A new approach for the selection of advanced manufacturing technologies: DEA with double frontiers

Selection of advanced manufacturing technologies (AMTs) is an important yet complex decision which requires careful consideration of various performance criteria. This paper proposes the use of data envelopment analysis (DEA) with double frontiers for the selection of AMTs, which considers not only the best (optimistic), but also the worst (pessimistic) relative efficiencies of each AMT. Compared with the traditional DEA, the DEA approach with double frontiers can identify the best AMT correctly and easily without the need to impose any weight restriction or the need to calculate the cross-efficiency matrix, which requires a large number of computations and may also result in inconsistent conclusions by aggressive and benevolent cross-efficiency models. Four numerical examples are examined using the DEA approach with double frontiers to illustrate its simplicity and usefulness in AMT selection and justification.     

Robotic U-shaped assembly line balancing using particle swarm optimization

Automation in an assembly line can be achieved using robots. In robotic U-shaped assembly line balancing (RUALB), robots are assigned to workstations to perform the assembly tasks on a U-shaped assembly line. The robots are expected to perform multiple tasks, because of their capabilities. U-shaped assembly line problems are derived from traditional assembly line problems and are relatively new. Tasks are assigned to the workstations when either all of their predecessors or all of their successors have already been assigned to workstations. The objective function considered in this article is to maximize the cycle time of the assembly line, which in turn helps to maximize the production rate of the assembly line. RUALB aims at the optimal assignment of tasks to the workstations and selection of the best fit robot to the workstations in a manner such that the cycle time is minimized. To solve this problem, a particle swarm optimization algorithm embedded with a heuristic allocation (consecutive) procedure is proposed. The consecutive heuristic is used to allocate the tasks to the workstation and to assign a best fit robot to that workstation. The proposed algorithm is evaluated using a wide variety of data sets. The results indicate that robotic U-shaped assembly lines perform better than robotic straight assembly lines in terms of cycle time.     

Robot selection using a fuzzy regression-based decision-making approach

Industrial robots, which enable manufacturing firms to produce high-quality products in a cost-effective manner, are important components of advanced manufacturing technologies. The performance of industrial robots is determined by multiple and conflicting criteria that have to be simultaneously considered in a robust selection study. In this study, a decision model based on fuzzy linear regression is presented for industrial robot selection. Fuzzy linear regression provides an alternative approach to statistical regression for modelling situations where the relationships are vague or the data set cannot satisfy the assumptions of statistical regression. The results obtained by employing fuzzy linear regression are compared with those of earlier studies applying different analytical methods to a previously reported robot selection problem.     

Computer aided robot selection: the ‘multiple attribute decision making’ approach

This paper presents an efficient approach for a computer-based solution to the problem of selection of an ‘optimum robot’ specifically to aid industries. The approach breaks new ground by applying Multiple Attribute Decision Making (MADM) ideology to equipment selection (robots) which is of consequence because selection is an implicit aspect of design. Indeed, the approach is also applicable to a robot design problem. The robot selection procedure allows rapid convergence from a very large number to a manageable shortlist of potentially suitable robots using an ‘elimination search’ routine based on a few pertinent attributes of the robots. Subsequently, the selection procedure proceeds to rank the alternatives in the shortlist by employing a MADM method termed TOPS1S (Technique for Order Preference by Similarity to Ideal Solution). An expert system has been developed as part of the software package to assist an inexperienced user to establish priorities, and to ‘oversee’ the selection process at various stages. The procedure is also illustrated by an example.     

A neural network approach for safety and collision avoidance in robotic systems

A major factor which has limited the application of robots in industrial and human service applications has been the lack of robust sensing and control algorithms for detection and prevention of collision conditions. This paper discusses an approach to the collision avoidance control of robots using a neural network methodology for the integration of sensory input data from the robot's environment. The paper presents a formulation of the collision avoidance problem using the occupancy grid formulation, and discusses the use of a combination of Dempster-Shafer inference and neural networks in fusing the sensory information and making robot movement decisions. Initial studies have shown this approach to be both robust and computationally tractable in providing enhanced safety capabilities.     

Robot selection using an integrated approach based on quality function deployment and fuzzy regression

The decision-makers have been experiencing difficulties in determining the most suitable robot alternative due to the increase in number of robots and the diversity in their application areas. A robust decision framework for robot selection should consider multiple and conflicting criteria and the dependencies among them. This paper introduces a decision model for robot selection based on quality function deployment (QFD) and fuzzy linear regression. The proposed approach benefits from the fact that QFD focuses on delivering value by taking into account the customer requirements and then by deploying this information throughout the development process, and applies this perspective to robot selection. Fuzzy linear regression is considered as an alternative decision aid for robot selection problems where imprecise relationships among system parameters exist. An example robot selection problem is presented to illustrate the proposed decision-making approach.     

An integrated linguistic MCDM approach for robot evaluation and selection with incomplete weight information

Nowadays selecting the most suitable robot is a difficult task for manufacturing firms due to increase in production demands and availability of various robot models. Robot evaluation and selection can be regarded as a multiple criteria decision-making (MCDM) problem and three key issues are the assessment of robots, the determination of criteria weights and the prioritisation of alternatives. This paper aims to propose an integrated model based on hesitant 2-tuple linguistic term sets and an extended QUALIFLEX approach for handling robot selection problems with incomplete weight information. The new model can not only manage uncertain and imprecise assessment information of decision-makers with the aid of hesitant 2-tuple linguistic term sets, but also derive the important weights of criteria objectively when the weight information is incompletely known. Moreover, based on the extended QUALIFLEX algorithm, the priority orders of robots can be clearly determined and a more reasonable and credible solution can be yielded in a particular industrial application. Finally, a robot selection case study is carried out, and comparative experiments indicate the practicality and effectiveness of the proposed integrated linguistic MCDM approach.     

Integrating AHP with QFD for robot selection under requirement perspective

Selection of a robot is an important task, as improper selection may adversely affect a firm's production by reducing the quality of the product, thereby reducing productivity as well as profitability. To effectively select a robot for a specified job, several factors have to be considered. The objective of this paper is to explain how, using a combined AHP/QFD model, the authors are able to determine if the deployment of robots in industry helped in performance enhancement from requirement perspective. Incorporating a simple and novel cost factor measure in the proposed integrated AHP/QFD model aids justification of the implementation of a robotic system in a manufacturing firm from an economic point of view also. The proposed integrated approach also identifies technical requirements followed by customer requirements. In this paper, an integrated model combining AHP and QFD has been delineated for the industrial robot selection problem. Seven technical requirement factors have been considered for the case study.     

Supplier evaluation and selection: an augmented DEA approach

Evaluating and selecting suppliers is an essential part of effectively managing today's dynamic and global supply chains. In this paper, we propose a supplier evaluation and selection methodology based on an extension of data envelopment analysis (DEA) that can evaluate suppliers in an efficient manner. Through the incorporations of a range of virtual standards, the proposed methodology termed augmented DEA, has enhanced discriminatory power over basic DEA models to rank suppliers. In addition, weight constraints are introduced to reduce the possibility of having inappropriate input and output factor weights. We demonstrate the application of augmented DEA with comparison experiments and find that the augmented DEA model has advantages over the basic DEA model as well as the cross-efficiency and super-efficiency models. Finally, we present a case application with data obtained from a communication and aviation electronics company to demonstrate the applicability and use of augmented DEA.     

基于模糊迭代Q-学习的冶金工业机器人轨迹跟踪控制研究

冶金工业机器人在现代工业生产中扮演着越来越不可替代的角色。由于工业自动化程度的大幅提升,人们对冶金工业机器人的性能也不断地提出新要求,尤其是对其控制系统的稳定性提出了更高的要求。针对目前冶金工业机器人轨迹跟踪精度较低且不具有自适应动态调节特性等问题,提出了一种模糊迭代Q-学习控制算法。以6-DOF（six degree-of-freedom,六自由度）双臂机器人为研究对象,利用Fuzzy工具箱编写模糊控制规则,以机器人产生的位置误差以及位置误差的变化率为模糊控制器的输入量,并引入Q-学习策略,以调整模糊控制器中的量化因子、比例因子以及迭代学习控制中的PD（proportional derivative,比例微分）参数,完成模糊迭代Q-学习控制器的设计。然后,联合ADAMS（automatic dynamic analysis of mechanical systems,机械系统动力学自动分析）和MATLAB软件搭建6-DOF双臂机器人仿真平台,开展高精度轨迹跟踪的轴孔装配任务模拟。仿真结果表明,6-DOF双臂机器人关节空间的轨迹跟踪精度较高,同时可以完成双臂轴孔协调装配任务,验证了所提出控制算法的有效性和先进性。研究结果可为双臂协作机器人实现高精度轨迹跟踪的轴孔装配提供参考,具有一定的实际应用价值。     

A comparison of stochastic dominance and stochastic DEA for vendor evaluation

Vendor selection involves decisions balancing a number of conflicting criteria. Data envelopment analysis (DEA) is a mathematical programming approach capable of identifying non-dominated solutions, as well as assessing relative efficiency of dominated solutions. A simple multi-attribute utility function can be applied to a small set of alternatives, providing a tool to assess relative value, but is subject to error if estimated measures are not precise. This paper compares stochastic DEA with a multiple-criteria model in a vendor selection model involving multiple criteria, reporting simulation experiments varying the degree of uncertainty involved in model parameters.     

基于BP神经网络和DEA的物流供应商选择

把交叉模型的评价结果设定为样本的期望值,将BP神经网络和交叉评价模型有机结合起来,构建了物流供应商选择模型。以2006年22家上市物流供应商的数据为样本进行了研究。该方法从统计数据出发,避免了主观因素对评价结果的影响,使物流供应商选择结果具有相对客观性。实例表明,该选择模型能够确定出备选物流供应商的评价等级和大小,为物流供应商的选择提供了一种新方法。     

Knowledge-based functional design of industrial robots

This paper presents an expert functional design model and software modeling environment for designing the architecture of industrial robots. The modeling environment comprises an integrated knowledge base, an inference engine, a working memory, and an explanation unit and is implemented in CLIPS. The functional design model is based on the authors’ behaviour-driven, function-environment-structure (B-FES) formalism, which has been tailored to meet the special requirements of industrial robot design. A universal robot template has been created and a library of typical components of a robot has been compiled. Nine customized templates were generated from the universal template. Web links to the websites of manufacturers/suppliers provide easy access to data on robot components. The architectural design solutions are assessed by a set of user-defined performance criteria, such as precision, flexibility and short cycle time. Application of the approach is demonstrated through a case study of the functional design of a printed circuit board assembly robot. The authors argue that this approach is new for configuring robots and can significantly reduce the time, effort and number of errors made.     

双臂机器人协作空间的解算

双臂机器人能极大的拓展机器人的活动能力,在空间科学领域中得到了越来越多的应用.双臂机器人工作空间的形状边界的精确计算在它的优化设计和应用中非常重要.本文针对一种双臂空间机器人的二维地面实验系统,给出了双臂的运动学模型.依据从关节空间到工作空间的运动学映射,采用蒙特卡洛方法分别建立每只操作臂的工作空间.在此基础上针对机器人双臂工作空间的公共区域即协作空间,采用计算机图形学理论中的射线相交方法加以分析计算,得到了协作空间的几何形状.     

Efficient heuristics for robot acquisition planning for a CIM system

In this paper, a planning model and three efficient heuristics are developed for equipment acquisition planning for a CIM system using multiple-type robots. Our planning model considers selection of a proper mix of multiple-type robots such that operational requirements (i.e., time and space) from a given number of work stations are satisfied at minimal system cost. In specific, each robot is characterized by its fixed charge and subject to two capacity constraints on machine time and work space; and each work station has known demands for both machine time and work space, and is to be served by only one robot. The model is formulated as a pure 0–1 mathematical program and is shown to be harder than two-dimensional bin packing, a well-known NP-hard problem. The three heuristics developed are: a greedy heuristic, tabu thresholding, and simulated annealing. All heuristics are tested by solving 450 randomly generated problems. Computational results indicate that all three heuristics are effective and efficient in solving problems of a practical size (i.e., 50 work stations and a maximum of 20 robots). However, none of the heuristics are overwhelmingly better than the others in terms of both solution time and quality. Future research issues are also discussed.     

An innovation funnel process for set-based conceptual design via DOE exploration,DEA selection and computer simulation

This paper presents a conceptual design approach including pattern creation from designers, alternative exploration with a DOE matrix, alternative analysis via computer simulation and alternative selection by DEA analysis. Designers possessing domain knowledge create various design patterns to meet the requirements of product performance and customer expectations. Then, based on these design patterns, the alternatives, considered as decision-making units (DMUs), are extracted from various quality level combinations by following the use of the DOE matrix. The nature of the DOE matrix ensures that distinctive representatives are constructed for all design alternatives. The total alternatives (DMUs) consist of the alternatives associated with all the patterns. Computer simulation with ANSYS software is introduced to convert the quality level combination of each alternative (DMU) into simulated outputs, which are further categorised into DEA inputs and DEA outputs for DEA frontier analysis. Four DEA methods, CCR-min input, CCR-max output, BCC-min input and BCC-max output, are used for analysing typical market representatives resulting from market uncertainty. The found efficiencies are used to rank and select the explored alternatives (DMUs) for the next stage of the detailed design. A bike-frame product is chosen as an example to demonstrate the proposed approach. The results clearly show that the proposed approach enables designers to economically select appropriate design alternatives that satisfy performance expectations during the conceptual design stage.     

Proposing integrated Shannon’s entropy–inverse data envelopment analysis methods for resource allocation problem under a fuzzy environment

In this study, a two-phase methodology for resource allocation problems under a fuzzy environment is proposed. In the first phase, the imprecise Shannon’s entropy method and the acceptability index are suggested, for the first time in the literature, to select input and output variables to be used in the data envelopment analysis (DEA) application. In the second step, an interval inverse DEA model is executed for resource allocation in a short run. In an effort to exemplify the practicality of the proposed fuzzy model, a real case application has been conducted involving 16 cement firms listed in Borsa Istanbul. The results of the case application indicated that the proposed hybrid model is a viable procedure to handle input–output selection and resource allocation problems under fuzzy conditions. The presented methodology can also lend itself to different applications such as multi-criteria decision-making problems.     

Collaborative reinforcement learning for a two-robot job transfer flow-shop scheduling problem

A two-robot flow-shop scheduling problem with n identical jobs and m machines is defined and evaluated for four robot collaboration levels corresponding to different levels of information sharing, learning and assessment: Full – robots work together, performing self and joint learning sharing full information; Pull – one robot decides when and if to learn from the other robot; Push – one robot may force the second to learn from it and None – each robot learns independently with no information sharing. Robots operate on parallel tracks, transporting jobs between successive machines, returning empty to a machine to move another job. The objective is to obtain a robot schedule that minimises makespan (C_max) for machines with varying processing times. A new reinforcement learning algorithm is developed, using dual Q-learning functions. A novel feature in the collaborative algorithm is the assignment of different reward functions to robots; minimising robot idle time and minimising job waiting time. Such delays increase makespan. Simulation analyses with fast, medium and slow speed robots indicated that Full collaboration with a fast–fast robot pair was best according to minimum average upper bound error. The new collaborative algorithm provides a tool for finding optimal and near-optimal solutions to difficult collaborative multi-robot scheduling problems.     

Supplier post performance evaluation: the effects of buyer preference weight variance

We use empirical post performance data of suppliers and buyers to demonstrate that degrees of variability of buyer evaluation team weight preferences can have varying effects on the perception of supplier performance attributes. We first justify then use data envelopment analysis (DEA) as a research tool to integrate buyer performance and buyer weight preferences with supplier performance and then use the results of the dyadic DEA evaluation model to analyse three propositions. This integration is essential to a dyadic approach. Results indicate that buyer preference weights on supplier performance attributes influence the perception of supplier performance sometimes in unexpected ways dependent on the degree of conformity versus disparity in the factor weights that was put into operation using assurance regions. As a result, buyer performance factors and preferences must be measured and their effects understood before post performance supplier evaluation is completed and other components of a relationship management plan are implemented.     

工业机器人三维在线自校准系统的研制

针对工业机器人校准成本与校准精度难以平衡的问题,研究了一种针对工业机器人的三维低成本高精度接触式自校系统。该系统的三维校准器安装于机器人末端位置（TCP）,采用三个互相正交的高精度位置传感器,通过研究工业机器人D-H参数校准算法,并对专用四球校准台精密迭代测量,可实现对工业机器人D-H参数的低成本、高精度在线自校准。通过与激光跟踪仪进行验证试验,各测量点的最大En值为0.63,小于1,验证结果满意。