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This paper investigates the deployment issue of an emergency medical service (EMS) system to maintain the preferred service coverages under different considerations. Specifically, two coverage levels are introduced to reflect the requirements under the regular situation and the situation with ambulance unavailable. We propose the two-stage robust optimization (RO) models to design a reliable ambulance system subject to unavailability of the ambulances, with and without the ambulance relocation. For the RO problem with mixed-integer recourse for relocation, we customize the column and constraint generation method with an approximation strategy to handle the computational challenge. Our numerical study: 1) demonstrates that our RO formulations have a strong modeling capacity on designing the EMS system; 2) shows that our approximation algorithm performs very well; and 3) provides a quantitative evaluation of, including, relocation operations on the system performance. Note to Practitioners—This paper presents the novel optimization models to help ambulance deployment. Due to ambulance unavailability and relocation operations, traditional optimization formulations might not be sufficient for modeling or might be hard for computation. In this paper, we provide an uncertainty set-based approach to capture ambulance unavailability and to build the robust optimization models (with relocation recourse decisions). Also, efficient algorithms are designed to support practical instances. Numerical results are very supportive to our new models and computational methods. [ABSTRACT FROM AUTHOR]

Polyculture farming, where multiple crop species are grown simultaneously, has potential to reduce pesticide and water usage while improving the utilization of soil nutrients. However, it is much harder to automate polyculture than monoculture. To facilitate research, we present AlphaGardenSim, a fast, first order, open-access polyculture farming simulator with single plant growth and irrigation models tuned using real world measurements. AlphaGardenSim can be used for policy learning as it simulates inter-plant dynamics, including light and water competition between plants in close proximity and approximates growth in a real greenhouse garden at 25, $000\times $ the speed of natural growth. This paper extends earlier work with a new action space that includes planting, which dynamically finds new seed locations that increases resources utilization, and an adaptive sampling technique to reduce the number of actions taken at each timestep without affecting performance. We also evaluate other automation policies using a novel metric that combines plant diversity and canopy coverage. Code and supplementary material can be found at https://github.com/BerkeleyAutomation/AlphaGarden. Note to Practitioners—Monoculture farming is often characterized by heavy agrichemical inputs, such as chemical fertilizers and pesticides, and increased vulnerability to disease and pestilence. This paper is motivated by the lack of long-term sustainability of industrial agriculture, and its implications for human food security. Although polyculture is a sustainable alternative to monoculture farming, it requires more human labor and is more challenging to automate. In this paper we propose a fast, first order simulator that simulates the growth of plants in a polyculture setting. Simulation experiments suggest that the simulator can be used to learn a planting, watering and pruning plan a robot can follow to produce maximal yield from a diverse set of plants with limited irrigation, however it has not yet been tested on a physical garden. In future research we will develop a fully automated controller that will operate planting, irrigation and pruning tools in a physical garden over multiple plant growth cycles. [ABSTRACT FROM AUTHOR]

Inventory–production coordination for customer orders is becoming increasingly important for companies to increase customer responsiveness and achieve economic purposes. In this paper, the joint optimization of inventory and production is considered for stochastic customer orders to maximize the throughput. Demands of customer orders dynamically arrive at the inventory department, and each incoming order consists of multiple product types with random workloads. To process the workloads, certain amounts of a common raw material are required and need to be drawn from the inventory department. A customer order will be lost if there do not exist enough raw materials in the inventory department. With the necessary materials, workloads of accepted orders will be assigned to a set of unrelated parallel servers to be processed in the production department. This paper intends to maximize the effective throughput through proper coordination of the inventory and the production departments. For this problem, system bottlenecks are identified and analyzed, and mathematical programming models are developed to determine the optimal throughput and the corresponding inventory and production policies. Several special cases are also explored to provide intuitive insights into the relationship between the system parameters and optimal throughput. Relationships between key model parameters and effective throughput are identified through sensitivity analysis and further validated by the results of computational experiments. Note to Practitioners—Coordination of production and inventory is crucial for system efficiency improvement. In order to better operate the system and respond to customer demands, this paper explores such coordination for stochastic customer orders to achieve the maximum effective throughput. Particularly, we consider the case where production is constrained by material inventory. System bottlenecks for various cases are identified, which can facilitate system diagnosis and performance evaluation. Based on the bottleneck analysis, an optimization problem is formulated to provide the optimal throughput and policy. Several important practical cases are also discussed, including single product type, identical/uniform servers, and speed-identical/uniform product types. Besides, sensitivity analysis is conducted to show how the changes of parameters such as expected workload and server speed will affect throughput performance. In the future research, more complex production environments and cost-related constraints will be further considered to help practitioners achieve desirable system outputs. [ABSTRACT FROM AUTHOR]

The radio block center (RBC) is one of the most essential ground systems in a high-speed train control system both in Europe and in China. The RBC handover procedure is an important function of RBC, which affects the transport efficiency, reliability and safety of railways. Analysis of crucial states in the RBC handover procedure is helpful to determine whether there are potential risks in the procedure, and to locate the fault in time when a fault occurs. In this paper, we study a property, called C-detectability, of the RBC handover. This property has been defined in discrete event systems and requires that the crucial states can be determined uniquely by observing the system output. Taking the RBC handover procedure in the Chinese train control system level 3 (CTCS-3) as an example, we first model the RBC handover procedure using Petri nets, which are a graphical and mathematical modeling tool to formalize the behavior of discrete event systems. Then, based on the notion of basis reachability graph, an efficient approach is used to check C-detectability of the Petri net modeling the handover procedure. Note to Practitioners—The railway system is a safety-critical system. System safety is essential to the railway system. It is necessary to provide formal methods to model the system and analyze its properties. The motivation of this work is to present a general modeling framework of railway systems for the crucial states estimation. The crucial states estimation of the railway system has not been discussed yet in the literature despite its importance due to its relationship with the safety of the railway. In particular, estimating the crucial states in the railway system helps to determine whether there are potential risks in the system. In this paper, we investigate a new state estimation property (C-detectability), which is related to fault diagnosis, marking estimation and fault identification. Future research will consider the use of timed Petri nets to increase the modeling power and to allow the performance analysis of the railway system. [ABSTRACT FROM AUTHOR]

This paper presents a controlled tying approach for knots using fixtures and simple pulling motions applied to the ends of string. Each fixture is specific to a particular knot; the paper gives a design process that allows a suitable fixture to be designed for an input knot. Knot tying is separated into two phases. In the first phase, a fixture is used to loosely arrange the string around a set of rods, with the required topology of the given knot. In the second phase, the string is pulled taut around the tightening fixtures. Two tightening fixture designs are presented. The first design is a fixture with no moving parts; tilted rods whose cross-sections get closer near the tips, guiding string in a controlled fashion as string slides up the rods during tightening. The second design is a collection of straight rods that can move passively along predefined paths during tightening. Successful tying is shown for three interesting cases: a “cloverleaf knot” design, a “double coin” knot design, and the top of a shoelace knot. [ABSTRACT FROM AUTHOR]

Battery management system has attracted mounting research attention recently, within which cell equalization plays a key role. Although many research and practices have been devoted to developing various structures of cell equalizers, there are still substantial opportunities for performance improvement yet to investigate. In particular, mathematical modeling and systematic analysis of equalizer systems are limited. In this paper, the performance analysis of the modularized global equalizer system for Lithium-ion battery cell equalization is conducted analytically. Specifically, a mathematical model is developed to emulate the equalization dynamics by considering both charging/discharging and energy loss. Analytical formulas are derived to evaluate the performance of the global equalizer. The introduced model is also compared with the state-of-the-art structures in terms of equalization speed and energy loss. Numerical studies show that the modularized global equalization outperforms others by its substantial reduction on energy loss with similar equalization performance and much less equalizers. In addition, a module segmentation guide is provided to facilitate the equalization system design. [ABSTRACT FROM AUTHOR]

This paper aims to devise efficient combination chemotherapy schedules that determine the dosages of drugs administered to cancer patients with drug resistance that, as a Gordian knot to cancer chemotherapy, may weaken the efficacy of chemotherapy. To characterize cell growth, we use the existing cell cycle-specific model, in which the mechanism of acquired drug resistance is incorporated. Subsequently, the determination of the optimal chemotherapy schedule for the patients is formulated as a nonlinear optimization problem, with the objective of minimizing not only the quantity of tumor cells but also the posttreatment chemotherapy-induced toxicity. To overcome the difficulty in finding a satisfactory solution to the problem due to its nonlinear nature, we develop a memetic algorithm (MA) with an advanced local search strategy. The efficiency of the proposed MA is validated by comparison with other state-of-the-art methods. In addition, we compare the best found solution to the problem in the presence of drug resistance with that in the absence of drug resistance. The resultant findings reveal that drug resistance is a crucial factor in the determination of chemotherapy schedules. Note to Practitioners—The efficacy of chemotherapy is limited by drug toxicity and is weakened by drug resistance. This paper deals with the problem of optimization of chemotherapy schedules with mathematical modeling and heuristic algorithms and is the first to consider the drug specificity, drug combination, and drug resistance simultaneously. The proposed algorithm is flexible. It can be applied to other optimization problems with proper adaptations. The experimental results suggest that the best found solution to the problem in the absence of drug resistance may, however, not remain as the best found solution, or even become an infeasible one, in the presence of drug resistance. Existing clinical regimens may be improved with the assistance of our method that aims to optimize the chemotherapy schedule in the presence of drug resistance. The parameters of our model should be refined with experimental and clinical data to confer more benefits on real treatments in clinical practice in the future. [ABSTRACT FROM AUTHOR]

Deep drawing is a metalworking procedure aimed at getting a cold metal sheet plastically deformed in accordance with a pre-defined mould. Although this procedure is well-established in industry, it is still susceptible to several issues affecting the quality of the stamped metal products. In order to reduce defects of workpieces, process control approaches can be performed. Typically, process control employs simple proportional-integral-derivative (PID) regulators that steer the blank holder force (BHF) based on the error on the punch force. However, a single PID can only control single-input single-output systems and cannot handle constraints on the process variables. Differently from the state of the art, in this paper we propose a process control architecture based on Model Predictive Control (MPC), which considers a multi-variable system model. In particular, we represent the deep drawing process with a single-input multiple-output Hammerstein-Wiener model that relates the BHF with the draw-in of $n$ different critical points around the die. This allows the avoidance of workpiece defects that are due to the abnormal sliding of the metal sheet during the forming phase. The effectiveness of the proposed process controller is shown on a real case study in a digital twin framework, where the performance achieved by the MPC-based system is analyzed in detail and compared against the results obtained through an ad-hoc defined multiple PID-based control architecture. Note to Practitioners—This work is motivated by the emerging need for the effective implementation of the zero-defect manufacturing paradigm in the Industry 4.0 framework. Especially in the deep drawing process, various quality issues in stamped parts can lead to significant product waste and manufacturing inefficiencies. This turns into considerable economic losses for companies, particularly in the automotive sector, where deep drawing is one of the most used cold sheet metal forming techniques. In most applications, only sample inspections are performed on batches of finished-product, with subsequent losses of time and resources. For the sake of improving the workpiece quality, innovative strategies for real-time process control represent a viable and promising solution. In this context, the proposed MPC-based process control approach allows the correct shaping of the metal sheet that is getting deformed during the forming stroke, thanks to the draw-in monitoring at various locations around the die. The draw-in is indeed one of the most effective forming variables to control in order to provide a correct BHF during the forming stroke. A useful and easy-to-implement non-linear metal sheet deep drawing process model is provided by this paper to perform an innovative process control strategy. A comprehensive methodology is applied in detail to an automotive case study, ranging from process modeling (model identification and validation based on experimental data acquisition) to MPC implementation (controller tuning and testing and software-in-the-loop system validation). The presented method can be easily implemented on any real deep drawing press, providing the multivariable constrained process with a suitable control system able to make the stamped parts well formed. [ABSTRACT FROM AUTHOR]

Swarm foraging is a common test case application for multi-robot systems. In this paper RepAtt algorithm is used for improving coordination of a robot swarm by selectively broadcasting repulsion and attraction signals. This is a chemotaxis-inspired search behaviour where robots use the temporal gradients of these signals to navigate towards more advantageous areas. Hardware experiments were used to model and validate realistic, noisy sound communication and vision system. We then show through extensive simulation studies that RepAtt significantly improves swarm foraging time and robot efficiency under realistic communication and vision models. Note to Practitioners—This research developed a swarm foraging algorithm that takes into consideration the vision and communication sensing noise levels faced by robots in real world applications. The algorithm, known as RepAtt, was developed with the aim of emphasizing algorithmic simplicity and limiting the hardware requirements for the robots in the swarm. In this paper, we have focused on the problem of deploying swarm robots to forage litter in an environment such as a park. The communication model of the robots was based on the physics of sound, while their vision system was modelled using experiments with deep neural networks based object detectors. The results show that the RepAtt algorithm is robust to different distributions of targets (or litter) in the search space, exhibits good swarm efficiency with changes in swarm population and is robust to noise in its communication and vision systems. Apart from the RepAtt algorithm, other contributions made by this research include modelling of robot vision system to aid extensive study of the impact of communication and vision noise on swarm coordination. This will be relevant for extensive testing and validation before deployment to swarm robots hardware. The sound communication used in this research limits the kinds of environment the robots can be deployed in. Echoes within an enclosed environment and bandwidth limitation for communication frequency and public disturbance due to sound emitted by the robots can all contribute to this limitation. Thus, this research can be improved by investing in the development of a communication technology with similar physics. Other areas of improvement include adopting better obstacle avoidance algorithms and implementing suitable manipulators for handling litter objects. The algorithm can be extended to make it applicable for solving other problems such as search and rescue operations where foraging targets could be disaster survivors; demining and hazardous waste cleanup, where targets are the mines or waste material; and planetary exploration, where targets could be interesting features of the planets are the targets searched for by the robots. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic pricing mechanism in the industrial park with demand response programs. A Lagrangian relaxation based dynamic pricing model for electricity and thermal coupled industrial park is formulated, taking into account energy balance, feeder exchange and other systems operating constraints. Considering two-markets clearing mechanism and two types of demand response programs, a dynamic prices prediction model is proposed by long short-term memory (LSTM) technique. Based on the prediction model, we proposed a real-time prices spike detection model for industrial park, which can detect prices spike hourly by history data and give rolling prices spike warning for next short-term operating horizon. Simulation experiments validate the theoretic results and show the effectiveness of the dynamic prices spike detection model. Note to Practitioners—This paper focuses on the dynamic pricing mechanism and prices spike detection for the customers in the industrial park. We improve the pricing model based on the Lagrangian relaxation method and develop a dynamic prices prediction model to handle the uncertainty in real-time. Furthermore, we develop a prices spike detection mechanism, which can achieve rolling detect whether the electricity and thermal prices may exceeded the threshold in the next short-term operating horizon. This technique can give the customers a prices spike early warning service and let them to reschedule their own strategy to minimize their operation cost with respect to the uncertainties in the energy price. Experimental results show that the proposed prices spike detection mechanism can issue spike warnings correctly in most supply-demand mismatching cases. [ABSTRACT FROM AUTHOR]

This paper presents an urban-decision support system (U-DSS) devoted to manage, in a unified framework, the logistic services of the smart cities, such as postal delivery (PD) and waste collection (WC) services. The U-DSS architecture is proposed by describing its main components. In particular, this paper focuses on the core of the U-DSS, i.e., the model component that provides the solutions of a general vehicle assignment and routing optimization problem with the aim of minimizing the length of the routes and satisfying time and capacity constraints. In order to solve the vehicle routing problems in reasonable time, a two-phase heuristic algorithm is proposed based on a clustering strategy and a farthest insertion heuristic for the solution of a traveling salesman problem. The applicability of the proposed U-DSS is enlightened by comparing the proposed heuristic algorithm solutions with the mixed integer linear programming problem solutions of the PD and WC services. Moreover, the discussion of the real case studies of the city of Bari (Italy) assesses the proposed approach. [ABSTRACT FROM PUBLISHER]

This paper describes hybrid model predictive controllers that switch between two predictor functions based on the uncontrollable divergence metric. The uncontrollable divergence metric relates the computational capabilities of the model predictive controller, to the error of the system due to model mismatch of the predictor function during computation of the solution. The contribution of this paper is in its treatment of the model predictive controller to permit optimization to take multiple timesteps to occur, but still rely on the uncontrollable divergence metric. The results demonstrate the approach for control of a vertical takeoff-and-landing aerial vehicle. [ABSTRACT FROM AUTHOR]

Serial lines and assembly systems are two of the most fundamental production system structures used in practical manufacturing environments. While serial lines have been studied extensively in production systems literature and practice, assembly systems have received much less attention. Among existing results on assembly systems, most are focused on steady state performance evaluation, system properties, and improvement. On the other hand, transient behavior of such systems remains largely unexplored. In the framework of assembly systems with Bernoulli machines and finite buffers, this paper develops a mathematical model for transient analysis and derives closed-form formulas to calculate the real-time production rate, consumption rates, work-in-process, and probabilities of machine starvation and blockage, during transients. Then, an improved computationally efficient algorithm for transient performance evaluation in Bernoulli serial lines is proposed. Based on the improved algorithm, an effective and efficient method for transient performance evaluation in Bernoulli assembly systems is derived. Finally, the method is extended to complex assembly system structures. [ABSTRACT FROM AUTHOR]

Timed event graphs (TEGs) are suitable to model manufacturing systems in which synchronization and delay phenomena appear. Since 1980s, TEGs are studied as a class of linear discrete event systems in idempotent semirings such as the (min,+) algebra. In this paper, we consider the class of weighted TEGs (WTEGs) that corresponds to TEGs where the edges have integer weights. By introducing nonunitary weights, WTEGs widen the class of manufacturing systems that can be modeled, especially systems with batches and duplications. Moreover, a subclass of WTEGs called weight-balanced TEGs (WB-TEGs) can be studied with the algebraic tools that stem from the theory of (min,+) linear systems. In this paper, the focus lies on some modeling issues for manufacturing systems. Besides cutting and palletization operations, it is shown that WB-TEGs are also well adapted to describe periodic routing policies and, in a symmetrical way, how to merge flows similar to a multiplexer. In order to simplify the modeling step, a class of cycloweighted TEGs (CW-TEGs) is introduced. It is an extension of WTEGs where the weights of the edges can change according to a periodic sequence. Finally, we propose some elements of modeling that can be described by CW-TEGs or equivalently with an input–output transfer relation in an appropriate idempotent semiring of operators. Note to Practitioners— The (min,+) linear system theory used in this paper aims at obtaining linear models for a subclass of man-designed systems, such as automated manufacturing systems or traffic networks. This theory has many analogies with the conventional linear system theory (for continuous systems) and it provides the basis to develop a specific control theory for man-made systems. More precisely, the theory of (min,+) linear systems is well suited to systems where the prevailing phenomena are synchronizations, delays, duplications, and batches. These phenomena arise, for example, in operations such as assembly/matching, cutting/lot splitting, and palletization/lot making. Among the possible representations, we can describe these systems by transfer functions obtained by the combination of a finite number of basic operators. This is analogous to block diagram in the conventional system theory, i.e., a transfer function describes the complete input-output behavior of a system. In the context of manufacturing systems, a transfer function describes the way a system maps an input flow of materials (raw part inputs) into an output flow (finished parts), without the necessity of simulation tools to predict this. Moreover, the transfer function thus obtained can be used to compute controllers in order to regulate the internal flows of a system, for instance to decrease internal stocks. In the case of an automated system, the obtained controllers can be implemented on a programmable logic controller as supplementary code. This paper focuses on the use of these algebraic tools in the model process of manufacturing systems and, in particular, on their ability to describe splitting and merging flows of materials. [ABSTRACT FROM PUBLISHER]

Piezo-actuated stages have become more and more promising in nanopositioning applications due to the excellent advantages of the fast response time, large mechanical force, and extremely fine resolution. Modeling and control are critical to achieve objectives for high-precision motion. However, piezo-actuated stages themselves suffer from the inherent drawbacks produced by the inherent creep and hysteresis nonlinearities and vibration caused by the lightly damped resonant dynamics, which make modeling and control of such systems challenging. To address these challenges, various techniques have been reported in the literature. This paper surveys and discusses the progresses of different modeling and control approaches for piezo-actuated nanopositioning stages and highlights new opportunities for the extended studies. [ABSTRACT FROM AUTHOR]

This paper presents a novel fault detection approach for industrial batch processes. The batch processes under consideration are characterized by the interaction between discrete system modes and non-stationary continuous dynamics. Therefore, a stochastic hybrid process model (SHPM) is introduced, where process variables are modeled as time-variant Gaussian distributions, which depend on hidden system modes. Transitions between the system modes are assumed to be either autonomous or to be triggered by observable events such as on/off signals. The model parameters are determined from training data using expectation-maximization techniques. A new fault detection algorithm is proposed, which assesses the likelihoods of sensor signals on the basis of the stochastic hybrid process model. Evaluation of the proposed fault detection system has been conducted for a penicillin production process, with the results showing a significant improvement over the existing baseline methods.Note to Practitioners—Automatic fault detection makes it possible to limit the effects of faults by taking countermeasures at an early stage. In this work, a data-driven fault detection method for industrial batch processes is proposed, in which the underlying process model is learned from training data. The proposed fault detection system can be used for various industrial batch processes without the need for complex and error-prone manual configuration. In contrast to many other data-driven approaches such as neural networks, only a few process cycles are required to create a robust process model. It should be noted that in data-driven fault detection methods, the training data should cover a large part of the process states that occur during error-free process cycles. The developed method is therefore particularly suitable for cyclical processes, which, however, can have alternative process paths and variability between the process cycles. [ABSTRACT FROM AUTHOR]

Recycling, reusing, and remanufacturing of end-of-life (EOL) products have been receiving increasing attention. They effectively preserve the ecological environment and promote the development of economy. Disassembly sequencing and line balancing problems are indispensable to recycling and remanufacturing EOL products. A set of subassemblies can be obtained by disassembling an EOL product. In practice, there are many different types of EOL products that can be disassembled on a disassembly line, and a high-level uncertainty exists in the disassembly process of those EOL products. Hence, this paper proposes a stochastic multi-product multi-objective disassembly-sequencing-line-balancing problem aiming at maximizing disassembly profit and minimizing energy consumption and carbon emission. A simulated annealing and multi-objective discrete grey wolf optimizer with a stochastic simulation approach is proposed. Furthermore, real cases are used to examine the efficiency and feasibility of the proposed algorithm. Comparisons with multi-objective discrete grey wolf optimization, non-dominated sorting genetic algorithm II, Multi-population multi-objective evolutionary algorithm, and multi-objective evolutionary algorithm demonstrate the superiority of the proposed approach. Note to Practitioners—Disassembly line balancing has been widely recognized as the most ecological way of retrieving EOL products. Through in-depth research, we present a Stochastic Multi-product Multi-objective Disassembly-sequencing-line-balancing Problem. Furthermore, we consider that the uncertainty of products might cause disassembly failure. To solve this problem effectively and quickly, we combine the simulated annealing algorithm with the Grey Wolf Optimizer. The results show that the algorithm can effectively solve the proposed problem. The disassembly scheme provided by the obtained solution set offers a variety of options for decision-makers. [ABSTRACT FROM AUTHOR]

Improving quality in large volume battery manufacturing systems for hybrid and electric vehicles is of significant importance. In this paper, we present a flow model to analyze and improve product quality in electrical vehicle battery assembly lines with 100% inspections and repairs for defective parts. Specifically, a battery assembly line consisting of multiple inspection stations is considered. After each inspection, defective parts will be repaired and sent back to the line. A quality flow model is introduced to analyze quality propagations along the battery production line. Analytical expressions of final product quality are derived and structural properties, such as monotonicity and sensitivities, are investigated. A bottleneck identification and mitigation method is introduced to improve quality performance. Finally, a case study is presented to illustrate the applicability of the method. [ABSTRACT FROM PUBLISHER]

Water environment system is a complex system, and an agent-based model presents an effective approach that has been implemented in water resource management research. Urban water resource optimal allocation is a challenging and critical issue in water environment systems, which belongs to the resource optimal allocation problem. In this paper, a novel approach based on multiagent Q-learning is proposed to deal with this problem. In the proposed approach, water users of different regions in the city are abstracted into the agent-based model. To realize the cooperation among these stakeholder agents, a maximum mapping value function-based Q-learning algorithm is proposed in this study, which allows the agents to self-learn. In the proposed algorithm, an adaptive reward value function is used to improve the performance of the multiagent Q-learning algorithm, where the influence of multiple factors on the optimal allocation can be fully considered. The proposed approach can deal with various situations in urban water resource allocation. The experimental results show that the proposed approach is capable of allocating water resource efficiently and the objectives of all the stakeholder agents can be successfully achieved. [ABSTRACT FROM PUBLISHER]

Provides instructions and guidelines to prospective authors who wish to submit manuscripts. [ABSTRACT FROM AUTHOR]

Crude oil transportation is a central logistics operation in petrochemical industry because its cost represents a significant part in the cost of petrochemical products. In this paper, we consider the transportation by tankers or trucks. We show that under some realistic assumptions, this problem can be transformed into a single item lot sizing problem with limited production and inventory capacities. We develop a strongly polynomial dynamic programming algorithm to solve it. The problem of crude oil transportation is very difficult. There are few efficient methods in this domain. In the model considered in this paper, crude oil is directly shipped from a supplier port to n client ports to satisfy customer demands over T future periods. The supplier port disposes a fleet of identical tankers with limited capacity. The inventory capacities of customers are limited and time-varying. The backlogging is admitted. The objective is to find an optimal shipment plan minimizing the total cost over the T-period horizon. When the number of tankers is unlimited and customer demands are independent, shipment plans of different customers become independent. This problem can be considered as n independent problems. Each of them can be transformed into a single item lot sizing problem with limited production and inventory capacities, where tanker capacity corresponds to production capacity in classical lot sizing models. The main contributions of this paper are: 1) transformation of a transportation planning problem into a lot-sizing problem; 2) an O(T^3) algorithm is proposed to solve it; and 3) the results can also be applied to terrestrial transportation with direct deliveries. [ABSTRACT FROM PUBLISHER]

This paper introduces a new type of automated container terminal system, which utilizes multistory frame bridges and rail-mounted trolleys to transport containers between the quay and the yard. For this new design concept, this paper makes an explorative study to identify the challenges and opportunity for it to be applied in transshipment hubs. We compare the transport efficiency and stacking capacity between this new system and the widely used AGV-based system. Several analytical models and performance measures are proposed for the quantitative comparisons between these two ACT systems. [ABSTRACT FROM PUBLISHER]

Failure mechanisms of electromechanical systems usually involve several degraded health-states. Tracking and forecasting the evolution of health-states and impending failures, in the form of remaining-useful-life (RUL), is a critical challenge and regarded as the Achilles' heel of condition-based-maintenance (CBM). This paper demonstrates how this difficult problem can be addressed through Hidden Markov models (HMMs) that are able to estimate unobservable health-states using observable sensor signals. In particular, implementation of HMM based models as dynamic Bayesian networks (DBNs) facilitates compact representation as well as additional flexibility with regard to model structure. Both regular HMM pools and hierarchical HMMs are employed here to estimate online the health-state of drill-bits as they deteriorate with use on a CNC drilling machine. Hierarchical HMM is composed of sub-HMMs in a pyramid structure, providing functionality beyond an HMM for modeling complex systems. In the case of regular HMMs, each HMM within the pool competes to represent a distinct health-state and adapts through competitive learning. In the case of hierarchical HMMs, health-states are represented as distinct nodes at the top of the hierarchy. Monte Carlo simulation, with state transition probabilities derived from a hierarchical HMM, is employed for RUL estimation. Detailed results on health-state and RUL estimation are very promising and are reported in this paper. Hierarchical HMMs seem to be particularly effective and efficient and outperform other HMM methods from literature. [ABSTRACT FROM AUTHOR]

Wheeled mobile robots (WMRs) are often used for maintenance of round pipes or ducts, which can typically be represented as a cylindrical workspace. Working in round pipes or ducts, kinematic models of WMRs are different from those applying on a plane and thus pose significant challenges in terms of kinematic analysis and motion control. To address these challenges, the kinematic properties of WMRs in a cylindrical workspace are analyzed in this paper. First, we discuss the kinematic properties of a single wheel in a cylindrical workspace. Then, we analyze the geometric constraints of WMRs in round pipes or ducts with analytical geometry. Based on these analyses, kinematic properties of WMRs in cylindrical workspaces are discussed with screw theory. A control law based on biaxial clinometer information is proposed, and it enables the robot to move horizontally in round pipes or ducts. Finally, the motion of a single wheel purely rolling in a cylindrical workspace is simulated. Experiments using a car-like mobile robot moving in round ducts are carried out to show the feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a general modeling framework for Intermodal Freight Transport Terminals (IFTTs). The model allows simulating and evaluating the performance of such key elements of the intermodal transportation chain. Hence, it may be used by the decision maker to identify the IFTT bottlenecks, as well as to test different solutions to improve the IFTT dynamics. The proposed modeling framework is modular and based on timed Petri Nets (PNs), where places represent resources and capacities or conditions, transitions model inputs, flows, and activities into the terminal and tokens are intermodal transport units or the means on which they are transported. The model is able to represent the different types of existing IFTTs. Its effectiveness is tested first on an example from the literature and then on a real case study, the railroad inland terminal of a leading Italian intermodal logistics company, showing its ease of application. In the real case study, using the proposed formalism we test the as-is IFTT performance and evaluate alternative possible to-be improvements in order to identify and eliminate emerging criticalities in the terminal dynamics. [ABSTRACT FROM AUTHOR]

On the hospital floor, prompt detection and appropriate treatment of clinical deterioration of ward patients are essential for successful rescue. In this paper, a continuous time Markov chain model is presented to describe the ward patient status and analyze the patient rescue processes, which are characterized by the transitions between different patient states, such as risk, non-risk, intervention by care providers (nurse, physician, rapid response team), or elevation to intensive care, etc. Closed formulas to calculate the probability of the patient in different states are developed for single patient case. A system-theoretic method, referred to as shared resource iteration (SRI), is developed to study the multiple patients scenario. It is justified that such an iterative method is convergent and results in a high accuracy in estimation of patient state probabilities through numerical experiments. Moreover, monotonic properties have been investigated to provide guidance for continuous improvement. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of designing in-vehicle driver-assist systems that warn or override the driver to prevent collisions with a guaranteed probability. The probabilistic nature of the problem naturally arises from many sources of uncertainty, among which the behavior of the surrounding vehicles and the response of the driver to on-board warnings. We formulate this problem as a control problem for uncertain systems under probabilistic safety specifications and leverage the structure of the application domain to reach computationally efficient implementations. Simulations using a naturalistic data set show that the empirical probability of safety is always within 5% of the theoretical value in the case of direct driver override. In the case of on-board warnings, the empirical value is more conservative due primarily to drivers decelerating more strongly than requested. However, the empirical value is greater than or equal to the theoretical value, demonstrating a clear safety benefit. [ABSTRACT FROM AUTHOR]

While flow line models have been studied for decades, excepting some cases with three servers or less, their equilibrium probabilities remain elusive. As such, approximations based on aggregation or decomposition methods are generally employed. In this paper, by focusing on flow lines with deterministic service durations and a renewal arrival process, we develop exact methods for steady-state analysis. Our starting point is the investigation of recursions for customer delay based on exact decomposition methods. We demonstrate that the delay a customer faces in each server possesses a Markovian property. For discrete-time flow lines, we obtain a multidimensional discrete-time time-homogeneous Markov chain for the delays; there are an infinite number of balance equations for the equilibrium probabilities. Exploiting a similarity between our system and the GI/D/1 queue allows us to reduce these to a finite number of balance equations that can be solved numerically. We also investigate the implications for continuous-time flow lines and consider an example inspired by production time windows in semiconductor manufacturing. To our knowledge, these are the first results that allow one to exactly obtain the equilibrium probabilities in flow lines consisting of more than three servers. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a two-step bloodmobile collection planning framework. The first step is the annual planning to determine weeks of collection at each mobile site in order to ensure regional self-sufficiency of blood supply. The second step is the detailed weekly planning to determine days of collections at each mobile site and to form corresponding transfusion teams. Only key resource requirements are considered for annual planning while detailed resource requirements and transportation times are considered for weekly planning. Two Mixed Integer Programming models are proposed for annual planning by assuming fixed or variable mobile collection frequencies. A new donation forecast model is proposed based on population demographics, donor generosity, and donor availability. A new concept of bloodmobile collection configurations is proposed for compact and efficient mathematical modeling of weekly planning in order to minimize the total working time. Field data from the French Blood Service (EFS) in the Auvergne-Loire Region are used to design numerical experiments and to assess the efficiency of the proposed models. [ABSTRACT FROM PUBLISHER]

This paper describes an adaptable system which is able to perform manipulation operations (such as Peg-in-Hole or Laying-Down actions) with flexible objects. As such objects easily change their shape significantly during the execution of an action, traditional strategies, e.g., for solve path-planning problems, are often not applicable. It is therefore required to integrate visual tracking and shape reconstruction with a physical modeling of the materials and their deformations as well as action learning techniques. All these different submodules have been integrated into a demonstration platform, operating in real-time. Simulations have been used to bootstrap the learning of optimal actions, which are subsequently improved through real-world executions. To achieve reproducible results, we demonstrate this for casted silicone test objects of regular shape. [ABSTRACT FROM AUTHOR]

This paper examines the problem of determining the distribution of a number of indistinguishable moving bodies located in regions separated by sensor beams that can detect whether a body moves across them. We characterize the conditions under which an exact distribution of bodies can be determined, and compute bounds on the expected number of sensor observations required to determine this exact distribution for a certain movement model of the bodies. [ABSTRACT FROM AUTHOR]

Despite a large number of proofs of concept in nanotechnologies (e.g., nanosensors), nanoelectromechanical systems (NEMS) hardly come to the market. One of the bottlenecks is the packaging of NEMS which require handling, positioning, assembling and joining strategies in the mesoscale (from 100 nm to 10 \ \mum, between nanoscale and microscale). It requires models of the interaction forces and adhesion forces dedicated to this particular scale. This paper presents several characteristics of the mesoscale in comparison with nanoscale and microscale. First, it is shown that the distributions of charges observed on the micro-objects and meso-objects would have negligible effects on the nano-objects. Second, the impact of both chemical functionalization and physical nanostructuration on adhesion are presented. Third, the van der Waals forces are increased by local deformations on the mesoscale contrary to the nanoscale where the deformation is negligible. This paper shows some typical characteristics of the mesoscale. [ABSTRACT FROM AUTHOR]

This paper aims to provide a solution method for the real-world hybrid flowshop scheduling problem resulting from a steelmaking process, which has important applications in modern iron and steel industry. We first present a mixed integer mathematic model based on a comprehensive investigation. Then, we develop a heuristic method and two improvement procedures for a given schedule based on the problem-specific characteristics. Finally, we propose an effective artificial bee colony (ABC) algorithm with the job-permutation-based representation for solving the scheduling problem. The proposed ABC algorithm incorporates the heuristic and improvement procedures as well as new characteristics including a neighboring solution generation method and two enhanced strategies. To evaluate the proposed algorithm, we present several adaptations of other well-known and recent metaheuristics to the problem and conduct a serial of experiments with the instances generated according to real-world production process. The results show that the proposed ABC algorithm is more effective than all other adaptations after comprehensive computational comparisons and statistical analysis. [ABSTRACT FROM PUBLISHER]

Remanufacturing is a practice of growing importance due to increasing environmental awareness and regulations. However, little research focuses on stochastic remanufacturing process routings (RPR). This paper presents an analytical method, where four Graphical Evaluation and Review Technique (GERT)-based RPR models are proposed to mathematically represent and analyze the variability of remanufacturing task sequences. In particular, with the method, the probability of individual processes being taken in a remanufacturing system and the time associated with them can be efficiently determined. The proposed method is demonstrated through the remanufacturing of used lathe spindles and telephones, and verified by Arena simulation. Numerical experiments that investigate the relationships between RPR dynamics and other system parameters (such as inventory control for due-time performance and time buffer size for bottleneck control) are included. [ABSTRACT FROM AUTHOR]

In this paper, we apply control-theoretic approach to the design of inventory policy for systems with perishable goods. In the considered systems, the stock used to fulfill unknown, variable demand is subject to exponential decay. It is replenished from multiple supply sources characterized by different lead times. The challenge is to achieve high demand satisfaction with minimum costs when replenishment orders are realized with non-negligible delay. In contrast to the classical stochastic, or heuristic approaches, a formal design methodology based on discrete time sliding mode (DSM) control is employed. The proposed DSM controller with the sliding plane selected for a dead-beat scheme ensures full demand satisfaction for arbitrary bounded demand. It achieves a given service level with smaller holding costs and reduced order-to-demand variance ratio as compared to the classical order-up-to policy. [ABSTRACT FROM PUBLISHER]

The development and analysis of increasingly complex systems require the intensive use of models and of sophisticated approaches to systems modeling. This paper focuses on workflows supporting the solution of complex, composed, formal models used to study and/or develop real-world systems. The workflows we deal with orchestrate multiple distributed tools and applications in order to provide the user with a powerful, composed solution environment. The aim is to automate and reproduce analysis and simulation tasks starting from a high level, graph-based description of the model to be solved. This paper thus introduces solution workflows and presents the Solution Process Definition Language (SPDL) for the specification of solution workflows processes. One of the key elements of SPDL is its formal semantics, which allow for unambiguous specification of its constructs and validation of the workflows. A workflow pattern analysis of SPDL is also provided. SPDL and its execution environment, the OsMoSys framework, are then applied to a homeland security scenario. The OsMoSys framework and the SPDL language provide a practical contribution to the applicability of model engineering techniques by enabling the semiautomatic solution of complex models. [ABSTRACT FROM PUBLISHER]

The paper specifies an Integrated System (IS) devoted to the management of Intermodal Transportation Networks (ITNs) to take both tactical decisions, i.e., in an offline mode, and operational decisions, i.e., in real-time. Both the resulting IS structures rely on a closed-loop approach that is able to tune the choices with the current system conditions. In either case, the core of the presented IS are a reference model and a simulation module. In particular, the reference model uses information from the real system, obtained by modern Information and Communication Technologies (ICTs) and the simulation module evaluates the impact of the management decisions. In order to obtain a systematic model suitable to describe a generic ITN, the paper proposes a metamodeling approach that describes in a thorough and detailed way the structure and the behavior of ITNs. Moreover, the metamodeling procedure is a top-down technique based on the well-known Unified Modeling Language (UML), a graphic and textual formalism able to describe systems from structural and behavioral viewpoints. In order to show the IS application at the tactical decision level, the paper specifies the IS for an ITN case study that is constituted by the port of Trieste (Italy) and the inland terminal of Gorizia (Italy). The results show how the IS can improve the performance of the ITN by applying ICT tools and information-based services. [ABSTRACT FROM PUBLISHER]

Concurrent engineering (CE) is a methodology applied to product lifecycle development so that high quality, well designed products can be provided at lower prices and in less time. Many research works have been proposed for efficiently modeling of different domains in CE. However, an integration of these works with consistent data flow is absent and still in great demand in industry. In this paper, we present a generic integration framework with a semantic feature model for knowledge representation and reasoning across domains in CE. An implementation of the proposed semantic feature model is presented to demonstrate its advantage in knowledge representation by feature transformation across domains in CE. [ABSTRACT FROM AUTHOR]

This paper presents a method of geo-referencing the video data acquired by a small low-cost UAV, which is specifically designed as an economical, moderately functional, small airborne platform intended to meet the requirement for fast-response to time-critical events in many small private sectors or government agencies for the small areas of interest. The developed mathematical model for geo-locating video data can simultaneously solve the video camera's interior orientation parameter (lOP) (including lens distortion), and the exterior orientation parameters (EOP5) of each video frame. With the experimental data collected by the UAV at the established control field located in Picayune, Mississippi, the results reveal that the boresight matrix, describing the relationship between attitude sensor and video camera, in a low-cost UAV system will not be able to remain a constant. This result is inconsistent with the calibrated results from the previous airborne mapping system because the boresight matrix was usually assumed to be a constant over an entire mission in a traditional airborne mapping system. Thus, this paper suggests that the exterior orientation parameters of each video frame in a small low-cost UAV should be estimated individually. With the developed method, each video is geo-orthorectified and then mosaicked together to produce a 2-D planimetric mapping. The accuracy of the 2-D planimetric map can achieve 1-2 pixels, i.e., 1-2 m, when comparing with the 43 check points measured by differential GPS (DGPS) survey. Note to Practitioners-This paper was motivated by the fact that the existing unmanned aerial vehicle (UAV) system, including hardware and software, cannot meet the requirement of real-time response to time-critical disasters due to the barriers of UAVs mobile operation capability in remote site and huge time-consuming of data postprocessing and computation. This paper presented an end-to-end, systematic design, and implementation of a small and low-cost UAV system, including hardware and software. The entire UAV system is housed on a mobile vehicle (called field control station) for providing command, control and data recording to and from the UAV platform, and real-time data processing in order to meet the requirement of fast-response to time-critical disaster. The field control station houses the data stream monitoring and UAV position interface computer, radio downlinks, antenna array and video terminal. All data (GPS data, UAV position and attitude data, and video data) are transmitted to the ground receiver station via wireless communication, with real-time data processing in field. The processed geo-referenced video flow, with an accuracy of 1-2 pixels, can be immediately merged with GIS data, so that the real-time response to disaster can be promptly deployed. [ABSTRACT FROM AUTHOR]

The industrial sector is one of the largest energy consumers in the world. To alleviate the grid’s burden during peak hours, time-of-use (TOU) electricity pricing has been implemented in many countries around the globe to encourage manufacturers to shift their electricity usage from peak periods to off-peak periods. In this paper, we study the unrelated parallel machine scheduling problem under a TOU pricing scheme. The objective is to minimize the total electricity cost by appropriately scheduling the jobs such that the overall completion time does not exceed a predetermined production deadline. To solve this problem, two solution approaches are presented. The first approach models the problem with a new time-interval-based mixed integer linear programming formulation. In the second approach, we reformulate the problem using Dantzig–Wolfe decomposition and propose a column generation heuristic to solve it. Computational experiments are conducted under different TOU settings and the results confirm the effectiveness of the proposed methods. Based on the numerical results, we provide some practical suggestions for decision makers to help them in achieving a good balance between the productivity objective and the energy cost objective. [ABSTRACT FROM AUTHOR]

Programmable logic controllers (PLCs) are computers that are hardened for industrial environments and have I/O that are used to monitor and control a physical process. Learning automata specifications from PLCs provides an interface to verification tools that use an automata language, such as Uppaal. This paper introduces the time delay Mealy machine and demonstrates that it is sufficiently expressive to model PLC software. Using the LearnLib library, we implement a custom learning method to learn models from several industrial examples and analyze the efficiency. We show that the method is able to learn from simple PLC software, but the time required to learn increases rapidly with the scale of the software. [ABSTRACT FROM AUTHOR]

Feature-based technologies are widely researched for manufacturing automation. However, in current feature models, features once defined remain constant throughout the whole manufacturing lifecycle. This static feature model is inflexible to support adaptive machining when facing frequent changes to manufacturing resources. This paper presents a new machining feature concept that facilitates responsive changes to the dynamics of machining features in 2.5/3D machining. Basic geometry information for feature construction of complex parts with various intersecting features is represented as a set of meta machining features (MMF). Optimum feature definition is generated adaptively by choosing optimum merging strategies of MMFs according to the capabilities of the selected machine tool, cutter, and cutting parameters. A composite function block for dynamic machining feature modelling is designed with Basic Machining Feature Function Block, Meta Machining Feature Extraction Function Block and Feature Interpreter Function Block. Once changes of the selected machining resources occur, they are informed as input events and machining features are then updated automatically and adaptively based on the event-driven model of function blocks. An example is provided to demonstrate the feasibility and benefits of the developed methodology. [ABSTRACT FROM AUTHOR]

In this paper, a novel backtracking scheme is investigated to shorten the time of the attitude determination-based initial alignment. Based on the investigated backtracking scheme, the recorded data during the forward process is reversed and connected with the forward data, which is further used to construct the vector observations for the attitude determination based initial alignment. The investigated method can improve the accuracy and robustness of the initial alignment within only a very short period of time. The car-mounted experiment demonstrates the validity of the investigated method. [ABSTRACT FROM PUBLISHER]

In the framework of automated manufacturing systems (AMS), Petri nets are widely used to model, analyze, and control them. Resolving deadlocks is of paramount significance because their emergence may likely zero a systems throughput, if not necessarily. Supervisory control technique is the most widely adopted method to resolve them. A control policy can be converted into satisfying a set of inequalities, each of which corresponds to a siphon in a Petri net structure. The number of siphons can be exponential in the worst case, so does the number of inequalities. Taking into account the independent and dependent inequalities, this paper proposes a method to remove all the dependent inequalities, while preserving only the independent ones. This method can significantly reduce the size of a supervisory controller. Examples are presented to illustrate the effectiveness and efficiency of this method. [ABSTRACT FROM PUBLISHER]

Extended labeled Petri nets (ELPNs), i.e., labeled Petri nets with inhibitor arcs, are usually used to model the desired closed-loop behavior of a controlled discrete-event system, and, as such, their states are formed with both the controller and the plant states. However, the control logic is based on the controller states only and the interaction between controller and plant is carried out through sensor readings from the plant and control actions (forced events) from the controller. This makes ELPN not suitable for modeling the controller. Control interpreted Petri nets (CIPNs), on the other hand, include control actions in the places and sensor readings in the transitions as part of their formal structure, and so provide a better formalism for controller modeling. In this paper, we propose a two-step approach to discrete-event controller implementation, as follows: (i) we first propose a set of transformation rules to convert the initial ELPN to an equivalent CIPN, therefore extracting the control logic from the desired closed-loop behavior and (ii) we present a straightforward systematic way to translate the CIPN into a ladder diagram. We apply the results presented here to the implementation of the automation system of a plastic molding machine. [ABSTRACT FROM PUBLISHER]

In this paper, we focus on labeled Petri nets with silent transitions that may either correspond to fault events or to regular unobservable events. We address the problem of deriving a procedure to determine if a given net system is diagnosable, i.e., the occurrence of a fault event may be detected for sure after a finite observation. The proposed procedure is based on our previous results on the diagnosis of discrete-event systems modeled with labeled Petri nets, whose key notions are those of basis markings and minimal explanations, and is inspired by the diagnosability approach for finite state automata proposed by Sampath in 1995. In particular, we first give necessary and sufficient conditions for diagnosability. Then, we present a method to test diagnosability that is based on the analysis of two graphs that depend on the structure of the net, including the faults model, and the initial marking. [ABSTRACT FROM PUBLISHER]

This paper presents existence and uniqueness results for a propagative model of simultaneous impacts that is guaranteed to conserve energy and momentum in the case of elastic impacts with extensions to perfectly plastic and inelastic impacts. A corresponding time-stepping algorithm that guarantees conservation of continuous energy and discrete momentum is developed, also with extensions to plastic and inelastic impacts. The model is illustrated in simulation using billiard balls and a two-dimensional legged robot as examples; the latter is optimized over geometry and gait parameters to achieve unique simultaneous impacts. [ABSTRACT FROM PUBLISHER]

In this paper, we present a case-study on application of Ramadge-Wonham supervisory control theory (abbreviated by SCT in the sequel) to a patient support system of a magnetic resonance imaging (MRI) scanner. We discuss the whole developmental cycle, starting from the mathematical models of the uncontrolled system and of the control requirements, and ending with the implementation of the obtained controller on the actual hardware. The obtained controller was tested on the physical system. In this case study, we attempted to build the models in a modular way, in order to decrease the computational complexity of the controller synthesis and to improve the adaptability of the models. An important advantage of SCT is that it allows automatic generation of the controller, and that it can thus improve adaptability of the control software. We also briefly discuss our experience on the adaptability of the control software, obtained in the course of this case study. [ABSTRACT FROM PUBLISHER]

Microrobots are promising tools for micromanipulation and minimally invasive interventions. Robust electromagnetic control of microrobots can be achieved through precisely modeled magnetic steering systems and accurate localization. Error-free modeling and position information, however, are not realistic assumptions, and microrobots need to be controlled under force and localization uncertainties. In this paper, methods to account for these types of uncertainties are presented. Initially, the uncertainties in electromagnetic force generation of a new class of manipulation systems are quantified. Subsequently, a drag-force uncertainty model for linear dynamics is proposed. This model can be employed for microrobots whose fluid dynamics are not well understood. A set of performance measures is introduced in the design of controllers, and a PID and a robust \cal H_\infty controller are synthesized and evaluated through simulations. To demonstrate the capabilities of the synthesized controllers under localization and force uncertainties, low update rates are considered. The \cal H_\infty controller can provably respect the performance measures under higher uncertainties than the PID controller, and its performance is further quantified through experiments in a prototype electromagnetic control system. [ABSTRACT FROM PUBLISHER]

The paper presents a model for freeway traffic performance evaluation and control in a First-Order Hybrid Petri Net (FOHPN) framework. Such a hybrid Petri net formalism includes continuous places holding fluid, discrete places containing a non-negative integer number of tokens and transitions, which are either discrete or continuous. In order to suitably describe the dynamics of the freeway traffic flow, we allow updating the transition firing speed as a function of the markings modeling the freeway traffic, as described by the stationary flow-density relationship. Moreover, we propose an online optimal control coordination of speed limits with the objective of maximizing the flow density. The use of FOHPNs offers several significant advantages with respect to the model existing in the related literature: the graphical feature enables an easy modular modeling approach and the mathematical aspects efficiently allow simulating and optimizing the system. The effectiveness of the FOHPN formalism is shown by applying the proposed modeling and control technique to a stretch of a freeway in the North-East of Italy, where a solution of an accident situation is considered. [ABSTRACT FROM PUBLISHER]