Synthesis and Stochastic Assessment of Cost-Optimal Schedules

We present a novel approach to synthesize good schedules for a class of scheduling problems that is slightly more general than the scheduling problem FJm,a|gpr,r_j,d_j|early/tardy. The idea is to prime the schedule synthesizer with stochastic information more meaningful than performance factors with the objective to minimize the expected cost caused by storage or delay. The priming information is obtained by stochastic simulation of the system environment. The generated schedules are assessed again by simulation. The approach is demonstrated by means of a non-trivial scheduling problem from lacquer production. The experimental results show that our approach achieves in all considered scenarios better results than the extended processing times approach

A synthesis of logic and bio-inspired techniques in the design of dependable systems

Much of the development of model-based design and dependability analysis in the design of dependable systems, including software intensive systems, can be attributed to the application of advances in formal logic and its application to fault forecasting and verification of systems. In parallel, work on bio-inspired technologies has shown potential for the evolutionary design of engineering systems via automated exploration of potentially large design spaces. We have not yet seen the emergence of a design paradigm that effectively combines these two techniques, schematically founded on the two pillars of formal logic and biology, from the early stages of, and throughout, the design lifecycle. Such a design paradigm would apply these techniques synergistically and systematically to enable optimal refinement of new designs which can be driven effectively by dependability requirements. The paper sketches such a model-centric paradigm for the design of dependable systems, presented in the scope of the HiP-HOPS tool and technique, that brings these technologies together to realise their combined potential benefits. The paper begins by identifying current challenges in model-based safety assessment and then overviews the use of meta-heuristics at various stages of the design lifecycle covering topics that span from allocation of dependability requirements, through dependability analysis, to multi-objective optimisation of system architectures and maintenance schedules

Stochastic Approximation and Modern Model-Based Designs for Dose-Finding Clinical Trials

In 1951 Robbins and Monro published the seminal article on stochastic approximation and made a specific reference to its application to the "estimation of a quantal using response, nonresponse data." Since the 1990s, statistical methodology for dose-finding studies has grown into an active area of research. The dose-finding problem is at its core a percentile estimation problem and is in line with what the Robbins--Monro method sets out to solve. In this light, it is quite surprising that the dose-finding literature has developed rather independently of the older stochastic approximation literature. The fact that stochastic approximation has seldom been used in actual clinical studies stands in stark contrast with its constant application in engineering and finance. In this article, I explore similarities and differences between the dose-finding and the stochastic approximation literatures. This review also sheds light on the present and future relevance of stochastic approximation to dose-finding clinical trials. Such connections will in turn steer dose-finding methodology on a rigorous course and extend its ability to handle increasingly complex clinical situations.Comment: Published in at http://dx.doi.org/10.1214/10-STS334 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

Techno-economic energy models for low carbon business parks

To mitigate climate change, global greenhouse gas emissions need to be reduced substantially. Industry and energy sector together are responsible for a major share of those emissions. Hence the development of low carbon business parks by maximising energy efficiency and changing to collective, renewable energy systems at local level holds a high reduction potential. Yet, there is no uniform approach to determine the optimal combination and operation of energy technologies composing such energy systems. However, techno-economic energy models, custom tailored for business parks, can offer a solution, as they identify the configuration and operation that provide an optimal trade-off between economic and environmental performances. However, models specifically developed for industrial park energy systems are not detected in literature, so identifying an existing model that can be adapted is an essential step. In this paper, energy model classifications are scanned for adequate model characteristics and accordingly, a confined number of models are selected and described. Subsequently, main model features are compared, a practical typology is proposed and applicability towards modelling industrial park energy systems is evaluated. Energy system evolution models offer the most perspective to compose a holistic, but simplified model, whereas advanced energy system integration models can adequately be employed to assess energy integration for business clusters up to entire industrial sites. Energy system simulation models, however, provide deeper insight in the system’s operation

Mastering operational limitations of LEO satellites – The GOMX-3 approach

When working with space systems the keyword is resources. For a satellite in orbit all resources are sparse and the most critical resource of all is power. It is therefore crucial to have detailed knowledge on how much power is available for an energy harvesting satellite in orbit at every time – especially when in eclipse, where it draws its power from onboard batteries. This paper addresses this problem by a two-step procedure to perform task scheduling for low-earth-orbit (LEO) satellites exploiting formal methods. It combines cost-optimal reachability analyses of priced timed automata networks with a realistic kinetic battery model capable of capturing capacity limits as well as stochastic fluctuations. The procedure is in use for the automatic and resource-optimal day-ahead scheduling of GOMX-3, a power-hungry nanosatellite currently orbiting the earth. We explain how this approach has overcome existing problems, has led to improved designs, and has provided new insights