Process planning optimization for parallel drilling of blind holes using a two phase genetic algorithm

Determining the optimal process parameters and machining sequence is essential in machining process planning since they significantly affect the cost, productivity, and quality of machining operations. Process planning optimization has been widely investigated in single-tool machining operations. However, for the research reported in process planning optimization of machining operations using multiple tools simultaneously, the literature is scarce. In this paper, a novel two phase genetic algorithm (GA) is proposed to optimize, in terms of minimum completion time, the process parameters and machining sequence for two-tool parallel drilling operations with multiple blind holes distributed in a pair of parallel faces and in multiple pairs of parallel faces. In the first phase, a GA is used to determine the process parameters (i.e., drill feed and spindle speed) and machining time for each hole subject to feed, spindle speed, thrust force, torque, power, and tool life constraints. The minimum machining time is the optimization criterion. In the second phase, the GA is used to determine the machining sequence subject to hole position constraints (i.e., the distribution of the hole locations on each face is fixed). The minimum operation completion time is the optimization criterion in this phase. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm in solving the process planning optimization problem for parallel drilling of blind holes on multiple parallel faces. In order to evaluate the performance of proposed algorithm, the simulation results are compared to a methodology that utilizes the exhaustive method in the first phase and a sorting algorithm.     

Capacity planning with ant colony optimization for TFT-LCD array manufacturing

Array manufacturing in thin film transistor-liquid crystal display (TFT-LCD) production network is characterized as a capital-intensive and capacity-constrained production system with re-entrance and batch operations. Effectively using associated machines through optimal capacity planning and order scheduling decisions is a critical issue for array manufacturing. This study develops a capacity planning system (CPS) for TFT-LCD array manufacturing. CPS uses information including master production schedule, order due date, process routing, processing time, and number of machines. In addition, CPS derives the order release time, estimated machine start and finish time, machine allocation, and order completion time to maximize machine workload, improve lateness, and eliminate setup time. This research also develops ant colony optimization (ACO) to seek the optimal order release schedule to maximize a combination of the above objectives. The preliminary experiments are first applied to identify the optimal tuning parameters of the ACO algorithm. Computational experiments are then conducted to evaluate the significance and the robustness of the proposed algorithm compared with other competitive algorithms by full factorial experimental design.     

A computer program for the optimization of the machining variables

In practice, the values of the machining variables (cutting speed, feed and depth of cut) are determined either by mere experience as usually done by machine tool operators or selected from the available engineering tables which is the usual practice for engineers and technologists. Both methods do not take the process constraints into consideration and merely depend on the personal experience of the employed personnel and hence lead to values which are too far from the economic values. The present paper present a technological optimization technique for turning operations. A computer program has been developed for this purpose, by means of which those optimum values of the machining variables can be obtained, which satisfy the encountered technical and technological constraints and lead to minimum manufacturing cost or maximum production rate as required.     

CSNEO:协同服务器与网络能耗优化的VM映射算法

已有针对虚拟机映射问题的研究,主要以提高服务器资源及能耗效率为目标.综合考虑虚拟机映射过程中对服务器及网络设备能耗的影响,在对物理服务器、虚拟机资源及状态,虚拟机映射、网络通信矩阵等概念定义的基础上,对协同能耗优化及网络优化的虚拟机映射问题进行了建模.将问题抽象为多资源约束下的装箱问题与二次分配QAP问题,并设计了基于蚁群算法ACO与局部搜索算法2-exchange结合的虚拟机映射算法CSNEO来进行问题的求解.通过与MDBP-ACO、vector-VM等四种算法的对比实验结果表明:CSNEO算法一方面在满足多维资源约束的前提下,实现了更高的虚拟机映射效率;另一方面,相比只考虑网络优化的虚拟机放置算法,CSNEO在实现网络优化的同时具有更好的能耗效率.     

Honey bees mating optimization algorithm for process planning problem

Process planning is a very important function in the modern manufacturing system. It impacts the efficiency of the manufacturing system greatly. The process planning problem has been proved to be a NP-hard problem. The traditional algorithms cannot solve this problem very well. Therefore, due to the intractability and importance of process planning problem, it is very necessary to develop efficiency algorithms which can obtain a good process plan with minimal global machining cost in reasonable time. In this paper, a new method based on honey bees mating optimization (HBMO) algorithm is proposed to optimize the process planning problem. With respect to the characteristics of process planning problem, the solution encoding, crossover operator, local search strategies have been developed. To evaluate the performance of the proposed algorithm, three experiments have been carried out, and the comparisons among HBMO and some other existing algorithms are also presented. The results demonstrate that the HBMO algorithm has achieved satisfactory improvement.     

Petri net techniques for process planning cost estimation

The problem under consideration is the cost estimation of operation sequencing for nonlinear process planning, i.e. taking into consideration processing alternatives. In order to determine overall costs for feasible process plans, we take into account in our Petri net model of manufacturing process planning the costs caused by machine, setup and tool changing in addition to the pure operation cost. We present two modelling and cost estimation techniques based on Petri nets. Both are based on a new Petri net model: the PP-net (Process Planning net) which represents manufacturing knowledge in the form of precedence constraints and incorporates the cost of machining operation in each operation transition. The first method is based on building a complex Petri net called PPC-system (Process Planning Cost system) by integrating the PP-net and separate Petri nets describing the costs of machine, setup and tool changing. The second method proceeds in the cost calculation by attaching a specific data structure to each PP-net transition which describes the associated machine, setup and tool for the operation modelled by that transition. We apply the developed methods and calculate the optimum process plan to an industrial case study of a mechanical workpiece of moderate complexity.     

A novel approach to CNC machining center processing parameters optimization considering energy-saving and low-cost

NC machining is currently a machining method widely used in mechanical manufacturing systems. Reasonable selection of process parameters can significantly reduce the processing cost and energy consumption. In order to realize the energy-saving and low-cost of CNC machining, the cutting parameters are optimized from the aspects of energy-saving and low-cost, and a process parameter optimization method of CNC machining center that takes into account both energy-saving and low -cost is proposed. The energy flow characteristics of the machining center processing system are analyzed, considering the actual constraints of machine tool performance and tool life in the machining process, a multi-objective optimization model with milling speed, feed per tooth and spindle speed as optimization variables is established, and a weight coefficient is introduced to facilitate the solution to convert it into a single objective optimization model. In order to ensure the accuracy of the model solution, a combinatorial optimization algorithm based on particle swarm optimization and NSGA-II is proposed to solve the model. Finally, take plane milling as an example to verify the feasibility of this method. The experimental results show that the multi-objective optimization model is feasible and effective, and it can effectively help operators to balance the energy consumption and processing cost at the same time, so as to achieve the goal of energy conservation and low-cost. In addition, the combinatorial optimization algorithm is compared with the NSGA-II, the results show that the combinatorial optimization algorithm has better performance in solving speed and optimization accuracy.     

Shape optimization of heavy load carrying components for structural performance and manufacturing cost

This paper addresses the trade-off between structural performance and manufacturing cost of heavy load carrying components by incorporating virtual machining (VM) technique in computer-aided design (CAD)-based shape optimization problem. A structural shape optimization problem is set up to minimize total cost, subject to the limits on structural performance measures. For every design iteration, finite element analysis (FEA) is conducted to evaluate structural performance, and VM is employed to ascertain machinability and estimate machining time. Design sensitivity coefficients of objective function and constraints are computed and supplied to the optimization algorithm. Based on the gradients, the algorithm determines design changes, which are used to update FEA and VM models. The process is repeated until specified convergence criterion is satisfied. Application programs developed to integrate commercially available CAD/CAM/FEA/Design optimization tools enable implementation in virtual environment and facilitate automation. The application programs can be reused for similar design problems provided that the same set of tools is used.     

Application of genetic algorithm to computer-aided process planning in preliminary and detailed planning

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in the computer integrated manufacturing (CIM) environment. A good process plan of a part is built up based on two elements: (1) optimized sequence of the operations of the part; and (2) optimized selection of the machine, cutting tool and tool access direction (TAD) for each operation. On the other hand, two levels of planning in the process planning is suggested: (1) preliminary and (2) secondary and detailed planning. In this paper for the preliminary stage, the feasible sequences of operations are generated based on the analysis of constraints and using a genetic algorithm (GA). Then in the detailed planning stage, using a genetic algorithm again which prunes the initial feasible sequences, the optimized operations sequence and the optimized selection of the machine, cutting tool, and TAD for each operation are obtained. By applying the proposed GA in two levels of planning, the CAPP system can generate optimal or near-optimal process plans based on a selected criterion. A number of case studies are carried out to demonstrate the feasibility and robustness of the proposed algorithm. This algorithm performs well on all the test problems, exceeding or matching the solution quality of the results reported in the literature for most problems. The main contribution of this work is to emerge the preliminary and detailed planning, implementation of compulsive and additive constraints, optimization sequence of the operations of the part, and optimization selection of machine, cutting tool and TAD for each operation using the proposed GA, simultaneously.     

An effective cellular particle swarm optimization for parameters optimization of a multi-pass milling process

In the milling process, the selection of machining parameters is very important as these parameters determine the processing time, quality, cost and so on, especially in the high-accuracy machine tools. However, the parameters optimization of a multi-pass milling process is a nonlinear constrained optimization problem which is difficult to be solved by the traditional optimization techniques. Therefore, in order to solve this problem effectively, this paper proposes a novel parameters optimization method based on the cellular particle swarm optimization (CPSO). To address the constraints efficiently, the proposed method combines two constraints handling techniques, including the penalty function method and the constraints handling strategy of PSO. In the proposed CPSO, the smart cell constructs its neighborhood with self-adaptive function and constraints handling techniques, which guide the unfeasible particles to move to the feasible regions and search for better solutions. A case is adopted and solved to illustrate the effectiveness of the proposed CPSO algorithm. The results of the experiment study are analyzed and compared with those of the previous algorithms. The experimental results show that the proposed approach outperforms other algorithms and has achieved significant improvement.     

Ant colony optimization approach to a fuzzy goal programming model for a machine tool selection and operation allocation problem in an FMS

Due to the global competition in manufacturing environment, firms are forced to consider increasing the quality and responsiveness to customization, while decreasing costs. The evolution of flexible manufacturing systems (FMSs) offers great potential for increasing flexibility and changing the basis of competition by ensuring both cost effective and customized manufacturing at the same time. Some of the important planning problems that need realistic modelling and quicker solution especially in automated manufacturing systems have assumed greater significance in the recent past. The language used by the industrial workers is fuzzy in nature, which results in failure of the models considering deterministic situations. The situation in the real life shop floor demands to adopt fuzzy-based multi-objective goals to express the target set by the management. This paper presents a fuzzy goal programming approach to model the machine tool selection and operation allocation problem of FMS. An ant colony optimization (ACO)-based approach is applied to optimize the model and the results of the computational experiments are reported.     

Integration of process planning and scheduling using chaotic particle swarm optimization algorithm

Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi-objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.     

Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning

This study develops an enhanced ant colony optimization (E-ACO) meta-heuristic to accomplish the integrated process planning and scheduling (IPPS) problem in the job-shop environment. The IPPS problem is represented by AND/OR graphs to implement the search-based algorithm, which aims at obtaining effective and near-optimal solutions in terms of makespan, job flow time and computation time taken. In accordance with the characteristics of the IPPS problem, the mechanism of ACO algorithm has been enhanced with several modifications, including quantification of convergence level, introduction of node-based pheromone, earliest finishing time-based strategy of determining the heuristic desirability, and oriented elitist pheromone deposit strategy. Using test cases with comprehensive consideration of manufacturing flexibilities, experiments are conducted to evaluate the approach, and to study the effects of algorithm parameters, with a general guideline for ACO parameter tuning for IPPS problems provided. The results show that with the specific modifications made on ACO algorithm, it is able to generate encouraging performance which outperforms many other meta-heuristics.     

基于深度邻域搜索PSO 算法的装配序列优化问题

针对产品结构特征建立几何约束矩阵, 以最大化满足几何约束条件装配次数和最小化装配方向改变次数为目标, 研究产品装配序列优化问题. 利用值变换的粒子位置和速度更新规则, 基于具有随机性启发式算法产生初始种群, 提出一种带有深度邻域搜索改进策略的粒子群算法解决装配序列问题. 通过装配实例验证了所提出算法的性能并对装配序列质量进行了评价, 所得结果表明了该算法在解决装配序列优化问题上的有效性与稳定性.

     

基于混合蚁群算法的冷链电动汽车车辆路径问题

用电动汽车进行冷链物流配送符合绿色物流的发展趋势。针对电动汽车冷链配送需消耗更多能源以维持低温环境,而电动汽车续驶里程短、充电时间长,致使运营成本高的现象,思考了电动汽车配送中的冷链车辆路径问题（REVRP）。考虑电动汽车能耗特点和社会充电站的充电需求,构建了以总配送成本最小为优化目标的线性规划模型,而目标函数由固定成本和可变成本构成,其中可变成本包含运输成本和制冷成本。模型考虑容量约束和电量约束,并设计混合蚁群（HACO）算法对其进行求解,其中重点设计了适合社会充电站的转移规则以及4种局部优化算子。在改进Solomon基准算例的基础上,形成了小规模和大规模两个算例集,并通过实验比较了蚁群（ACO）算法和局部优化算子的性能。实验结果表明,在小规模算例集中,传统ACO算法与CPLEX求解器均能找到精确解,而ACO算法在运算时间方面可节省99.6%;而在大规模算例集中,与ACO算法相比,结合4种局部优化算子的HACO算法的平均优化效率提升了4.45%。所提算法能够在有限时间内得出电动汽车REVRP的可行解。     

时间依赖型车辆路径问题的一种改进蚁群算法

时间依赖型车辆路径规划问题(TDVRP),是研究路段行程时间随出发时刻变化的路网环境下的车辆路径优化.传统车辆路径问题(VRP)已被证明是NP-hard问题,因此,考虑交通状况时变特征的TDVRP问题求解更为困难.本文设计了一种TDVRP问题的改进蚁群算法,采用基于最小成本的最邻近法(NNC算法)生成蚁群算法的初始可行解,通过局部搜索操作提高可行解的质量,采用最大--最小蚂蚁系统信息素更新策略.测试结果表明,与最邻近算法和遗传算法相比,改进蚁群算法具有更高的效率,能够得到更优的结果;对于大规模TDVRP问题,改进蚁群算法也表现出良好的性能,即使客户节点数量达到1000,算法的优化时间依然在可接受的范围内.     

Drilling reconfigurable machine tool selection and process parameters optimization as a function of product demand

Special purpose machines (SPMs) are customized machine tools that perform specific machining operations in a variety of production contexts, including drilling-related operations. This research investigates the effect of optimal process parameters and SPM configuration on the machine tool selection problem versus product demand changes. A review of previous studies suggests that the application of optimization in the feasibility analysis stage of machine tool selection has received less attention by researchers. In this study, a simulated model using genetic algorithm is proposed to find the optimal process parameters and machine tool configuration. During the decision-making phase of machine tool selection, unit profit is targeted as high as possible and is given by the value of the following variables: SPM configuration selection, machining unit assignment to each operation group, and feed and cutting speed of all operations. The newly developed model generates any random chromosome characterized by feasible values for process parameters. Having shown how the problem is formulated, the research presents a case study which exemplifies the operation of the proposed model. The results show that the optimization results can provide critical information for making logical, accurate, and reliable decisions when selecting SPMs.     

A novel hybrid immune algorithm for global optimization in design and manufacturing

This paper presents a new hybrid optimization approach based on immune algorithm and hill climbing local search algorithm. The purpose of the present research is to develop a new optimization approach for solving design and manufacturing optimization problems. This research is the first application of immune algorithm to the optimization of machining parameters in the literature. In order to evaluate the proposed optimization approach, single objective test problem, multi-objective I-beam and machine-tool optimization problems taken from the literature are solved. Finally, the hybrid approach is applied to a case study for milling operations to show its effectiveness in machining operations. The results of the hybrid approach for the case study are compared with those of genetic algorithm, the feasible direction method and handbook recommendation.     

Reliability optimization of topology communication network design using an improved ant colony optimization

Network design problem is a well-known NP-hard problem which involves the selection of a subset of possible links or a network topology in order to minimize the network cost subjected to the reliability constraint. To overcome the problem, this paper proposes a new efficiency algorithm based on the conventional ant colony optimization (ACO) to solve the communication network design when considering both economics and reliability. The proposed method is called improved ant colony optimizations (IACO) which introduces two addition techniques in order to improve the search process, i.e. neighborhood search and re-initialization process. To show its efficiency, IACO is applied to test with three different topology network systems and its results are compared with those obtained results from the conventional approaches, i.e. genetic algorithm (GA), tabu search algorithm (TSA) and ACO. Simulation results, obtained these test problems with various constraints, shown that the proposed approach is superior to the conventional algorithms both solution quality and computational time.     

Ant colony optimization for multi-objective flow shop scheduling problem

Flow shop scheduling problem consists of scheduling given jobs with same order at all machines. The job can be processed on at most one machine; meanwhile one machine can process at most one job. The most common objective for this problem is makespan. However, multi-objective approach for scheduling to reduce the total scheduling cost is important. Hence, in this study, we consider the flow shop scheduling problem with multi-objectives of makespan, total flow time and total machine idle time. Ant colony optimization (ACO) algorithm is proposed to solve this problem which is known as NP-hard type. The proposed algorithm is compared with solution performance obtained by the existing multi-objective heuristics. As a result, computational results show that proposed algorithm is more effective and better than other methods compared.