Product Grouping and Resource Allocation in Multiline Manufacturing Systems

This paper studies the product grouping problem and the resource allocation problem in the design of manufacturing systems where multiple production lines are used to manufacture a range of products. Each production line in the product grouping problem is dedicated to manufacturing a group of products. A mathematical model is developed to determine the number of product groups and the composition of each product group in the manufacturing system. For the resource allocation problem, a mathematical model is developed to determine the optimal resource allocation scheme for each production line and the optimal inventory level for each product. A genetic-based algorithm is proposed to solve the product grouping problem and the resource allocation problem simultaneously, and its results are compared to those of the conventional heuristic approaches. The proposed genetic approach is a simple but effective means of solving these problems.     

Comparative Analysis of Conventional and Non-Conventional Optimisation Techniques for CNC Turning Process

This paper describes various optimisation procedures for solving the CNC turning problem to find the optimum operating parameters such as cutting speed and feedrate. Total production time is considered as the objective function, subject to constraints such as cutting force, power, tool–chip interface temperature and surface roughness of the product. Conventional optimisation techniques such as the Nelder Mead simplex method and the boundary search procedure, and non-conventional techniques such as genetic algorithms and simulated annealing are employed in this work. An example is given to illustrate the working procedures for determining the optimum operating parameters. Results are compared and their performances are analysed.     

Production Scheduling and Cell Formation for Virtual Cellular Manufacturing Systems

In this paper, an approach using the concept of genetic algorithms is proposed as a powerful but simple means of scheduling the manufacturing operations of a virtual cellular manufacturing system (VCMS). A mathematical model is developed to describe the characteristics of a VCMS, which includes the constraints related to the delivery due dates of the various products and the maximum capacities of the manufacturing resources. The objectives are to set up virtual manufacturing cells and to formulate feasible production schedules for all manufacturing operations, in order to minimise the total material and component travelling distance incurred in manufacturing the products. A new genetic based scheduling algorithm is proposed as an optimisation tool to determine the solution. The proposed algorithm differs from the conventional genetic algorithms in that the populations of the candidate solutions consist of individuals from various age-groups, and each individual is incorporated with an age attribute to enable its birth and survival rates to be governed by predefined ageing patterns. By generating the evolution of the populations with the genetic operators of selection, crossover and mutation, the proposed approach provides excellent results by maintaining a better balance between the exploitation and the exploration of the solution space, and thus improves the computational speed and the solution quality. The condition ensuring stable search performance is also derived. The superiority of the proposed algorithm is illustrated by solving the production-scheduling and cell-formation problems for a virtual cellular manufacturing system, and the results are compared with those obtained by using a conventional optimisation technique.     

A Modified Genetic Algorithm for Job Shop Scheduling

As a class of typical production scheduling problems, job shop scheduling is one of the strongly NP-complete combinatorial optimisation problems, for which an enhanced genetic algorithm is proposed in this paper. An effective crossover operation for operation-based representation is used to guarantee the feasibility of the solutions, which are decoded into active schedules during the search process. The classical mutation operator is replaced by the metropolis sample process of simulated annealing with a probabilistic jumping property, to enhance the neighbourhood search and to avoid premature convergence with controllable deteriorating probability, as well as avoiding the difficulty of choosing the mutation rate. Multiple state generators are applied in a hybrid way to enhance the exploring potential and to enrich the diversity of neighbour-hoods. Simulation results demonstrate the effectiveness of the proposed algorithm, whose optimisation performance is markedly superior to that of a simple genetic algorithm and simulated annealing and is comparable to the best result reported in the literature.     

基于多代理的敏捷制造单元调度研究

在分析敏捷环境下敏捷制造单元调度特点的基础上 ,提出了基于多代理协作的敏捷制造单元模型 ,并根据该模型设计了一种混合遗传模拟退火 (SAGA)单元调度算法。并给出SAGA和GA两种方法的比较结果。应用实例表明 ,该方法调度性能良好 ,调度过程快 ,支持任务的随机加入 ,为制造企业快速有效的响应市场 ,提高敏捷性提供了强有力的理论与技术支持。     

Machining Parameters Optimisation for Turning Cylindrical Stock into a Continuous Finished Profile Using Genetic Algorithm (GA) and Simulated Annealing (SA)

The economics of machining have been of interest to many researchers. Many researchers have dealt with the optimisation of machining parameters for turning operations with constant diameters only. All CNC machines produce finished components from bar stock. Finished profiles consist of straight turning, facing, taper and circular machining. This research concentrates on optimising the machining parameters for turning cylindrical stock into continuous finished profiles. Arriving at a finished profile from a cylindrical stock is done in two stages, rough machining and finish machining. Rough machining consists of multiple passes and finish machining consists of single-pass contouring after the stock is removed in rough machining. The machining parameters in multipass turning are depth of cut, cutting speed and feed. The machining performance is measured by the production cost. In this paper the optimal machining parameters for continuous profile machining are determined with respect to the minimum production cost, subject to a set of practical con-straints. The constraints considered in this problem are cutting force, power constraint and tool tip temperature. Due to high complexity of this machining optimisation problem, a simulated annealing (SA) and genetic algorithm (GA) are applied to resolve the problem. The results obtained from GA and SA are compared. ID="A2"Correspondance and offprint requests to: Dr P. Asokan, Department of Production Engineering, Regional Engineering College, Tiruchirap–palli–620 015, Tamil Nadu, India. E-mail: asokan@rect.ernet.in     

A Genetic Algorithm for the Design of a Single-Row Layout in Automated Manufacturing Systems

In this paper the machine layout problem with a linear single-row flow path in automated manufacturing systems is investigated. Traditional machine layout approaches can produce an inappropriate layout design because they often do not consider flow path characteristics, such as flow path configuration and feasible flow path direction. This paper investigates the effects of such flow path characteristics on machine layouts. There are six flow-line analysis methods reported in the literature. Of these, the two best methods (FLA-5 for bidirectional flow lines and FLA-6 for unidirectional flow lines) are considered here. Two heuristic search algorithms, one combining FLA-5 and a genetic algorithm and other combining FLA-6 and a genetic algorithm, are proposed. A wide variety of data sets are used to test the proposed algorithms.This revised version with a corrected online cover date was published online in April 2004.     

A New Approach to Fuzzy Part-Family Formation in Cellular Manufacturing Systems

Group technology (GT) is one of the major issues in successful implementation of cellular manufacturing systems. The success of GT implementation depends only on an effective formation of part-families. The effective formation of part-families depends mainly on an effective formation of a similarity coefficient measure. Many similarity coefficients have been produced over the past three decades, but better similarity coefficient measures are required. The decision-making process in a manufacturing system often involves uncertainties and ambiguities. Under such circumstances, fuzzy methodologies have proved to be effective tools for taking fuzziness into consideration. In this paper, we use fuzzy cluster analysis to form part-families and assign parts to existing part-families. We have established a new approach to convert a fuzzy clustering matrix into a zero-one incidence matrix. We have also developed a new similarity coefficient measure and this coefficient measure is used to form a part-family. A mathemat-ical model that uses this similarity coefficient for solving optimally the part-family formation problems in cellular manufacturing is developed. Finally, it is compared with other models by giving an illustration with a numerical example.     

The Role of Fuzzy and Genetic Algorithms in Part Family Formation and Sequence Optimisation for Flexible Manufacturing Systems

Group technology (GT) is one of the key issues in the successful implementation of flexible manufacturing systems (FMSs). The success of GT implementation is in the effective formation of part families (PFs) and similarity coefficients measures. Over the past three decades, many similarity coefficients have been proposed, but a better similarity coefficient measure is required. The decision-making process in a manufacturing system often involves uncertainties and ambiguities. Under such circumstances, fuzzy methodologies have proved to be an effective tool for taking fuzziness into consideration. The first part of this paper deals with the fuzzy part-family formation. This was achieved in the following ways: 1. A new similarity coefficient measure has been developed and this coefficient measure is used to form a part-family. 2. A mathematical model that uses this similarity coefficient for solving the part-family formation problems optimally in an FMS is developed. The fuzzy approach has the special advantage of producing more accurate results than conventional clustering and other methods. It not only reveals the specific part family that a part belongs to, but also provides the degree of membership of a part associated with each part family. This will give a balanced work load for the machine. In the second part of this paper, the introduction of the concept of genetic algorithms is proposed to eliminate more job sequences and, finally, the optimum sequence is obtained through the minimum penalty cost. Software is developed and implemented to obtain an optimum sequence and, finally, a numerical example is given as an illustration.     

Real-Time Manipulation of Alternative Routeings in Flexible Manufacturing Systems: A Simulation Study

This paper presents the results of a simulation study of a typical flexible manufacturing system (FMS) that has routeing flexibility. The objective is this study is to test the effectiveness of the dissimilarity maximisation method (DMM) for real-time FMS scheduling. DMM is an alternative process plan selection method developed for routeing selection in off-line FMS sched-uling. An integrated framework that consists of a computer simulation model, which mimics a physical system, a C++ module, and a linear program solver is used to evaluate the effects of various operational control rules on the system performance. The hypothetical FMS employed in this study consists of seven machining centres, a loading and an unloading area, and six different part types. Owing to the existence of identical machining centres in the system, the part types have alternative routeings. For selecting an incoming part and later routeing it to a machining centre for its next operation, three control rules, namely, first-in first-out/first available (FIFO/FA), equal probability loading (EPL), and dissimilarity maximisation method/first-in first-out (DMM/ FIFO) are used. In this study, DMM is 1. Used as a real-time decision-making tool to select routeings for the parts that are in the system. 2. Tested and benchmarked against FIFO/FA and EPL. The results show that DMM/FIFO outperforms FIFO/FA and EPL on system throughput. Other measures such as average waiting time, average transportation time, and percentage utilisation rates are also investigated to provide insights for the effectiveness of the DMM rule for real-time FMS control applications.