Search

Accurate vibration time series modeling can mine the internal law of data and provide valuable references for reliability assessment. To improve the prediction accuracy, this study proposes a hybrid model – called the AR–SVR–CPSO hybrid model – that combines the auto regression (AR) and support vector regression (SVR) models, with the weights optimized by the chaotic particle swarm optimization (CPSO) algorithm. First, the auto regression model with the difference method is employed to model the vibration time series. Second, the support vector regression model with the phase space reconstruction is constructed for predicting the vibration time series once more. Finally, the predictions of the AR and SVR models are weighted and summed together, with the weights being optimized by the CPSO. In addition, the data collected from the reliability test platform of high-speed train transmission systems and the "NASA prognostics data repository" are used to validate the hybrid model. The experimental results demonstrate that the hybrid model proposed in this study outperforms the traditional AR and SVR models. [ABSTRACT FROM AUTHOR]

Scheduling problems with resource allocation have received much attention in the academic and industrial communities in recent years. This study addresses a parallel‐machine scheduling problem with resource allocation under the objective of minimizing the makespan, where jobs need an additional resource that is allocated to machines in advance. The processing time of jobs is a function of the amount of allocated resources on their corresponding machines. In order to deal with this problem, three decisions must be made, i.e. resource allocation among machines, job assignment on machines, and job sequence on each machine. To solve this problem efficiently, a constructive heuristic approach and a hybrid nested partition method are designed to make these three decisions. Simulation experiments are carried out on a set of instances, and two existing algorithms are chosen to be compared with the proposed algorithms. Experimental results show that the proposed algorithms are promising in coping with this problem. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Synergistic improvement on low carbon manufacturing is not taken full consideration in flow shop scheduling in literatures. To fill the gap, this article extends a flow shop scheduling problem by considering low carbon emission and variable machining parameters. The difference between the extended scheduling problem and the traditional one is that the former investigates the comprehensive effects of machine and scheduling levels on production throughput and environmental impact. A mathematical model of the extended problem is formulated to find the Pareto frontier, which is the set of scheduling solutions that are all Pareto efficient. Moreover, two problem-specific scheduling methods are presented and their effects on the considered problem are proved. A novel multi-objective heuristic is developed to deal with the extended problem because it is a non-deterministic polynomial acceptable problem. An actual case is studied to demonstrate the feasibility and applicability of the proposed extended problem and two scheduling methods. These finds can be employed in manufacturing execution systems and do not require additional cost. [ABSTRACT FROM AUTHOR]

Recently, the ow shop scheduling problem with deteriorating jobs has gained increasing concern from academic communities and industrial areas. The de- teriorating job is that the processing time of the jobs depends on their starting time. Due to the essential complexity of this problem, most studies focus on the two and three stages setting, and there are few studies that have considered a multiple stage setting. In this paper, a multiobjective ow shop deteriorating scheduling problem is considered, where the objectives are to minimize the makespan and the total tardiness, simultaneously. In order to solve it e_ciently, a novel adaptive multipopulation mul- tiobjective genetic algorithm is proposed. In the proposed algorithm, multiple scalar optimization problems of the multiobjective ow shop deteriorating scheduling prob-lem are developed, which will be introduced into the iteration course in multiple stages. An adaptive multipopulation strategy is designed to construct multiple subpopulations to search the optimal solutions of several scalar optimization problems in parallel. In addition, some special strategies, i.e. selection, crossover, mutation and the evolution of external archives are designed to improve the performance of the adaptive multi-population multiobjective genetic algorithm for the investigated multiobjective ow shop deteriorating scheduling problem. Based on a set of test instances on the multiob-jective ow shop deteriorating scheduling problem, simulation experiments are carried out to examine the performance of adaptive multipopulation multiobjective genetic al-gorithm in comparison with several peer multiobjective evolutionary algorithms. The experimental results show that the proposed adaptive multipopulation multiobjective genetic algorithm performs well when solving the multiobjective ow shop deteriorating scheduling problem. [ABSTRACT FROM AUTHOR]

In order to provide an accurate estimation of energy consumption, this work proposes a novel energy consumption modeling and prediction approach for a milling process from a multistage perspective. Based on its work stages, each stage's energy consumption model is established by sliding filter, multiple linear regression, and improved gene expression programming (variable neighborhood search–based gene expression programming) methods and then the total energy consumption is predicted through their combination. A case study is given to illustrate the proposed model and its effectiveness. Compared with the full quadratic model, which can fully consider the interaction between cutting factors, the proposed method can achieve the higher accuracy to predict the energy consumption of the milling process. [ABSTRACT FROM AUTHOR]

Materials selection, as a essential link for manufacturing enterprises, has an important driving-force to comprehensively upgrade material properties and service life, especially in building and decoration fields. To qualitatively select the optimal green decoration materials, a hybrid multi-criteria decision making (MCDM) approach integrating analytical hierarchy process (AHP) and grey correlation technique for order performance by similarity to ideal solution (GC-TOPSIS) is proposed. The weights vector of hierarchical index structure, which is established based on interior environmental characteristics, i.e., physiological comfort, psychological satisfaction and interior environmental effect, is determined by AHP. GC-TOPSIS is applied to obtain the final ranking of green decoration materials to select the optimal one. A case study, i.e., 10 kinds of solid woods, is illustrated to validate the proposed method. Additionally, a sensitivity analysis of nine experiments is carried out to monitor the robustness of solution ranking to changes. The results proved that this method furnishes an rational and efficient decision support tool for performance assessment of green decoration materials. [ABSTRACT FROM AUTHOR]

While the concept of remanufacturing is gaining popularity in practice, its effect on environmental protection and energy saving is significant, which has attracted widespread concerns, especially in the automotive components field. Automotive components remanufacturing (ACR), as a prominent part in automotive life-cycle development, can dramatically reduce resources-waste and mitigate energy-crisis, which has become a prominent direction to promote sustainable development of Chinese economy. Thus, influence factors analysis and enterprise operation patterns assessment which primarily contribute to ACR industry development are of paramount importance. This paper elaborates the criteria influencing the ACR industry operation and analyzes the main operation patterns for ACR in China. To overcome the uncertainty of the evaluation process, a new hybrid multi-criterion decision-making (MCDM) model that combines fuzzy AHP (analytical hierarchy process) and fuzzy G-TOPSIS (combining gray relation analysis and technique for order performance by similarity to ideal solution) is proposed to determine weights of influence criteria and evaluate ACR production operation patterns. In addition, sensitivity analysis is applied to monitor the robustness of the method. The study refers to the experience of Chinese end-of-life vehicle (ELV) laws and implementation requirements of extended producer responsibility (EPR). The obtained results provide the new guideline to revise the regulations of Chinese ACR industry. [ABSTRACT FROM AUTHOR]

Disassembly sequencing has significant effects on the performance of remanufacturing and recycling of used or discarded products. Studies on disassembly sequence optimization have largely focused on sequential disassembly. However, for large or complex products sequential disassembly takes long time to complete and is rather inefficient since it removes only one part or subassembly at a time with only one operator assigned to disassemble a product. This work studies selective cooperative disassembly sequence planning (SCDSP) problem which is essential to disassemble large or complex products in an efficient way. Similar to sequential disassembly planning, SCDSP aims at finding the optimal disassembly task sequence, but is more complicated. SCDSP is a nonlinear NP-complete combinatorial optimization problem, and evolutionary algorithms can be adopted to solve it. In this paper exclusive and cooperative relationships are introduced as additional constraints besides the common precedence relationship. A novel procedure to generate feasible cooperative disassembly sequences (GFCDS) is proposed. A mathematical programming model of SCDSP is developed based on the parallel disassembly characteristics with two optimization objectives i.e. disassembly time and profit, considered. A multi-objective evolutionary algorithm (MOEA), i.e., multi-objective discrete artificial bee colony optimization (MODABC), is adopted to solve the problem to create the Pareto frontier. This approach is applied to real-world disassembly processes of two products (a small product and a medium/large one) to verify its feasibility and effectiveness. Also, the proposed method is compared with the well-known NSGA-II. For our comparative study, the nondominated solutions of the two MOEAs are compared in both cases, and two quantitative metrics, i.e., inverted generational distance ( IGD ) and spacing ( SP ), are adopted to further measure the algorithm performance. Results indicate that the set of nondominated solutions from MODABC are better for each instance tested, and the Pareto front is overall superior to that from NSGA-II. For both cases, IGD and SP are decreased by up to 81.5% and 62.2%, respectively. [ABSTRACT FROM AUTHOR]

A new force is introduced in the social force model (SFM) for computing following behavior in pedestrian counterflow, whereby an individual tries to approach others in the same direction to avoid conflicts with pedestrians from the opposite direction. The force, like a kind of gravitation, is modeled based on the movement state and visual field of the pedestrian, and is added to the classical SFM. The modified model is presented to study the impact of following behavior on the process of lane formation, the conflict, the number of lanes formed, and the traffic efficiency in the simulations. Simulation results show that the following behavior has a significant effect on the phenomenon of lane formation and the traffic efficiency. [ABSTRACT FROM AUTHOR]

Multilevel programming is developed for modeling decentralized decision-making processes. For different management requirements and risk tolerances of different-level decision-makers, the decision-making criteria applied in different levels cannot be always the same. In this paper, a hybrid multilevel programming model with uncertain random parameters based on expected value model (EVM) and dependent-chance programming (DCP), named as EVM-DCP hybrid multilevel programming, is proposed. The corresponding concepts of Nash equilibrium and Stackelberg-Nash equilibrium are given. For some special case, an equivalent crisp mathematical programming is proposed. An approach integrating uncertain random simulations, Nash equilibrium searching approach and genetic algorithm is designed. Finally, a numerical experiment of uncertain random supply chain pricing decision problem is given. [ABSTRACT FROM AUTHOR]