The Assembly Line Balancing Problem (ALBP) is of great relevance for manufacturing companies improving the line efficiency and productivity and thus maximizing production profits. Multiple exact, heuristic and meta-heuristic methods have been applied to solve the ALBP. These optimization methods consist in producing a feasible line balance, i.e. the partitioning of assembly tasks among available work stations based on, among others, the precedence graph. Such a graph describes the technological and organizational precedence constraints between tasks. Unfortunately, the assembly precedence relations, in the automotive and related industries for example, are often outdated, incomplete or altogether unavailable. This limits the applicability of the available approaches to real-world assembly systems. Grounded in an industry use-case, we propose a novel approach for the assistance in the upfront assignment of assembly tasks to stations. We recommend station assignments relying on historical data of prior feasible assembly balances of different products. We evaluate our approach against real industry data. On average, our approach is able to provide station assignment recommendations for 91% of the tasks at 82% precision.

Cyber-physical production systems are composed of a multitude of subsystems from diverse vendors and integrators, connected in a distributed fashion. An undesirable phenomenon in one system might cause a misbehavior in another connected system. Searching for the root cause of this misbehavior quickly becomes very tedious as many possible search directions exist. This paper proposes an approach and algorithm to tie together information available in design-time and runtime models. This then allows, in conjunction with observed and desired status of a system, to recommend search options and concrete solution steps to guide workers along the fixing process without being overwhelmed by the complexity of the overall system of systems. We demonstrate the feasibility of our approach using a lab-scal production cell model.

Unforeseen situations on the shopfloor cause the assembly process to divert from its expected progress. To be able to overcome these deviations in a timely manner, assembly process monitoring and early deviation detection are necessary. However, legal regulations and union policies often limit the direct monitoring of human-intensive assembly processes. Grounded in an industry use case, this paper outlines a novel approach that, based on indirect privacy-respecting monitored data from the shopfloor, enables the near real-time detection of multiple types of process deviations. In doing so, this paper specifically addresses uncertainties stemming from indirect shopfloor observations and how to reason in their presence.

As manufacturing companies move towards producing highly customizable products in small lot sizes, assembly workers remain an integral part of production systems. However, with workers in the loop, it is necessary to monitor the production process for timely detection of deviations and timely provisioning of worker assistance. Grounded in an industrial case study describing the assembly of construction vehicles, we outline a generic heuristic-based approach for monitoring progress in human-intensive assembly systems. Specifically, we highlight the challenges in dealing with uncertainty stemming from the limitations in accurately, timely, and completely observing human physical assembly steps. We discuss a motivating example to showcase these challenges and present a set of heuristics that manages to accurately infer assembly progress from indirect and incomplete observations of deviating worker behavior. Validated against ground truth obtained from a real industrial assembly line, on average our approach correctly estimates completion times for steps that are associated with shopfloor observations within 14 seconds or less of their true value.

Cyber physical production systems (CPPS) focus on increasing the flexibility and adaptability of industrial production systems, systems that comprise hardware such as sensors and actuators in machines as well as software controlling and integrating these machines. The requirements of customised mass production imply that control and integration software needs to be adaptable after deployment in a shop floor (factory), possibly even without interrupting production. Today, software frameworks provide support to model and execute manufacturing processes. They, however, provide little support for reuse. In this paper, we present a framework based on capabilities, which supports manufacturing process templates. These templates are bound/allocated to a specific shopfloor setup, a specific set of machines, and executed using a distributed set of process engines. This enables the reuse of manufacturing processes, as well as transmitting and executing changed processes. The framework is implemented using the Eclipse Milo implementation of OPC UA in Java. It is used to control a lab-scale modular shopfloor programmed in IEC61499 and Java.