At present, passive balancing methods dominate energy storage applications, however, they suffer from a long balancing duration. In this article, we took advantage of a modular architecture, where ...several modular power converters replace a central dc-dc converter for fast charging and balancing of a supercapacitor stack. A strategy has been proposed to control how power is shared among the converters during the charging period in order to balance the supercapacitors. However, some converters enter control saturation due to voltage differences between supercapacitors caused by their nonuniform conditions and characteristics. The originality of this article lies in taking into account the saturation by modifying an energy-based strategy to correct the power shares and make balancing the supercapacitors possible. Simulation and experimental case studies were used to demonstrate the strategy's performance and limitations.
In this paper, a new mixed integer programming (MIP) formulation is developed for balancing and scheduling of mixed model assembly lines with disjunctive precedence constraints among assembly tasks. ...To represent alternative precedence relations, AND/OR assembly graph was adopted. In case of alternative precedence relations, for each product multiple assembly plans exist, which can be represented by a set of alternative precedence subgraphs and only one of such subgraphs should be selected for each product. As the number of subgraphs exponentially increases with the number of disjunctive relations among the tasks, the computational complexity of simultaneous balancing and scheduling along with the assembly subgraph selection increases with the number of alternative precedence relations. Unlike the other MIP approaches known from the literature, the new model does not need the alternative assembly subgraphs to be to explicitly enumerated as input data and then used for indexing the variables. Instead, a new disjunctive precedence selection and task assignment variable and new constraints are introduced to optimally choose one relation for each subset of alternative precedence relations. The optimal solutions for computational examples of balancing and scheduling problems illustrate a superior performance of the new modelling approach.
Manufacturers need to arrange the recovery of product components and subassemblies for reuse, remanufacture and recycling to extend the life of materials in use and reduce the disposal volume owing ...to increasing environmental concerns. The disassembly line balancing problem (DLBP) is the process of allocating a set of disassembly tasks to an ordered sequence of workstations. A novel mathematical model is presented for the DLBP by considering resource and labour constraints. Utilizing a transformed AND/OR graph as the main input ensures the feasibility of the precedence relationships among the tasks. The objective is to minimize the number of labourers used under the predetermined cycle time. This study proposes a three-phase heuristic adaptive genetic algorithm (AGA) to optimize the number of labourers in the disassembly line. Experimental results indicate that the proposed method is superior to the existing approaches for medium- and large-scale DLBPs.
The cycle life and efficiency of a battery pack get enhanced by employing an intelligent supporting system with it called the Battery Management System (BMS). A novel Proportional Integral (PI) ...controller and an Artificial Neural Network (ANN)-based controller for controlling the Passive Cell Balancing (PCB) technology have been implemented for BMS. The Scaled Conjugate Gradient, Bayesian Regularization, and Levenberg Marquardt algorithms of ANN are employed individually for the control operation. Each of the techniques is executed and analyzed in a MATLAB Simulink environment. With PI controllers, improved performance of cell balancing is achieved as compared with the conventional PCB method without employing controllers. Whereas, on implementing the ANN-based controllers, more improvement in the results occurs in terms of SoC balancing, voltage balancing, power dissipation, heat dissipation, and temperature rise across the bleeding resistors connected to each cell. The average current flowing across the bleeding resistors decreases from 1.5620 A to 0.8756 A, and to 0.2032 A on utilizing the conventional PCB, the novel PI-controller, and ANN-controller-based PCB techniques respectively, indicating better SoC balancing. Consequently, the average power dissipated decreases from 2.9807 W to 1.1275 W and 0.0838 W, while the average heat dissipated decreases from 2.0224 KJ to 0.1921 KJ and 0.0052 KJ. Thus, the average temperature rise also reduces from 2.3541°C to 1.0331°C and 0.1091°C. Hence, the efficiency further gets enhanced by employing the ANN-based controllers for their satisfactory use in BMS. So, these technologies ensure improving the performance and driving range of electric vehicles effectively.
The promise of quantum computing lies in harnessing programmable quantum devices for practical applications such as efficient simulation of quantum materials and condensed matter systems. One ...important task is the simulation of geometrically frustrated magnets in which topological phenomena can emerge from competition between quantum and thermal fluctuations. Here we report on experimental observations of equilibration in such simulations, measured on up to 1440 qubits with microsecond resolution. By initializing the system in a state with topological obstruction, we observe quantum annealing (QA) equilibration timescales in excess of one microsecond. Measurements indicate a dynamical advantage in the quantum simulation compared with spatially local update dynamics of path-integral Monte Carlo (PIMC). The advantage increases with both system size and inverse temperature, exceeding a million-fold speedup over an efficient CPU implementation. PIMC is a leading classical method for such simulations, and a scaling advantage of this type was recently shown to be impossible in certain restricted settings. This is therefore an important piece of experimental evidence that PIMC does not simulate QA dynamics even for sign-problem-free Hamiltonians, and that near-term quantum devices can be used to accelerate computational tasks of practical relevance.
Display omitted
•Iterative branch, bound and remember is developed as an exact method for SALBP-2.•Iterative beam search is developed as a heuristic algorithm for SALBP-2.•Improvements are made to ...enhance the proposed methods.•Iterative branch, bound and remember is the new state-of-the-art methodology.•Seven new upper bounds are obtained for tested benchmarks.
This study presents an enhanced iterative branch, bound and remember (IBBRe) algorithm as an exact method and an enhanced iterative beam search (IBSe) approach as a heuristic method to tackle the type II assembly line balancing problem. The proposed IBBRe is enhanced by utilizing additional lower bounds, a new search strategy and a new sequence of applying the lower bounds and dominance rules. The proposed IBSe is also enhanced by utilizing additional lower bounds, more dominance rules and a new station load selection criterion. The computational study demonstrates that both the proposed methods, namely IBBRe and IBSe, outperform the current state-of-the-art method (iterative beam search), and they update the upper bounds for seven cases which have been open for over two decades. Furthermore, IBBRe outperforms IBSe by achieving the best-known solutions in 301 out of the 302 test cases and might be regarded as the new state-of-the-art methodology.
The biggest difference between a disassembly line and an assembly line is that there are many uncertainties in structure and quality of the disassembled products in a disassembly line. The ...disassembly line balancing problem, considering the effect of end-of-life states caused by the uncertainty of the structure or the quality of the disassembled products, is addressed in this paper. A multi-objective mathematical model for the addressed problem is built with three optimization goals: minimizing the number of workstations, minimizing the idle index and minimizing the number of resources. Then a multi-objective hybrid migrating birds optimization algorithm is proposed, which uses a greedy random search operation based on embedding mechanism to generate neighborhood individuals. To avoid the problem of easily being trapped into a local optimum by a basic migrating birds optimization algorithm, a reset mechanism based on simulated annealing operation is set up to accept other solutions with a certain probability, so that the algorithm can escape out of a local optimum. By solving disassembly examples of different scales in the literature and comparing with the existing algorithms, the effectiveness and superiority of the proposed multi-objective hybrid migrating birds optimization algorithm is validated. Finally, the proposed model and algorithm are applied to solving two disassembly instances, and the solving results are compared with the single-objective optimal solution solved by LINGO 11.0 solver and the basic migrating birds optimization algorithm to further identify the performance of the proposed algorithm.
PurposeMulti-manned assembly lines are designed to produce large-sized products, such as automobiles. In this paper, a multi-manned assembly line balancing problem (MALBP) is addressed in which a ...group of workers simultaneously performs different tasks on a workstation. The key idea in this work is to improve the workstation efficiency and worker efficiency of an automobile plant by minimizing the number of workstations, the number of workers, and the cycle time of the MALBP.Design/methodology/approachA mixed-integer programming formulation for the problem is proposed. The proposed model is solved with benchmark test problems mentioned in research papers. The automobile case study problem is solved in three steps. In the first step, the authors find the task time of all major tasks. The problem is solved in the second step with the objective of minimizing the cycle time for the sub-tasks and major tasks, respectively. In the third step, the output results obtained from the second step are used to minimize the number of workstations using Lingo 16 solver.FindingsThe experimental results of the automobile case study show that there is a large improvement in workstation efficiency and worker efficiency of the plant in terms of reduction in the number of workstations and workers; the number of workstations reduced by 24% with a cycle time of 240 s. The reduced number of workstations led to a reduction in the number of workers (32% reduction) working on that assembly line.Practical implicationsFor assembly line practitioners, the results of the study can be beneficial where the manufacturer is required to increased workstation efficiency and worker efficiency and reduce resource requirement and save space for assembling the products.Originality/valueThis paper is the first to apply a multi-manned assembly line balancing approach in real life problem by considering the case study of an automobile plant.
The Joint Assembly Line Balancing and Feeding Problem (JALBFP) assigns a line feeding mode to each component (Assembly Line Feeding Problem) and each task to a workplace of a station (Assembly Line ...Balancing Problem). Current literature offers numerous optimisation models that solve these problems sequentially. However, only few optimisation models, provide a joint solution. To solve the JALBFP for a multi-manned assembly line, we propose a Mixed Integer Linear Programming (MILP) model and a heuristic that relies on the Adaptive Large Neighborhood Search (ALNS) framework by considering multiple workplaces per station and three different feeding policies: line stocking, travelling kitting and sequencing. The objective function minimises the cost of the whole assembly system which considers supermarket, transportation, assembly operations, and investment costs. Although the JALBFP requires higher computation times, it leads to a higher total cost reduction compared to the sequential approach. Through a numerical study, we validate the heuristic approach and find that the average deviation to the MILP model is around 1%. We also compare the solution of the JALBFP with that of the sequential approach and find an average total cost reduction of 10.1% and a maximum total cost reduction of 43.8%.
•We put forward a novel cost-oriented line balancing problem.•Several properties and special cases of the problem are studied.•We design a memetic algorithm that hybridises these properties with a ...genetic algorithm.•Computational experiments highlight the contributions of each component to the quality of the proposed method.
In order to minimize costs, manufacturing companies have been relying on assembly lines for the mass production of commodity goods. Among other issues, the successful operation of an assembly line requires balancing work among the stations of the line in order to maximize its efficiency, a problem known in the literature as the assembly line balancing problem, ALBP.
In this work, we consider an ALBP in which task assignment and equipment decisions are jointly considered, a problem that has been denoted as the robotic ALBP. Moreover, we focus on the case in which equipment has different costs, leading to a cost-oriented formulation. In order to solve the problem, which we denote as the cost-oriented robotic assembly line balancing problem, cRALBP, a hybrid metaheuristic is proposed. The metaheuristic embeds results obtained for two special cases of the problem within a genetic algorithm in order to obtain a memetic algorithm, applicable to the general problem. An extensive computational experiment shows the advantages of the hybrid approach and how each of the components of the algorithm contributes to the overall ability of the method to obtain good solutions.
PDF
