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Many cities around the world have adopted Bike Sharing Systems to offer an alternative to personal vehicles and classic public transport. These systems present many challenges. One of those challenges is the availability of both bikes and docking stations, which has a direct influence on the user satisfaction level. Operator of such a system uses a vehicle fleet dedicated to the repositioning of bikes from full stations to empty ones. This operation, often performed during the night when the demand is negligible, is referred in the literature as static rebalancing. The problem consists in determining the number of bikes to pickup and/or deliver at each station, in order to increase, or even optimize, the availabilities of both bikes and docking stations. The models proposed in the literature are generally linear programming models, which may be hard to establish without proper knowledge of mathematical programming techniques. In this paper, we propose a method for helping the building of these mathematical models through the use of UML graphical class diagrams which describe the structure and properties of a bike sharing system. Transformation rules are defined to extract some constraints of the problem from the system class diagram. The mathematical model is then completed by adding additional constraints as well as the objective function that cannot be expressed in UML language. We will use the case of the Parisian bike sharing system (Vélib’) to illustrate the method.

During their life cycle, products can be re-designed due to the requested changes. One or more changes can be applied to the products in order to improve, upgrade and adapt it to new requirements. These changes can have serious impacts. For one design change, some impacts can be predictable while others occur due to unexpected propagations on other parts of the product. Dealing with this risk, companies look for an approach to evaluate and model the propagation of changes impacts to minimize their consequences. The main objective of this work is to characterise this change impact by identification of the dependencies among product components, which is a first and necessary step to evaluate change propagation. We develop a novel approach based on Design Structure Matrix and graphs to identify and characterise the type of dependency existent between product parameters and compute the change propagation impacts. Our findings show that functional dependency is more detailed than quantitative and qualitative dependency characterisation, already used in the literature. A case of 2D geometric model of bicycle is presented at the end of paper.

We look at understanding the main mechanisms behind the renewal of complex systems. These systems, such as trains or airplanes, have a long operation or service duration. They should be renewed and their renewal projects are often partial for economic reasons. A renewal act is performed through integration of engineering changes. Any functional or structural change could have cascading effects on consecutive subsystems. The system architecture can be used as the main driver of renewal acts definition and planning. We position the renewal problem and define some key concepts usable for complex systems renewal. They allow to define the renewal possibilities as scenarios that should be qualified for ultimate selection. This paper explores these mechanisms and suggests a first set of propagation mechanisms. Throughout the paper, we illustrate our purposes by using examples from a train renewal project.

In the last two decades, the failure of multiple engineering projects has highlighted the importance of adopting risk management practices. While risk identification and risk assessment have been widely studied in the literature, only few authors have proposed formal tools for helping project managers to evaluate the effectiveness of their risk response plan. While some risk response measures might be easily validated, overlapping, a commonly used mitigation measure in engineering projects is difficult to evaluate because of the complex interactions between activities and resources. This paper proposes an evaluation model to measure the effectiveness of overlapping strategy as a risk response in terms of additional cost and total maximum time reduction. Results based on a large set of generated projects highlight the importance of three factors in the effectiveness of an overlapping strategy: the number of opportunities of overlapping, the maximum overlapping amount allowed, and the level of resource constraints.