coordination of activities and tasks across the supply network, fall short when it comes to facilitate knowledge sharing and negotiation during early system design. Empirical observations show that when system-level requirements are not available or not mature enough, engineers dealing with the development of long lead-time sub-systems tend to target local optima, rather than opening up the design space. This phenomenon causes design teams to generate solutions that do not embody the best possible configuration for the overall system. The aim of this paper is to show how methodologies for value-driven design may address this issue, facilitating early stage design iterations and the resolution of early stage design trade-offs. The paper describes how such methodologies may help gathering and dispatching relevant knowledge about the ‘design intent’ of a system to the cross-functional engineering teams, so to facilitate a more concurrent process for requirement elicitation in SE. The paper also describes EVOKE (Early Value Oriented design exploration with KnowledgE maturity), a concept selection method that allows benchmarking design options at sub-system level on the base of value-related information communicated by the system integrators. The use of EVOKE is exemplified in an industrial case study related to the design of an aero-engine component. EVOKE’s ability to raise awareness on the value contribution of early stage design concepts in the SE process has been further verified with industrial practitioners in ad-hoc design episodes.     

An Economic Model-Based Matching Approach Between Buyers and Sellers Through a Broker in an Open E-Marketplace

A broker in an open e-marketplace enables buyers and sellers to do business with each other. Although a broker plays an important role in e-marketplaces, theory and guidelines for matching between buyers and sellers in multi-attribute exchanges are limited. Therefore, a challenge for a broker’s responsibility is how to maximize a buyer’s total satisfaction degree as its goals under the consideration of trade-off between a buyer’s buying quantity and price paid to a seller, and other attributes. To solve this challenge, this paper proposes an economic model-based matching approach between a buyer’s requirements and a seller’s offers. The major contributions of this paper are that (i) a broker can model a seller’s price policy as per a buyer’s buying quantity through communication between a broker and a seller; (ii) due to each buyer’s different quantity demand, a broker models a buyer’s satisfaction degree as per a buyer’s buying quantity based on communication between a broker and a buyer; and (iii) to carry out a broker’s matching processes, an objective function and a set of constraints are generated to help a broker to maximize a buyer’s total satisfaction degree. Experimental results demonstrate the good performance of the proposed approach.     

Two Innovative Coalition Formation Models for Dynamic Task Allocation in Disaster Rescues

This paper addresses the problem of multi-objective coalition formation for task allocation. In disaster rescue, due to the dynamics of environments, heterogeneity and complexity of tasks as well as limited available agents, it is hard for the single-objective and single (task)-to-single (agent) task allocation approaches to handle task allocation in such circumstances. To this end, two multi-objective coalition formation for task allocation models are proposed for disaster rescues in this paper. First, through coalition formation, the proposed models enable agents to cooperatively perform complex tasks that cannot be completed by single agent. In addition, through adjusting the weights of multiple task allocation objectives, the proposed models can employ the linear programming to generate more adaptive task allocation plans, which can satisfy different task allocation requirements in disaster rescue. Finally, through employing the multi-stage task allocation mechanism of the dynamic programming, the proposed models can handle the dynamics of tasks and agents in disaster environments. Experimental results indicate that the proposed models have good performance on coalition formation for task allocation in disaster environments, which can generate suitable task allocation plans according to various objectives of task allocation.     

A Simulation Method for Analyzing and Evaluating Rail System Performance Based on Speed Profile

This paper presents and discusses a simulation method for analyzing and evaluating system performance on a rail line from the perspective of speed profile. Dynamic analysis for train motions is introduced, and a discrete time-operation graph is proposed to represent the relation between speed profile and energy consumption. Based on them, an analytical model is formulated to provide a quick insight into the system performance. The discrete-time simulation (DTS) method is then implemented to study the system in detail. Compared to the existing simulations, two innovations are included in the DTS: (1) the analytical lookup tables that can simplify the dynamic computation and, (2) the speed profile adjustment process that forecasts and avoids future conflicts based on practical constraints. The numerical results show that the DTS speed profile has advantages over existing methods. Finally, the DTS method is used to analyze and evaluate the system performance of the current timetable on Beijing Yizhuang Metro Line. The results suggest that the current timetable is not robust enough, and thus possible improvements are discussed at both scheduling and operating stages. The proposed method is verified to be effective and reliable for practical uses.     

Advance selling in the presence of product diffusion effect

Product diffusion refers to the phenomenon that the later demand is dependent on the early sales. To investigate how a firm’s advance selling strategy is affected by the effect of product diffusion, we consider a monopolist seller who sells a fashionable product in a market that comprises of myopic and strategic consumers over two periods (i.e., the advance selling season and the regular selling season). For a linear product diffusion effect we find that, when the effect of product diffusion is positive, the seller may set an extremely high advance selling price to induce the strategic consumers to purchase in the regular selling season, which is counter-intuitive. Moreover, the optimal procurement quantity for the seller may increase in the negative effect of product diffusion and decrease in the amount of strategic consumers. When we extend our model to consider a concave quadratic product diffusion effect, however, the optimal procurement quantity is concave in the amount of strategic consumers. Numerical studies are further presented to discuss the managerial insights.     

Risk reduction in a project portfolio

The positive impacts of managing projects as a portfolio are quantified by comparing the value of the integrated risk of a project portfolio and the aggregation of single project risks implemented separately. Firstly, the integrated risk is defined by proposing risky events based on set theory. Secondly, as projects interact with each other in a project portfolio, the integrated risk is evaluated by using a Bayesian network structure learning algorithm to construct an interdependent network of risks. Finally, the integrated risk of a practical case is assessed using this method, and the results show that the proposed method is an effective tool for calculating the extent of risk reduction of implementing a project portfolio and identifying the most risky project, so as to assist companies in making comprehensive decisions in the phase of portfolio selection and portfolio controlling.     

Optimal bundling and pricing decisions for complementary products in a two-layer supply chain

This article explores bundling and pricing decisions for two complementary products in a two-layer supply chain consisting of a multi-product manufacturer and a retailer. We establish four different pricing models under cases of decentralized decision, while considering different portfolio-bundling strategies of the manufacturer and the retailer. A game-theoretical method is used to characterize the corresponding equilibrium outcomes in each scenario. By further analyzing and comparing the maximum profits of all four possible scenarios, optimal bundling and pricing decisions for the manufacturer and the retailer are obtained, respectively. Model extensions and numerical examples are enriched to highlight the factors affecting optimal decision-making. Finally, valuable and interesting managerial insights are summarized. Results show that the upstream manufacturer always profits more when he sells complementary products separately. However, the optimal bundling decision of the downstream retailer is jointly determined by product complementarity (as a major factor) and the difference of product profitability (as a secondary factor). Market power cannot exert an influence on both optimal bundling decisions, but it can partly affect pricing decisions.