The DeFacto project, supported by the European Commission via a Marie Skłodowska-Curie Global Individual Fellowship, tackles the complexity arising from the transformation of industrial manufacturing systems into intricate cyber-physical systems. This evolution offers unprecedented opportunities but also poses intellectual and engineering challenges. DeFacto aims to advance the design automation of cyber-physical production systems by developing innovative modeling paradigms, scalable algorithms, software architectures, and tools. In the DeFacto approach, production systems are managed through service-oriented manufacturing software architectures. System-level models capture the features and the requirements of production systems, representing both production and computational processes as services provided by the infrastructure. Methodologies for system analysis and optimization rely on compositional abstractions of system behaviors grounded in assume-guarantee contracts. This paper outlines key research endeavors, findings, and lessons learned from the DeFacto project.

We present ContrArc, a methodology for the exploration of cyber-physical system architectures aiming to minimize a cost function while adhering to a set of heterogeneous constraints. We assume a system topology, defined as a graph, where components (nodes) are selected from an implementation library, and connections between components (edges) are drawn from a finite set of possible connection choices. ContrArc uses assume-guarantee contracts to formalize different viewpoints in the system requirements, such as timing and power consumption, as well as the interface of different components, and translate the exploration problem into a mixed integer linear programming problem. It then searches for efficient solutions by relying on contract decompositions and a method based on sub graph isomorphism to iteratively prune infeasible architectures out of the search space. Experiments on a reconfigurable production line and an aircraft power distribution network show up to two orders of magnitude acceleration in architectural exploration with respect to comparable approaches.

We address the warehouse servicing problem (WSP) in automated warehouses, which use teams of mobile agents to bring products from shelves to packing stations. Given a list of products, the WSP amounts to finding a plan for a team of agents which brings every product on the list to a station within a given timeframe. The WSP consists of four subproblems, concerning what tasks to perform (task formulation), who will perform them (task allocation), and when (scheduling) and how (path planning) to perform them. These subproblems are NP-hard individually and are made more challenging by their interdependence. The difficulty of the WSP is compounded by the scale of automated warehouses, which frequently use teams of hundreds of agents. In this paper, we present a methodology that can solve the WSP at such scales. We introduce a novel, contract-based design framework which decomposes an automated warehouse into traffic system components. By assigning each of these components a contract describing the traffic flows it can support, we can syn-thesize a traffic flow satisfying a given WSP instance. Component-wise search-based path planning is then used to transform this traffic flow into a plan for discrete agents in a modular way. Evaluation shows that this methodology can solve WSP instances on real automated warehouses.

We address the Warehouse Servicing Problem (WSP) in automated warehouses, which use teams of mobile robots to move products from shelves to packaging stations. Given a list of products, the WSP amounts to finding a motion plan which brings every product on the list from a shelf to a packaging station within a given time limit. The WSP consists of four subproblems, namely, deciding where to source and deposit a product (task formulation), who should transport each product (task assignment) and when (scheduling) and how (motion planning). These problems are NP-Hard individually and made more challenging by their interdependence. The difficulty of the WSP is compounded by the scale of automated warehouses, which use teams of hundreds of agents to transport thousands of products. In this paper, we present Contract-based Cyclic Motion Planning (CCMP), a novel contract-based methodology for solving the WSP at scale. CCMP decomposes a warehouse into a set of traffic system components. By assigning each component a contract which describes the traffic flows it can support, CCMP can generate a traffic flow which satisfies a given WSP instance. CCMP then uses a novel motion planner to transform this traffic flow into a motion plan for a team of robots. Evaluation shows that CCMP can solve WSP instances taken from real industrial scenarios with up to 1 million products while outperforming other methodologies for solving the WSP by up to 2.9×.

This paper proposes an automated framework for quantitative verification and design space exploration of cyber-physical systems in the presence of uncertainty, leveraging assume-guarantee contracts expressed in Stochastic Signal Temporal Logic (StSTL). We introduce quantitative semantics for StSTL and formulations of the quantitative verification and design space exploration problems as bi-level optimization problems. We show that these optimization problems can be effectively solved for a class of stochastic systems and a fragment of bounded-time StSTL formulas. Our algorithm searches for partitions of the upper-level design space such that the solutions of the lower-level problems satisfy the upper-level constraints. A set of optimal parameter values are then selected within these partitions. We illustrate the effectiveness of our framework on the design of a multi-sensor perception system and an automatic cruise control system.

Assume-guarantee (A/G) contracts are mathematical models enabling modular and hierarchical design and verifi-cation of complex systems by rigorous decomposition of system-level specifications into component-level specifications. Existing A/G contract frameworks, however, are not designed to effectively capture the behaviors of cyber-physical systems where multiple agents aim to maximize one or more objectives, and may interact with each other and the environment in a cooperative or non-cooperative way toward achieving their goals. We propose an extension of the A/G contract framework, namely optimizing A/G contracts, that can be used to specify and reason about properties of component interactions that involve optimizing objectives. The proposed framework includes methods for constructing new contracts via conjunction and composition, along with algorithms to verify system properties via contract refinement. We illustrate its effectiveness on a set of case studies from connected and autonomous vehicles.