Human–machine collaborative optimization method for dynamic worker allocation in aircraft final assembly lines

통상적으로 표준적인 최적배분은 층별 조사비용을 고려하여 표본을 배분한다. 만일 조사단위당 비용이 서로 다를 경우 보다 현실적인 배분방법을 고려할 필요가 있다. 즉, 개별 조사단위의 특성에 따라 이익비용비를 최대로 하는 단위를 먼저 표본으로 고려하는 동적배분을 고려하였다. 이러한 관점에서 층별 표본수를 배분하고, 배분된 표본규모에 따라 임의로 표본을 선정하는 방식인 표준적인 최적배분과는 차이가 있다. 이 논문은 약물오용, 낙태, 알콜중독 등과 같은 민감한 특성을 조사하는 층화확률화 응답기법에 대해 각 층별로 표본을 배분할 경우 최적 동적배분을 고려하여 보다 현실적인 문제를 해결하고자 하였으며, 수치적 예제를 통해 동적배분 방법의 효과성을 증명하였다. Typically the standard optimal allocation method distributes the sample for each stratum considering survey cost. In case of varying survey cost for each survey unit, we need to consider more practical allocation method. In other words, according to characteristics of an individual unit, we consider the optimal dynamic allocation method which first selects the survey unit having maximum value of benefit cost ratio. In terms of this, the proposed allocation method is different from standard optimal allocation method which allocate samples for each stratum and selects the random sample according to each size of sample. This paper is considered the dynamic optimal allocation method for the stratified randomized response technique which surveys for sensitive characteristic of survey units such as drug abuse, abortion, alcoholic. We prove the practical usefulness of proposed method using the numerical example.

This paper uses the modified Backstepping (BS) Control method and the Dynamic Control Allocation (DCA) method to solve the problem that when the fighter aircraft under different flight conditions, it will be adaptively tracking different commands. Firstly, we introduce the classical Lyapunov method and classical backstepping control method, and then propose a modified backstepping control method to be applied by general flight control system model. Finally, for the problem that the common control allocation method can't compensate for ignoring the actuator dynamics, a dynamic control allocation method is introduced, and it is successfully applied to the design of the whole closed loop control system. The controller reserves the stable nonlinear term of the system, eliminates the unstable nonlinear term, and minimize the impact of ignoring the actuator dynamic through the dynamic allocation. The simulation results show that the methods in this paper have a good response. It can be implemented in different flight conditions with a good performance of command tracking and lots of robustness.

Due to progressive displacement of conventional power plants by wind turbines, dynamic security of large-scale wind integrated power systems is significantly compromised. In this paper we first highlight the importance of dynamic reactive power support/voltage security in large-scale wind integrated power systems with least presence of conventional power plants. Then we propose a mixed integer dynamic optimization based method for optimal dynamic reactive power allocation in large-scale wind integrated power systems. One of the important aspect of the proposed methodology is that unlike static optimal power flow based approaches, the proposed method considers detailed system dynamics and wind turbine grid code compliance while optimizing the allocation of dynamic reactive power sources. We also advocate that in large-scale wind integrated power systems, 1) better utilization of existing wind turbines especially wind farms with additional grid support functionalities like dynamic support (e.g., dynamic reactive power support, etc.) and 2) refurbishment of existing conventional central power plants to synchronous condensers could be one of the efficient, reliable and cost-effective option to address not only the issue of dynamic voltage security but also to strengthen other dynamic capabilities of the system including system inertia, etc. that are also significant challenges in large-scale wind penetrated power system. The proposed methodology is applied to the detailed model of the western Danish power system which is characterized by large-scale wind integration and least presence of central power plants.

Sectoring is a group control algorithm that is used in elevator traffic control systems by grouping passengers that have common destinations or common origins into elevator cars that serve these floors. The building is split into sectors usually comprising contiguous floors. Two different alternative algorithms for sectoring are discussed in this paper. The first approach is based on dynamic allocation with equal sector allocation. The second approach is based on static allocation with unequal sector sizes. Under static allocation, the same elevator car is allocated to the same sector in every round trip. Under dynamic allocation, each elevator car is allocated to a different sector in each round trip. Under the dynamic allocation scheme suggested in this paper, the elevator cars are sequenced to the various sectors in the buildings in a round-robin fashion. It is important to note that under both schemes, the provided (relative) handling capacity of different sectors is equalised. Five different buildings have been analysed using the two suggested sectoring algorithms. The building is first designed by finding the required number and speed of elevators assuming conventional control. Each building is then analysed using one of the two suggested sectoring algorithms. In order to compare the performance of the two sectoring algorithms, the provided (relative) handling capacity is calculated. The provided (relative) handling capacity of the two suggested algorithms is then compared. Very little difference was found between the two algorithms. The dynamic sectoring with equal sector sizes offers the convenience of having equal sector sizes. The static sectoring with unequal sector sizes is more convenient for passengers that are familiar with the building. Practical application: This paper analyses two different options for sectoring the control system of an elevator system in a building. Each of the two sectoring methods is suitable for different situations. The dynamic allocation method is more suitable for destination group control systems and offers the group controller more flexibility. The static allocation method with unequal sector sizes ensures that the passengers remain familiar with the elevators that they use to get to their floors. The programmer of the elevator group controller can programme both methods in the controller and allow it to switch to the most suitable algorithm depending on the prevailing conditions.

Worldwide, Internet of Things (IoT) devices will surpass a range of five billion by 2025 and developed countries will extend to advance by supplying almost two-thirds of such connections. With existing infrastructure, allocating bandwidth to billions of IoT devices is going to be cumbersome. This paper addresses the problem of Dynamic bandwidth allocation in IoT devices. We enhanced the dynamic bandwidth allocation algorithms to support QoS in different bandwidth ranges. Our Proposed innovative Machine learning-based Intelligent Dynamic Bandwidth Allocation (IDBA) algorithm allocates the bandwidth effectively between IoT devices based on utilization patterns observed through machine learning methods. Moreover, we showed that an IDBA algorithm results in supporting quality of service in terms of ensuring uninterrupted bandwidth to critical IoT application where bandwidth tolerance is zero percent, along with that IDBA increasing the network throughput correlated to other dynamic bandwidth allocation algorithms. We demonstrate simulations in different applications. The results show that IDBA achieves better throughput even in low bandwidth range.

Due to the large uncertainty of signal reception, the transmission speed of the wireless communication network resource allocation method is low. Based on social relations, a dynamic allocation method for wireless communication network resources is designed. Establish a wireless ad hoc network composed of a random number of nodes to determine the upper limit of its security capacity, and the nodes at the edge need to undertake more communication transmission loads on average. According to the similarity between users, a triple closed model is used to measure the social trust relationship in wireless communication networks. Considering the social trust and encounter frequency among users, the optimal relay node is selected for the wireless network communication link. The sum of data transmission rates under each allocation scheme is calculated and sorted in descending order, and resources are dynamically allocated based on the principle of maximum overall network throughput. Compared with other schemes through simulation, the dynamic allocation method of wireless communication network resources based on social relations can increase the network transmission speed and improve the communication performance.

Dynamic task allocation of unmanned aerial vehicle swarms for ground targets is an important part of unmanned aerial vehicle (UAV) swarms task planning and the key technology to improve autonomy. The realization of dynamic task allocation in UAV swarms for ground targets is very difficult because of the large uncertainty of swarms, the target and environment state, and the high real-time allocation requirements. Hence, dynamic task allocation of UAV swarms oriented to ground targets has become a key and difficult problem in the field of mission planning. In this work, a dynamic task allocation method for UAV swarms oriented to ground targets is comprehensively and systematically summarized from two aspects: the establishment of an allocation model and the solution of the allocation model. First, the basic concept and trigger scenario are introduced. Second, the research status and the advantages and disadvantages of the two allocation models are analyzed. Third, the research status and the advantages and disadvantages of several common dynamic task allocation algorithms, such as the algorithm based on market mechanisms, intelligent optimization algorithm, and clustering algorithm, are evaluated. Finally, the specific problems of the current UAV swarm dynamic task allocation method for ground targets are highlighted, and future research directions are established. This work offers important reference significance for fully understanding the current situation of UAV swarm dynamic task allocation technology.

Optimal stochastic scenario-based allocation of smart grids’ renewable and non-renewable distributed generation units and protective devices

In this paper, we propose a grouping-based dynamic routing, core, and spectrum allocation (RCSA) method for preventing spectrum fragmentation and inter-core crosstalk in elastic optical path networks based on multi-core fiber environments. Multi-core fibers enable us to considerably enhance the transmission capacity of optical links; however, this induces inter-core crosstalk, which degrades the quality of optical signals. We should thus avoid using the same frequency bands in adjacent cores in order to ensure high-quality communications. However, this simple strategy leads to inefficient use of frequency-spectrum resources, resulting in spectrum fragmentation and a high blocking probability for lightpath establishment. The proposed method allows one to overcome this difficulty by grouping lightpath-setup requests according to their required number of frequency slots. By assigning lightpath-setup requests belonging to the same group to cores according to their priority, the proposed method aims to suppress inter-core crosstalk. Furthermore, the proposed method is designed to mitigate spectrum fragmentation by determining the prioritized frequency bandwidth for lightpath-setup requests according to their required number of frequency slots. We show that the proposed method reduces the blocking of lightpath establishment while suppressing inter-core crosstalk through simulation experiments.

Optimal effort allocation in stratified, large area trawl surveys, with application to shrimp surveys in the Barents Sea

For the efficient use of limited frequency resources, the spectrum sharing allocation between mobile networks and different existing radio systems is promising. In this scheme, we focus on an allocation method that involves sharing spectrum bands with a mobile network operator (MNO). Recently, to improve the spectrum efficiency, dynamic spectrum allocation methods according to the MNO demand were proposed. However, in demand-based dynamic allocation, channel discontinuity in the frequency and time directions in the same MNO occurs when mapping the channel to MNOs. This channel discontinuity reduces the utility of shared frequencies. To address this problem, we propose a novel spectrum allocation method that maximizes frequency and time continuity for demand-based dynamic spectrum sharing. Our performance evaluation shows that compared with the existing methods, the proposed algorithm can improve the spectrum efficiency by 6%, channel continuity by 23%, and fairness by 5%.

Resource allocation is one of the principal stages of query processing in relational data grid systems. Specific characteristics of the data grid environment, such as dynamicity, heterogeneity and large scale, impose serious restrictions to the resource allocation process. Static resource allocation before the query execution may be far from optimal due to the dynamic changes of the system. One possible optimization is to adjust dynamically the allocation of resources during the query execution. Some methods of dynamic resource allocation have been proposed, however, most of them use centralized control mechanisms. In this study we argue that the decentralized approach meets better the requirements of the data grid systems. In this study we propose a decentralized method of dynamic resource allocation that is based on the mobile agent paradigm. We consider the participating nodes as autonomous and independent elements of the system, each of which can detect if it is overloaded and make the decision to react. Then we consider each relational operation as a mobile agent running on the allocated node, meaning that, it keeps track of its own status and can migrate to another node at any time. A two-level cooperation mechanism between such autonomous nodes and autonomous operations is described in detail. Performance evaluation proves the efficiency of the proposed method.

To solve the question of guaranteeing the safety of a system with a complex structure and tight couplings, a new method of optimal allocation exploiting the dynamical planning theory is presented. Two key factors that can affect system safety, i.e., accident loss and accident rate are analyzed. Then, to measure comprehensively requirements of system safety, accident safety level is defined based on these two factors. Subsequently, optimal allocation for complex systems’ safety resource is implemented according to the network properties of the inherent structures of the systems and constraint conditions for safe investments. Finally, a case study shows that the method using dynamical planning for optimal allocation of system safety can ensure preferential allocations of the parts with high safety requirements. Besides, this optimal allocation method is feasible and valid.

The traditional balanced assembly line designs can perform quite inefficiently under the presence of high labor turnover, low operator learning rates, and stochastic processing times. In these situations assembly line designs based on dynamic work allocation and work sharing principles have been shown to render a higher and less variable throughput. However, for situations where low labor turnover conditions and high operator learning rates exist, the traditional balanced lines still tend to be most productive. This paper has two objectives: to introduce a method based on work sharing principles, which we denominate Modified Work Sharing (MWS), and to develop some simple analytical tools that will allow us to compare the performance of this method with the traditional and other dynamic work allocation line designs. These results suggest that the traditional method is the most affected by the introduction of new operators in the production lines and thus the most affected by variability in general. On the other hand, dynamic work allocation methods appear to better absorb the variability introduced by new operators by sharing the workload of new operators with the more experienced members of the line.

In order to solve the problems of poor balance and low efficiency of traditional dynamic allocation method of human resources, this paper proposes a new dynamic allocation method of human resources based on improved ant colony optimization. In order to improve the convergence of the traditional ant colony optimization algorithm and avoid falling into the local optimal solution, the ant colony optimization algorithm is improved from three aspects: heuristic function, state selection strategy and pheromone allocation mechanism. Based on the improved ant colony optimization algorithm, the objective function of human resource dynamic allocation is constructed on the basis of human resource workload, work efficiency and work ability index, so as to obtain the optimal allocation results and complete the dynamic allocation of human resources. The experimental results show that, compared with the traditional human resource allocation method, the distribution equilibrium and efficiency of the proposed method are higher, and the distribution equilibrium is basically consistent with the standard value, indicating that the practical application performance of the proposed method is stronger.