Chao Yong Zhang

A Cooperative Simulated Annealing Algorithm for the Optimization of Process Planning

Process planning is an essential component of computer aided process planning (CAPP), which involves operations selection from design features and operations sequencing of these selected operations. It makes process planning a complex combinatorial optimization problem to conduct of these two steps simultaneously. In this paper, we propose a cooperative simulated annealing (CoSA) approach for the process planning problem to minimize total manufacturing cost. The proposed CoSA algorithm employed a novel optimization strategy different from all the existing approaches in the literature. Simulated annealing was utilized to optimize the four components of a process plan individually and sequentially. The approach is tested on two parts from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.

A Hybrid Genetic Algorithm for Flexible Job-Shop Scheduling Problem

In this paper, a hybrid genetic algorithm is presented for the flexible job-shop scheduling problem with makespan criterion. A new machine assignment strategy is proposed to improve the initial population. A modified coding scheme is presented, and a population improvement strategy is performed when the best solution of the population did not improve during some generations. This hybrid algorithm is tested on a series of benchmarks instances. Experimental results show that this hybrid algorithm is efficient and competitive compared to some existing methods.

An Improved Genetic Algorithm for Multi-Objective Flexible Job-Shop Scheduling Problem

Flexible job shop scheduling problem (FJSP) is an extended traditional job shop scheduling problem, which more approximates to real scheduling problems. This paper presents a multi-objective genetic algorithm (GA) based on immune and entropy principle to solve the multi-objective FJSP. In this improved multi-objective GA, the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Advanced crossover and mutation operators are proposed to adapt to this special chromosome structure. The proposed algorithm is evaluated on three representative instances and the computational results and comparison with some other approaches show that the proposed multi-objective algorithm is effective and potential.

An Improved TLBO Algorithm for Balancing Stochastic Two-Sided Assembly Line

The stochastic two-sided assembly line is a special line in which workers perform assembly tasks in both sides of the line and the task time is not deterministic. In the improved teaching-learning-based optimization (ITLBO) algorithm, teacher and learner phases are modified for the discrete problem, a novel merging method is proposed to construct the new evolutionary population and Late Acceptance Hill-Climbing (LAHC) is integrated into the self-learning phase to obtain high quality solutions. The experimental results validate the effectiveness of the proposed algorithm.

Minimizing Total Flow Time in a Flow Shop with Blocking Using a Hybrid Variable Neighborhood Search and Simulated Annealing Algorithm

In this paper, a hybrid methodology that incorporates a simulated annealing (SA) approach into the framework of variable neighborhood search (VNS) is proposed to solve the blocking flow shop scheduling problem with the total flow time minimization. The proposed hybrid algorithm adopts SA as the local search method in the third stage of VNS, and uses a perturbation mechanism consisting of three neighborhood operators in VNS to diversify the search. To enhance the intensification search, best-insert operator is adopted to generate the neighbors in SA. To evaluate the performance of the proposed hybrid algorithm, computational experiments and comparisons were conducted on the well-known Taillard’s benchmark problems. The computational results and comparisons validate the effectiveness of the proposed algorithm.

Flexible Job-Shop Scheduling Problem with Maintenance Activities Considering Energy Consumption

With the developing of industry, the topic of energy-saving is attracting more and more attentions. However, the attentions have focused primarily on the design activities of the firm. The planning/scheduling activities of manufacturing are neglected. In this paper, a mathematical model, which considers the energy consumption and preventive maintenance of equipment in the flexible job shop, is established, three objectives, such as the minimization of maximum completion time, the minimization of total production energy costs and the minimization of total energy costs of maintenance that are quantized by electricity, are taken into account. The algorithm of NSGA-II is presented to solve the optimization model, and experimental results validate the effectiveness of the proposed approach.

Complex Network Based Modeling and Analysis of Resource Nodes for MG

Manufacturing Grid (MG) is one of the most promising manufacturing modes in recent years, which has been proved to be a complex network characterized with scale-free. In this paper, the resource nodes of MG are treated as the nodes of a network and the linkage among each node is treat as the edges or arcs, based on which a three-element model of G = ( V, E, R) is proposed, and a scene of the optimal composition strategy among the 50 candidate nodes aiming at task A is modeled. Next the static parameter of the degree for a resource node and the synchronization performance of the complex network model for the resource nodes are studied. Results of the paper can promote the management of the manufacturing resource nodes and improve the efficiency of sharing MG resources, so as to facilitate the application and popularization of MG.

An Adaptive Memetic Algorithm for Packing Problems of Irregular Shapes

Packing problem, which occurs frequently in sheet metal, clothing and furniture industry, cut product patterns from raw materials most efficiently and maximize material utilization. In this paper, an adaptive memetic algorithm is proposed to solve the problem of irregular shapes packed on the rectangular sheets. First, operators and parameters of evolution is researched, and second, local search method is proposed. Finally, this study compares benchmarks presented by other authors. The results show that the material utilization efficiency by using the adaptive memetic algorithm is higher compared to other methods.

Research on Modeling of Cutting Force on CNC Lathe Based on Orthogonal Experiment

Through experiments and orthogonal regression, the cutting force model of machining bars on a CNC lathe HCK60 equipped with the Huazhong CNC system is established. The model is simplified by using the knowledge of matrix theory, and we got the cutting force prediction empirical formula. The verifiable experiments show that the errors between predicted values and measured values are almost within 10%, which validates its high credibility. In addition, it can provide boundary conditions for the finite element analysis of CNC machining deformation, which is very important for application value.

The Implementation of SLA Mechanism in Manufacturing Grid

. In MG environment, resource consumers choose the optimal resource service according to their non-functional demands for quality of service (QoS) in terms of service execution time, reliability, and availability. Non-functional requirements of QoS can be organized and managed by utilizing Service Level Agreement (SLA) standards. In this study, architectures with QoS oriented mechanism with four layers for MG on the basis of SLA are provided. On the basis of the QoS oriented architectures, the management processes of SLA creation, negotiation and matching, monitoring and verification are discussed. Finally, a simple case is provided.