Traffic flow prediction plays an important role in intelligent transportation applications, such as traffic control, navigation, path planning, etc., which are closely related to people’s daily life. In the last twenty years, many traffic flow prediction approaches have been proposed. However, some of these approaches use the regression based mechanisms, which cannot achieve accurate short-term traffic flow predication. While, other approaches use the neural network based mechanisms, which cannot work well with limited amount of training data. To this end, a light weight tensor-based traffic flow prediction approach is proposed, which can achieve efficient and accurate short-term traffic flow prediction with continuous traffic flow data in a limited period of time. In the proposed approach, first, a tensor-based traffic flow model is proposed to establish the multi-dimensional relationships for traffic flow values in continuous time intervals. Then, a CANDECOMP/PARAFAC decomposition based algorithm is employed to complete the missing values in the constructed tensor. Finally, the completed tensor can be directly used to achieve efficient and accurate traffic flow prediction. The experiments on the real dataset indicate that the proposed approach outperforms many current approaches on traffic flow prediction with limited amount of traffic flow data.

To avoid the decrease of system reliability due to insufficient component maintenance and the resource waste caused by excessive component maintenance, identifying the critical components of complex products is an effective way to improve the efficiency of maintenance activities. Existing studies on identifying critical components of complex products are mainly from two aspects i.e., topological properties and functional properties, respectively. In this paper, we combine these two aspects to establish a hybrid intuitionistic fuzzy set to incorporate the different types of attribute values. Considering the mutual correlation between attributes, a combination of AHP (Analytic Hierarchy Process) and Improved Mahalanobis-Taguchi System (MTS) is used to determine the λ-Shapley fuzzy measures for attributes. Then, the λ-Shapley Choquet integral intuitionistic fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is proposed for calculating the closeness degrees of components to generate their ranking order. Finally, a case study which is about the right gear of airbus 320 is taken as an example to verify the feasibility and effectiveness of this method. This novel methodology can effectively solve the critical components identification problem with different types of evaluation information and completely unknown weight information of attributes, which provides the implementation of protection measures for the system reliability of complex products.

Extreme learning machine(ELM) is a feedforward neural network with a single layer of hidden nodes, where the weight and the bias connecting input to hidden nodes are randomly assigned. The output weight between hidden nodes and outputs are learned by a linear model. It is interesting to ask whether the training error of ELM is significantly affected by the hidden layer output matrix H, because a positive answer will enable us obtain smaller training error from better H. For single hidden layer feedforward neural network(SLFN) with one input neuron, there is significant difference between the training errors of different Hs. We find there is a reliable strong negative rank correlation between the training errors and some singular values of the Moore-Penrose generalized inverse of H. Based on the rank correlation, a selection algorithm is proposed to choose robust appropriate H to achieve smaller training error among numerous Hs. Extensive experiments are carried out to validate the selection algorithm, including tests on real data set. The results show that it achieves better performance in validity, speed and robustness.

Home health care (HHC) includes a wide range of healthcare services that are performed in customers’ homes to help them recover. With the constantly increasing demand for health care, HHC policymakers are eager to address routing and scheduling problems from the perspective of optimization. In this paper, a bi-level programming model for HHC routing and scheduling problems with stochastic travel times is proposed, in which the degree of satisfaction with the visit time is simultaneously considered. The upper-level model is formulated for customer assignment with the aim of minimizing the total operating cost, and the lower-level model is formulated as a routing problem to maximize the degree of satisfaction with the visit time. Consistent with Stackelberg game decision-making, the trade-off relationship between these two objectives can be achieved spontaneously so as to reach an equilibrium state. A three-stage hybrid algorithm combining an iterated local search framework, which uses a large neighborhood search procedure as a sub-heuristic, a set-partitioning model, and a post-optimization method is developed to solve the proposed model. Numerical experiments on a set of instances including 10 to 100 customers verify the effectiveness of the proposed model and algorithm.

Motivated by the fact that the product remanufacturing operations are increasingly performed as firms’ competitive advantage and may also play an important role in the choice of channel structure, we construct game-theoretical models to examine the manufacturer’s optimal sales channel strategy in a closed-loop supply chain (CLSC), in which the manufacturer is responsible for used product recycling and remanufacturing and the retailer operates a traditional retail channel. We show that the manufacturer’s optimal sales channel selection depends on the customers’ acceptance of the direct channel and the remanufacturing efficiency. Specifically, in the centralized system, the manufacturer would prefer the dual-channel strategy rather than either the exclusive direct or retail channel, and becomes more willing to introduce a direct channel as the remanufacturing cost savings increase. However, in a decentralized system, there exists a Pareto improvement zone where both the manufacturer and the retailer are better off in the dual-channel format, and the increasing remanufacturing efficiency spills over to the retailer via a lower wholesale price and thus a higher retail demand. Moreover, we extend the study to the retailer-collecting mode and demonstrate that the main results of the original model remain robust.

During the execution of imaging tasks, satellites are often required to observe natural disasters, local wars, and other emergencies, which regularly interferes with the execution of existing schemes. Thus, rapid satellite scheduling is urgently needed. As a new generation of three degree-of-freedom (roll, pitch, and yaw) satellites, agile earth observation satellites (AEOSs) have longer variable-pitch visible time windows for ground targets and are capable of observing at any time within the time windows. Thus, they are very suitable for emergency tasks. However, current task scheduling models and algorithms ignore the time, storage and energy consumed by pitch. Thus, these cannot make full use of the AEOS capabilities to optimize the scheduling for emergency tasks. In this study, we present a fine scheduling model and algorithm to realize the AEOS scheduling for emergency tasks. First, a novel time window division method is proposed to convert a variable-pitch visible time window to multiple fixed-pitch visible time windows. Second, a model that considers flexible pitch and roll capabilities is designed. Finally, a scheduling algorithm based on merging insertion, direct insertion, shifting insertion, deleting insertion, and reinsertion strategies is proposed to solve conflicting problems quickly. To verify the effectiveness of the algorithm, 48 groups of comparative experiments are carried out. The experimental results show that the model and algorithm can improve the emergency task completion efficiency of AEOSs and reduce the disturbance measure of the scheme. Furthermore, the proposed method can support hybrid satellite resource scheduling for emergency tasks.