A hybrid approach for dynamic routing planning in an automated assembly shop

Highly turbulent environment of dynamic job-shop operations affects shop floor layout as well as manufacturing operations. Due to the dynamic nature of layout changes, essential requirements such as adaptability and responsiveness to the changes need to be considered in addition to the cost issues of material handling and machine relocation when reconfiguring a shop floor’s layout. Here, based on the source of uncertainty, the shop floor layout problem is split into two sub-problems and dealt with by two modules: re-layout and find-route. GA is used where changes cause the entire shop re-layout, while function blocks are utilised to find the best sequence of robots for the new conditions within the existing layout. This paper reports the latest development to the authors’ previous work.     

Heuristic transporter routing model for manufacturing facility design

In this study, a transporter routing problem is analyzed and adapted for use in manufacturing facility design. Given fixed facility layout and predetermined material flow paths, this study determines the minimum number of transporters required to transfer material within a given manufacturing facility with minimal handling effort. The manufacturing facility design problem is particularly complex and involves the sub-problems such as design of the material network and the transporter routing problem, which provides the fleet size and the routing of each transporter over the flow network. The problem is formulated as an integer program. To solve the problem, we used a heuristic and integrated vehicle routing model. We also developed a heuristic solution program and several tests along with an industrial example to indicate the effectiveness of this method.     

绿色车间调度优化研究进展

当今社会环境问题日益严重,绿色制造的研究备受关注.作为现代化制造模式,绿色制造旨在保证产品功能与质量,降低生产成本,提高生产效益,同时减少环境污染与能源浪费,实现经济指标和绿色指标的协同优化.绿色车间调度是绿色制造的重要环节,比传统调度问题的求解难度更高,更具学术研究意义和工程应用价值.绿色车间调度通过资源分配、操作排序和运作模式的合理优化,实现增效、节能、减排、降耗.为此,分析绿色车间调度问题的复杂性和相应的处理机制,介绍绿色车间调度在问题、方法、应用层面的代表性研究进展,进而指出各层面有待进一步研究的若干方向和内容,以促进智能制造与绿色制造的研究与发展.     

An integrated model and a decomposition-based approach for concurrent layout and material handling system design

This paper presents an integer programming formulation that integrates decisions concerning the layout of the resource groups on the shop floor with the design of the material handling system. The model reflects critical practical concerns, including the capacity of the material flow network and of the handling transporters, as well as the tradeoff between fixed (construction and acquisition) and variable (operational) costs. For realistic industrial cases, the size of the problem prevents the solution using explicit or implicit enumeration methods. Instead, the global model is decomposed into standard optimization problems: quadratic assignment, fixed charge capacitated network design, and non-depot distance-constrained vehicle routing. An integrated solution method, guided by a simulated annealing scheme, solves the global shop design problem. The algorithm takes advantage of the proposed decomposition and converges to a final design which is feasible with respect to all problem constraints. The method is applied to redesign the facility of a large manufacturer of radar antennas. The resulting shop configuration exhibits substantially decreased material handling effort, and requires significantly smaller investment costs compared to the existing facility.     

基于MAS的车间调度控制系统的研究

讨论了分布式控制结构和传统的递阶式控制结构的特点，面向任务建立了基于MAs的调度控制系统模型并建立了agent间的协作模型以及通信模型。整个系统运用动态重组的思想，采用分级分布式控制和并行处理相结合的组织结构和运行模式，将整个车间的调度问题分解为一系列子调度问题，满足了现代制造系统对动态性、柔性和敏捷性的要求。     

A mathematical programming model for AGVS planning and control in manufacturing systems

In this paper, problems of the planning and control of automated guided vehicles in manufacturing systems are discussed. A mixed integer programming model is developed to minimize the total material handling cost in manufacturing systems. In order to solve this NP-complete problem efficiently, a decomposition approach following the Lagrangian relaxation method is used. The decomposed sub-problems can be solved by a dynamic programming method. An efficient algorithm is developed to solve the entire problem and a numerical example is presented to illustrate the method of solution.     

Design of efficient hybrid neural networks for flexible flow shop scheduling

Abstract: Although neural networks have been successfully used in performing pattern recognition, their application for solving optimization problems has been limited. In this paper we design a neural network to solve a well‐known combinatorial problem, namely the flexible flow shop problem. A key feature of our neural network design is the integration of problem structure and heuristic information in the network structure and solution. We compare the performance of our neural network with well‐known current heuristics with respect to solution quality. The results indicate that our approach outperforms the heuristics.     

Series production in a basic re-entrant shop to minimize makespan or total flow time

This paper addresses a real life shop scheduling problem in a manufacturing company. In this problem, a set of n identical jobs are to be processed on two machines. Every job visits one of the machines more than once. This is therefore a re-entrant shop. Due to the fact that the jobs are identical, the decision version of this problem is even not in the class NP. We give an optimal schedule to minimize the makespan. Since the total flow time depends on the relations between the processing times, we decompose this problem into sub-problems according to the relations between the processing times. We prove various properties of optimal solutions and, based on these properties, we provide an optimal solution for all the sub-problems except one of them. For the sole remaining sub-problem, we prove a dominance property which allows to consider a part of schedules to find an optimal one.     

求解流水车间批量流集成调度的离散入侵杂草优化算法

提出一种离散入侵杂草优化算法,用来解决最大完工时间目标的流水车间批量流集成调度问题.该调度问题包含两个紧密耦合的子问题:批次分割问题和考虑启动时间的批次调度问题.设计了两段字符串编码,用来表示两个子问题.与基本入侵杂草优化算法不同,所提算法基于适应度和年龄确定杂草种子数量,基于正切函数和连续邻域操作产生种子.8种邻域算子的混合应用与局部搜索增强了算法的求解能力.仿真实验表明了所提算法的有效性.     

The design of an automated material handling system for a job shop

This paper describes the application of techniques developed for the management and control of Flexible Manufacturing Systems (FMS's) to a traditional job shop manufacturing sheet metal parts on typical sheet fabricating machines. The problem was the design and evaluation of an automated material handling subsystem (MHS) to support the shop. The MHS controlled the shop loading and floor control by its choice of orders to transport and enter the shop. Detailed simulation models were used to simulate the existing shop, the shop with a MHS, and several algorithms for loading and sequencing orders through the shop. This paper describes the shop, the models developed, the algorithms tested, and the simulation results. A highlight of the study was the ease and speed with which multiple models were developed using an interactive simulation generator, CAPS. Of particular interest is the procedure for shop scheduling, downstream pull, which uses heavily loaded machines to provide sequencing alternatives with the objective of “pulling” work through them to reach more lightly loaded machines. Shop output was estimated to increase from 26 orders per day to 45, a 73% increase, and turnaround time to decrease from ten days to two days.     

Integrated process planning using tool/process capabilities and heuristic search

CAD–CAM integration has involved either design with standard manufacturing features (feature-based design), or interpretation of a solid model based on a set of predetermined feature patterns (automatic feature recognition). Thus existing approaches are limited in application to predefined features, and also disregard the dynamic nature of the process and tool availability in the manufacturing shop floor. To overcome this problem, we develop a process oriented approach to design interpretation, and model the shape producing capabilities of the tools into tool classes. We then interpret the part by matching regions of it with the tool classes directly. In addition, there could be more than one way in which a part can be interpreted, and to obtain an optimal plan, it is necessary for an integrated computer aided process planning system to examine these alternatives. We develop a systematic search algorithm to generate the different interpretations, and a heuristic approach to sequence operations (set-ups/tools) for the features of the interpretations generated. The heuristic operation sequencing algorithm considers features and their manufacturing constraints (precedences) simultaneously, to optimally allocate set-ups and tools for the various features. The modules within the design interpretation and process planner are linked through an abstracted qualitative model of feature interactions. Such an abstract representation is convenient for geometric reasoning tasks associated with planning and design interpretation.     

Design of robust cellular manufacturing system for dynamic part population considering multiple processing routes using genetic algorithm

In this paper, a comprehensive mathematical model is proposed for designing robust machine cells for dynamic part production. The proposed model incorporates machine cell configuration design problem bridged with the machines allocation problem, the dynamic production problem and the part routing problem. Multiple process plans for each part and alternatives process routes for each of those plans are considered. The design of robust cell configurations is based on the selected best part process route from user specified multiple process routes for each part type considering average product demand during the planning horizon. The dynamic part demand can be satisfied from internal production having limited capacity and/or through subcontracting part operation without affecting the machine cell configuration in successive period segments of the planning horizon. A genetic algorithm based heuristic is proposed to solve the model for minimization of the overall cost considering various manufacturing aspects such as production volume, multiple process route, machine capacity, material handling and subcontracting part operation.     

Improved genetic algorithm based on multi-layer encoding approach for integrated process planning and scheduling problem

Integrated process planning and scheduling (IPPS) is of great significance for modern manufacturing enterprises to achieve high efficiency in manufacturing and maximize resource utilization. In this paper, the integration strategy and solution method of IPPS problem are deeply studied, and an improved genetic algorithm based on multi-layer encoding (IGA-ML) is proposed to solve the IPPS problem. Firstly, considering the interaction ability between the two subsystems and the multi-flexibility characteristics of the IPPS problem, a new multi-layer integrated encoding method is designed. The encoding method includes feature layer, operation layer, machine layer and scheduling layer, which respectively correspond to the four sub-problems of IPPS problem, which provides a premise for a more flexible and deeper exploration in the solution space. Then, based on the coupling characteristics of process planning and shop scheduling, six evolutionary operators are designed to change the four-layer coding interdependently and independently. Two crossover operators change the population coding in the unit of jobs, and search the solution space globally. The four mutation operators change the population coding in the unit of gene and search the solution space locally. The six operators are used in series and iteratively optimized to ensure a fine balance between the global exploration ability and the local exploitation ability of the algorithm. Finally, performance of IGA-ML is verified by testing on 44 examples of 14 benchmarks. The experimental results show that the proposed algorithm can find better solutions (better than the optimal solutions found so far) on some problems, and it is an effective method to solve the IPPS problem with the maximum completion time as the optimization goal.     

Customer order management in service oriented holonic manufacturing

One of the most important problems when considering the design of manufacturing systems based on SOA paradigms is the integration of shop floor devices in the business processes at the enterprise level. This paper presents the design and implementation of the Customer Order Management (COM) module based on SOA architecture in the context of holonic manufacturing systems. The COM module is integrating with SOA enabled shop floor devices using industry standards. The implementation leverages a multi agent system suited for industrial applications integrated in a SOA environment capable of dynamic BPEL workflow generation and execution. The prototype consists in a SCA application for core COM module functionality and an extension for NetLogo MAS platform for SOA integration. The COM module interacts with the MES layer using real time events handled by the BPEL process implementation in the execution stage. A web based portal frontend for the COM module has been developed to allow real time tracking of customer orders, providing data about product batch execution and individual progress of each product on the production line.     

基于OpenCV的物料分拣搬运机器人的设计

针对当今制造业中物料分拣搬运任务的自动化问题,设计出一套新型物料分拣机器人,结合2019年广东省工科大学生实验综合技能竞赛的机器人设计要求,提出了一种基于OpenCV的物料分拣搬运机器人的设计。以结构、控制、识别及通讯四方面对机器人的设计进行介绍。在基于STM32芯片的主控制系统的控制下,以树莓派4B为核心的图像识别系统运行OpenCV对摄像头采集的图像数据进行处理,通过CAMshift跟踪算法对目标进行跟踪,得到目标在图像中的位置,机器人根据该位置对物料进行抓取并搬运至物料投放点,在机器人运动同时通过Wi-Fi和Flask框架可在浏览器网页上对机器人的识别情况进行监视。实验及竞赛结果充分证明机器人在物料分拣搬运任务的完成上具有极大的高效性、准确性及稳定性,对制造业自动化的技术推进具有极大的前景。     

Integrating data envelopment analysis and analytic hierarchy for the facility layout design in manufacturing systems

Facility layout design (FLD) has a very important effect on the performance of a manufacturing system. The concept of FLD is usually considered as a multiobjective problem. For this reason, a layout generation and its evaluation are often challenging and time consuming due to their inherent multiple objectives in nature and their data collection process. In addition, an effective facility layout evaluation procedure necessitates the consideration of qualitative criteria, e.g., flexibility in volume and variety and quality related to the product and production, as well as quantitative criteria such as material handling cost, adjacency score, shape ratio, and material handling vehicle utilization in the decision process. This paper presents a decision-making methodology based on data envelopment analysis (DEA), which uses both quantitative and qualitative criteria, for evaluating FLD. The criteria that are to be minimized are viewed as inputs whereas the criteria to be maximized are considered as outputs. A computer-aided layout-planning tool, VisFactory, is adopted to facilitate the layout alternative design process as well as to collect quantitative data by using exact and vague data by means of fuzzy set theory. Analytic hierarchy process (AHP) is then applied to collect qualitative data related to quality and flexibility. The DEA methodology is used to solve the layout design problem by simultaneously considering both the quantitative and qualitative data. The purposed integrated procedure is applied to a real data set of a case study, which consists of 19 FLDs provided of the plastic profile production system.     

一个基于多代理的并行工程设计系统模型研究

并行工程设计方法试图在产品的早期设计时期就考虑产品生命周期中的各方面因素,但是要真正实现却是很困难的,该文给出了一个基于多代理的并行工程设计系统框架,来解决“考虑制造的设计（ＤＦＭ）”这一总理２,同样地,并行工程的其它考虑,如可装配性,可维护性与可服务性都能以同样的方式集成到这个框架中来。     

Intelligent scheduling and control of rail-guided vehicles and load/unload operations in a flexible manufacturing system

This paper presents a framework of intelligent manufacturing scheduling and control with specific applications to operations of rail-guided vehicle systems (RGVS). A RGVS control architecture is discussed with a focus on a simulated experiment in operations of the load/unload area of a real industrial flexible manufacturing system (FMS). In the operation stage of a material handling system (MHS), all shop floor data are subject to change as time goes. These data can be collected using a data acquisition device and stored in a dynamic database. The RGVS simulator used in this experimental study is designed to incorporate some possible situations representing existing material handling scenarios in order to evaluate alternative control policies. At the development stage of the controller, all possible combinations of most commonly encountered scenarios such as RGV failures, production schedule changes, machine breakdowns, and rush orders are to be simulated and corresponding results collected. The data are then structured into training data pairs to properly train an artificial neural network. The neural network, trained by using input/output data sets obtained from a number of simulation runs, will then provide control strategy recommendations. At the application stage, whenever an abnormal scenario occurs, a pre-processor will be activated to pre-screen and prepare an input vector for the trained neural network. If such an abnormal scenario falls outside the existing domain of data sets employed to train the neural network, as judged by the MHS supervisory controller, an off-line training module will be activated to eventually update the neural network. The recommended control strategies will be transmitted to the MHS control for real-time execution. If there is no further abnormal event detected, the dynamic data base (DDB) module simply continues to monitor the MHS activities. The proposed MHS control system combines the features of example based neural network technology and simulation modeling for true intelligent, on-line, pseudo real-time control. Not only will the system assure that feasible material handling control actions be taken, but also it will implement better control decisions through continuous learning from experiences captured as the operation time of the MHS accumulates.     

Real-time holonic scheduling of material handling operations in a dynamic manufacturing environment

In a real-world manufacturing environment, finding the right sequences and associated schedules with resource, precedence, and timing constraints is a difficult task. Moreover, a decision time period of hours or even minutes is simply too long. Good solutions are often needed in real time. One approach to overcome the limitations of classical scheduling is the use of distributed schemes such as agent or holonic-based control architectures. This paper presents a solution for scheduling material handling devices in the cellular manufacturing environment using the holonic control approach. In holonic systems, under real-time constraints, a feasible schedule for the material handling devices emerges from the combination of individual material handling holons’ schedules. Internal evaluation and allocation algorithms and specific cooperation mechanisms between the holons in the architecture are the basis for the resultant emergent schedules. These evaluation algorithms are developed using several scenarios that take into account uncertainties that usually exist in a dynamic manufacturing environment, such as new orders entering into the system. The study results obtained show that the holonic system is capable of accommodating new arriving jobs and delivers good solutions in real time.     

Sequencing material handling equipment in production facilities

In this article we consider the problem of sequencing material handling equipment in manufacturing systems, subject to constraints that restrict the start and end time of each production activity, according to pre‐specified daily resource operational schedules and process planning information. The underlying decision problem is modeled using an integer programming formulation similar to vehicle routing with time windows (VRPTW). To take advantage of standard approaches for the VRPTW, we develop a transformation schema that allows a one‐to‐one mapping between the manufacturing problem and VRPTW. An efficient heuristic to solve the resulting transformed problem is proposed. The method, which is a penalty‐based sequential insertion heuristic, allows routes to be constructed by exploiting the tradeoff between material handling and resource starvation costs. The steps of the method are illustrated via a comprehensive example and results on benchmark problems are reported.