基于遗传算法的多资源作业车间智能优化调度

提出一种基于遗传算法的调度算法，用于解决作业车间的加工受到机床、操作工人和机器人等多种生产资源制约条件下的优化调度。以生产周期为目标进行的优化调度，将遗传算法和分派规则相结合，通过交叉、交异等遗传操作，得到目标的最优或次优解。最后对算法进行了仿真研究，并给出了算法运行结果，仿真结果表明该算法是可行的。     

柔性装配单元任务规划的时间优化方法

详细研究了柔性装配单元中的任务规划问题,采用时延Petri网对系统行为进行建模并提出了任务规划的时间优化算法。为装配序列规划提供了重要的评价指标,并可用于指导装配系统设计。     

软体机器人流体驱动方式综述

软体机器人是由柔性材料制成的新型机器人,具有刚度小、柔顺性高等特点,其运动性能、应用环境范围主要取决于驱动方式。目前的驱动方式主要有流体驱动、线驱动、形状记忆合金驱动、电活性聚合物驱动、混合驱动等,其中流体驱动由于其形式的多样性、响应的快速性、高承载性而受到青睐。根据流体驱动介质的不同,将软体机器人流体驱动方式分为气压驱动、液压驱动、微流体驱动等,同时进一步根据气压驱动的结构类型将其分为纤维编织型、螺旋型、网格型、折纸型和特殊型等;介绍了目前流体驱动的软体机器人制造技术,分析了软体机器人流体驱动方式面临的一些问题,并提出了其未来发展方向。     

Integrated assembly and machining planning for electronic products using a genetic algorithm approach

To produce an electronic product, both assembly operations and machining operations are required in the process plan. In most cases, the assembly operations and machining operations need to be combined in a continued order with an integrated sequence. This is different from the traditional process planning approaches in which machining operations and assembly operations are separated as two independent tasks with no interactions. For an electronic product, the two types of operations and the associated costs may affect each other in an interactive way. Therefore, the sequence planning of assembly operations and machining operations must be analyzed with an integrated model. In this research, a graph-based model is presented to represent the assembly and machining operations in an integrated model. The related operation cost functions are developed to evaluate the costs for the integrated assembly and machining sequences. The integrated sequence planning problem is solved using a genetic algorithm approach with an objective of lowest operation costs. As a result, the assembly operations and machining operations can be planned in an integrated sequence suitable for producing electronic products. The result shows that the developed method using the genetic algorithm approach is efficient for solving the integrated sequence planning problem. Example products are demonstrated and discussed.     

Lot streaming in a two-machine mixed shop

Most classical scheduling models overlook the fact that products are often produced in job lots and assume that job lots are indivisible single entities, although an entire job lot consists of many identical items. However, splitting an entire lot (process batch) into sublots (transfer batches) to be moved to downstream machines allows the overlapping of different operations on the same product while work needs to be completed on the upstream machine. This approach is known as lot streaming in scheduling theory. In this study, the lot streaming problem of multiple jobs in a two-machine mixed shop where there are two different job types as flow shop and open shop is addressed so as to minimize the makespan. The optimal solution method is developed for the mixed shop scheduling problem in which lot streaming can improve the makespan.     

Optimization of workcell layouts in a mixed-model assembly line environment

Mixed-model assembly lines enable automotive manufacturers to build different vehicles using the same equipment and facility. They allow a more rapid response to changes in customer demand. However, the increased production flexibility creates new challenges regarding assembly line planning and balancing. Part of this problem is how to allocate stock within the workcells so that non-value added operations, such as walking and waiting, are minimized. This paper addresses the issue of workstation layout optimization. Different formulations can be used to minimize non-value added time depending on the level of sophistication desired. We present three different optimization formulations and give an example of an optimized workstation layout.     

Experimental comparison of dynamic tracking performance of iGPS and laser tracker

External metrology systems are increasingly being integrated with traditional industrial articulated robots, especially in the aerospace industries, to improve their absolute accuracy for precision operations such as drilling, machining and jigless assembly. While currently most of the metrology assisted robotics control systems are limited in their position update rate, such that the robot has to be stopped in order to receive a metrology coordinate update, some recent efforts are addressed toward controlling robots using real-time metrology data. The indoor GPS is one of the metrology systems that may be used to provide real-time 6DOF data to a robot controller. Even if there is a noteworthy literature dealing with the evaluation of iGPS performance, there is, however, a lack of literature on how well the iGPS performs under dynamic conditions. This paper presents an experimental evaluation of the dynamic measurement performance of the iGPS, tracking the trajectories of an industrial robot. The same experiment is also repeated using a laser tracker. Besides the experiment results presented, this paper also proposes a novel method for dynamic repeatability comparisons of tracking instruments.     

基于时延Petri网模型的机器人装配任务规划

采用时延Petri网针对有代表性的柔性装配单元进行建模 ,柔性装配单元包括装配机器人、拧螺钉机器人、变位器和加工中心等自动装配和制造中的常用设备 ,具有较大的通用性。推导了顺序作业和并行作业的时间消耗计算方法 ,针对具体任务提出了任务规划的时间优化算法 ,通过仿真和试验两种方法对最优规划序列进行了验证。该项研究可在作业时间要求严格的场合产生优化的装配任务序列 ,并可用于指导装配系统设计     

Multiple-Objective Scheduling for the Hierarchical Control of Flexible Manufacturing Systems

This paper presents a hierarchical approach to scheduling flexible manufacturing systems (FMSs) that pursues multiple performance objectives and considers the process flexibility of incorporating alternative process plans and resources for the required operations. The scheduling problem is solved at two levels: the shop level and the manufacturing system level. The shop level controller employs a combined priority index developed in this research to rank shop production orders in meeting multiple scheduling objectives. To overcome dimensional complexity and keep a low level of work-in-process inventory, the shop controller first selects up to three production orders with the highest ranking as candidates and generates all possible release sequences for them, with or without multitasking. These sequences are conveyed to the manufacturing system controller, who then performs detailed scheduling of the machines in the FMS using a fixed priority heuristic for routing parts of multiple types while considering alternative process plans and resources for the operations. The FMS controller provides feedback to the shop controller with a set of suggested detailed schedules and projected order completion times. On receiving these results, the shop controller further evaluates each candidate schedule using a multiple-objective function and selects the best schedule for execution. This allows multiple performance objectives of an FMS to be achieved by the integrated hierarchical scheduling approach.     

Aggregation and disaggregation of end items in a class of multistage production systems

Aggregation of end items in a class of multistage production systems within a hierarchical scheduling framework is considered. This class is one of flexible machining and assembly systems where each end item is assembled from a number of components. Each component undergoes a sequence of operations on various machines. The assembly structure of all items is flat. At least for some operations on some machines, set-ups are not negligible. Aggregation of constraints and items is employed and the problem of aggregate capacitated lot-size scheduling is formulated. The solution of the original detailed scheduling problem is found by disaggregating the solution of the aggregate scheduling problem. The process of disaggregation is formulated as a mixed integer programming problem, which is shown to be a fixed-charge transshipment problem. An efficient branch and bound/transshipment solution scheme for the disaggregation problem is proposed and validated. A case study on real plant data is presented. This work was supported by the ACME Directorate of the Science and Engineering Research Council, Grant No. GR/D 51476, and was carried out in collaboration with Lucas Aerospace (Engines Divisions)     

仿生水下机器人研究现状及其发展趋势

仿生水下机器人以其高度灵活性及逐渐智能化的特点,成为近年来机器人领域研究的热点。仿生水下机器人按照模仿水下生物的运动方式可分为:仿鱼水下机器人、仿多足爬行动物水下机器人和仿蠕虫水下机器人。文中具体地阐述了这三类中具有代表性的仿生水下机器人的特点。随着科学技术的发展,仿生水下机器人在智能材料制成的驱动装置、游动机理方面会不断地完善,在个体的智能化和群体的协作方面也会有很大的发展。     

Automated manufacturing complexes with industrial robots used

Application of the basic manufacturing equipment ensuring high-level combination and concentration of operations together with automatic program-controlled manipulators of various types offers possibilities for developing automated production systems which differ in purpose, structure and a degree of automation of the main and auxiliary operations and control as well.In the field of mass and large-lot production the development of flexible versatile transfer lines served by robots to perform, in particular, loading/unloading operations in the initial and final positions of the lines and their segments is of high promise. The use of robots enables solving not only the problem of quick changeover but also no less important a problem of completing and mastering transfer lines in a shorter period of time. In the field of lot and small-lot production it is most suitable to combine multi-program robots with NC machine tools. This permits the small-lot production to be automated on the base of using a multiple machining method.     

A Genetic Algorithm Approach to the Scheduling of FMSs with Multiple Routes

Usually, most of the typical job shop scheduling approaches deal with the processing sequence of parts in a fixed routing condition. In this paper, we suggest a genetic algorithm (GA) to solve the job-sequencing problem for a production shop that is characterized by flexible routing and flexible machines. This means that all parts, of all part types, can be processed through alternative routings. Also, there can be several machines for each machine type. To solve these general scheduling problems, a genetic algorithm approach is proposed and the concepts of virtual and real operations are introduced. Chromosome coding and genetic operators of GAs are defined during the problem solving. A minimum weighted tardiness objective function is used to define code fitness, which is used for selecting species and producing a new generation of codes. Finally, several experimental results are given.     

Analysis of reactive deadlock-free scheduling in flexible job shops

Reactive scheduling is a procedure followed in production systems to react to unforeseen events that disturb the normal operation of the system. In this paper, a novel operations insertion heuristic is proposed to solve the deadlock-free reactive scheduling problem in flexible job shops, upon the arrival of new jobs. The heuristic utilizes rank matrices (Latin rectangles) to insert new jobs in schedules, while preventing the occurrence of deadlocks or resolving them using the available buffer space (if any). Jobs with alternative processing routes through the system are also considered. The heuristic can be employed to execute two reactive scheduling approaches in a timely efficient manner; to insert the new jobs in the already existing schedule (job insertion) or to reschedule all the jobs in the system (total rescheduling). Using experimental design and analysis of variance (ANOVA), the relative performance of the two approaches is studied and analyzed to provide some measures and guidelines for selecting the appropriate reactive scheduling approach for different problem settings. Three measures of performance are considered in the analysis; efficiency of the revised schedules in terms of the mean flow time, resulting system nervousness, and the required solution time. The results show that, on average, job insertion obtains revised schedules featuring significantly lower system nervousness and slightly higher mean flow time than total rescheduling. However, depending on the system size, number and processing times of the new jobs, and the available flexibility in the system, a trade-off between the two approaches should sometimes be considered.

Two meta-heuristics for three-stage assembly flowshop scheduling with sequence-dependent setup times

In this paper, we consider a three-stage assembly flowshop scheduling problem with bi-objectives, namely the mean flow time and maximum tardiness. This problem can be considered as a production system model consisting of three stages: (1) different production operations are done in parallel, concurrently and independently, (2) the manufactured parts are collected and transferred to the next stage, and (3) these parts are assembled into final products. In this paper, sequence-dependent setup times and transfer times are also considered as two important presumptions in order to make the problem more realistic. We present a novel mathematical model for a production system with a new lower bound for the given problem. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. Thus, we propose two meta-heuristics, namely simulated annealing and tabu search, to solve a number of test problems generated at random. Finally, the computational results are illustrated and compared in order to show the efficiency of the foregoing meta-heuristics.     

变扰动强度下柔性装配作业车间调度性能分析

柔性装配作业车间是柔性作业车间的一类现实化扩展,其调度问题既要考虑复杂的加工路径柔性,还要考虑零件间的装配关联约束,以及由其带来的关联零件生产进度协同难题。首先给出了柔性装配作业车间调度问题的数学模型;然后考虑现实生产中普遍存在的随机扰动,采用了完全反应式与预测-反应式两类动态调度策略,并提出了相应的优先度规则算法和周期性滚动遗传算法。前者能快速协同各关联任务,但其决策分散,缺乏全局优化力度;后者进行全局周期决策,但扰动将导致性能的下降。最后构建了一般化的仿真模型,并设计了大量的比较实验,分析了不同综合扰动强度对两种调度策略的影响,为实际生产调度策略选择提供了有效的依据。     

Integrating Flexible Process Plans with Scheduling in Flexible Manufacturing Systems

This paper highlights the importance of integration between process planning and scheduling in flexible manufacturing systems (FMS). An effective integration increases the potential for enhanced system performance and enhanced decision making A framework that integrates flexible process plans with off-line (predictive) scheduling in FMS is presented. The flexibility in process planning, including process flexibility, sequence flexibility, and alternative machine tools, is discussed. The proposed framework consists of four integrated stages with the objective of reducing the completion time. The integrated stages include: 1. Machine tool selection. 2. Process plan selection. 3. Scheduling. 4. Re-scheduling modules. In addition, the paper proposes a new approach, namely the Dissimilarity Maximisation Method (DMM), for selecting the appropriate process plans for a part mix where parts have alternative process plans. The recursive structure of the framework provides a different approach, namely overlapping schedules, which considers a longer scheduling period as comprising several short scheduling periods. Knowing that neither the process plans nor the planned (predicted) schedules are truly followed on the shop floor, the related literature and the corresponding approaches are compared in order to envisage new approaches for closing the gap between process planning and scheduling.     

An approach to real-time flexible scheduling

Two types of flexibility are important in manufacturing scheduling in general and in real-time scheduling in particular. The first is flexibility with respect to the criteria that can be considered in the scheduling decisions. The second is flexibility with respect to the trade-off between decision quality and computational burden: that is, the ability to arrive at a solution that makes maximum use of theavailable computational capacity and computation time. This paper describes a procedure which meets the above requirements. The procedure is justified using a theoretical analysis based on probability. Experimental results of the procedure's performance are also presented. The results show that random selection (which is used in the procedure) can play a useful role in the real-time scheduling problem.     

TOC or LP? [production control]

The authors address the production scheduling problem from two fundamentally different approaches, the theory of constraints (TOC) and linear programming (LP). LP is a complicated mathematical approach while TOC is much less quantitative in nature. Both approaches were applied to the real operations data from a fibre manufacturer. It was found that the schedule produced by TOC is very similar to the one generated by LP     

A dispatching rule-based hybrid genetic algorithm focusing on non-delay schedules for the job shop scheduling problem

Job shop scheduling is an important decision process in contemporary manufacturing systems. In this paper, we aim at the job shop scheduling problem in which the total weighted tardiness must be minimized. This objective function is relevant for the make-to-order production mode with an emphasis on customer satisfaction. In order to save the computational time, we focus on the set of non-delay schedules and use a genetic algorithm to optimize the set of dispatching rules used for schedule construction. Another advantage of this strategy is that it can be readily applied in a dynamic scheduling environment which must be investigated with simulation. Considering that the rules selected for scheduling previous operations have a direct impact on the optimal rules for scheduling subsequent operations, Bayesian networks are utilized to model the distribution of high-quality solutions in the population and to produce the new generation of individuals. In addition, some selected individuals are further improved by a special local search module based on systematic perturbations to the operation processing times. The superiority of the proposed approach is especially remarkable when the size of the scheduling problem is large.