Combinatorial and optimization problems are fundamental to many industrial AI applications. Solving large-scale real-world instances of such problems typically requires careful problem formalization, specialized solvers, and expert-designed heuristics. Thus, experts need to specify not only what solutions are, but also how they are derived. By introducing the tool CHECKMATE, we show that algorithm generation via code evolution represents a paradigm shift by eliminating the need to formulate the how. CHECKMATE solely relies on the what. Specifically, a formal specification ensures solutions' correctness and enables systematic performance evaluation of the generated programs, while a natural language description guides the evolutionary process. The effectiveness of our method is demonstrated on selected problems from two industrial domains: configuration and scheduling. In all cases, the evolved algorithms consistently outperform state-of-the-art solvers. This underscores the potential of formal methods in guiding code evolution for automatically solving complex real-world problems.

Sustainable manufacturing requires energy-efficient scheduling, especially in metalworking, where machines like bandsaws consume significant power. Based on a real-world use case from an Austrian steel-cutting company, we extend the Energy-Aware Double-Flexible Job-Shop Scheduling Problem (E-DFJSP)-which already comprises machine and worker flexibility-with machine modes, as well as setup and transport operations. To tackle this problem, we propose a Constraint Programming (CP) model, implemented using the state-of-the-art IBM CP Optimizer (CPO), to minimize job tardiness, energy consumption, and makespan. We evaluate our approach on two datasets representing present and future production scenarios, each with up to 500 jobs. While CPO fails to find feasible solutions for several large instances in the less flexible scenario, it successfully solves all instances in the more flexible one, indicating that higher resource flexibility improves search performance. In terms of solution quality, CPO demonstrates stronger scalability in makespan than in tardiness minimization, with the addition of machines and workers further reducing the optimal makespan and easing problem-solving. Finally, we identified a bug in CPO affecting staticLex (fixed-priority multi-objective minimization) when combined with tolerance-based early stopping; to avoid it, we optimized each goal separately.

Answer set programming (ASP) aims to realize the AI vision: The user specifies the problem, and the computer solves it. Indeed, ASP has made this vision true in many application domains. However, will current ASP solving techniques scale up for large configuration problems? As a benchmark for such problems, we investigated the configuration of electronic systems, which may comprise more than 30,000 components. We show the potential and limits of current ASP technology, focusing on methods that address the so-called grounding bottleneck, i.e., the sharp increase of memory demands in the size of the problem instances. To push the limits, we investigated the incremental solving approach, which proved effective in practice. However, even in the incremental approach, memory demands impose significant limits. Based on an analysis of grounding, we developed the method constraint-aware guessing, which significantly reduced the memory need.

The immense complexity of semiconductor production demands for advanced dispatching strategies that transcend traditional rule-based systems. This paper introduces a novel approach to dispatching rules derived from swarm intelligence techniques, specifically designed to tackle the intricate dynamics of large-scale semiconductor manufacturing processes. Our approach integrates simulation and optimization methods to investigate and enhance operational efficiency, addressing both the scalability of schedules and their practical implementation. We employ a customizable simulation framework to model a semiconductor manufacturing environment, wherein various dispatching rules as well as our proposed swarm intelligence-driven method are assessed. The effectiveness of these dispatching rules is quantitatively evaluated through a series of simulations that measure key performance indicators such as work-in-progress levels, throughput, and operational variability across different production scenarios. This study not only elucidates the potential of swarm intelligence techniques in refining production dispatching strategies, but also provides a simulation-based evaluation framework that can assist in the further development of intelligent dispatching systems.

Prescriptive analytics in supply chain management and manufacturing addresses the question of "what" should happen "when", where good recommendations require the solving of decision and optimization problems in all stages of the product life cycle at all decision levels. Artificial intelligence (AI) provides general methods and tools for the automated solving of such problems. We start our contribution with a discussion of the relation between AI and analytics techniques. As many decision and optimization problems are computationally complex, we present the challenges and approaches for solving such hard problems by AI methods and tools. As a running example for the introduction of general problem-solving frameworks, we employ production planning and scheduling. First, we present the fundamental modeling and problem-solving concepts of constraint programming (CP), which has a long and successful history in solving practical planning and scheduling tasks. Second, we describe highly expressive methods for problem representation and solving based on answer set programming (ASP), which is a variant of logic programming. Finally, as the application of exact algorithms can be prohibitive for very large problem instances, we discuss some methods from the area of local search aiming at near-optimal solutions. Besides the introduction of basic principles, we point out available tools and practical showcases.

Domain-specific heuristics are an essential technique for solving combinatorial problems efficiently. Current approaches to integrate domain-specific heuristics with Answer Set Programming (ASP) are unsatisfactory when dealing with heuristics that are specified non-monotonically on the basis of partial assignments. Such heuristics frequently occur in practice, for example, when picking an item that has not yet been placed in bin packing. Therefore, we present novel syntax and semantics for declarative specifications of domain-specific heuristics in ASP. Our approach supports heuristic statements that depend on the partial assignment maintained during solving, which has not been possible before. We provide an implementation in Alpha that makes Alpha the first lazy-grounding ASP system to support declaratively specified domain-specific heuristics. Two practical example domains are used to demonstrate the benefits of our proposal. Additionally, we use our approach to implement informed search with A*, which is tackled within ASP for the first time. A* is applied to two further search problems. The experiments confirm that combining lazy-grounding ASP solving and our novel heuristics can be vital for solving industrial-size problems.

. Domain-specific heuristics are an essential technique for solving configuration problems efficiently. Current approaches to integrate domain-specific heuristics with Answer Set Programming (ASP) are unsatisfactory when dealing with heuristics that are spec-ified non-monotonically on the basis of partial assignments. Such heuristics frequently occur in practice, for example, when picking a component that has not yet been placed in a configuration problem. Therefore, we present novel syntax and semantics for declarative specifications of domain-specific heuristics in ASP. Our approach supports heuristic statements that depend on the partial assignment maintained during solving, which has not been possible before. We provide an implementation in A LPHA that makes A LPHA the first lazy-grounding ASP system to support declaratively speci-fied domain-specific heuristics. Two well-known configuration problems are used to demonstrate the benefits of our proposal. The experiments confirm that combining lazy-grounding ASP solving and our novel heuristics can be vital for solving industrial-size configuration problems.

Generalising and re-using knowledge learned while solving one problem instance has been neglected by state-of-the-art answer set solvers. We suggest a new approach that generalises learned nogoods for re-use to speed-up the solving of future problem instances. Our solution combines well-known ASP solving techniques with deductive logic-based machine learning. Solving performance can be improved by adding learned non-ground constraints to the original program. We demonstrate the effects of our method by means of realistic examples, showing that our approach requires low computational cost to learn constraints that yield significant performance benefits in our test cases. These benefits can be seen with ground-and-solve systems as well as lazy-grounding systems. However, ground-and-solve systems suffer from additional grounding overheads, induced by the additional constraints in some cases. By means of conflict minimization, non-minimal learned constraints can be reduced. This can result in significant reductions of grounding and solving efforts, as our experiments show. (Under consideration for acceptance in TPLP.)

Conflict-Driven Nogood Learning (CDNL) [6, 10, 14] is a major success factor for high-performance state-of-the-art ASP systems. When a conflict occurs, new propositional nogoods are learned that prevent the same conflict from re-occurring, which improves search performance. We present an extension of CDNL that learns non-ground constraints. The idea is that whole parts of the search tree can be pruned when these learned constraints are added to the original program. We aim to derive non-ground constraints from the problem encoding that are valid for all possible inputs and which can be employed to speed up solving new instances. CDNL and Explanation-Based Learning (EBL) [2, 7, 8, 12] are our starting point. EBL is a well-known logic-based machine learning technique which learns first-order rules that are entailed by the background knowledge (in our case, the problem encoding). We combine CDNL with EBL to learn non-ground nogoods while solving prior problem instances. Since the number of generalised nogoods can be overwhelming, choosing those that will actually pay off is particularly challenging. Our basic idea is to generalise those non-ground conflicts that occur most often, i.e., we generalise propositional nogoods learned from frequently violated nogoods. The underlying assumption is that nogoods learned from frequent conflicts will also be able to prevent many conflicts. Previous work by Lühne et al. [4, 11] used different means to generalise learned nogoods for future re-use: In their approach, propositional constraints are extracted while solving, then generalized by minimization and abstraction, and finally validated by means of proof techniques. We use a realistic hardware configuration example (the House Reconfiguration Problem (HRP) [3, 13]) and a graph colouring problem for demonstration and experimentation purposes. In simplified terms, the HRP is about assigning things to cabinets and cabinets to rooms s.t. every room contains things belonging to only one person. This is expressed by the following rule and constraint: personTOroom(P,R)← personTOthing(P,T ), cabinetTOthing(C,T ), roomTOcabinet(R,C). ← personTOroom(P1,R), personTOroom(P2,R), P1 < P2.

Answer-Set Programming (ASP) is a powerful and expressive knowledge representation paradigm with a significant number of applications in logic-based AI. The traditional ground-and-solve approach, however, requires ASP programs to be grounded upfront and thus suffers from the so-called grounding bottleneck (i.e., ASP programs easily exhaust all available memory and thus become unsolvable). As a remedy, lazy-grounding ASP solvers have been developed, but many state-of-the-art techniques for grounded ASP solving have not been available to them yet. In this work we present, for the first time, adaptions to the lazy-grounding setting for many important techniques, like restarts, phase saving, domain-independent heuristics, and learned-clause deletion. Furthermore, we investigate their effects and in general observe a large improvement in solving capabilities and also uncover negative effects in certain cases, indicating the need for portfolio solving as known from other solvers. Under consideration for acceptance in TPLP.

The traditional ground-and-solve approach to Answer Set Programming (ASP) suffers from the grounding bottleneck, which makes large-scale problem instances unsolvable. Lazy grounding is an alternative approach that interleaves grounding with solving and thus uses space more efficiently. The limited view on the search space in lazy grounding poses unique challenges, however, and can have adverse effects on solving performance. In this paper we present a novel characterization of degrees of laziness in grounding for ASP, i.e. of compromises between lazily grounding as little as possible and the traditional full grounding upfront. We investigate how these degrees of laziness compare to each other formally as well as, by means of an experimental analysis using a number of benchmarks, in terms of their effects on solving performance. Our contributions are the introduction of a range of novel lazy grounding strategies, a formal account on their relationships and their correctness, and an investigation of their effects on solving performance. Experiments show that our approach performs significantly better than state-of-the-art lazy grounding in many cases.

The 32nd meeting of the International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems was held July 9‐11, 2019, in Graz, Austria. The conference focus for 2019 was on automated driving, autonomous systems, robotics, and AI in tourism.

The traditional ground-and-solve approach to Answer Set Programming (ASP) suffers from the grounding bottleneck, which makes large-scale problem instances unsolvable. Lazy grounding is an alternative approach that interleaves grounding with solving and thus uses space more efficiently. The limited view on the search space in lazy grounding poses unique challenges, however, and can have adverse effects on solving performance. In this paper we present a novel characterization of degrees of laziness in grounding for ASP, i.e. of compromises between lazily grounding as little as possible and the traditional full grounding upfront. We investigate how these degrees of laziness compare to each other formally as well as, by means of an experimental analysis using a number of benchmarks, in terms of their effects on solving performance. Our contributions are the introduction of a range of novel lazy grounding strategies, a formal account on their relationships and their correctness, and an investigation of their effects on solving performance. Experiments show that our approach performs significantly better than state-of-the-art lazy grounding in many cases.

The 32nd meeting of the International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems was held July 9–11, 2019, in Graz, Austria. The conference focus for 2019 was on automated driving, autonomous systems, robotics, and AI in tourism.