The impact of replenishment rules with endogenous lead times on supply chain performance..

In dit proefschrift beperken we ons tot een basis supply chain met één klant en één producent. We bestuderen verschillende bestelpolit ieken van de klant, en meten de impact van deze bestelregels op de produ ctie van de producent. We modelleren het productieproces als een wachtli jn- of queueing model. Uit de analyse van dit productiemodel vinden we de levertijden, die op hun beurt gebruikt worden in het voorra admodel van de klant. De methodologie die hiervoor gebruikt wordt, is tw eevoudig. Enerzijds maken we gebruik van statistische technieken om de v oorraad te beheren en bestellingen te plaatsen. Anderzijds maken we gebr uik van wachtlijntheorie en Markov ketens om de doorlooptijden te bepale n. Eerst onderzoeken we een eenvoudige "chase sales" bestelpolit iek: de klant plaatst elke periode een bestelling die gelijk is aan de c onsumentenvraag. We ontwikkelen een efficiënte procedure om de impact va n deze bestelregel op de doorlooptijden te berekenen op basis van

Milk Run Design: Definitions, Concepts and Solution Approaches

Efficient inbound networks in the European automotive industry rely on a set of different transport concepts including milk runs - understood as regularly scheduled pickup tours. The complexity of designing such a mixed network makes decision support necessary: In this book we provide definitions, mathematical models and a solution method for the Milk Run Design problem and introduce indicators assessing the performance of established milk runs in relation to alternative transport concepts

Adaptive Performance and Power Management in Distributed Computing Systems

The complexity of distributed computing systems has raised two unprecedented challenges for system management. First, various customers need to be assured by meeting their required service-level agreements such as response time and throughput. Second, system power consumption must be controlled in order to avoid system failures caused by power capacity overload or system overheating due to increasingly high server density. However, most existing work, unfortunately, either relies on open-loop estimations based on off-line profiled system models, or evolves in a more ad hoc fashion, which requires exhaustive iterations of tuning and testing, or oversimplifies the problem by ignoring the coupling between different system characteristics (\ie, response time and throughput, power consumption of different servers). As a result, the majority of previous work lacks rigorous guarantees on the performance and power consumption for computing systems, and may result in degraded overall system performance. In this thesis, we extensively study adaptive performance/power management and power-efficient performance management for distributed computing systems such as information dissemination systems, power grid management systems, and data centers, by proposing Multiple-Input-Multiple-Output (MIMO) control and hierarchical designs based on feedback control theory. For adaptive performance management, we design an integrated solution that controls both the average response time and CPU utilization in information dissemination systems to achieve bounded response time for high-priority information and maximized system throughput in an example information dissemination system. In addition, we design a hierarchical control solution to guarantee the deadlines of real-time tasks in power grid computing by grouping them based on their characteristics, respectively. For adaptive power management, we design MIMO optimal control solutions for power control at the cluster and server level and a hierarchical solution for large-scale data centers. Our MIMO control design can capture the coupling among different system characteristics, while our hierarchical design can coordinate controllers at different levels. For power-efficient performance management, we discuss a two-layer coordinated management solution for virtualized data centers. Experimental results in both physical testbeds and simulations demonstrate that all the solutions outperform state-of-the-art management schemes by significantly improving overall system performance

Tactical design of production-distribution networks : safety stocks, shipment consolidation and production planning

VII+217hlm.;24c

Simulation of Alternative Airline Terminal Check-in Disciplines

Computer simulation has become a very useful and flexible tool in the planning process of passenger facilities. By this means the probability of queues, congestion and delays can be determined, and different design concepts and operational disciplines can be considered experimentally. Within this thesis two different check-in disciplines, restricted flight system, and common system are compared. The stochastic simulation models developed to evaluate the performance of the alternative check-in systems examined the impact of 1) changes in the number of passengers boarding per flight, 2) reduction in the number of counters, and 3) different time value to the passengers. Input to the model including 1) service times, 2) passengers rate of arrivals, 3) characteristics of the passenger groups, etc. allowed for testing both alternatives. Output from the model included 1) queuing times, 2) number of persons in queue, 3) density of crowds, and 4) counter utilization. After calibrating the model with data gathered at Knoxville\u27s airport, it was found that the common system has better performance than the restricted system. Also it was determined that the restricted system became inefficient for a large number of persons checking in per flight. Finally, by assigning monetary value to the passenger time, it was possible to select the number of counters which represented the minimum cost to the airlines, the airport operator, and the passengers

Measuring & Mitigating Electric Vehicle Adoption Barriers

Transitioning our cars to run on renewable sources of energy is crucial to addressing concerns over energy security and climate change. Electric vehicles (EVs), vehicles that are fully or partially powered by batteries charged from the electrical grid, allow for such a transition. Specifically, if hydro, solar, and wind generation continues to be integrated into the global power system, we can power an EV-based transportation network cleanly and sustainably. To this end, major car manufacturers are now producing and marketing EVs. Unfortunately, at the time of this writing, drivers are slow to adopt EVs due to a number of concerns. The two greatest concerns are range anxiety—the fear of being stranded without power and the fear that necessary charging infrastructure does not exist—and the unknown return on investment of EVs over their lifetime. This thesis presents computational approaches for measuring and mitigating EV adoption barriers. Towards measuring the barriers to adoption, we build a sentiment analysis system for programmatically mining detailed perceptions towards EVs from ownership forums. In addition, we design the most comprehensive electric bike trial to date, which allows us to study several aspects of electric vehicles, including range anxiety, at a much lower cost. Towards mitigation, we develop algorithms for managing a network of gasoline vehicles to be used by EV owners when a planned trip exceeds the range of their EV. Further, we design a model for taxi companies to compute whether it is profitable to transition a fraction of their fleet to EVs. To summarize our findings, we find that sentiments towards EVs are very positive, especially regarding performance and maintenance, but there are concerns over range anxiety and the higher initial price of EVs. There is a delicate balance between these two adoption barriers. Larger batteries cost more, so alleviating range anxiety with larger batteries leads to pricier vehicles. Conversely, EVs with low range capabilities can also induce costs, because drivers and fleets that own EVs may have to often acquire (or own as an additional vehicle) a gasoline vehicle to fully meet their mobility demands. As a result, EVs are best suited for drivers and fleets that are able to make long-term return on investment calculations, and whose mobility patterns do not include many very long trips. Fleets can greatly reduce their operating costs by adopting EVs because they have the capital to make upfront investments that are profitable long-term. We show that even under conservative assumptions about revenue loss due to battery depletion, EVs are already profitable (the company saves more than enough money to recoup all initial investments) for a large taxi company in San Francisco. Similarly, EVs can be profitable for two-car families (those who already have a gasoline car) and for those who can easily acquire a gasoline vehicle when needed, hence our work on sizing networks of gasoline-vehicle pools for EV owners. Finally, we find that not only are electric bikes and EVs operationally similar, the sentiments towards the two technologies are as well. Advancements made in the battery sector, especially those that reduce costs or weight, are likely to accelerate sales in both markets. The results presented in this thesis, as well as in prior work, suggest that EVs are suitable for many drivers and will hence serve a role in our eventual transition away from fossil fuels

An analysis on vendor hub

Master'sMASTER OF SCIENCE (MANAGEMENT