20 research outputs found
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
LIPIcs, Volume 244, ESA 2022, Complete Volume
LIPIcs, Volume 244, ESA 2022, Complete Volum
Algorithmic Developments in Two-Stage Robust Scheduling
This thesis considers the modelling and solving of a range of scheduling problems, with a particular focus on the use of robust optimisation for scheduling in two-stage decision-making contexts. One key contribution of this thesis is the development of a new compact robust counterpart for the resource-constrained project scheduling problem with uncertain activity durations. Resource conflicts must be resolved under the assumption of budgeted uncertainty, but start times can be determined once the activity durations become known. This formulation is also applied to the multi-mode version of this problem. In both cases, computational results show the clear dominance of the new formulation over the prior decomposition-based state-of-the-art methods. This thesis also demonstrates the first application of the recoverable robust framework to single machine scheduling. Two variants of this problem are considered, in which a first-stage schedule is constructed subject to uncertain job processing times, but can be amended in some limited way following the realisation of these processing times. The first of these problems is considered under general polyhedral uncertainty. Key results concerning the second-stage subproblem are derived, resulting in three formulations to the full problem which are compared computationally. The second of these problems considers interval uncertainty but allows for a more general recovery action. A 2-approximation is derived and the performance of a proposed greedy algorithm is examined in a series of computational experiments. In addition to these results on two-stage robust scheduling problems, a new deterministic resource-constrained project scheduling model is developed which, for the first time, combines both generalised precedence constraints and flexible resource allocation. This model is introduced specifically for the application of scheduling the decommissioning of the Sellafield nuclear site. A genetic algorithm is proposed to solve this model, and its performance is compared against a mixedinteger programming formulation
Automated Deduction â CADE 28
This open access book constitutes the proceeding of the 28th International Conference on Automated Deduction, CADE 28, held virtually in July 2021. The 29 full papers and 7 system descriptions presented together with 2 invited papers were carefully reviewed and selected from 76 submissions. CADE is the major forum for the presentation of research in all aspects of automated deduction, including foundations, applications, implementations, and practical experience. The papers are organized in the following topics: Logical foundations; theory and principles; implementation and application; ATP and AI; and system descriptions
Embedded System Design
A unique feature of this open access textbook is to provide a comprehensive introduction to the fundamental knowledge in embedded systems, with applications in cyber-physical systems and the Internet of things. It starts with an introduction to the field and a survey of specification models and languages for embedded and cyber-physical systems. It provides a brief overview of hardware devices used for such systems and presents the essentials of system software for embedded systems, including real-time operating systems. The author also discusses evaluation and validation techniques for embedded systems and provides an overview of techniques for mapping applications to execution platforms, including multi-core platforms. Embedded systems have to operate under tight constraints and, hence, the book also contains a selected set of optimization techniques, including software optimization techniques. The book closes with a brief survey on testing. This fourth edition has been updated and revised to reflect new trends and technologies, such as the importance of cyber-physical systems (CPS) and the Internet of things (IoT), the evolution of single-core processors to multi-core processors, and the increased importance of energy efficiency and thermal issues
Ordonnancement de tùches sous contraintes sur des métiers à tisser
Dans une usine de production de textile, il y a des mĂ©tiers Ă tisser. Ces mĂ©tiers Ă tisser peuvent ĂȘtre configurĂ©s de diffĂ©rentes façons. Des tĂąches doivent ĂȘtre exĂ©cutĂ©es sur ces mĂ©tiers Ă tisser et le temps dâexĂ©cution dâune tĂąche est fonction du mĂ©tier sur lequel elle est effectuĂ©e. De plus, chaque tĂąche est seulement compatible avec les mĂ©tiers Ă tisser Ă©tant configurĂ©s de certaines façons. Un temps de mise en course peut permettre de configurer ou prĂ©parer un mĂ©tier Ă tisser pour lâexĂ©cution dâune tĂąche. Le temps de mise en course est dĂ©pendant de la tĂąche qui prĂ©cĂšde et de celle qui suit. Nous souhaitons alors crĂ©er un horaire pour minimiser les temps de fabrication et les retards. Toutefois, certaines contraintes doivent ĂȘtre respectĂ©es. Lorsque des prĂ©parations surviennent sur des mĂ©tiers diffĂ©rents en mĂȘme temps, le nombre dâemployĂ©s doit ĂȘtre suffisant. Un mĂ©tier ne peut faire quâune seule action Ă la fois. Lâordonnancement dâune seule machine est un problĂšme NP-Difficile. Dans ce projet, il faut ordonnancer environ 800 tĂąches sur 90 machines dans un horizon de deux semaines, tout en respectant les contraintes de personnel. Des Ă©vĂšnements stochastiques doivent ĂȘtre pris en compte pour obtenir un meilleur horaire. Le bris dâun fil nâĂ©tant pas un Ă©vĂšnement rare, lâoccurrence des bris est donnĂ©e sous la forme dâune loi de Poisson. Nous proposons alors une approche de rĂ©solution utilisant une heuristique de branchement basĂ©e sur le problĂšme du commis voyageur. Cette approche permet dâobtenir de bonnes solutions pour le problĂšme dâordonnancement explorĂ©. Les solutions trouvĂ©es sont 5 Ă 30% meilleures en termes de fonction objectif quâune heuristique semblable Ă celle utilisĂ©e par lâĂ©quipe de planification de notre partenaire industriel. Nous prĂ©sentons aussi un algorithme pour garantir la robustesse dâun horaire. Notre algorithme permet de gĂ©nĂ©rer des horaires plus rĂ©alistes et qui rĂ©sistent bien aux Ă©vĂšnements imprĂ©vus. La combinaison de ces deux pratiques mĂšne Ă lâintĂ©gration et lâutilisation du produit final par notre partenaire industriel.In a textile factory, there are looms. Workers can configure the looms to weave different pieces of textiles. A loom can only weave a piece of textiles if the piece of textiles is compatible with its loom configuration. To change its configuration, a loom requires a setup. The setups are performed manually by workers. There are also sequence-dependent setups to prepare a loom for the upcoming piece of textiles. We wish to minimize the setups duration and the lateness. A solution must satisfy some constraints. The problem is subject to cumulative resources. The quantity of workers simultaneously configuring machines canât exceed the total number of employees. A loom can only weave a piece of textiles at a time. Scheduling tasks on a single loom is an NP-Hard problem. In this project, we must schedule tasks an average of 800 tasks on 90 looms with a two-week horizon. Stochastic events might occur and must be accounted for. We must design an algorithm to create robust schedules under uncertainty. As a thread breaking during the weaving process isnât a rare occurrence, a better schedule could greatly impact the performances of a company when applying the schedule to a real situation. We formulate that the number of breaks per task follows a Poisson distribution. First, we propose a branching heuristic based on the traveling salesperson problem in order to leverage computation times. The solutions found are 5 to 30% better according to their objective function than the ones of a greedy heuristic similar to what our industrial partner uses. We also present a filtering algorithm to guarantee robustness of solutions in respect to a confidence level. This algorithm improves robustness and creates more realist schedules. The algorithm is also efficient in computation time by achieving bound consistency in linear time. Combining both these techniques leads to the integration of our research in the decision system of our industrial partner
Survey on Combinatorial Register Allocation and Instruction Scheduling
Register allocation (mapping variables to processor registers or memory) and
instruction scheduling (reordering instructions to increase instruction-level
parallelism) are essential tasks for generating efficient assembly code in a
compiler. In the last three decades, combinatorial optimization has emerged as
an alternative to traditional, heuristic algorithms for these two tasks.
Combinatorial optimization approaches can deliver optimal solutions according
to a model, can precisely capture trade-offs between conflicting decisions, and
are more flexible at the expense of increased compilation time.
This paper provides an exhaustive literature review and a classification of
combinatorial optimization approaches to register allocation and instruction
scheduling, with a focus on the techniques that are most applied in this
context: integer programming, constraint programming, partitioned Boolean
quadratic programming, and enumeration. Researchers in compilers and
combinatorial optimization can benefit from identifying developments, trends,
and challenges in the area; compiler practitioners may discern opportunities
and grasp the potential benefit of applying combinatorial optimization
Linear-time filtering algorithms for the disjunctive constraint and a quadratic filtering algorithm for the cumulative not-first not-last
We present new filtering algorithms for Disjunctive and Cumulative
constraints, each of which improves the complexity of the state-of-theart
algorithms by a factor of log n. We show how to perform TimeTabling
and Detectable Precedences in linear time on the Disjunctive
constraint. Furthermore, we present a linear-time Overload Checking for
the Disjunctive and Cumulative constraints. Finally, we show how
the rule of Not-first/Not-last can be enforced in quadratic time for the
Cumulative constraint. These algorithms rely on the union find data
structure, from which we take advantage to introduce a new data structure
that we call it time line. This data structure provides constant time
operations that were previously implemented in logarithmic time by the
Î-tree data structure. Experiments show that these new algorithms are
competitive even for a small number of tasks and outperform existing algorithms
as the number of tasks increases. We also show that the time
line can be used to solve specific scheduling problems