Currently, the disassembly of retired electric vehicle battery packs relies on manpower and results in high cost, low efficiency, and poor stability. With the development of artificial intelligence, automated disassembly is an efficient method to largely reduce even completely replace human disassembly. However, the various kinds of battery packs and the uncertainty on their retired numbers and types lead to frequent changes of their disassembly processes. It is necessary to provide a method that can integrate valuable disassembly knowledge to enable automated disassembly. Thus, this study proposes an automated disassembly-oriented knowledge graph for retired battery packs which considers the properties of subassemblies (entities) and explicit physical connections/implicit associations among subassemblies (relations). A large amount of unstructured data exists regarding battery packs, such as product manuals and maintenance records, whereas the knowledge that can be available to guide the disassembly process is dispersed and sparse. To solve this, a candidate entity-based relational triple joint extraction method is developed to efficiently extract the disassembly knowledge, which consists of semantic feature learning, candidate entity recognition, and explicit/implicit relational triple identification. Finally, more than 10,000 sentences collected from multi-source unstructured texts are adopted to verify the proposed method. The experimental results demonstrate that our proposed method achieves an F1-score of 93.99% in candidate entity recognition and an F1-score of 95.6% in triple extraction. Also, the information of disassembly operations, disassembly tools, and subassembly properties can be recommended by the automated disassembly-oriented knowledge graph for retired battery packs.

A bike-sharing system (BSS) is easily unbalanced due to the uncertainty of user demand at each bike station during the day, which appeals for an effective bike reposition solution based on the accurate prediction of user demand. However, there is a discrepancy between the bike pickup/drop-off record (satisfied demand) and the user’s first choice of origin/destination stations (i.e., true user demand) since the BSS cannot capture the unsatisfied user demand (i.e., abandoned rentals and transferred rentals/returns) that occurs at either empty stations (failed rentals) or full stations (failed returns). To efficiently rebalance the BSS, this paper focuses on accurately forecasting the true user demand of the BSS. First, we extract the spatial-temporal features of bike usage and establish a spatio-temporal model for true user demand prediction. Then, an XGBoost-based three-stage prediction approach is proposed to accurately predict the true user demand including the station clustering, the system record rectification, and the true user demand prediction. The real data from the Citi Bike in New York is applied to verify the proposed method and the experimental results demonstrate that the proposed approach outperforms the existing methods.

This article studies the flexible job shop scheduling problem with a certain number of automatic guided vehicles (FJSP-AGVs), aiming to minimize the makespan. First, a novel constraint programming (CP) model is formulated to obtain optimal solutions. Specifically, the proposed CP model addresses the shortcomings of the existing CP model, which cannot solve instances with a machine processing two consecutive operations of the same job. Additionally, redundant and symmetry-breaking constraints are designed to accelerate constraint propagation and break problem symmetry, respectively. Then, to more effectively solve FJSP-AGVs, a CP-assisted meta-heuristic algorithm framework is designed, with a CP-assisted dual-population collaborative genetic algorithm (DCGA-CP) being developed as an example. Finally, experiments are performed on benchmark instances to demonstrate the effectiveness and superiority of the proposed CP model and DCGA-CP. Experimental results show that the proposed CP models first prove 29 new optimal solutions and improve 27 best-known solutions. Meanwhile, DCGA-CP first proves 29 new optimal solutions and improves 32 best-known solutions for benchmark instances.

Disassembly is the first phase to demanufacture end-of-life (EOL) products that are separated into parts/components for recovery. The quality conditions of EOL products are highly uncertain, which would result in some uncertain information during the disassembly process, e.g., the disassembly time and recovering revenue of each subassembly. It is quite challenging to determine the optimal/near-optimal disassembly solutions under uncertain information. This article studies uncertain disassembly planning (UDP) and proposes a self-adaptive learning approach to quickly identify the near-optimal disassembly solutions. First, we model the UDP by extending Petri nets, where not only disassembly operations but also EOL options of each subassembly are represented in the extended Petri Net. Based on the UDP model, we develop the self-adaptive learning approach, which integrates an approximation procedure for estimating uncertain disassembly information, aQ-learning algorithm for training disassembly samples, and a heuristic method for selecting the best disassembly solution. Finally, a hybrid Li-ion battery pack of Audi A3 Sportback e-tron is selected as the case study and applied to test the proposed self-adaptive learning approach. The experimental results demonstrate that our proposed method can efficiently find a better disassembly solution than the existing disassembly solution within 200 trainings in the case study.

Selecting reliable beacon nodes plays a significant role in relocalizing unknown nodes in a wireless sensor network. When the position of a beacon node is drifted or is spoofed, it becomes an unreliable beacon node, which would lead to a large relocalization deviation of unknown nodes in its neighbor. However, when selecting reliable beacon nodes, most relocalization algorithms only screen either drifting beacon nodes or malicious beacon nodes whose position is drifted or spoofed. This article proposes an algorithm that can simultaneously screen drifting beacon nodes and malicious beacon nodes. The algorithm is divided into four steps. First, three indicators are introduced, where two are for describing position drifting and one is for describing position spoofing. Second, the entropy method is used to weight the contributions of three indicators. Third, a technique for order preference by similarity to an ideal solution is used to construct a reliability evaluation model. Finally, using the reliability evaluation model select reliable beacon nodes. Experimental results illustrate that the detection accuracy of drifting beacon nodes and malicious beacon nodes of the proposed algorithm is 7.5% and 8.2% higher than that of the state-of-the-art algorithms, respectively.

The accurate information of sideslip angle (SA) and tire cornering stiffness (TCS) is essential for advanced chassis control systems. However, SA and TCS cannot be directly measured by in-vehicle sensors. Thus, it is a hot topic to estimate SA and TCS with only in-vehicle sensors by an effective estimation method. In this article, we propose a novel fuzzy adaptive robust cubature Kalman filter (FARCKF) to accurately estimate SA and TCS. The model parameters of the FARCKF are dynamically updated using recursive least squares. A Takagi–Sugeno fuzzy system is developed to dynamically adjust the process noise parameter in the FARCKF. Finally, the performance of FARCKF is demonstrated via both simulation and experimental tests. The test results indicate that the estimation accuracy of SA and TCS is higher than that of the existing methods. Specifically, the estimation accuracy of SA is at least improved by more than 48%, while the estimators of TCS are closer to the reference values.

Demanufacturing aims to recover value and conserve energy from end-of-life (EOL) products, contributing to sustainable manufacturing. To make the full use of EOL products, they are usually disassembled into components that have different values and embodied energy at different EOL options. This article studies a disassembly planning (DP) that integrates the decisions on disassembly sequence and EOL strategy to maximize the recovered value and energy conservation from EOL products. We propose a multiobjective DP based on the value recovery and energy conservation (MDPVE) model, which is different from the existing DP models by focusing on the embodied energy rather than the energy consumption during disassembly. An adapted multiobjective artificial bee colony (ABC) algorithm [multiobjective ABC (MOABC)] is developed to identify the Pareto solutions for the MDPVE and is compared with a well-known metaheuristic algorithm, Non-dominated Sorting Genetic Algorithm-II (NSGA-II). A real-world case study demonstrated the superior solution quality and computational efficiency of MOABC. Note to Practitioners-There is often more than one treatment option for EOL products or components, including reuse, remanufacturing, and recycling. However, the decision on which EOL option to select is not considered in most of the DP studies by assuming an EOL option given for each component. Hence, the disassembly plan with the EOL decision is focused in this article. As energy sustainability gains an increasing attention, it is essential to assess the profitability and energy conservation simultaneously for EOL products. Since there could be a tradeoff between recovered profit and conserved energy, a multiobjective evolutionary algorithm is developed for generating Pareto solutions which help decision-makers to find good solutions for both evaluation indicators.

Smart shared mobility is an emerging transportation strategy that promotes sustainable and intelligent transportation. Bike sharing is one mode of smart shared mobility and is gaining popularity in recent years. To ensure a smooth operation of a bike sharing system (BSS), it is essential to redistribute the bicycles, which includes picking up returned bicycles and relocating them to best serve customers. A bike sharing rebalancing problem (BSRP) has thus emerged. This paper addresses a static BSRP which operates during the night when shared bikes are rarely utilized or when the BSS is closed. We studied a single-vehicle BSRP (sBSRP) and multi-vehicle BSRP (mBSRP) with the objective of minimizing the depot inventory cost as well as the traveling cost. For mBSRP, six formulations are presented i.e. five mixed integer programming models (mBSRP1-mBSRP5) and a mixed integer linear programming model (mBSRP6). In addition, an iterative procedure combined with the branch-and-cut algorithm in the CPLEX solver is developed to solve this problem. A real-world case study is employed to test the effectiveness of the formulations, and a set of benchmark instances are adopted to further compare the performances of mBSRP5 and mBSRP6. The experimental results show that mBSRP6 performs the best among the six models, offering the best solution quality and computational efficiency. Finally, mBSRP6 is applied to determine the depot inventory for the case study using random demand datasets.

Due to the rapid technology advancement and market changes, products are becoming outdated and subsequently discarded faster than ever before. Recovery, recycling, and remanufacturing of end-of-life (EOL) products are getting more attention. Disassembly is indispensable to recycle and remanufacture EOL products, and a disassembly line is an efficient way to perform it. A disassembly line balancing problem (DLBP) aims at streamlining the disassembly activities such that the total disassembly time consumed at each workstation is approximately the same and approaching the cycle time. However, the assignment of disassembly operations to workstations in a disassembly shop should ensure the recovery of valuable components and reduce undesirable impact on the environment as much as possible. In this paper, a novel heuristic technique combining multicriterion decision making (MCDM) and general variable neighborhood search (GVNS) is proposed to solve the DLBP. Based on the characteristics of the DLBP, an innovative MCDM method based on fuzzy set theory, grey relational analysis, and Choquet fuzzy integral is developed to evaluate the performance scores and determine the ranking of disassembly tasks. Subsequently, an improved GVNS algorithm is employed to further balance a disassembly line with three objectives, in which a new metric is formulated to integrate with the ranking from MCDM. The proposed method not only takes a comprehensive objective system into consideration but effectively generates a good enough tradeoff disassembly solution. Finally, the proposed approach is illustrated with an example and compared with two other heuristics to show its efficacy in solving the DLBP.

Disassembly sequencing is important for remanufacturing and recycling used or discarded products. AND/OR graphs (AOGs) have been applied to describe practical disassembly problems by using “AND” and “OR” nodes. An AOG-based disassembly sequence planning problem is an NP-hard combinatorial optimization problem. Heuristic evolution methods can be adopted to handle it. While precedence and “AND” relationship issues can be addressed, OR (exclusive OR) relations are not well addressed by the existing heuristic methods. Thus, an ineffective result may be obtained in practice. A conflict matrix is introduced to cope with the exclusive OR relation in an AOG graph. By using it together with precedence and succession matrices in the existing work, this work proposes an effective triple-phase adjustment method to produce feasible disassembly sequences based on an AOG graph. Energy consumption is adopted to evaluate the disassembly efficiency. Its use with the traditional economical criterion leads to a novel dual-objective optimization model such that disassembly profit is maximized and disassembly energy consumption is minimized. An improved artificial bee colony algorithm is developed to effectively generate a set of Pareto solutions for this dual-objective disassembly optimization problem. This methodology is employed to practical disassembly processes of two products to verify its feasibility and effectiveness. The results show that it is capable of rapidly generating satisfactory Pareto results and outperforms a well-known genetic algorithm.

It is complex and difficult to perform the emergency scheduling of forest fires in order to reduce the operational cost and improve the efficiency of extinguishing fire services. A new research issue arises when: 1) decision-makers want to minimize the number of rescue vehicles (or fire-fighting ones) while minimizing the extinguishing time; and 2) decision-makers prefer to complete this task given limited vehicle resources. To do so, this paper presents a novel multiobjective scheduling model to handle forest fires subject to limited rescue vehicle (fire engine) constraints, in which a fire-spread speed model is introduced into this problem to better describe practical forestry fire. Moreover, a Multiobjective Hybrid Differential-Evolution Particle-Swarm-Optimization (MHDP) algorithm is proposed to create a set of Pareto solutions for this problem. This approach is applied to a real-world emergency scheduling problem of the forest fire in Mt. Daxing'anling, China. Its effectiveness is verified by comparing it with a genetic algorithm and particle swarm optimization algorithm. Experimental results show that the proposed approach is able to quickly produce satisfactory Pareto solutions.