In the control of complex systems, we observe two diametrical trends: model-based control derived from digital twins, and model-free control through AI. There are also attempts to bridge the gap between the two by incorporating learning-based AI algorithms into digital twins to mitigate mismatches between the digital twin model and the physical system. One of the most straightforward approaches to this is direct input adaptation. In this paper, we ask whether it is useful to employ a generic learning algorithm in such a setting, and our conclusion is “not very”. We denote an algorithm to be more useful than another algorithm based on three aspects: 1) it requires fewer data samples to reach a desired minimal performance, 2) it achieves better performance for a reasonable number of data samples, and 3) it accumulates less regret. In our evaluation, we randomly sample problems from an industrially relevant geometry assurance context and measure the aforementioned performance indicators of 16 different algorithms. Our conclusion is that blackbox optimization algorithms, designed to leverage specific properties of the problem, generally perform better than generic learning algorithms, once again finding that “there is no free lunch”. Note to Practitioners—Digital twins have the potential to improve productivity and quality in complex systems such as manufacturing systems. Their impact on system performance hinges on the accuracy of their digital models around the system’s operating points. Difficult to measure phenomena, such as wear and tear of equipment, however, may cause a mismatch between the digital twin model and the physical system. In this paper, we formalize this problem and compare 16 potential solution strategies under practical aspects. We argue that readily available off-the-shelf blackbox optimization algorithms may prove more useful for this problem, than more recent learning-based approaches. Specifically, gradient-based algorithms will perform best in systems with high-dimensional, continuous, and non-linear performance functions–even in the presence of white measurement noise.

Safety critical systems are typically subjected to hazard analysis before commissioning to identify and analyse potentially hazardous system states that may arise during operation. Currently, hazard analysis is mainly based on human reasoning, past experiences, and simple tools such as checklists and spreadsheets. Increasing system complexity makes such approaches decreasingly suitable. Furthermore, testing-based hazard analysis is often not suitable due to high costs or dangers of physical faults. A remedy for this are model-based hazard analysis methods, which either rely on formal models or on simulation models, each with their own benefits and drawbacks. This paper proposes a two-layer approach that combines the benefits of exhaustive analysis using formal methods with detailed analysis using simulation. Unsafe behaviours that lead to unsafe states are first synthesised from a formal model of the system using Supervisory Control Theory. The result is then input to the simulation where detailed analyses using domain-specific risk metrics are performed. Though the presented approach is generally applicable, this paper demonstrates the benefits of the approach on an industrial human-robot collaboration system.

Recounts the career and contributions of Peter Luh.

This paper investigates the problem of reducing the energy use of robot stations with stochastic execution times in production lines in automotive factories. First, real stochastic cycle time data is used to analyze and improve the cycle time. The result shows that the cycle time mean can be decreased with the cost of an increase in cycle time variance. Second, the cycle time data is combined with energy models of real robot stations. A stochastic optimization problem is formulated where the goal is to reduce the energy use of the stations in the production line by lowering the robot velocities, while not affecting the cycle time of the production line. The optimization problem is solved and the resulting energy optimized station is simulated using the improved cycle time. The result shows that up to 23 percent of the energy use can be reduced by only marginally affecting the cycle time variance of the production line.

In the manufacturing industry, spray painting is often an important part of the manufacturing process. Especially in the automotive industry, the perceived quality of the final product is closely linked to the exactness and smoothness of the painting process. For complex products or low batch size production, manual spray painting is often used. But in large scale production with a high degree of automation, the painting is usually performed by industrial robots. There is a need to improve and simplify the generation of robot trajectories used in industrial paint booths. A novel method for spray paint optimization is presented, which can be used to smooth out a generated initial trajectory and minimize paint thickness deviations from a target thickness. The smoothed out trajectory is found by solving, using an interior point solver, a continuous non-linear optimization problem. A two-dimensional reference function of the applied paint thickness is selected by fitting a spline function to experimental data. This applicator footprint profile is then projected to the geometry and used as a paint deposition model. After generating an initial trajectory, the position and duration of each trajectory segment are used as optimization variables. The primary goal of the optimization is to obtain a paint applicator trajectory, which would closely match a target paint thickness when executed. The algorithm has been shown to produce satisfactory results on both a simple 2-dimensional test example, and a non-trivial industrial case of painting a tractor fender. The resulting trajectory is also proven feasible to be executed by an industrial robot.Note to Practitioners—The work is motivated by the need to generate well performing robot trajectories in robotized spray-painting booths. The described method applies to cases where robotic spray painting is to be used for painting a surface with an even layer of paint at a specified thickness. The method generates and optimizes robot trajectories and is shown to be able to generate a satisfactory paint cover for simple test cases as well as more realistic industrial cases. For a user it could be implemented as is, or be obtained as a standalone service, but there are some prerequisites that need to be fulfilled to make use of the optimization method. It is assumed that the surface to be painted is available as a CAD-model and that physical testing has been performed to determine the characteristics of the paint and nozzle. These physical tests amount to spraying paint on a flat piece of material at a few different distances from the surface and measuring the cross section of the paint thickness. The resulting trajectory can be executed on any industrial painting robot that can handle linear motion commands.

Compositional Optimization(CompOpt) was recently proposed for optimization of discrete-event systems of systems. A modular optimization model allows CompOpt to divide the optimization into separate sub-problems, mitigating the state space explosion problem. This paper presents the Modular Optimization Learner (MOL), a method that interacts with a simulation of a system to automatically learn these modular optimization models. MOL uses amodular learningthat takes as input a hypothesis structure of the system and uses the provided structural information to split the acquired learning into a set of modules, and to prune parts of the search space. Experiments show that modular learning reduces the state space by many orders of magnitude compared to a monolithic learning, which enables learning of much larger systems. Furthermore, an integrated greedy search heuristic allows MOL to remove many sub-optimal paths in the individual modules, speeding up the subsequent optimization.Note to Practitioners—Automation systems are becoming increasingly large and complex and the automation of more and more advanced tasks often requires the coordination of multiple subsystems. Optimization can have a great impact on the efficiency of these systems in terms of cost and operation speed. Finding optimal solutions is, however, a difficult task. As the number of tasks and subsystems of the automation system increases, the search space of the optimization problems tends to grow exponentially.Compositional Optimization(CompOpt) is a method specifically designed for the optimization of large-scale automation systems. A challenge with the application of CompOpt is that it takes as input a specific type of optimization model that divides the system into subsystems; like machines, vehicles, etc. Formulating these models requires a high level of expertise and system knowledge. This paper addresses this challenge with an algorithm that learns these models from a simulation of the system. The simulation can be implemented in any software as long as a suitable interface exists or can be constructed. To divide the learning into subsystems, the algorithm uses an initialplant structure hypothesis(PSH). This can be viewed as a meta-model that includes known structural information, such as the number of subsystems and which actions that affect each subsystem. The more structural information that is added to PSH, the more efficient the learning and subsequent optimization will be. The purpose of this is to reduce the level of expertise and system knowledge needed in the application of optimization, to simplify the transition to Industry 4.0.

This paper investigates the problem of reducing the energy use of the movements of robots in industrial robot stations that have variations in execution times. An online method is presented that repeatedly solves an optimization problem during the execution of the station, that tries to minimize the energy use by finding the optimal execution times of the robot movements while at the same ensuring that the deadline of the station is met with a high enough probability. The method involves reformulating the original optimization problem, which is stochastic and nonlinear, into a convex version that can be solved efficiently. The method is tested on a simulated robot station and the result shows that the method is fast enough to be useable online and reduces the energy use of the station.

In order for virtual commissioning to be applied in an industrial setting, it is required that the models used are predefined and standardized. These models also need to be able to protect intellectual property, so that systems containing such information also can be modeled and used. This paper presents and evaluates an alternative method for co-simulation, based on a differential algebraic equation (DAE) formulation. The method specifies how models should be created and used, such that sharing model information between interacting components can be avoided. The focus is specifically on co-simulation of rigidly interacting models, such as robots interacting with other mechanical components.

We propose an efficient optimization method, which addresses several performance criteria, such as makespan, maximum lateness, and the sum of tardiness for an automated guided vehicle (AGV) system, together with its energy consumption. We show that the most important factors in energy consumption of AGVs are their cruise velocities and traveled distances. We also demonstrate that optimizing the productivity-related performance criteria also reduces energy consumption through less traveled distance. It also allows for the reduction of the cruise velocity, which leads to more energy savings. Our experiments demonstrate that the optimization method outperforms the existing traffic controller with respect to the performance criteria and reduces energy consumption. The proposed method can reduce the energy consumption by around 38%, while the values of makespan, lateness, and tardiness remain better than those obtained from the existing traffic controller. An important advantage of this article is that the evaluations are based on collected data from a real large-scale manufacturing plant. Note to Practitioners-It is commonly believed that reduction of speed, for example, due to safety reasons in critical areas, in automated guided vehicle (AGV) systems leads to lower system efficiency. However, it has been shown that speed management is an effective strategy to reduce the energy consumption of mobile robots and robot stations. If one seeks to utilize the existing slacks in the schedule of the AGVs, it should be possible to reduce energy consumption without affecting system efficiency. It can, furthermore, be combined with better scheduling to even improve performance measures, such as makespan, while reducing energy. In this article, we propose an optimization method that seeks to minimize the number of performance measures, such as makespan, maximum lateness, and sum of tardiness for a real AGV system designed by AGVE, which operates at Volvo Cars, Gothenburg, Sweden. We also show that the optimization method allows for reduction of cruise speed, while the mentioned performance measures are still better than the one obtained from the original traffic controller. We will also show the importance of taking into account the temperature of the drive system of the AGVs when performing energy measurements.

An improved method for solving conflict-free scheduling and routing of automated guided vehicles is proposed in this article, with promising results. This is achieved by reformulating the mathematical model of the problem, including several improvements and speedup strategies of an existing Benders decomposition method. A new heuristic is also presented that quickly yields high-quality solutions. Moreover, a real-large-scale industrial instance is solved using an open-source satisfiability module theories solver and a commercial constraint programming solver. According to the results, both of these general-purpose solvers can effectively solve the proposed models. Note to Practitioners-The problem of conflict-free routing and scheduling of automated guided vehicles (AGVs) in large-scale manufacturing systems has been an ever-present challenge for many AGV companies. Although these companies have developed rather efficient control policies and algorithms, retrofitting the existing heuristic to future's denser, more complicated, and more demanding AGV layouts is not guaranteed to be easy. Furthermore, the installed system will not necessarily be as efficient as expected. Currently, it is common to use heuristics to allocate vehicles to orders and route them. There are also rules of thumbs to avoid collisions and deadlocks. However, with increasing demand for high-performance AGV solutions, it is of interest to employ optimization algorithms that handle the order allocation, scheduling, and routing in a more efficient way. In this article, we present an improved method to tackle this issue, with promising results. We have developed our work in collaboration with a Swedish AGV company, and we have investigated a real-large-scale industrial instance as our case study.

This article presents a framework for Plug & Produce that makes it possible to use configurations rather than programming to adapt a manufacturing system for new resources and parts. This is solved by defining skills on resources, and goals for parts. To reach these goals, process plans are defined with a sequence of skills to be utilized without specifying specific resources. This makes it possible to separate the physical world from the process plans. When a process plan requires a skill, e.g., grip with a gripper resource, then that skill may require further skills, e.g., move with a robot resource. This creates a tree of connected resources that are not defined in the process plan. Physical and logical compatibility between resources in this tree is checked by comparing several parameters defined on the resources and the part. This article presents an algorithm together with a multiagent system framework that handles the search and matching required for selecting the correct resources.

The following topics are dealt with: production engineering computing; learning (artificial intelligence); mobile robots; Internet of Things; control engineering computing; factory automation; protocols; embedded systems; manufacturing systems; condition monitoring.

In a diagnosability verifier with polynomial complexity, a non-diagnosable system generates uncertain loops. Such forbidden loops are in this paper transformed to forbidden states by simple detector automata. The forbidden state problem is trivially transformed to a nonblocking problem by considering all states except the forbidden ones as marked states. This transformation is combined with one of the most efficient abstractions for modular systems called conflict equivalence, where nonblocking properties are preserved. In the resulting abstraction, local events are hidden and more local events are achieved when subsystems are synchronized. This incremental abstraction is applied to a scalable production system, including parallel lines where buffers and machines in each line include some typical failures and feedback flows. For this modular system, the proposed diagnosability algorithm shows great results, where diagnosability of systems including millions of states is analyzed in less than a second.

This paper proposes an optimization model for optimizing the energy use of industrial robots in production systems affected by stochastic disturbances. In the model there are a number of operations that needs to be completed by the robots before a deadline. The operations are of two types, one type that can not be controlled and have stochastic execution times. The other type are robot movements, and by extending their execution times energy can be saved. The goal of the optimization is to find the optimal combination of execution times for the robot movements, while meeting the deadline with a given probability.

Home healthcare (HHC) is a service that dispatches caregivers to people in need of healthcare who live in the home. The task assignment and route generation for caregivers can be formulated as an extension of the well-known vehicle routing problem with time windows (VRPTW). Currently, the most successful exact algorithms for VRPTW are based on a framework combining column generation (CG) and branching. Although such methods could be successful for HHC routing and scheduling problem (HHCRSP) as well, fast approximate algorithms are appealing, especially for large problems. In an early version of this paper, we employed a heuristic distributed gossip algorithm to solve HHCRSP. In this paper, we integrate the gossip algorithm with a local solver based on CG, which makes it an effective algorithm for larger problem instances. As it will be shown with extensive numerical experiments, for large problem instances, gossip-CG performs better than the pure CG.

Many people in need of care still live in their homes, requiring the caretakers to travel to them. Assigning the people to caretakers and generating their schedules can be formulated as a mixed integer linear programming problem (MILP) that inherits many features of the well-known vehicle routing problem with time windows (VRPTW). Currently, the most successful exact algorithms for VRPTW are based on branch and price framework, which combine column generation (CG) and branching. While these methods could be successful for Home Healthcare Routing and Scheduling Problem (HHCRSP) as well, fast approximate algorithms are appealing, especially for large problems. We recently employed a heuristic distributed gossip algorithm to solve HHCRSP. The method had the potential to provide approximate solutions for relatively large problem instances, but its effectiveness was limited to the performance of its local MILP solver. In this paper, we integrate the gossip algorithm with a local solver based on CG, which makes it an effective algorithm for larger problem instances. We also provide numerical experiments and complexity evaluations of the improved gossip algorithm (gossip-CG) with the standard gossip (gossip-MILP) and CG, and show that gossip-CG outperforms the pure CG in case of large problems.

The Eleventh Annual IEEE International Conference on Automation Science and Engineering (CASE 2015) was held on August 24–28, at Elite Park Avenue Hotel in Gothenburg, Sweden. IEEE CASE represents the Flagship Automation Conference of the IEEE Robotics and Automation Society and constitutes the primary forum for cross-industry and multidisciplinary research in automation. The conference theme wasAutomation for a Sustainable Future, a global challenge emphasized at the conference by specific workshops and a number of oral sessions. The focus on sustainability involves energy saving, life science, as well as resource efficiency and reusability.

This paper, as an outcome of the EU project AREUS, heralds an optimization procedure that reduces up to 30% of energy consumption and up to 60% in peak power for the trajectories that have been tested on real industrial robots. We have evaluated a number of cost functions and tested our algorithm for a variety of scenarios such as varying cycle times, payloads, and single/multirobot cases in both ac- and dc-operated robot cells. The significance of our work is not only in the impressive savings, simplicity of implementation, and preserving path and cycle time, but also in the variety of test scenarios that include different kinds of KUKA robots. We have carried out the optimization and experiments in as realistic conditions as possible.

A systematic method to evaluate the conflict between robustness, stability, and energy consumption is proposed in this paper. Energy optimization is combined with robust scheduling techniques to analyze the tradeoff. In rescheduling, slack is often used to protect a schedule from disruptions. However, results from the literature on energy minimization show that a reduction in energy consumption is achieved by extending the execution time of operations. Thus, slack in schedules is diminished on behalf of longer execution times. The proposed method, which quantitatively shows this conflict, is based on a multiobjective optimization formulation where efficient computation of the involved criteria is developed. This includes a convex surrogate stability measure that makes it possible to evaluate different operation sequences by a mixed-integer nonlinear programming formulation. Previous works connecting the two research fields use simulation for analyzing the impact of disruptions in order to generate robust production schedules. Our results show that an increase in energy efficiency comes at a cost of reducing stability and robustness and hence becoming more sensitive to disruptions.

In this paper, a predicate transition model for discrete-event systems is generalized to include continuous dynamics, and the result is a modular hybrid predicate transition model. Based on this model, a hybrid Petri net including explicit differential equations and shared variables is also proposed. It is then shown how this hybrid Petri net model can be optimized based on a simple and robust nonlinear programming formulation. The procedure only assumes that desired sampled paths for a number of interacting moving devices are given, while originally equidistant time instances are adjusted to minimize a given criterion. This optimization of hybrid systems is also applied to a real robot station with interacting devices, which results in about 30% reduction in energy consumption. Moreover, a flexible online and event-based information architecture called the Tweeting Factory is proposed. Simple messages (tweets) from all kinds of equipment are combined into high-level knowledge, and it is demonstrated how this information architecture can be used to support optimization of robot stations.

In this work in progress paper, we formulate performance robust event-based control strategies with respect to uncertainties in operation processing times. The proposed policies are based on a time-optimal schedule of operation sequences. A logical operation model is used to specify relations between operations. The control policies generate conditions ensuring to maintain the execution order given in the schedule. One of the strategies is less restrictive in order to avoid unnecessary delays due to uncertainties in execution times. In this case, the generated conditions are evaluated and, if possible, relaxed for operations without a logical connection. For non-nominal execution times, the makespan of the less restrictive control policy is proven to always be better or equal to the makespan of the more restrictive control strategy.

It is challenging to get an overview and understanding of the activities and their relations at an emergency department (ED). This is due to the complicated relations among activities - called operations in this paper - and the always changing system behavior. Two key enablers to get this overview are the transformation of real-time events into understandable information and an operation-based behavior description. This paper presents an event-based information architecture for healthcare (EVAH) for gathering state changing events in realtime at an ED and how these are transformed into an operation-based representation. EVAH together with the tool Sequence Planner, will be used for visualization, online prognosis and optimization.

This paper presents an efficient diagnosability verification technique, based on a general abstraction approach. We exploit branching bisimulation including state labels with explicit divergence (BBSD), which preserves the temporal logic property that verifies diagnosability. Furthermore, using compositional abstraction for modular diagnosability verification offers additional state space reduction in comparison to state-of-the-art techniques.

Optimizing the configuration and overall performance of synchronized modular systems is considered in this paper. The synchronized modules can be considered as a hybrid system, including continuous-time dynamics of local moving devices, combined with high-level discrete event sequences. The continuous-time trajectories are approximated by the Gauss pseudospectral method, resulting in a nonlinear programming (NLP) problem. The optimal configuration generates the maximal production rate subject to dynamic constraints. A complete design procedure is presented and applied to a case study of a packaging machine, where an alternative optimal configuration is achieved compared to current industrial practices.

This paper concerns the energy optimization of systems with interacting robots. Pseudo spectral optimal control is used to collocate the continuous time dynamics of each robot. The resulting model is a multi stage, multi robot, trajectory planning problem with timing constraints. In the case of a given task sequence, the mathematical formulation is that of a Nonlinear Programming problem. We present two example problems where three 3-DoF robots work together. For the case the problem consists of undetermined sequences, we outline an algorithm which integrates Operations Research methods with those of Constraint Programming.

The execution behavior of a system or a product can often be specified by a set of operations (sometimes called tasks, actions or activities). The process to specify these operations seems to be a real challenge in various situations, for example when designing automation systems or keeping track of the work at an emergency department. To be flexible during design and development, is about coping with uncertainty. However, in practice, specifying operation behavior is often quite inflexible because every possible execution route is explicitly defined. This approach is sometimes refereed to as point-based engineering, which is characterized by the early selection and approval of a single “best” specific solution. This approach can result in neither a robust nor a flexible design process with many rework iterations. This paper instead suggests a set-based operation specification approach that does not explicitly defines operation routes, where instead the operation behavior is specified using the execution restrictions in transition conditions for each operation. This enables the possibility to create multiple projections of the operation relations to enable better understanding. This is accomplished by creating various Sequences of Operations, sequences of operations (SOP), including a multiplicity of sequences and operation relations. Two example case studies are presented, where the first show the development of an automation system and the second study uses sequence projections in an emergency department at a hospital to handle the complex and flexible operation (task) behavior.

A generic state-vector transition (SVT) model is suggested, including a flexible synchronous composition involving both shared variables and events. This model is analyzed, focusing on properties that are important for supervisor synthesis. A synthesis procedure is then developed for the SVT model, where supervisor guards are generated that guarantee a controllable, nonblocking and maximally permissive supervisor. Novel conditions are introduced, such that more flexible specifications can be applied than earlier suggested for related models. Since the SVT model includes automata and (colored) Petri nets, optionally extended with variables, guards and actions, as special cases, the suggested synthesis approach unifies supervisor synthesis for the main discrete event model classes. Finally, the SVT model is naturally represented and efficiently computed based on binary decision diagrams, and the resulting supervisor guards are easily implemented in industrial control systems.

In this paper, we symbolically represent timed discrete-event systems (TDES), which can be used to efficiently compute the supervisor in the supervisory control theory context. We model a TDES based on timed extended finite automata (TEFAs): an augmentation of extended finite automata (EFAs) by incorporating discrete time into the model. EFAs are ordinary automata extended with discrete variables, where conditional expressions and update functions can be attached to the transitions. The symbolic computations are based on binary decision diagrams (BDDs). We show how TEFAs can be represented by BDDs. The main feature of this approach is that the BDD-based fixed point computations are not based on tick models that have been commonly used in this area, leading to better performance in many cases. The approach has been implemented and applied to a simple case study and several large-scale benchmarks.

This paper presents a survey and evaluation of the efficiency of polynomial diagnosability algorithms for systems modeled by Petri nets and automata. A modified verification algorithm that reduces the state space by exploiting symmetry and abstracting unobservable transitions is also proposed. We show the importance of minimal explanations on the performance of diagnosability verifiers. Different verifiers are compared in terms of state space and elapsed time. It is shown that the minimal explanation notion involved in the modified basis reachability graph, a graph presented by Cabasino et al. [3] for diagnosability analysis of Petri nets, has great impact also on automata-based diagnosability methods. The evaluation often shows improved computation times of a factor 1000 or more when the concept of minimal explanation is included in the computation.

In this paper, we propose a logic-based Benders decomposition (LBBD), as well as an LBBD/gossip method to solve the heterogeneous multi-vehicle routing problem (HMVRP). HMVRP is a newly formalized extension of the NP-hard multi-traveling salesman problem (mTSP). First, a hybrid algorithm based on LBBD is formulated that decomposes the HMVRP into an assignment problem and a cluster of sequencing problems. The former is solved by a mixed integer linear programming (MILP) solver, and the latter by a dedicated TSP solver. Then, a gossip algorithm is constructed which utilizes the mentioned LBBD for local optimization to achieve better computational efficiency. The use of LBBD remarkably reduces the CPU time. Furthurmore, integrating the three layers of gossip algorithm, LBBD and the TSP solver, results in a very efficient solution method.

In this paper, we introduce a complete framework for integrating the design of the manufacturing process and control system. We show how operation sequences can be designed in a modeling tool, Sequence Planner (SP), and how relations between operations may be expressed using logical conditions. An approach to convert the SP model into a constraint programming model for optimization is presented. The time-based solution is transformed to an event-based description. Due to uncertainties in execution times, some logical restrictions based on the optimal schedule are relaxed to avoid unnecessary delays. The control logics to achieve the desired operation sequences are added to the SP model. Hence, the process designer can revise the sequences if necessary, and the control designer retrieves a logical description of the optimized process that can be automatically converted to control code.

This paper concerns the reduction of energy use in manufacturing industry. If individual robot movements in a system are preprocessed using Dynamic Programming, one can attain a Mixed Integer Nonlinear Program (MINLP) which models the energy consumption of the complete system. This model can then be solved to optimality using mathematical programming. We have previously shown proof of concept for this energy reduction method. In this paper, we apply state of the art MINLP methods to a number of problems in order benchmark their effectiveness. Algorithms used are Nonlinear Programming based Branch and Bound (NLP-BB), Outer Approximation (OA), LP/NLP based Branch and Bound (LP/NLP-BB) and Extended Cutting Plane (ECP). Benchmarks show that the NLP-BB does not perform well for nonlinear scheduling problems. This is due to the weak lower bounds of the integer relaxations. For scheduling problems with nonlinear costs, ECP and in particular LP/NLP-BB are shown to outperform both NLP-BB and OA. The resulting energy optimal schedules for the examples show a significant decrease in energy consumption.

The central theme of this Special Issue is emerging opportunities and future directions in automation for green manufacturing, where information technology based modeling, analysis, control and optimization are the focus areas. The purpose is to show the state-of-the-art research and applications in the general area of automation in green manufacturing, by bringing together researchers and practitioners from both academia and industry, to address the significant advancement, expose the unsolved challenges, present the critical needs for integration with new technologies, and provide visions for future research and development. This Special Issue presents original, significant and visionary automation papers describing scientific models, methods and technologies with both solid theoretical development and practical importance that improve process, efficiency, productivity, quality, and reliability in green manufacturing. The contributions in this Special Issue can be divided into the following categories in green manufacturing: renewable energy products and systems; energy savings in manufacturing processes and systems; remanufacturing system design; and emission reduction in supply chain management. Specifically, the following papers are included in this Special Issue. The first category addresses renewable energy source, which includes manufacturing and operation of alternative energy products, such as batteries for electric vehicles, and system design to support manufacturing activities using renewable energy sources, such as electricity generated by wind turbines and solar panels. The second category focuses on energy savings in manufacturing, such as operational control and robot scheduling to minimize energy consumptions in production, optimal design of facility and production to reduce energy cost. The third category extends the study to remanufacturing, which plays a significant role to achieve sustainability and multiple life cycles. This includes remanufacturing system analysis, design and optimization, such as strategies for recycling, reassembly, etc. Finally, the last category considers supply chain management, within which multiple firms work together to reduce negative environmental impact.

For flexible manufacturing systems, there are normally some durations in which a number of machines are idle and do not process any parts. Devising a control policy to turn off the idle machines and reduce their level of energy consumption is a significant contribution towards the green manufacturing paradigm. This paper addresses the design of such a control strategy for a closed-loop flow shop plant based on a one-loop pallet system. The main goal is to coordinate running of the machines and motion of pallets to gain the minimal energy consumption in idle machines, as well as to obtain the desired throughput for the plant. To fulfill this goal, first mathematical conditions, which economically characterize the on-off control for machines, are presented. Constrained to these conditions and the mathematical models describing the pallet system, a mixed integer nonlinear minimization problem with the energy monitor as the objective function is then developed. Provided that the problem computation time can be managed, the optimal control for the operation of the plant and the minimal energy consumption in the idle machines are computed. To deal with the time complexity, a linearized form of the model and a heuristic approach are introduced. These methods are applied to some examples of industrial size, and their impacts in practice are discussed and verified by using a discrete event simulation tool.

A proper control of a system to get a desired function and increase the system lifetime is a crucial step towards the sustainable paradigm. In this paper, such a control is designed for a cyclic pallet system to achieve a minimal force on its drive unit, meet safety conditions on the system chain tension force, and the momentum of pallets, and fulfill a desired production rate. The optimal values of control parameters, namely, number of pallets, conveyor velocity, and part set schedule, are obtained through solving a mixed integer linear optimization model. The objective function in the model defines the average force on the drive unit in a cycle production. In addition, the related constraints characterize the pallet system properties such as cyclic and dynamic behavior, buffer size, constant work in process, and safety specifications. This optimization model strongly suffers from the time complexity due to the binary decision variables defining the part set schedule. To reasonably handle the computation time, a heuristic search strategy based on a modified form of the weighted profile fitting algorithm is introduced. Furthermore, the robustness of the optimal control and the system design is analyzed, using worst control and worst but safe control strategies. The optimal control and the robustness analysis are applied to some case studies, and the results are evaluated and discussed.

The reduction of energy consumption is today addressed with great effort in manufacturing industry. In this paper, we improve upon a previously presented method for robotic system scheduling. By applying dynamic programming to existing trajectories, we generate new energy optimal trajectories that follow the same path but in a different execution time frame. With this new method, it is possible to solve the optimization problem for a range of execution times for the individual operations, based on one simulation only. The minimum energy trajectories can then be used to derive a globally energy optimal schedule. A case study of a cell comprised of four six-link manipulators is presented, in which energy optimal dynamic time scaling is compared to linear time scaling. The results show that a significant decrease in energy consumption can be achieved for any given cycle time.

In supervisory control theory, a supervisor restricts the plant in order to fulfill given specifications. A problem for larger industrial applications is that the resulting supervisor is not easily implemented and comprehensible for the users. To tackle this problem, an efficient method has recently been introduced to characterize a supervisor by tractable logic conditions, referred to as guards. This approach has been developed for a specific type of automata with variables called extended finite automata (EFAs). An extension of this approach to a more general class of models is presented in this paper. It means that classical supervisory control problems for automata and Petri nets are easily and efficiently solved, but also generalized based on the suggested approach. The synthesis procedure is naturally modeled and efficiently computed based on binary decision diagrams.

Lean design and control of an automated material handling system is investigated in this study. A universal framework for modeling and analysis of different types of material handling mechanisms is introduced to obtain a minimum number of resources in a system design and fulfill a desired throughput. This framework is developed in a discrete event simulation environment and applied to a case study based on a real pallet system technology. The minimal design of the pallet system is realized by devising the system universal model.

In order to perform efficient geometrical simulation and virtual commissioning in stamping, three fields are investigated namely: simulation building time, collision detection time and optimization time. Hence, reducing time is the main theme of this paper. To reduce simulation building time and optimization time, an efficient stamping simulation model is built and tested. Collision detection time is examined by a relative motion method based on 3D to 2D geometrical collision detection. The presented results mean that simulation and virtual commissioning can be performed at least ten times faster compared to standard approaches.

This paper presents a method for modeling robot and human resources in the context of assembly systems planning. In assembly systems, several redundant resources can be used to increase system flexibility. However, the “quality” of a sequence planning strongly depends on the “quality” of the system modeling. Furthermore, occurrence of unexpected events or variations in availability of resources may have significant impact on the actual planning. Instead of using simplistic models such as available or unavailable resources, the method presented in this paper proposes a more detailed modeling of resource abilities. Products and resources are considered on the same levels and matched together on a final step. The aim of this modeling is to permit analyses and to increase system flexibility.

The sequential behavior of a manufacturing system results from several constraints introduced during the product, manufacturing, and control logic development. This paper proposes methods and algorithms for automatically representing and visualizing this behavior from various perspectives throughout the development process. A new sequence planning approach is introduced that uses self-contained operations to model the activities and execution constraints. These operations can be represented and visualized from multiple perspectives using a graphical and formal language called Sequences of Operations (SOPs). The operations in a manufacturing system are related to each other in various ways, due to execution constraints expressed by operation pre- and post-conditions. These operation relations include parallel, sequence, arbitrary order, alternative, and hierarchy relations. Based on the SOP language, these relations are identified and visualized in various SOPs and sequences. A software tool, Sequence Planner, has been developed, for organizing the operations into SOPs that visualize only relevant operations and relations.

Reduction of energy consumption is important for reaching a sustainable future. This paper presents a novel method for optimizing the energy consumption of robotic manufacturing systems. The method embeds detailed evaluations of robots' energy consumptions into a scheduling model of the overall system. The energy consumption for each operation is modeled and parameterized as function of the operation execution time, and the energy-optimal schedule is derived by solving a mixed-integer nonlinear programming problem. The objective function for the optimization problem is then the total energy consumption for the overall system. A case study of a sample robotic manufacturing system and an experiment on an industrial robot are presented. They show that there exists a real possibility for a significant reduction of the energy consumption in comparison to state-of-the-art scheduling approaches.

Simulation-based programmable logic controller (PLC) code verification is a part of virtual commissioning, where the control code is verified against a virtual prototype of an application. With today's general OPC interface, it is easy to connect a PLC to a simulation tool for, e.g., verification purposes. However, there are some problems with this approach that can lead to an unreliable verification result. In this paper, four major problems with the OPC interface are described, and two possible solutions to the problems are presented: a general IEC 61131-3-based software solution, and a new OPC standard solution.

In flexible manufacturing systems a large number of operations need to be coordinated and supervised to avoid blocking and deadlock situations. The synthesis of such supervisors soon becomes unmanageable for industrial manufacturing systems, due to state space explosion. In this paper we therefore develop some reduction principles for a recently presented model based on self-contained operations and sequences of operations. First sequential operation behaviors are identified and related operation models are simplified into one model. Then local transitions without interaction with other operation models are removed. This reduction principle is applied to a synthesis of non-blocking operation sequences, where collisions among moving devices are guaranteed to be avoided by a flexible booking process. The number of states in the synthesis procedure and the computation time is reduced dramatically by the suggested reduction principle.

It is a great challenge to obtain an efficient algorithm for global optimisation of nonlinear, nonconvex and high dimensional objective functions. This paper shows how the combination of DIRECT and Nelder-Mead algorithms can improve the efficiency in the parameter tuning of a sheet-metal press line. A combined optimisation algorithm is proposed that determines and utilises all local optimal points from DIRECT algorithm as Nelder-Mead starting points. To reduce the total optimisation time, all Nelder-Mead optimisations can be executed in parallel. Additionally, a Collision Inspection Method is implemented in the simulation model to reduce the evaluation time. Altogether, this results in an industrially useful parameter tuning method. Improvements of an increased production rate of 7% and 40% smoother robot motions have been achieved.

Supervisory Control Theory (SCT) is a model based framework for automatically synthesizing a supervisor that minimally restricts the behavior of a plant such that a given specification is fulfilled. A problem, which prevents SCT from having a major breakthrough industrially, is that the supervisory synthesis often suffers from the state-space explosion problem. To alleviate this problem, a well-known strategy is to represent and explore the state-space symbolically by using Binary Decision Diagrams. Based on this principle, an efficient symbolic state-space traversal approach, depending on the disjunctive partitioning technique, is presented and the correctness of it is proved. Finally, the efficiency of the presented approach is demonstrated on a set of benchmark examples.

In the supervisory control theory, a supervisor is generated based on given plant and specification models. The supervisor restricts the plant in order to fulfill the specifications. A problem that is typically encountered in industrial applications is that the resulting supervisor is not easily comprehensible for the users. To tackle this problem, we introduce an efficient method to characterize a supervisor by tractable logic conditions, referred to as guards, generated from the models. The guards express under which conditions an event is allowed to occur to fulfill the specifications. To obtain tractable guard expressions, we reduce them by exploiting the structure of the given models. In order to be able to handle complex systems efficiently, the models are symbolically represented by binary decision diagrams and all computations are performed on these data structures. The algorithms have been implemented in a supervisory control tool and applied to an industrially relevant example.

A method for restart after an error in a manufacturing system is introduced. The method is able to restart systems even after nonforeseen errors that cannot be planned for, and the online part of the restart method does not require use of more powerful computers than a standard Programmable Logic Controller. It is shown what properties the control function must have to ensure that there is at least one restart state for each controller state. Sufficient conditions to guarantee the possibility to restart a system regardless of where an error occurs are given, along with indications on how the system could otherwise be rebuilt to be restartable.

In order to obtain a unified information flow from early product design to final production, an integrated framework for product, process and automation design is presented. The framework is based on sequences of operations and includes a formal relation between product properties and process operations. This relation includes liaisons (interfaces) and precedence relations, where the precedence relations generate preconditions for the related process operations. From this information a set of sequences of operations (SOPs) is generated. A formal graphical language for hierarchical operations and SOPs is then introduced and defined based on automata extended with variables. Since the operations are self-contained they can be grouped and viewed from different angles, e.g., from a product or a resource perspective. These multiple views increase the interoperability between different engineering disciplines. A case study is performed on a car manufacturing cell, where the suggested modeling framework is shown to give comprehensible SOPs.

In a cyclic manufacturing system, the number of pallets in a handling and locating pallet system (HLPS) can have a huge effect on the production cycle time. The complexity of calculating the optimal number of pallets that satisfies the minimal cycle time poses a challenging design problem. In this paper the optimal solution is presented for deterministic cyclic manufacturing systems having determined schedule of tasks for multi-product applications. Furthermore, a specific domain which includes the optimal number of pallets is obtained for any arbitrary schedule of various product types in an HLPS. To verify the optimal solution, a reconfigurable Colored Petri Net (CPN) model is developed for a simple X85 pallet system. For various cases of machine processing times, the optimal number of pallets is obtained for the suggested framework.

An off-line optimization of a sheet-metal press line is performed with improved production performances, both in terms of increased production rate and smoother robot motions. Smooth motions prevent the sheet-metal to slide out of position or even fall out, thereby causing lengthy down times. The simulation based method use a process optimizer connected to a time synchronized virtual manufacturing model including real industrial control systems, e.g. PLC. The key benefit herein is that all tuned control system parameters can be directly downloaded to the real press line without any post-processing or transformations. The challenge to find suitable optimization algorithms for a press line that handle highly nonlinear, discontinuous functions; considerable number of parameters; and long evaluation times is reached.

The aim of this paper is to show how the international standard STEP-AP214 can be used for communication and storing of process specifications. Even though there are several software tools available for the generation of both product and resource information systems, there is still a lack of tools related to the STEP standard for producing process information, e.g., sequence of operations and system capabilities for resource allocation. Therefore, such a tool is suggested, which makes use of a high-level language for discrete-event systems based on process algebra and Petri nets. This language, called process algebra Petri net), has been developed in accordance with the process relations defined in STEP-AP214. More specifically, it is shown how process specifications created with the PPN tool can be mapped to the STEP AP-214 format. Note to Practioners-Rapidly changing market needs is making demands on flexibility and ability to shorten lead times. Standards for exchanging information, as well as formal methods for automatic development of programmable controller code have been important research topics for many years. There are a lot of software tools available for the generation of both product- and resource information, but there is still a lack of tools for producing process information. Moreover, the connection between information exchange standards and such tools is absent, which makes the development of programmable controller code an isolated activity. This activity is often time consuming and performed in an ad hoc manner resulting in unnecessary production delay. The aim of this paper is to show how the international standard STEP-AP214 (a standard for exchange of product-, process-, and resource related information) can be used for communicating and storing process specifications. In order to achieve this, a tool which makes use of a formal high-level language is suggested. This tool can be used for automatic control generation and has been developed in accordance with the process relations defined in STEP-AP214. A further aim is to shown how the mentioned tool can be used to specify complex systems in a compact, yet highly readable manner, which is a crucial incentive for industry to use formal methods. The presented method will guarantee that expected information is delivered quickly and without errors caused by the human factor, something that is very important in our ambition to achieve shortened lead times. The quick information exchange also makes it possible to perform simulation, supervisor synthesis, and verification early in the development phase. This is a first attempt at using a formal language for creating a tool that can automatically generate specifications in accordance with the international STEP-standard.

This paper introduces the concept of a hybrid system and some of the challenges associated with the stability of such systems, including the issues of guaranteeing stability of switched stable systems and finding conditions for the existence of switched controllers for stabilizing switched unstable systems. In this endeavour, this paper surveys the major results in the (Lyapunov) stability of finite-dimensional hybrid systems and then discusses the stronger, more specialized results of switched linear (stable and unstable) systems. A section detailing how some of the results can be formulated as linear matrix inequalities is given. Stability analyses on the regulation of the angle of attack of an aircraft and on the PI control of a vehicle with an automatic transmission are given. Other examples are included to illustrate various results in this paper.

Based on some modeling primitives from automata, Petri nets and process algebra, an architecture for a general routing and resource booking problem is presented. The architecture is based on general models for a set of resources, desired routing specifications for a set of objects (products, data packets, vehicles) and a controller that synchronizes the objects utilization of the available resources. High level graphical routing specifications for the objects are also introduced, together with corresponding Petri nets, in order to simplify the specification of desired routes. Two specific operators, event synchronization and arbitrary order including an algebra of events, are then used in the formal Petri net specifications.

This paper presents part of the results from a research project aimed at increasing flexibility and reusability of cell-control software. First to be discussed are the concept of flexibility and the advantages and disadvantages of various types of modular controllers. Then guidelines, generic models that describe the behavior of manufacturing resources, and a message-passing structure are given. The guidelines and the models should be used as the basis to support system developers when implementing modular control software for machining cells. The two main case studies examined to achieve the models are also briefly described.