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Movement modules regarding the rotatory movement of the AGV (ω = 2.4rad/s, mLoad = 27.455kg, β = 180∘)
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Due to the advancing energy system transformation and the increasingly complex and dynamic environment in which factories have to operate, the energy efficiency and flexible design of production systems are becoming more important. Since the use of automated guided vehicles is a promising approach to enhance the flexibility of intralogistics, their...
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The performance of factory-internal logistic systems plays a central role for the overall productivity of the factory of the future. A key element is the optimization of logistic systems based on predictive analytics of transport tasks in order to anticipate and to adapt to changes of the production flows in the factory. Although this information m...
Citations
... The most related works on modeling power and energy requirements of AGVs are [15,16]. Hamdy [15] presents a simulation model to determine the optimal number of vehicles considering energy constraints. ...
... Finally, the work of Meissner and Massalski [16] is mentioned, in which the electrical power of the drives of an AGV with differential drives was measured and analyzed for the procedure. In a state-based simulation model, the states accelerating, driving, and decelerating were represented and reported a relative average deviation in the energy requirements of the drives as ∆E = −5.59%. ...
... Acceleration Lifting without load Lifting load absorption Lifting under full load Breaking Figure 2. State-based activity model for an LHD of an AGV. Own illustration, based on [16]. ...
Saving energy and resources has become increasingly important for industrial applications. Foremost, this requires knowledge about the energy requirement. For this purpose, this paper presents a state-based energy requirement model for mobile robots, e.g., automated guided vehicles or autonomous mobile robots, that determines the energy requirement by integrating the linearized power requirement parameters within each system state of the vehicle. The model and their respective system states were verified using a qualitative process analysis of 25 mobile robots from different manufacturers and validated by comparing simulated data with experimental data. For this purpose, power consumption measurements over 461 operating hours were performed in experiments with two different industrial mobile robots. System components of a mobile robot, which require energy, were classified and their power consumptions were measured individually. The parameters in the study consist of vehicle speed, load-handling duration, load, utilization, material flow and layout data, and charging infrastructure system frequency, yet these varied throughout the experiments. Validation of the model through real experiments shows that, in a 99% confidence interval, the relative deviation in the modeled power requirement for a small-scale vehicle is [−1.86%,−1.14%], whereas, for a mid-scale vehicle, it is [−0.73%,−0.31%]. This sets a benchmark for modeling the energy requirement of mobile robots with multiple influencing factors, allowing for an accurate estimation of the energy requirement of mobile robots.
... These instances are designated as Sal01 ∼ Sal30: in instances Sal01 ∼ Sal10, each job has five operations that are to be processed on three machines, in instances Sal11 ∼ Sal20, each job has 10 operations that are to be processed on seven machines, and in instances Sal21 ∼ Sal30, each job has 25 operations, that are to be processed on three machines. Following on Meißner and Massalski (2020), which empirically investigates the energy requirements of automated guided vehicles (AGVs) in a manufacturing shop, we consider vehicles to have three possible travelling speeds, namely: 0.9, 1.2, and 1.5 metres per second (m/s). The corresponding power consumption being 63, 75, and 86 watts per second when travelling empty and 74, 90, and 108 watts per second when travelling loaded. ...
This work extends the energy-efficient job shop scheduling problem with transport resources by considering speed adjustable resources of two types, namely: the machines where the jobs are processed on and the vehicles that transport the jobs around the shop-floor. Therefore, the problem being considered involves determining, simultaneously, the processing speed of each production operation, the sequence of the production operations for each machine, the allocation of the transport tasks to vehicles, the travelling speed of each task for the empty and for the loaded legs, and the sequence of the transport tasks for each vehicle. Among the possible solutions, we are interested in those providing trade-offs between makespan and total energy consumption (Pareto solutions). To that end, we develop and solve a bi-objective mixed-integer linear programming model. In addition, due to problem complexity we also propose a multi-objective biased random key genetic algorithm that simultaneously evolves several populations. The computational experiments performed have show it to be effective and efficient, even in the presence of larger problem instances. Finally, we provide extensive time and energy trade-off analysis (Pareto front) to infer the advantages of considering speed adjustable machines and speed adjustable vehicles and provide general insights for the managers dealing with such a complex problem.
... For this purpose, the selection of the energy source and the correct management of the energy are of great importance. There are studies on the energy consumption of AGVs, which are expected to work independently from the energy source as much as possible and to be able to do this for as long as possible, according to the route, the load carried and the route-tracking performance [32][33][34][35]. Batteries are generally used as an energy source in AGVs. ...
For automatic guided vehicles (AGVs), maximizing the operating time with maximum energy efficiency is the most important factor that increases work efficiency. In this study, the fuel-cell-powered AGV (FCAGV) system was modeled and optimized control and design were carried out to obtain high tracking performance with minimum power consumption. Firstly, a full mathematical model of FCAGV, which involves the AGV, the fuel cell, DC/DC converters and motors, was obtained. Then, particle swarm optimization (PSO)-based intelligent PID and I controllers were developed for maximizing the route-tracking performance of AGV and voltage-tracking performance of the DC/DC converter with reduced power consumption. PSO was used to determine the optimal parameters of controllers and the values of DC/DC converters’ components. The performance of the full AGV system was analyzed for different paths. The results show that the sufficient path-tracking and voltage-tracking performance was obtained for AGV and DC/DC converters, respectively. The average tracking errors according to global coordinate system are 0.0061 m at the x axis, 0.0572 m at the y axis and 0.0228 rad at rotational axis. The obtained average voltage-tracking errors for each DC/DC converters were approximately 0.8033 V. These results indicate that the developed controllers with optimal coefficients work successfully with small voltage and path-tracking errors. During this motion, the average consumed power from the fuel cell was observed as 58.2675 W. These results show that the designed optimized intelligent controllers have sufficient performance with high energy efficiency and maximum route tracking.
... Improvement in energy efficiency can significantly boost the battery life of CAVs as the Achilles' heel of electric vehicles. The enhancement of Lidar-based autopilot with vehicle-to-vehicle communication technology in CAVs enables smoothing of acceleration/deceleration and reducing stop-and-go driving patterns to reduce energy consumption [16,17,18,19,20]. Research shows that optimizing the powertrain in real-time using an anticipative car-following approach can significantly reduce energy consumption in various driving scenarios and improve the battery life of electric vehicles [21,22,23]. ...
The integration of artificial intelligence and wireless communication technologies in communicant autonomous vehicles (CAVs) enables coordinating the movement of CAV platoons at signal-free intersections. The capacity of signal-free intersections can be significantly improved by adjusting traffic variables at a macroscopic scale; however, the resulting improvement in the capacity does not necessarily have a positive impact on the energy consumption of CAVs at the network level. In this research, we develop an analytical model to enhance energy efficiency by optimizing macroscopic traffic variables in signal-free networks. To this end, we adopt a macroscopic modeling approach to estimate the operational capacity by accounting for the stochasticity resulting from the error in synchronizing the arrival and departure of consecutive platoons in crossing directions at intersections. We also develop a macrolevel analytical model to estimate expected energy loss during the acceleration/deceleration maneuver required for resynchronization at intersections as a function of synchronization success probability. We then maximize energy efficiency by minimizing expected energy loss and maximizing expected capacity in a biobjective optimization framework. We solve the energy efficiency problem using an analytical approach to derive a closed-form solution for the optimal traffic speed and the length of the marginal gap between the passage of consecutive platoons in crossing directions through intersections for a (general) normal distribution of the operational error. Having the closed-form solution of the energy efficiency problem, we balance the trade-off between energy loss and operational capacity at a large scale by extending the analytical model to the network level using the Macroscopic Fundamental Diagram (MFD) concept. The results of our two-ring simulation model indicate the accuracy of the proposed analytical model in estimating the macroscopic relationship between the expected energy loss at intersections and the vehicular density in signal-free networks. Our numerical results also show that optimizing the traffic speed and marginal gap length can improve energy efficiency by 31% at the cost of a 16% decrease in maximum capacity.
... GV/AV motion features A.Herrmann, Brenner, and Stadler (2018);Hirz, Walzel, and Brunner (2021);Meißner and Massalski 2020;Schönauer (2020b); Ullrich and Albrecht (2019); VDI 2010 AGV detection, localization and navigation features Almeida et al., (2020); Borges et al., (2013); Cronin, Conway, and Walsh (2019); Dixon, Bright, and Harley (2012); Herrero, Villagra, and Martinez (2013); Melacini et al., (2019); Run and Z.-Y. Xiao (2018); Ullrich and Albrecht (2019) AGV connectivity/ communication features Bujari et al., (2020); Burmeister et al., (2021); Cupek et al., (2021); Cupek et al., (2020); Horatiu et al., (2019); Indri et al., (2019); Javed et al., (2021); Ullrich and Albrecht (2019); VDA 2020b ...
The automotive industry is on the brink of transitioning to autonomous vehicles (AVs). This will require highly flexible assembly systems. This paper focuses on exploiting the capabilities of the technology base, e.g., sensors and image recognition, of AVs as assembly items and employing their self-driving function in assembly systems. This fundamentally new approach to matrix manufacturing systems based on autonomously navigating automated guided vehicles (AGVs) and the elimination of set assembly sequences is a growing topic of discussion. This study develops a conceptual framework, based on a systematic literature review and interviews with fifteen experts from three carmakers, for exploring the field of research and assessing the feasibility of employing the technology base of autonomous driving instead of AGVs. This study is intended for assembly planners and researchers of assembly systems in automotive manufacturing.
... The remaining constraints ensure the correctness of the completion time constraints that interconnect the AGVs and QCs decisions for i) loading tasks (constraints (20) to (22)), ii) unloading tasks (constraints (23) to (25)), and for iii) mixed loading and unloading tasks (constraints (26) to (27)). Finally, constraints (28) and (29) state that the makespan is the latest tasks completion time among loading and unloading tasks. ...
Maritime transportation has been, historically, a major factor in economic development and prosperity since it enables trade and contacts between nations. The amount of trade through maritime transport has increased drastically; for example, about 90% of the European Union’s external trade and one-third of its internal trade depend on maritime transport. Major ports, typically, incorporate multiple terminals serving containerships, railways, and other forms of hinterland transportation and require interterminal and intraterminal container transport. Many factors influence the productivity and efficiency of ports and hence their economic viability. Moreover, environmental concerns have been leading to stern regulation that requires ports to reduce, for example, greenhouse gas emissions. Therefore, port authorities need to balance economic and ecological objectives in order to ensure sustainable growth and to remain competitive. Once a containership moors at a container terminal, several quay cranes are assigned to the ship to load/unload the containers to/from the ship. Loading activities require the containers to have been previously made available at the quayside, while unloading ones require the containers to be removed from the quayside. The containers are transported between the quayside and the storage yard by a set of vehicles. This chapter addresses the intraterminal container transport scheduling problem by simultaneously scheduling the loading/unloading activities of quay cranes and the transport (between the quayside and the storage yard) activities of vehicles. In addition, the problem includes vehicles with adjustable travelling speed, a characteristic never considered in this context. For this problem, we propose bi-objective mixed-integer linear programming (MILP) models aiming at minimizing the makespan and the total energy consumption simultaneously. Computational experiments are conducted on benchmark instances that we also propose. The computational results show the effectiveness of the MILP models as well as the impact of considering vehicles with adjustable speed, which can reduce the makespan by up to 16.2% and the total energy consumption by up to 2.5%. Finally, we also show that handling unloading and loading activities simultaneously rather than sequentially (the usual practice rule) can improve the makespan by up to 34.5% and the total energy consumption by up to 18.3%.
... 3) Energy-efficient design of traction system considering the most efficient operating points: Detailed analysis of power and energy consumption of AGV is studied in [54][55]. In [54], it is reported that the higher AGV speeds yield higher efficiency and less battery energy consumption. ...
... 3) Energy-efficient design of traction system considering the most efficient operating points: Detailed analysis of power and energy consumption of AGV is studied in [54][55]. In [54], it is reported that the higher AGV speeds yield higher efficiency and less battery energy consumption. This is due to the operation of motors at higher efficiency points. ...
... Tests are performed without any payload. The results are in parallel to the results reported in [54]. Energy consumption of an AGV is higher at lower speeds and lower at higher speeds for the same distance. ...
... Among the numerous previous studies on green vehicle routing problem (GVRP), the energy consumption or fuel consumption is closely relevant to the real-time payload weight of vehicles body [8][9][10]. Nevertheless, as shown as Figure 1, reference [11] through implemented simulation model and statistical analysis of experimental data, it is confirmed that the servo motor output power of the AGV body rises with increasing payload weight, but this power fluctuation phenomenon shows only minor deviations. Considering this energy consumption characteristic of AGVs, based on reasonable assumptions and quantitative analysis of the motion state of AGVs transportation process, an AGV path planning model was established with two performance indicators of transportation distance and energy consumption. ...
... Section 3 established Figure 1. Influence of the payload weight on the electrical power [11]. ...
... Influence of the payload weight on the electrical power[11]. ...
In this study, we present and discuss a variant of the classical vehicle routing problem (VRP), namely the heterogeneous multitype fleet green automated guided vehicle (AGV) routing problem with time windows (HFGVRPTW) applied in the workshops of flexible manufacturing systems (FMS). Specifically, based on the analysis of AGV body structure and motion state, transport distance and energy consumption are selected as two optimization objectives. According to the characteristics and application context of the problem, this paper designs a hybrid genetic algorithm with large neighborhood search (GA-LNS) considering the farthest insertion heuristic. GA-LNS is improved by increasing the local search ability of genetic algorithm to enhance the solution optimal quality. Extensive computational experiments which are generated from Solomon’s benchmark instances and a real case of FMS are designed to evaluate and demonstrate the efficiency and effectiveness of the proposed model and algorithm. The experimental results reveal that compared with using the traditional homogeneous fleet, the heterogeneous multitype AGV fleet transportation mode has a huge energy-saving potential in workshop intralogistics.
... Example of required power of an AGV depending on the driving mode[27]. ...
Due to the growing number of automated guided vehicles (AGVs) in use in industry, as well as the increasing demand for limited raw materials, such as lithium for electric vehicles (EV), a more sustainable solution for mobile energy storage in AGVs is being sought. This paper presents a dual energy storage system (DESS) concept, based on a combination of an electrical (supercapacitors) and an electro-chemical energy storage system (battery), used separately depending on the required transport distance. Each energy storage unit (ESU) in this DESS is capable of supplying the AGV completely. The concept takes into account requirements for a complex material flow as well as minimizing the energy storage capacity required for the operation of the AGV. An energy flow analysis is performed and further used as a basis to derive three possible circuit concepts for the technical realization. The circuit concepts are compared to other approaches from related work, differentiating the functionality to hybrid energy storage systems (HESS). The functionality of the concepts was validated by mapping the energy flow states to active circuit components. Finally, an approach for implementing the control strategy as a state machine is given, and conclusions for further investigations are drawn.
... In addition to the distance traveled and the time spent, the energy consumption of an AGV depends on many factors, such as its speed, weight, and the transported cargo quantity. Hence, the higher the payload weighs, the more power an AGV requires [17]. ...
Order picker routing refers to the process of collecting a set of products with the minimum travel time. Recently, a new generation of Automated Guided Vehicles (AGVs) has been developed to assist human order pickers in order to minimize their travel time. These vehicles are using battery as energy source. However, the routing energy efficiency aspect of these systems remains unexplored. Yet any improvement in power consumption will ultimately reduce the DOD (depth of discharge) of the battery and increase its lifespan. For example, in many real AGV applications incorporating the effect of load mass has been neglected, although its importance. In most studies, the methodology proposed for the order picking routing problem does not allow neither the integration of the mass of each Stock Keeping Unit (SKU) nor the calculation of associated energy costs. Those studies are generally limited to ensure that all the items requested by an order are picked up with minimum travel time/distance. In this paper, an Energy Efficient Order Picking Routing algorithm named
EE-OPR
is proposed to realize an efficient AGV tour with an acceptable trade-off between energy preservation and travel time minimization. The proposed approach takes into account the mass of loads and its accumulation throughout the pick tour since it intensifies the rolling resistance losses on flat ground, especially at lower speeds. In this regard, an optimization method by means of dynamic states graph is developed. This method is applied to different warehouse layouts. The performance of the suggested algorithm is evaluated by comparing it with an approach minimizing only travel time consumption. Results show that the optimized tours, offered by
EE-OPR
are effective and robust, with an 18% average saving on the total cost of picking tour.
