Schedules for Dynamic Bidirectional Simulations on Parallel Computers

For adjoint calculations, parameter estimation, and similar purposes one may need to reverse the execution of a computer program. The simplest option is to record a complete execution log and then to read it backwards. This requires massive amounts of storage. Instead one may generate the execution log piecewise by restarting the ``forward'' calculation repeatedly from suitably placed checkpoints. This thesis extends the theoretical results of the parallel reversal schedules. First a algorithm was constructed which carries out the ``forward'' calculation and distributes checkpoints in a way, such that the reversal calculation can be started at any time. This approach provides adaptive parallel reversal schedules for simulations where the number of time steps is not known a-priori. The number of checkpoints and processors used is optimal at any time. Further, an algorithm was described which makes is possible to restart the initial computer program during the program reversal. Again, this can be done without any additional computation at any time. Hence, optimal parallel reversal schedules for the bidirectional simulation are provided by this thesis.Bei der Berechnung von Adjungierten, zum Debuggen und für ähnliche Anwendungen kann man die Umkehr der entsprechenden Programmauswertung verwenden. Der einfachste Ansatz, nämlich das Erstellen einer kompletten Mitschrift der Vorwärtsrechnung, welche anschließend rückwärts gelesen wird, verursacht einen enormen Speicherplatzbedarf. Als Alternative dazu kann man die Mitschrift auch stückweise erzeugen, indem die Programmauswertung von passend gewählten Checkpoints wiederholt gestartet wird. In dieser Arbeit wird die Theorie der optimalen parallelen Umkehrschemata erweitert. Zum einen erfolgt die Konstruktion von adaptiven parallelen Umkehrschemata. Dafür wird ein Algorithmus beschrieben, der es durch die Nutzung von mehreren Prozessen ermöglicht, Checkpoints so zu verteilen, daß die Umkehrung des Programmes jederzeit ohne Zeitverlust erfolgen kann. Hierbei bleibt die Zahl der verwendeten Checkpoints und Prozesse innerhalb der bekannten Optimalitätsgrenzen. Zum anderen konnte für die adaptiven parallelen Umkehrschemata ein Algorithmus entwickelt werden, welcher ein Restart der eigentlichen Programmauswertung basierend auf der laufenden Programmumkehr erlaubt. Dieser Restart kann wieder jederzeit ohne Zeitverlust erfolgen und die entstehenden Checkpointverteilung erfüllen wieder sowohl Optimalitäts- als auch die Adaptivitätskriterien. Zusammenfassend wurden damit in dieser Arbeit Schemata konstruiert, die bidirektionale Simulationen ermöglichen

CSM429: Abstract Geometric Crossover for the Permutation Representation

Abstract crossover and abstract mutation are representation-independent operators that are well-defined once a notion of distance over the solution space is defined. They were obtained as generalization of genetic operators for binary strings and real vectors. In this paper we explore how the abstract geometric framework applies to the permutation representation. This representation is challenging for various reasons: because of the inherent difference between permutations and the representations that inspired the abstraction; because the whole notion of geometry over permutation spaces radically departs from traditional geometries and it is almost unexplored mathematical territory; because the many notions of distance available and their subtle interconnections make it hard to see the right distance to use, if any; because the various available interpretations of permutations make ambiguous what a permutation represents, hence, how to treat it; because of the existence of various permutation-like representations that are incorrectly confused with permutations; and finally because of the existence of many mutation and recombination operators and their many variations for the same representation. This article shows that the application of our geometric framework naturally clarifies and unifies an important domain,the permutation representation and the related operators, in which there was little or no hope to find order. In addition the abstract geometric framework is used to improve the design of crossover operators for well-known problems naturally connected with the permutation representation

Theoretical and Computational Research in Various Scheduling Models

Nine manuscripts were published in this Special Issue on “Theoretical and Computational Research in Various Scheduling Models, 2021” of the MDPI Mathematics journal, covering a wide range of topics connected to the theory and applications of various scheduling models and their extensions/generalizations. These topics include a road network maintenance project, cost reduction of the subcontracted resources, a variant of the relocation problem, a network of activities with generally distributed durations through a Markov chain, idea on how to improve the return loading rate problem by integrating the sub-tour reversal approach with the method of the theory of constraints, an extended solution method for optimizing the bi-objective no-idle permutation flowshop scheduling problem, the burn-in (B/I) procedure, the Pareto-scheduling problem with two competing agents, and three preemptive Pareto-scheduling problems with two competing agents, among others. We hope that the book will be of interest to those working in the area of various scheduling problems and provide a bridge to facilitate the interaction between researchers and practitioners in scheduling questions. Although discrete mathematics is a common method to solve scheduling problems, the further development of this method is limited due to the lack of general principles, which poses a major challenge in this research field

Development and demonstration of an on-board mission planner for helicopters

Mission management tasks can be distributed within a planning hierarchy, where each level of the hierarchy addresses a scope of action, and associated time scale or planning horizon, and requirements for plan generation response time. The current work is focused on the far-field planning subproblem, with a scope and planning horizon encompassing the entire mission and with a response time required to be about two minutes. The far-feld planning problem is posed as a constrained optimization problem and algorithms and structural organizations are proposed for the solution. Algorithms are implemented in a developmental environment, and performance is assessed with respect to optimality and feasibility for the intended application and in comparison with alternative algorithms. This is done for the three major components of far-field planning: goal planning, waypoint path planning, and timeline management. It appears feasible to meet performance requirements on a 10 Mips flyable processor (dedicated to far-field planning) using a heuristically-guided simulated annealing technique for the goal planner, a modified A* search for the waypoint path planner, and a speed scheduling technique developed for this project

Gainsharing: A Critical Review and a Future Research Agenda

This paper provides a critical review of the extensive literature on gainsharing. It examines the reasons for the fast growth in these programs in recent years and the major prototypes used in the past. Different theoretical formulations making predictions about the behavioral consequences and conditions mediating the success of these programs are discussed and the supporting empirical evidence is examined. The large number of a theoretical case studies and practitioner reports or gainsharing are also summarized and integrated. The article concludes with a suggested research agenda for the future

Relaxation Adaptive Memory Programming For The Resource Constrained Project Scheduling Problem

The resource constrained project scheduling problem (RCPSP) is one of the most intractable problems in operations research; it is NP-hard in the strong sense. Due to the hardness of the problem, exact solution methods can only tackle instances of relatively small size. For larger instances commonly found in real applications heuristic solution methods are necessary to find near-optimal solutions within acceptable computation time limits. In this study algorithms based on the relaxation adaptive memory programming (RAMP) method (Rego, 2005) are developed for the purpose of solving the RCPSP. The RAMP algorithms developed here combine mathematical relaxation, including Lagrangian relaxation and surrogate constraint relaxation, with tabu search and genetic algorithms. Computational tests are performed on an extensive set of benchmark instances. The results demonstrate the capability of the proposed approaches to the solution of RCPSPs of different sizes and characteristics and provide meaningful insights to the potential application of these approaches to other more complex resource-constrained scheduling problems