By far the most common planning procedure found in practice is to approximate the solution to an infinite horizon problem by a series of rolling finite horizon solutions. Although many empirical studies have been done, this so-called rolling horizon procedure has been the subject of few analytic studies. We provide a cost error bound for a general rolling horizon algorithm when applied to infinite horizon nonhomogeneous Markov decision processes, both in the discounted and average cost cases. We show that a Doeblin coefficient of ergodicity acts much like a discount factor to reduce this error. In particular, we show that the error goes to zero for any fixed rolling horizon as this Doeblin measure of control over the future decreases. The theory is illustrated through an application to vehicle deployment.
Throughput analysis is important for the design, operation and management of manufacturing systems. Due to the large state space, exact analytical results only exist for two-machine serial lines. To analyse longer lines or more complex systems, the two-machine line model becomes the building block for further development. Therefore, many two-machine line models have been presented in the literature. A thorough understanding of the nature and differences of the various two-machine line models is critical for future analysis and development.In this paper, we study eight different two-machine models, categorized by synchronous and asynchronous lines with both time-dependent and operationdependent failures, respectively. For each model, we introduce and compare the assumptions, calculation formulae, and their performance in system throughput.The results show that all the models exhibit similar performance with small differences, comparable to (or less than) the accuracy of data collection on the factory floor.
In the late 1980s, General Motors Corporation (GM) initiated a long-term project to predict and improve the throughput performance of its production lines to increase productivity throughout its manufacturing operations and provide GM with a strategic competitive advantage. GM quantified throughput performance and focused improvement efforts in the design and operations of its manufacturing systems through coordinated activities in three areas: (1) it developed algorithms for estimating throughput performance, identifying bottlenecks, and optimizing buffer allocation, (2) it installed real-time plant-floor data-collection systems to support the algorithms, and (3) it established common processes for identifying opportunities and implementing performance improvements. Through these activities, GM has increased revenue and saved over $2.1 billion in over 30 vehicle plants and 10 countries.
Managing production throughput variability is necessary to reduce system costs and improve throughput. Critical in this management is predicting how system changes, like reduced repair times, will change throughput variability. Many manufacturing plants produce products in ® xed lot sizes to meet production targets which causes system variability to be manifested as variability in the time to produce the lot. We derive analytical approximations of the density function and variance of the time to produce a ® xed lotsize on a single workstation with deterministic processing times and random downtimes. These expressions can be applied to larger production systems, by modelling the system as a single workstation with parameters that approximate the system's output. This result quanti® es how workstation failures, repair times, and speed impact the density and variance of time to produce a ® xed lotsize. The density function also generates a cycle time distribution when the lost size is one. This is useful in discrete event simulation.
SLI lead-acid batteries are still the most commonly used technology in automotive applications around the world. Despite its relatively low gravimetric and volumetric energy density in comparison with other battery solutions it is still installed in the newest micro-hybrid and conventional cars due to its low cost. To facilitate the design of multi-physical systems as complex as modern automobiles, it is critical to have a precise battery aging model that incorporates various operation conditions. This paper presents a comprehensive battery model which consists of electrical, thermal and aging part that provides crucial information about the current state of SLI battery during lifetime for any conditions like climate, driving style, charging strategy or different battery management approaches. The described model is based on empirical, physico-chemical and equivalent circuit solutions and can be calibrated to simulate any SLI lead-acid battery without design limitations.
Each year, the INFORMS Edelman Award celebrates the best and most impactful implementations of operations research, management science, and analytics. As the Edelman Award approaches its 50-year mark, we provide a history and characterization of the award’s finalists and winners. We provide some basic descriptive analytics about the participating organizations and authors, the impact of their work, and the methods they employed. We also conduct predictive analytics on finalist submissions, gauging contributors to success in establishing winning entries. We find that predicting Edelman winners a priori is extremely difficult; however, given a set of finalists, predictive models based on monetary impact could have predicted the winner over half the time in recent years, but would have had less predictive success in the early years of the competition. We suggest that, by characterizing the finalists, we can give future entrants a better picture of what it takes to compete for the Edelman Award.
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