Search

Cooperation among multiple connected vehicles (CVs) brings swarm intelligence to our transportation systems and helps improve their performance. This study proposes a fuel economy optimization approach for a platooning vehicle swarm via distributed economic model predictive control (DEMPC). Within the DEMPC framework, each CV shares its assumed trajectory in the predictive horizon with its neighboring CVs at each control loop. With its neighbors’ and its own assumed trajectories, each CV first solves an open-loop control optimization problem for platoon formation, and then solves an open-loop economic optimization problem for direct fuel economy improvement. In particular, the optimal cost of the former optimization problem is used in the latter one to build an upperbound constraint for stability guarantee. The asymptotic convergence of assumed terminal states is given in the analysis, and the recursive feasibility of the two optimization problems are proved with an explicit constraint on the weight matrices in the open-loop control optimization problem. Based on these analyses, the asymptotic stability of the closed-loop system is finally proved through Lyapunov analysis. Numerical simulation results validate the effectiveness of the proposed approach in terms of closed-loop stability and fuel economy improvement. Note to Practitioners—This work aims to optimize the fuel economy for a swarm of vehicles running in a platoon. Existing approaches on platoon control mainly focus on the accurate tracking of the following vehicles, but this may cause aggressive control and hence lead to poor fuel economy. Therefore, this paper proposes a distributed economic model predictive control approach to explicitly optimize the fuel consumption rate of each vehicle. The proposed method can improve fuel economy and reduce communication requirements and burdens for platooning vehicle swarms. The paper assumes no communication time delay and packet drops, so the impact of unreliable communication will be studied in the future work. [ABSTRACT FROM AUTHOR]

As natural habitats protection has become a global priority, smart sensing-nets are ever-increasingly needed for effective environmental observation. In a practical monitoring network, it is critical to deploy sensors with sufficient automated intelligence and motion flexibility. Recent advances in robotics and sensors technology have enabled automated mobile sensors deployment in a smart sensing-net. Existing deployment algorithms can be employed to calculate adequate destinations (goals) for sensors to perform respective monitoring tasks. However, given the calculated goal positions, the problem of how to actually coordinate a fleet of robots and schedule moving paths from random initials to reach their goals safely, without collisions, remains largely unaddressed in the wireless sensor networking (WSN) literature. In this paper, we investigate this problem and propose polynomial-time collision-free motion algorithms based on batched movements to ensure all the mobile sensors reach their goals successfully without incurring collisions. We observe that the grouping (batching) strategy is similar to the coloring procedure in graph theory. By constructing a conflict graph, we model the collision-free path scheduling as the well-known k-coloring problem, from which we reduce to our k-batching problem (determining the minimum number of required batches for a successful deployment) and prove its NP completeness. Since the k-batching problem is intractable, we develop CFMA (collision-free motion algorithm), a simple yet effective batching (coloring) heuristic mechanism, to approximate the optimal solution. Performance results show that our motion algorithms outperform other existing path-scheduling mechanisms by producing 100% sensors reachability (success probability of goals reaching), time-bounded deployment latency with low computation complexity, and reduced energy consumption. Note to Practitioners— This research was originally motivated by an oceanography project, which studied marine microbes by sending a team of tiny robots (sensors) randomly scattered on the ocean floor. For hard-to-access habitats like deserts or oceans, where manual placement of sensors is costly or impossible, automatically scheduling robots movements to calculated positions from random initials is essential for an effective monitoring. Our contribution is unique in two ways. First, traditional path-planning research focuses more on independent robots navigating the environment, whereas we target on the network automation problem, coordinating a fleet of robots working together to perform an environmental sensing task. Second, we observe that existing movement methods are too complicated and energy-consuming due to the calculation on-the-go nature. To provide a practical solution, we suggest and design our motion algorithms from a new perspective: pre-scheduled batched movements. Our approach requires a central server for grouping (batching) calculations, but can effectively reduce computation complexity and save significant energy expenditure exerted on resource-limited sensors. The proposed collision-free motion algorithm (CFMA) is a suboptimal yet efficient batching solution, which can be easily applied in real-life open-space monitoring networks. Insights from this foundational research will facilitate future opportunities to implement and verify our method in operational monitoring testbeds. [ABSTRACT FROM AUTHOR]

In the past decade, there is an increasing interest in the deployment of unmanned surface vehicles (USVs) for undertaking ocean missions in dynamic, complex maritime environments. The success of these missions largely relies on motion planning algorithms that can generate optimal navigational trajectories to guide a USV. Apart from minimising the distance of a path, when deployed a USVs’ motion planning algorithms also need to consider other constraints such as energy consumption, the affected of ocean currents as well as the fast collision avoidance capability. In this paper, we propose a new algorithm named anisotropic GPMP2 to revolutionise motion planning for USVs based upon the fundamentals of GP (Gaussian process) motion planning (GPMP, or its updated version GPMP2). Firstly, we integrated the anisotropy into GPMP2 to make the generated trajectories follow ocean currents where necessary to reduce energy consumption on resisting ocean currents. Secondly, to further improve the computational speed and trajectory quality, a dynamic fast GP interpolation is integrated in the algorithm. Finally, the new algorithm has been validated on a WAM-V 20 USV in a ROS environment to show the practicability of anisotropic GPMP2. Note to Practitioners—The work reported in this article will be significant for USVs to conduct missions in complex, dynamic maritime environments where various obstacles and time-varying ocean currents exit. We develop this novel motion planning algorithm based on Gaussian process and optimise the trajectory using probabilistic inferences. The new algorithm can generate collision free trajectories that also minimise the influences caused by adverse ocean currents in a highly efficient way. In addition, the planning has been undertaken in a continuous-time domain making the generated trajectory have a guaranteed smoothness and readily feasible for autopilots to track. We use a coastal area with time-varying vortexes to present a challenging practical maritime environment. The presented algorithm integrates the available information about a fluid field regarding energy consumption and hazard level, along with the density of obstacles to plan a navigational route efficiently. To increase the practical performance of the proposed method, diverse models for generating ocean currents need to be developed in the future to tackle unpredictable situations. [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]

This special section of the IEEE Transactions on Automation Science and Engineering (T-ASE) focuses on new models, methods, and technologies for energy efficiency and sustainability in transportation and energy systems. In this section, the focus is thus on articles considering sustainable transportation, such as electric vehicles (EVs), integrated with the smart grid requirements. As guest editors, we are very pleased to present the selected 12 papers, whose topics are specifically related to optimal planning of charging stations (CSs), sustainable transportation and mobility, EVs integration in smart grids, reliability, reduction of consumption, demand response and smart grid modeling, optimal scheduling, routing and charging of fleets of EVs, as well as smart parking. [ABSTRACT FROM AUTHOR]

Because that the light-energy consumption (LC) is growing quite dramatically over the past decades, the light-energy efficiency is regarded as a key component of the modern energy system in major cities. In this paper, a novel and sophisticated algorism of lighting control is presented to improve light-energy efficiency and cut down energy consumption, when considering the lighting system with multilighting equipment. In this paper, a multivariate extremum seeking controller is used to manipulate, respectively, the luminance of multilighting equipment, in order to track the minimal LC efficiently. Meanwhile, a proportional–integral–differential (PID) approach is adopted to achieve the required level of illumination. The proposed extremum seeking control (ESC) is designed to ensure the amplitude of the dither signal converge exponentially to zero, by making the amplitudes change with the extremum estimation error. The presented frame helps to improve search speed for the minimal LC value and remove the steady-state oscillations. The comparative experiments reveal that, compared with the convergence velocity of the Newton- and gradient-based ESCs, the developed method for the track of the minimal LC can have higher accuracy and faster speed. Note to Practitioners—In a modern large commercial building, the exorbitant LC is an important problem for the engineering technicians and managers, and the hybrid lighting system which combines artificial light with natural light offers a solution to this problem. However, the minimum light-energy optimal control is a very key problem in manipulating multigroup lighting equipment, since the required level of illumination usually varies according to the exterior lighting environment. This paper contributes to a useful multivariate extremum seeking controller to solve these light-energy control problems for multiple-input single-output hybrid lighting system, whereas the PID controller is also designed to maintain the required level of illumination simultaneously. This paper discusses how to seek the minimal energy necessary for the required levels of illumination by separately regulating different light equipment. The research results of this paper will be beneficial for a practical application of extremum seeking control method in enhancing the lighting energy efficiency of lighting system. [ABSTRACT FROM AUTHOR]

This paper constructs an integrated virtual reconfiguration model that can simultaneously group workstations, schedule virtual cells, and select energy consumption levels. If managers prefer the physical proximity of machines in a certain virtual cell, the material handling cost in it can be reduced. However, the distances among machines in other virtual cells are probably large, which may cause greater material handling cost. If a virtual cell for a certain product type has priority to be created, the backorder cost of that type can be decreased or even avoided. Nevertheless, the creation of virtual cells for other product types may be delayed, perhaps leading to the higher backorder cost of other product types. In addition, managers can choose a high energy consumption level of a machine to expedite its production to reduce backorder cost at the expense of more energy consumption cost. To minimize the total operational cost, we develop a novel Discrete Imperialist Competitive Algorithm with a Priority rule-based heuristic (DICAP). It includes a colony movement strategy, a competition strategy, a collapse mechanism, a development strategy, and a sufficient convergence policy. Numerical experiments and t-test are conducted to validate that the proposed DICAP outperforms genetic algorithm and simulated annealing. Note to Practitioners—Virtual cellular manufacturing systems can create an expectation of improving machine utilization and productivity, reducing reconfiguration cost, and adaptation to product specification changes in reality. However, managers often feel difficult to make appropriate decisions for three interrelated issues, i.e., workstation grouping, virtual cell creation and release, and energy consumption options. Workstation grouping depends on the availability of machines and workers, and the availability may be related to the creation and release time of some virtual cells. Whether a virtual cell is created in time has an influence on the selection of energy consumption levels, because managers need to consider both backorder cost and energy consumption cost. In addition, there often exists a bottleneck workstation in each virtual cell. Therefore, it is essential and worthwhile to select appropriate energy consumption levels of machines to smooth the production efficiency of grouped workstations. To effectively address the issues, this paper presents an integrated virtual reconfiguration model. A novel Discrete Imperialist Competitive Algorithm with a Priority rule-based heuristic is developed to minimize the total operational cost. Numerical experiments and t-test results indicate that the proposed algorithm outperforms two commonly used ones, i.e., genetic algorithm and simulated annealing in solution quality. It is suitable for virtual reconfiguration problem with industrial size in practice. [ABSTRACT FROM AUTHOR]

Based on the Petri net (PN) models of the flexible manufacturing systems (FMSs), this paper focuses on solving the scheduling problem of minimizing the total energy consumption of FMSs. In the view of different energy consumption rates of resources under different working statuses, two energy consumption functions are considered. The dynamic programming (DP) models of the scheduling problems based on PNs are established, where a reachable marking of a PN model, start processing time vector, route vector, and the transition sequence leading to the reachable marking from the initial one, is regarded as a state, and the Bellman equation is based on transition firing. For small-size scheduling problems, their optimal solutions can be obtained by the presented DP algorithm. However, it is difficult to solve larger-scale ones since the number of explored states increases exponentially with the problem size, which makes DP computationally infeasible. To obtain an optimal or suboptimal schedule in an acceptable time, modified DP (MDP) algorithm is proposed, in which fewer states are explored. In MDP, two ways are introduced through which only the most promising states are explored. One is keeping only one transition sequence for each marking through an evaluation function. Another is selecting the most promising states in each stage for further exploration through a heuristic function. To guarantee that the generated states are safe, a deadlock controller is applied in the recursion procedure of MDP. Experimental results on manufacturing systems and comparisons with existing works are provided to show the effectiveness of MDP. Note to Practitioners—This paper is motivated by the need of optimizing the energy consumption of manufacturing systems, considering the increasing energy cost and environmental concerns. Existing studies on energy optimization rarely focus on flexible manufacturing systems (FMSs) which exhibit a high degree of resource sharing and route flexibility and can be highly adaptable to various production plans and goals. Scheduling becomes more challenging when facing deadlock-prone FMSs. This paper provides a method to solve this complex scheduling problem efficiently. In this paper, dynamic programming (DP) is formulated for it, and the optimal energy consumption schedules are found. Considering the computational burden of implementing DP in the scheduling of large-size FMSs, a novel scheduling method named modified DP (MDP) is proposed. Experimental tests on an FMS and a stamping system suggest that MDP can successfully find feasible solutions. Besides, it can be applied to other FMS scheduling problems and industrial cases, once their processing time of operations and energy consumption of resources per unit time are known. [ABSTRACT FROM AUTHOR]

In this paper, a method is presented for lowering the energy consumption and/or increasing the speed of a standard manipulator spray painting a surface. The approach is based on the observation that a small angle between the spray direction and the surface normal does not affect the quality of the paint job. Recent results in set-based kinematic control are utilized to develop a switched control system, where this angle is defined as a set-based task with a maximum allowed limit. Four different set-based methods are implemented and tested on a UR5 manipulator from Universal Robots. Experimental results verify the correctness of the method, and demonstrate that the set-based approaches can substantially lower the paint time and energy consumption compared to the current standard solution. Note to Practitioners—This paper is motivated by the observation that uniform paint coating can be achieved even when the spray direction is not normal to the spray surface. By allowing a small orientation error, the manipulator maintains a constant spray velocity along the surface while following a more energy efficient trajectory at the end-effector. By doing so, this velocity can also be increased as the proposed method requires less torque during the turns than the current industry standard. The method proposed in this paper is based on a kinematic control method and generates reference trajectories for the manipulator configuration and velocity every time step which are inputs to the manipulator dynamic controller. If tracked, the references will allow the paint gun to follow the trajectory, including both straight line segments and turns, with constant velocity and thus achieve uniform paint coating. We show how to calculate the reference state of the manipulator and present the explicit expressions for solving and implementing the algorithms. The approach can also be used for other applications where an offset in the end-effector orientation improves performance, such as welding and 3-D printing. [ABSTRACT FROM AUTHOR]

The quay crane scheduling problem (QCSP) is one of the most important problems for the operations at container ports. The QCSP aims to decide a QC schedule for loading and unloading containers so as to minimize the vessel turnaround time. The QCSP is subject to various kinds of constraints, e.g., task precedence constraints and QC noninterference constraints. This paper extends the QCSP by taking into consideration the stability constraints, which are crucial for the safety reason but often omitted in the existing literature. We provide a mathematical model for the QCSP with stability constraints (QCSPSCs). A bicriteria evolutionary algorithm is proposed to solve the QCSPSC. The algorithm consists of a sliding-window heuristic to fix the schedule, which violates the stability constraints. Extensive experiments are conducted to demonstrate the effectiveness of the algorithm. The computational results of the traditional QCSP and the QCSPSC are also compared and analyzed. Note to Practitioners—Stability is a vital factor for the safety of the ship and the goods inside. At a terminal, when a berthed container ship receives loading and unloading operations performed by quay cranes (QCs), it is required that the ship stability criterion does not exceed a certain threshold. In practice, ship operation managers are keen in minimizing the ship’s turnaround time via an efficient QC schedule for loading and unloading containers without leaning the ship to one side. The schedule is then implemented by automatic scheduling systems or simulation tools in the terminal routines. This paper presents some practical stability constraints and integrates them into the traditional QC scheduling model. The results show that by considering the stability constraints, the ship will become more stable, but the turnaround time will not be affected significantly. We believe that the introduction of ship stability constraints can not only fill in the gap between the research on QC scheduling and the real-world ship handling practices but also enrich the studies on logistics planning and port operations. [ABSTRACT FROM AUTHOR]

In recent days, many interacted shape products have been developed by manufacturing industries for different applications in various fields such as defense, aerospace, and space centers. In manufacturing, 30% of time consumption is due to assembly operation compared with the remaining processes in manufacturing. It is very difficult to get optimal sequence because assembly sequence planning is a multimodel optimization problem. As the number of parts in the assembly increases, the possible number of sequences increases exponentially therefore obtaining the optimal assembly sequence becomes more difficult and time consuming. There exist many mathematical algorithms to obtain optimal assembly sequences. But, recent studies state that they perform poorly when it comes to multiobjective optimal assembly sequence. In recent years, researchers have developed several soft computing-based algorithms for solving assembly sequence problems. In this paper, assembly subset detection method has been introduced. The proposed method is applied for the first time to solve assembly sequence problems. This method eliminates those assembly sets that have more directional changes and require more energy. The method is compared with other algorithms, namely, genetic algorithm (GA), enhanced GA, ant colony optimization (ACO), memetic algorithm, imperialistic harmonic search algorithm, and flower pollination algorithm (FPA), and is found to be successful in achieving the optimal assembly sequence for an industrial product with smaller number of iterations. Note to Practitioners—This paper is motivated by the redesign of helicopter cowling of a Canadian aircraft company using concepts of design for assembly. Though we could reduce the number of parts using advanced composite materials and manufacturing processes, obtaining a feasible assembly for the new assembly structure required a lot of computation time. Hence, the researchers studied the existing literature on assembly sequence generation methods and their limitations, and came up with efficient automated optimal sequence generation method. [ABSTRACT FROM AUTHOR]

A growing number of companies deploy their applications in green data centers (GDCs) and provide services to tasks of global users. Currently, a growing number of GDC providers aim to maximize their profit by deploying green energy facilities and decreasing brown energy consumption. However, the temporal variation in the revenue, price of grid, and green energy in tasks’ delay bounds makes it challenging for GDC providers to achieve profit maximization while strictly guaranteeing delay constraints of all admitted tasks. Unlike existing studies, a time-aware task scheduling (TATS) algorithm that investigates the temporal variation and schedules all admitted tasks to execute in GDC meeting their delay bounds is proposed. In addition, this paper provides the mathematical modeling of task refusal and service rates. In each iteration, TATS solves the formulated profit maximization problem by hybrid chaotic particle swarm optimization based on simulated annealing. Compared with several existing scheduling algorithms, TATS can increase profit and throughput without violating delay constraints of all admitted tasks. Note to Practitioners—This paper investigates the profit maximization problem for a green data center (GDC) while meeting delay constraints for all admitted tasks. Previous task scheduling algorithms do not jointly investigate temporal variation in revenue, green energy, and price of grid. Thus, they fail to meet the delay constraints of all admitted tasks. In this paper, a new approach that overcomes drawbacks of existing algorithms is proposed. It is obtained by using a hybrid metaheuristic algorithm that solves a constrained nonlinear optimization problem. Simulation results show that compared with several existing algorithms, it increases both throughput and profit. It can be readily incorporated into real-life industrial GDCs. The future work needs to investigate the repair/failure effect of GDCs on the proposed time-aware task scheduling. [ABSTRACT FROM AUTHOR]

It becomes more and more critical for manufacturing enterprises to improve energy efficiency because of the escalating energy prices, increasing global competitions, and more rigorous government regulations. In this paper, a systematic method is developed to improve the energy efficiency of a multistage manufacturing system through production control. The method aims at reducing energy consumption with minimal negative impact on production. We start from the analysis of system dynamics and develop quantitative methods to estimate energy saving opportunities. A supervisory control algorithm is developed to improve system energy efficiency by periodically taking the saving opportunities. Simulation case studies are performed to validate the effectiveness of the control algorithm.

Note to Practitioners—Manufacturing systems are facing increasing pressure to reduce energy consumption, as global competition, sustainability, and green processes are becoming more prevalent. Although most of the research efforts on manufacturing energy saving have focused on developing individual energy efficient machines, it can be more cost-effective to improve energy efficiency through better control of the energy usage of the whole production system. Therefore, this paper presents a systematic method to improve system energy efficiency with a minimal negative impact on production. This paper continues the work by Chang et al. by extending the scope of energy saving opportunity theory from serial production systems to general serial–parallel production systems. It also develops analytical methods based on Markov chain models to estimate the energy saving opportunity accurately. [ABSTRACT FROM AUTHOR]

Modern datacenters rely on virtualization to deliver complex and scalable cloud services. To avoid inflating costs or reducing the perceived service level, suitable resource optimization techniques are needed. Placement can be used to prevent inefficient maps between virtual and physical machines. In this perspective, we propose a holistic placement framework considering conflicting performance metrics, such as the service level delivered by the cloud, the energetic footprint, hardware or software outages, and security policies. Unfortunately, computing the best placement strategies is nontrivial, as it requires the ability to trade among several goals, possibly in a real-time manner. Therefore, we approach the problem via model predictive control to devise optimal maps between virtual and physical machines. Results show the effectiveness of our technique in comparison with classical heuristics. Note to Practitioners—This paper is motivated by the success of cloud-based services. Specifically, we consider the case where the resources of datacenters are accessed through the infrastructure-as-a-service paradigm. To efficiently handle the load of requests and to tame operational costs, a proper optimization is needed. To this end, we focus on the selection of the best maps between virtual and physical machines. We propose a holistic placement framework for the deployment of user-requested virtual machines by pursuing different performance goals, such as counteract to hardware outages or reboots due to software aging issues, ensure proper security policies, maintain a suitable service level perceived by users, and reduce power requirements. The optimal strategies are computed with model predictive control, which allows to consider complex constraints and take advantage of future information. The proposed framework is tested in different scenarios characterized by a variety of workloads and traces gathered in a real cloud datacenter. Results indicate that our approach outperforms bin-packing techniques. [ABSTRACT FROM AUTHOR]

This paper proposes a resource-oriented, decentralized auction algorithm (RODAA) for multirobot task allocation considering multiple resources of the robots and limited robot communication range. The resources that this paper focuses on are the expendable supplies that a robot consumes and recharges while performing tasks, such as energy. In the proposed algorithm, each robot generates its cost for the task in a probabilistic manner considering multiple paths that visit none or different combinations of refill stations for performing the task based on the robot's residual resources. For robust and time-efficient task allocation with limited robot communication range in a dynamic network, a multihop-based auction algorithm is proposed. This paper also introduces a solar panel cleaning mission as a new application for multirobot systems and the proposed algorithm is implemented in the simulation of the mission. The simulation results demonstrate that the proposed algorithm is capable of completing the panel cleaning mission faster than other auction-based task allocation algorithms and has lower overall resource consumption. [ABSTRACT FROM AUTHOR]

In this paper, we consider “sinus-lifting motion” of a living snake, in which a snake lifts up some parts of its body from the ground, and switches the lifted parts dynamically. It is not clear whether imitating the sinus-lifting motion is the best locomotion or not for a snake like robot. The aim of this paper is to propose an appropriate motion pattern to a snake like robot considering the optimality of the sinus-lifting motion. We introduce two physical parameters, constraint forces and energy efficiency, as cost functions to optimize and propose switching strategies for generating optimal motion patterns of a snake like robot. [ABSTRACT FROM PUBLISHER]

Due to the limits of battery capacity of mobile devices, how to select cloud services to invoke in order to reduce energy consumption in mobile environments is becoming a critical issue. This paper addresses the problem of mobile service selection for composition in terms of energy consumption. It formally models this problem and constructs energy consumption computation models. Energy consumption aggregation rules for composite services with different structures are presented. It adopts the genetic algorithm to resolve it. A replanning mechanism is also proposed to deal with the changeable conditions and user behavior. A series of experiments are conducted to evaluate the performance of our method. The results show that our service selection method significantly outperforms traditional methods. Even if the conditions or user behavior is changeable, this method is still effective to recommend services. Moreover, the service selection method performs good scalability as the experimental scale increases. [ABSTRACT FROM PUBLISHER]

This paper addresses the endemic problem of the gap between predicted and actual energy performance in public buildings. A system engineering approach is used to characterize energy performance factoring in building intrinsic properties, occupancy patterns, environmental conditions, as well as available control variables and their respective ranges. Due to the lack of historical data, a theoretical simulation model is considered. A semantic mapping process is proposed using principle component analysis (PCA) and multi regression analysis (MRA) to determine the governing (i.e., most sensitive) variables to reduce the energy gap with a (near) real-time capability. Further, an artificial neural network (ANN) is developed to learn the patterns of this semantic mapping, and is used as the cost function of a genetic algorithm (GA)-based optimization tool to generate optimized energy saving rules factoring in multiple objectives and constraints. Finally, a novel rule evaluation process is developed to evaluate the generated energy saving rules, their boundaries, and underpinning variables. The proposed solution has been tested on both a simulation platform and a pilot building – a care home in the Netherlands. Validation results suggest an average 25% energy reduction while meeting occupants’ comfort conditions. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper explores energy efficient building design for manufacturing plants. Many efforts have been directed into the field of building design optimization concerning building energy performance, but most of the studies focus on residential buildings or public buildings. Very limited research results studying plants buildings have been reported. However, plants buildings have certain unique features that make the design problem more challenging. Furthermore, the approaches presented in the current publications could not guarantee the performance of their designs if the computation capacity is limited. This paper attempts to address these two issues. First, an EnergyPlus-integrated overall energy consumption estimation framework is developed for a class of manufacturing plants, where the environmental conditions would not affect the energy consumption of the production processes. Based on that, the building design problem for this type of manufacturing plants is formulated as a stochastic programming problem concerning uncertainties arising from the future weather conditions and energy prices, where seasonal production scheduling optimizing is incorporated when estimating the performance of building designs. Second, Ordinal Optimization (OO) method refid="ref1"/ is introduced to solve the problem so as to quantitatively guarantee a high probability of finding satisfactory designs while reducing the computation burden. A numerical example is provided, showing our solution method performs effectively in finding a satisfactory design. [ABSTRACT FROM AUTHOR]

Control of production operations is considered as one of the most economical methods to improve energy efficiency in manufacturing systems. This paper investigates energy consumption reduction in production systems through effective scheduling of machine startup and shutdown. Specifically, we consider serial production lines with finite buffers and machines having Bernoulli reliability model. This machine reliability model is applicable in production situations, where the downtime is relatively short and comparable to machine cycle time (e.g., automotive paint shops and general assembly). In this paper, using transient analysis of the systems at hand, an analytical performance evaluation technique is developed for Bernoulli serial lines with time-dependent machine efficiencies. In addition, tradeoff between productivity and energy-efficiency in production systems is discussed and the energy-efficient production problem is formulated as a constrained optimization problem. The effects and practical implications of operations schedule are demonstrated using a numerical study on automotive paint shop operations. [ABSTRACT FROM AUTHOR]

Near net shape (NNS) manufacturing refers to the production of products that require a finishing operation of some kind. NNS manufacturing is important because it enables a significant reduction in: machining work, raw material usage, production time, and energy consumption. This paper presents an integrated system for the production of near net shape components based on the Octree decomposition of 3-D models. The Octree representation is used to automatically decompose and approximate the 3-D models, and to generate the robot instructions required to create assemblies of blocks secured by adhesive. Not only is the system capable of producing shapes of variable precision and complexity (including overhanging or reentrant shapes) from a variety of materials, but it also requires no production tooling (e.g., molds, dies, jigs, or fixtures). This paper details how a number of well-known Octree algorithms for subdivision, neighbor findings, and tree traversal have been modified to support this novel application. This paper ends by reporting the construction of two mechanical components in the prototype cell, and discussing the overall feasibility of the system. [ABSTRACT FROM AUTHOR]

An important challenge to be addressed in the domain of Internet of Things (IoT) is the development of efficient services selection algorithms for an optimal management of both energy and Quality of Service (QoS) in the context of IoT services composition. This issue becomes crucial in the case of large-scale IoT environments composed of thousands of distributed entities. In this paper, an energy-centered and QoS-aware services selection algorithm (EQSA) is proposed for IoT services composition. The proposed selection approach consists of preselecting the services offering the QoS level required for user's satisfaction using a lexicographic optimization strategy and QoS constraints relaxation technique. In order to reduce the energy consumption of a composite service without affecting the user's satisfaction, the most suitable services among the preselected ones are then selected using the concept of relative dominance of services in the sense of Pareto. The relative dominance of a candidate service depends on its energy profile and QoS attributes, and user's preferences. The proposed algorithm has been evaluated through several simulation scenarios. The obtained results show clearly the good performances of the EQSA algorithm in terms of selection time, energy efficiency, composition lifetime, and optimality and its added value in comparison with algorithms dealing separately with QoS and energy consumption. [ABSTRACT FROM PUBLISHER]

Current manufacturing facilities lack proper performance indicators that can accurately pinpoint areas of energy inefficiency on the manufacturing line. This paper establishes new indices that utilize readily available, real sensor information from the production line, such as buffer levels, machine throughput, etc., to find the machine that is the least energy efficient. This is the machine that causes the line to waste the most energy without producing parts. The energy structure of the production line is analyzed to better understand the complex system dynamics and to find the root cause of the energy inefficiencies. A baseline energy consumption is established, which is the least amount of energy that is needed to produce a certain number of parts on the line. Using this knowledge, the new sustainable manufacturing performance indicators are defined to properly monitor the performance of the line. These indices utilize the energy structure to illustrate the static (energy used when there are no downtime events) and dynamic (energy lost due to downtime) portions of the energy consumption. The concepts of the downtime energy bottleneck and the rated power bottleneck are introduced and proven analytically. These methods are verified using simulation studies in Simulink/MATLAB. [ABSTRACT FROM AUTHOR]

In this paper, we propose a robust approach for scheduling of smart appliances and electrical energy storages (EESs) in active apartments with the aim of reducing both the electricity bill and the \CO2 emissions. The proposed robust formulation takes the user behavior uncertainty into account so that the optimal appliances schedule is less sensitive to unpredictable changes in user preferences. The user behavior uncertainty is modeled as uncertainty in the cost function coefficients. In order to reduce the level of conservativeness of the robust solution, we introduce a parameter allowing to achieve a trade-off between the price of robustness and the protection against uncertainty. Mathematically, the robust scheduling problem is posed as a multi-objective Mixed Integer Linear Programming (MILP), which is solved by using standard algorithms. The numerical results show effectiveness of the proposed approach to increase both the electricity bill and \CO2 emissions savings, in the presence of user behavior uncertainties. Mathematical insights into the robust formulation are illustrated and the sensitivity of the optimum cost in the presence of uncertainties is investigated. Although home appliances and EESs are considered in this work, we point out that the proposed scheduling framework is generally applicable to many use cases, e.g., charging and discharging of electrical vehicles in an effective way. In addition, it is applicable to various scenarios considering different uncertainty sources, different storage technologies and generic programmable electrical loads, as well as different optimization criteria. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The objective of this paper is to develop an optimal planning framework for a production system utilizing multisource distributed energy supplies, in order to improve its energy efficiency and to reduce the dependency on grid energy and outage risk. The energy consumption of an automotive seat assembly plant is modeled using a discrete-event simulation, which consumes energy supplied by wind and solar, as well as traditional power grids. Machines can be halted for designed time period to avoid high peak load and operate efficiently in terms of power consumption. On the other hand, the inventory level can be planned by adjusting production goal to better utilize alternative energy and accommodate demand uncertainty using stochastic programming method. The results from case studies show that optimizing waiting time for each process can effectively reduce outage risk while meeting customer demands. Having the optimal production goal to over-produce when alternative energy supply is high can reduce the dependency on grid energy. Also, the benefit of considering predicted future customer demands would have the effect of safety inventory, which can prevent shortages during extremely high customer demand periods. [ABSTRACT FROM AUTHOR]

One of the measures for saving energy is the implementation of control strategies that reduce energy consumption during the machine idle periods. This paper proposes a framework that integrates different control policies for switching the machine off when the production is not critical, and on when the part flow has to be resumed. A general policy is formalized by modeling explicitly the power consumed in each system state. With this policy, the service can be resumed according to time threshold and when N parts have accumulated in the buffer. The behavior of the control strategy under different scenarios is numerically evaluated. Numerical results are based on data acquired with dedicated experimental measurements on a real machining center. A comparison with the common practice in manufacturing and with control policies that do not consider buffer information is also reported. [ABSTRACT FROM AUTHOR]

This paper presents a district energy management strategy devoted to monitor and control the district power consumption in a twofold human-centered perspective: the respect of user’s comfort preferences and the minimization of the power consumption and costs. The presented district energy management system forwards the power profile determined the day ahead to each building energy management system that, in turn, minimizes its real-time power consumption and costs (based on rewards and penalties), respecting the comfort preferences. Successively, the power is redistributed among the district buildings in order to minimize the penalties by applying two approaches: a centralized approach for public buildings and a distributed methodology for private buildings. Such optimization problems are formalized by defining some linear programming problems: two case studies are solved to show the applicability of the proposed management strategies. [ABSTRACT FROM AUTHOR]

Optimizing HVAC operation becomes increasingly important because of the rising energy cost and comfort requirements. In this paper, an innovative event-based approach is developed within the Lagrangian relaxation framework to minimize an HVAC's day-ahead energy cost. To solve the HVAC optimization problem based on events is challenging since with time-dependent uncertainties in weather, cooling load, etc., the optimal policy is not stationary. The nonstationary policy space is extremely large, and it is time consuming to find the optimal policy. To overcome the challenge, we develop an event-based approach to make the nonstationary optimal policy stationary in the planning horizon. The key idea is to augment state variables to include the time-dependent variables that make the optimal policy nonstationary and then define events based on the extended state variables. In addition, we develop within the Lagrangian relaxation framework a Q-learning method where Q-factors are used to evaluate event-action pairs and to obtain the optimal policy. Numerical results demonstrate that, as compared with time-based approaches, the event-based approach maintains similar levels of energy costs and human comfort, but reduces computational efforts significantly and has a much faster response to events. [ABSTRACT FROM AUTHOR]

Public Bicycle-Sharing Systems (PBSS) have been appearing in more and more cities around the world in the last few years. Although their apparent success as an alternative form of public transportation mode, there are major challenges confronting the operators while few scientific works are available to support such complex dynamical systems to influence their economic viability and operational efficiency. One of the most crucial factors for the success of a PBS system is its ability to ensure that bicycles are available for pick up and vacant berths available for bicycle drop off at every station. In this paper, we develop an original discrete event approach for modelling and performance evaluation of public bicycle-sharing systems by using Petri nets with time, inhibitor arcs and variable arc weights. [ABSTRACT FROM AUTHOR]

Due to the large amount of energy consumed in buildings, building energy savings has attracted more and more attention recently. The total energy consumed during building operations is determined by the building energy efficiency and the total demand. On the one hand, though most existing studies focus on improving building energy efficiency, there are limits. On the other hand, the demand grows fast and without limit. Therefore it is important to coordinate the supply and demand in buildings. We consider this important problem in this paper and make the following major contributions. First, the concept of average price of electricity (APE) is defined to measure the average generation cost of electricity using multiple devices. Second, a comfort model of occupant is developed to capture the tradeoff between thermal comfort and cost. Human building interaction allows the user to adjust their temperature set ranges according to the APE in real time. Third, an iterative solution method is developed to solve the supply demand coordination optimization problem. Numerical examples show that significant energy saving is possible through exploring the soft comfort requirement of the occupants, and the iterative method achieves a solution which is close to that of the centralized method, but in a much faster way. We hope this work brings insight to building energy saving in general. [ABSTRACT FROM PUBLISHER]

Energy saving in production plants is becoming more and more relevant due to the pressure from governments to contain the environmental impact of manufacturing and from companies to reduce costs. One of the measures for saving energy is the implementation of control strategies that reduce energy consumption during the machine idle periods. This paper proposes a framework that integrates different control policies for switching the machine off when production is not critical and on when the part flow has to be resumed. A general policy is formalized by modelling explicitly the energy consumed at each machine state. The policy parameters that minimize the requested machine expected energy are provided analytically for general distributions. Numerical results are based on data acquired with dedicated experimental measurements on a real machining centre, and a comparison with the most common practices in manufacturing is also reported. [ABSTRACT FROM PUBLISHER]

To increase the energy efficiency of Ethernet networks, in 2010, the IEEE published the IEEE 802.3az amendment, known as Energy Efficient Ethernet (EEE). The amendment introduces a new operational mode, defined as Low Power Idle (LPI), that allows to considerably reduce the power consumption of inactive Ethernet links. In this paper, we address the application of EEE to Real-Time Ethernet (RTE) networks, the popular communication systems typically employed in factory automation, characterized by tight timing requirements. We start with a description of the EEE basics and, subsequently, focus on the introduction of EEE in the industrial communication scenario. Then, we specifically address the implementation of effective EEE strategies for some popular RTE networks. The analysis is carried out on configurations commonly deployed at low levels of factory automation systems. The obtained results show that considerable power savings can be achieved with very limited impact on network performance. [ABSTRACT FROM PUBLISHER]

Schedule optimization is crucial to reduce energy consumption of flexible manufacturing systems (FMSs) with shared resources and route flexibility. Based on the weighted p-timed Petri Net (WTPN) models of FMS, this paper considers a scheduling problem which minimizes both productive and idle energy consumption subjected to general production constraints. The considered problem is proven to be a nonconvex mixed integer nonlinear program (MINLP). A new reachability graph (RG)-based discrete dynamic programming (DP) approach is proposed for generating near energy-optimal schedules within adequate computational time. The nonconvex MINLP is sampled, and the reduced RG is constructed such that only reachable paths are retained for computation of the energy-optimal path. Each scheduling subproblem is linearized, and each optimal substructure is computed to store in a routing table. It is proven that the sampling-induced error is bounded, and this upper bound can be reduced by increasing the sampling frequency. Experiment results on an industrial stamping system show the effectiveness of our proposed scheduling method in terms of computational complexity and deviation from optimality. [ABSTRACT FROM AUTHOR]

This paper quantitatively reports about potential energy savings on robotic assembly lines for the automotive industry. At first, a detailed system model is described, which improves previously published results by explicitly considering both manipulator and electrical drive dynamics. The model closely captures experimental data in terms of actuation torques and servodrive voltages, which are directly used to derive the plant input power. Two practical methods are then evaluated for reducing the overall energy consumption. The methods rely on: 1) implementation of energy-optimal trajectories obtained by means of time scaling, concerning the robots' motion from the last process point to the home positions and 2) reduction of energy consumption by releasing the actuator brakes earlier when the robots are kept stationary. Simulation results, based on the production timing characteristics measured at a real plant, clearly shows that the system energy consumption can be effectively reduced without negative effects on the production rate. [ABSTRACT FROM AUTHOR]

Modern manufacturing facilities waste many energy savings opportunities (ESO) due to the lack of integration between the facility and the production system. To explore the energy savings opportunities, this paper combines the two largest energy consumers in a manufacturing plant: the production line and the heating, ventilation, and air conditioning (HVAC) system. The concept of the energy opportunity window (OW) is utilized, which allows each machine to be turned off at set periods of time without any throughput loss. The recovery time of each machine is the minimum amount of time a machine must be operational between opportunity windows to guarantee zero production loss and it is explored both analytically and numerically. The opportunity window for the production line is synced with the peak periods of energy demand for the HVAC system to create a heuristic rule to optimize the energy cost savings. This integrated system is modeled and tested using simulation studies. [ABSTRACT FROM AUTHOR]

Automotive painting shops consume electricity and natural gas to provide the required temperature and humidity for painting processes. The painting shop is not only responsible for a significant portion of energy consumption with automobile manufacturers, but also affects the quality of the product. Various storage devices play a crucial role in the management of multiple energy systems. It is thus of great practical interest to manage the storage devices together with other energy systems to provide the required environment with minimal cost. In this paper, we formulate the scheduling problem of these multiple energy systems as a Markov decision process (MDP) and then provide two approximate solution methods. Method 1 is dynamic programming with value function approximation. Method 2 is mixed integer programming with mean value approximation. The performance of the two methods is demonstrated on numerical examples. The results show that method 2 provides good solutions fast and with little performance degradation comparing with method 1. Then, we apply method 2 to optimize the capacity and to select the combination of the storage devices, and demonstrate the performance by numerical examples. [ABSTRACT FROM AUTHOR]

Conventionally, improving production efficiency, flexibility and responsiveness has been the primary research focus of production management, while energy consumption has received relatively little attention. Energy consumption plays a more and more important role in the manufacturing environment. This is mainly driven by energy cost and environmental concerns. When the energy system becomes complicated and coupled with ongoing production, it is very difficult to hunt the “hidden treasure” which affects the overall benefit of a manufacturing system. This paper provides a systematic method to search for energy saving opportunities and strategies. We start from dynamic production transient analysis and provide quantitative analysis for identifying energy saving opportunity in a system. Furthermore, energy saving strategy is justified through cost analysis for tradeoffs between energy savings and throughput loss. A case study is conducted to demonstrate its potential on energy savings in a multistage manufacturing system. [ABSTRACT FROM PUBLISHER]

Due to the complexity of modern manufacturing systems and the lack of optimal management of energy consumption, the energy efficiency of manufacturing systems in real industrial environment is much lower than designed level, which significantly increases operation cost, impedes company competitiveness in the global market, leads to high carbon dioxide emission, and results in destroyed environment and ecology. Compared with existing research efforts on energy management of single machine system in the literature, few works have been performed to study the opportunity for energy management of typical manufacturing systems with multiple machines and buffers. From the point-of-view of sustainability, considering stochastic factor and buffer utilization, this paper investigates the opportunity estimation for real-time energy control of typical multi-machine manufacturing systems without sacrificing system throughput. A numerical case study based on an automotive assembly line is used to illustrate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

Automatic train regulation (ATR) dominates the service quality, transport capacity, and energy usage of a metro-line operation. The train regulator aims to maximize the schedule/headway adherence while minimizing the energy consumption. This paper presents a traffic-energy model to characterize the complicated dynamics with regard to the traffic and the energy consumption of a metro line, and devises an adaptive-optimal-control (AOC) algorithm to optimize the train regulator through reinforcement learning. The updating rules for reinforcement learning are deduced from the discrete minimum principle. Testing with field traffic data, the AOC algorithm succeeds in the optimization of the train regulator; no matter the system is disturbed by passenger-flow fluctuations or by frequently minor delays. The results also show that better train regulation with less energy consumption is attainable through the running-time and dwell-time controls. [ABSTRACT FROM AUTHOR]

Chillers constitute a significant portion of energy consumption equipment in heating, ventilating and air-conditioning (HVAC) systems. The growing complexity of building systems has become a major challenge for field technicians to troubleshoot the problems manually; this calls for automated "smart-service systems" for performing fault detection and diagnosis (FDD). The focus of this paper is to develop a generic FDD scheme for centrifugal chillers and also to develop a nominal data-driven ("black-box") model of the chiller that can predict the system response under new loading conditions. In this vein, support vector machines, principal component analysis, and partial least squares are the candidate fault classification techniques in our approach. We present a genetic algorithm. based approach to select a sensor suite for maximum diagnosabilty and also evaluated the performance of selected classification procedures with the optimized sensor suite. The responses of these selected sensors are predicted under new loading conditions using the nominal model developed via the black-box modeling approach. We used the benchmark data on a 90-t real centrifugal chiller test equipment, provided by the American Society of Heating, Refrigerating and Air-Conditioning Engineers, to demonstrate and validate our proposed diagnostic procedure. The database consists of data from sixty four monitored variables of the chiller under 27 different modes of operation during nominal and eight faulty conditions with different seventies. [ABSTRACT FROM AUTHOR]