Constraint satisfaction or optimisation models – even if they are formulated in high-level modelling languages – need to be reduced into an equivalent format before they can be solved by the use of Quantum Computing. In this paper we show how Boolean and integer FlatZinc builtins over finite-domain integer variables can be equivalently reformulated as linear equations, linear inequalities or binary products of those variables, i.e. as finite-domain quadratic integer programs. Those quadratic integer programs can be further transformed into equivalent Quadratic Unconstrained Binary Optimisation problem models, i.e. a general format for optimisation problems to be solved on Quantum Computers especially on Quantum Annealers.

Obtaining Quadratic Unconstrained Binary Optimisation models for various optimisation problems, in order to solve those on physical quantum computers (such as the the DWave annealers) is nowadays a lengthy and tedious process that requires one to remodel all problem variables as binary variables and squeeze the target function and the constraints into a single quadratic polynomial into these new variables. We report here on the basis of our automatic converter from MiniZinc to QUBO, which is able to process a large set of constraint optimisation and constraint satisfaction problems and turn them into equivalent QUBOs, effectively optimising the whole process.

In this article we approach an extended Job Shop Scheduling Problem (JSSP). The goal is to create an optimized duty roster for a set of workpieces to be processed in a flexibly organized workshop, where the workpieces are transported by one or more Autonomous Ground Vehicles (AGV), that are included in the planning. We are approaching this extended, more complex variant of JSSP (still NP-complete) using Constraint Programming (CP) and Quantum Annealing (QA) as competing methods. We present and discuss: a) the results of our classical solution based on CP modeling and b) the results with modeling as quadratic unconstrained binary optimisation (QUBO) solved with hybrid quantum annealers from D-Wave, as well as with tabu search on current CPUs. The insight we get from these experiments is that solving QUBO models might lead to solutions where some immediate improvement is achievable through straight-forward, polynomial time postprocessing. Further more, QUBO proves to be suitable as an approachable modelling alternative to the expert CP modelling, as it was possible to obtain for medium sized problems similar results, but requiring more computing power. While we show that our CP approach scales now better with increased problem size than the hybrid Quantum Annealing, the number of qubits available for direct QA is increasing as well and might eventually change the winning method.

We propose and compare Constraint Programming (CP) and Quantum Annealing (QA) approaches for rolling stock assignment optimisation considering necessary maintenance tasks. In the CP approach, we model the problem with an Alldifferent constraint, extensions of the Element constraint, and logical implications, among others. For the QA approach, we develop a quadratic unconstrained binary optimisation (QUBO) model. For evaluation, we use data sets based on real data from Deutsche Bahn and run the QA approach on real quantum computers from D-Wave. Classical computers are used to evaluate the CP approach as well as tabu search for the QUBO model. At the current development stage of the physical quantum annealers, we find that both approaches tend to produce comparable results.

An aggregator is a business entity enabling smooth cooperation between a System Operator (SO) and small customers to trade electric power. In this cooperation each market actor (aggregator, small customer, system operator) looks for its own economic incentives. In this paper, we consider an aggregator, who manages a portfolio of domestic heat pumps (HPs). The aggregator aims at maximizing its profit while trading energy and providing balancing power in wholesale markets. The paper develops a Mixed Integer Linear Program (MILP) for optimal coordinated bidding of HPs consumption power in competitive day-ahead and real-time markets. The model enables an aggregator to shift the consumption of HPs to hours with lower market prices, while respecting the comfort of involved houses. A case has been studied based on data from Dutch pilot built in the scope of FLEXCoop project. Day-ahead and balancing market prices have been obtained from TenneT.

Almost climate neutral buildings are one of the core goals in terms of sustainability. Beside the support of the necessary design decisions for an integrated, interoperable, ecological and economical operation of building energy systems, innovative management solutions for scheduling the operation of decentralized energy systems are of great importance. The challenge is optimal interaction between energy system components in terms of own consumption, energy efficiency and resource consumption as well as greenhouse gas emissions. To achieve these goals a modular optimization approach based on Mixed Integer Programming is proposed. In detail, and to our knowledge the first time, a MIP model for the dynamic behavior of fuel cell Combined Heat and Power plants is presented. Our approach is evaluated for the operation of heat pumps showing that their energy efficiency can be increased significantly.

Die derzeitigen Entwicklungen und Herausforderungen in Wirtschaft und Gesellschaft im Rahmen von Digitalisierung und Digitaler Transformation stellen enorme Anforderungen an die Informatik und Wirtschaftsinformatik. Nur durch die aktiv getriebene Weiterentwicklung und den gezielten Einsatz von IT-Technologien, -verfahren und -methoden konnen wir es schaffen, uns den Herausforderungen unserer Zeit erfolgreich zu stellen und nachhaltige, zielkonforme, wirtschaftliche Losungen zu entwickeln. Beispieldomanen hierfur sind das agile Management (lokal und global) bei sich andernden politischen und wirtschaftlichen Randbedingungen (z.B. Brexit), das flexible Steuern und Uberwachen groser Verkehrs- und -Versorgungsnetze, das Schaffen und Betreiben agiler effizienter Unternehmensverbunde und die Energiewende. Wesentliche Aspekte hierfur sind „agilitatsbegunstigende“ und transparente Modellierung sowie die Optimierung von Prozessen und die Planung und Steuerung von effizienten und nachhaltigen Ressourcennutzungen. Der Workshop adressiert Aspekte der transparenten Modellierung, der Optimierung und der Simulation hochkomplexer Systeme im o.g. Sinne. Im Vergleich zu seinen Vorgangerworkshops fallt der MOC 2019, bezogen auf den vorgesehenen zeitlichen Umfang, gering aus. Neben der Beitragsprasentation liegen die Schwerpunkte auf kooperativem Brainstorming und Diskussion der thematisierten Aspekte. Hierfur sind impulsgebende Slots zu den Themengebieten „Shapley-Wert“ sowie „Deklarativitat und KI“ vorgesehen. Generell soll der Workshop Fachleuten, Anwendern und Interessierten die Moglichkeit zum Austausch und zur nutzbringenden Diskussion von Ideen, Ansatzen, Verfahren und Problemlosungen geben, wobei hierbei idealer Weise Grundlagen fur zukunftige, im o.g. Sinne zielfuhrende, Forschungsarbeiten entstehen sollen.

Motivated by the necessity to model the energy loss of energy storage devices, a Proportional Constraint is introduced in finite integer domain Constraint Programming. Therefore rounding is used within its definition. For practical applications in finite domain Constraint Programming, pruning rules are presented and their correctness is proven. Further, it is shown by examples that the number of iterations necessary to reach a fixed-point while pruning depends on the considered constraint instances. However, fixed-point iteration always results in the strongest notion of bounds consistency. Furthermore, an alternative modeling of the Proportional Constraint is presented. The run-times of the implementations of both alternatives are compared showing that the implementation of the Proportional Constraint on the basis of the presented pruning rules performs always better on sample problem classes.

Die aktuellen Entwicklungen und Herausforderungen in Gesellschaft und Wirtschaft stellen neue Anforderungen an die Informatik und Wirtschaftsinformatik. Nur durch die Weiterentwicklung und den gezielten Einsatz von IT-Technologien, -verfahren und -methoden sind die „Probleme unserer Zeit“ nachhaltig zu lösen. Beispieldomänen hierfür sind die Energiewende, das flexible Steuern und Überwachen großer Verkehrsnetze und das Schaffen und Betreiben agiler effizienter Unternehmensverbünde. Wesentliche Aspekte hierfür sind „agilitätsbegünstigende“ und validierbare Modellierung sowie die Optimierung der Prozesse und des Betriebes. Der Workshop adressiert Aspekte der Modellierung, der Optimierung und der Simulation hochkomplexer Systeme im o.g. Sinne, wobei insbesondere auch die zeitnahe Auswertung großer Datenmengen (Big Data) zur „optimalen Steuerung“ der modellierten Prozesse thematisiert werden soll. Besonderes Interesse besteht dabei an Verfahren, die neben Effizienz auch Deklarativität und Interaktivität bei der Behandlung von Simulationsund Optimierungsproblemen sichern. Ansätze bieten hierfür zum Beispiel ConstraintVerfahren. Deklarativität eine Beschreibung der gewünschten Lösung, anstelle einer prozeduralen Abarbeitungsvorschrift scheint mit Effizienz, insbesondere vor dem Hintergrund der Behandlung von Optimierungsaufgaben, kaum verträglich zu sein. Dennoch ist Deklarativität wünschenswert, um eine Problembehandlung möglichst einfach (Nichtinformatiker) und transparent (Anpassbarkeit) darzustellen und Verständlichkeit, Wartbarkeit und Erweiterbarkeit/ Adaptierbarkeit der Software zu erleichtern. Die Eigenschaft der Interaktivität kann Unterstützung bieten, Expertenwissen während des Optimierungsprozesses zu nutzen, um in kürzerer Zeit eine Lösung zu finden. Onthe-fly-Expertenwissen ist für Simulationsund Optimierungsverfahren schon deshalb ein unverzichtbares Potential, weil die zu behandelnden Probleme aufgrund wachsender Größe, steigender Suchraumgröße und Komplexität durch automatische Verfahren kaum oder nicht beherrschbar sind. Weiterhin erfordern viele Anwendungen, dass gute oder gar beste Lösungen innerhalb kürzester Zeit zu finden sind, um beispielsweise Betriebsabläufe bei unvorhergesehenen Störungen unmittelbar anzupassen und so die Wettbewerbsfähigkeit von Unternehmen und Unternehmensverbünden zu erhalten. Als erforderlich werden daher interaktive Simulationsund Optimierungssysteme angesehen, die auf Basis einer umfassenden Datenund Erkenntnislage (siehe oben) Anwender bei ihren operativen und auch strategischen Entscheidungen, z.B. durch "was-wäre-wenn"Szenarien oder durch Berechnen und Präsentieren von alternativen Lösungen, unterstützen. Der Workshop soll Fachleuten und Anwendern die Möglichkeit zum Austausch und zur fruchtvollen Diskussion von Ideen, Ansätzen, Verfahren und Problemlösungen geben, wobei hierbei idealer Weise Grundlagen für zukünftige, 1 http://www.constraint-programming.de/MOC-2014/

The aim of the presented constraint programming library firstCS is the integration of the constraint programming paradigm in the object-oriented programming language Java. This open-box library provides its users with the necessary concepts to model and solve constraint satisfaction problems and even constraint optimization problems over finite integer domains. The application focus of firstCS is constraint-based scheduling and resource allocations (e.g. Sandow in INFORMATIK 2011, LNI, vol. P-192, p. 248, 2011), however, it offers all primitives to realize new constraints and according propagation algorithms as well as problem-specific tree search heuristics to find good or even best solutions. Beyond related work and an overview over the general architecture of the system and the supported constraints, this presentation focuses on new aspects of the current version of firstCS, i.e. redundancy checking and the assembling of new search strategies from existing ones using the implementation language Java. The presentation is completed by code fragments showing interesting implementation details.

In this article a constraint-based modeling of clinical pathways, in particular of surgical pathways, is introduced and used for an optimized scheduling of their tasks. The addressed optimization criteria are based on practical experiences in the area of Constraint Programming applications in medical work flow management. Objective functions having empirical evidence for their adequacy in the considered use cases are formally presented. It is shown how they are respected while scheduling clinical pathways.

The recent success of the lazy clause generator (a hybrid of a FD and a SAT solver) on resource-constrained project scheduling problems (RCPSP) shows the importance of the global cumulative constraint to tackle these problems. A key for an efficient cumulative propagator is a fast and correct pruning of time-bounds. The not-first/not-last rule (which is not subsumed by other rules) detects activities that cannot be run at first/last regarding to an activity set and prunes their time bounds. This paper presents a new sound not-first/not-last pruning algorithm which runs in O(n2 log n), where n is the number of activities. It may not find the best adjustments in the first run, but after at most n iterations. This approach of iteration fits the setup of constraint propagation quite naturally offering the opportunity that a fixed point is reached more efficiently. Moreover, it uses a novel approach of generation of some "artificial" activities in the context of triggering pruning rules correctly. In experiments on RCPSP amongst others from the well-established PSPLib we show that the algorithm runs negligible more often than a complete algorithm while taking its advantage from the lower - to the best of our knowledge the lowest known - runtime complexity.

Optimized task scheduling is in general an NP-hard problem, even if the tasks are prioritized like surgeries in hospitals. Better pruning algorithms for the constraints within such constraint optimization problems, in particular for the constraints representing the objectives to be optimized, will result in faster convergence of branch & bound algorithms. This paper presents new pruning rules for linear weighted (task) sums where the summands are the start times of tasks to be scheduled on an exclusively available resource and weighted by the tasks' priorities. The presented pruning rules are proven to be correct and the speed-up of the optimization is shown in comparison with well-known general-purpose pruning rules for weighted sums.

Adaptive constraint processing with Constraint Handling Rules (CHR) allows the application of intelligent search strategies to solve Constraint Satisfaction Problems (CSP), but these search algorithms have to be implemented in the host language of adaptive CHR which is currently Java. On the other hand, CHR *** enables to explicitly formulate search in CHR, using disjunctive bodies to model choices. However, a naive implementation for handling disjunctions, in particular chronological backtracking (as implemented in Prolog), might cause "thrashing" due to an inappropriate order of decisions. In order to avoid this, a first combination of adaptive CHR and CHR *** is presented to offer a more efficient embedded search mechanism to handle disjunctions. Therefore, the refined operational semantics of CHR is extended for disjunctions and adaptation.