The study indicates that the DCM concept permit gains both in terms of performance and flexibility by capitalizing on the advances in computer science and ...
Pergamon
Computers ind. Engng Vol. 33, Nos 1-2, pp. 239-242, 1997 © 1997 Published by Elsevier Science Ltd Printed in Great Britain. All rights reserved 0360-8352197 $17.00 + 0.00 P I I : S0360-8352(97)00083-1
STUDYING THE PERFORMANCE OF A DYNAMIC CELLULAR MANUFACTURING SYSTEM Yannick Marcoux, Jocelyn Drolet, Georges Abdulnour D~partement de g~nie industriel, Universit~ du Quebec ~ Trois-Rivi~res Bombardier Sea-Doo/Ski-Doo Chair in SME Technological Change Management
ABSTRACT
This paper aims at studying and comparing the performance of a Dynamic Cellular Manufacturing System (DCMS) with a Classical Cellular Manufacturing System (CCMS). A case study has been realized through the use of a bench test and several measures of performance were collected. The study indicates that the DCM concept permit gains both in terms of performance and flexibility by capitalizing on the advances in computer science and information technology. Even though the dynamic cellular concept is in its early infancy, research being conducted tends to indicate that this new organization paradigm may be a serious rival to the classical cellular organization. © 1997 P u b l i s h e d by E l s e v i e r Science Ltd INTRODUCTION
International competition relentlessly places pressure on manufacturing systems to be more effective. Consumer goods show an increase in variety and a decrease in product life cycle. Product life tends to be much shorter than in the past; this forces manufacturing organizations t o increase responsiveness, increase flexibility, shorten setup time and lower work-in-process inventory. Many words have been used to describe the new manufacturing systems: agile, flexible, intelligent, responsive, etc., to name a few. They indicate a need for manufacturing systems that possess the "innate ability to respond, promptly and correctly, to changes in requirements". For this to happen the manufacturing system must be designed so that it supports a one o f a kind production. It is believed that the DCM concept that has been introduced a year ago could sustain this kind of environment. This paper aims at presenting a case study that has been realized through the use of a bench test. The DCM concept is described first. Then, the authors present the bench test briefly. Third, a case study is presented and results are analyzed. The paper finishes with a brief concluding remark and a few projects for futur developments. DYNAMIC CELLULAR MANUFACTURING
The literature reports several hundreds of success stories that resulted from the utilization of classical cellular manufacturing systems. These systems are not a major technological advantage anymore. To stay ahead of the competition, manufacturers must look for something new, a manufacturing system designed for maximum efficiency at all time and capable to support a one of a kind production at a minimum cost. For a great number of manufacturers, the answer is perhaps the dynamic cellular manufacturing concept [RHE95]. This approach appears to be very efficient in a highly turbulent environment as it is often the case for subcontracting manufacturers. Typically, these manufacturers are specialized in a few processes and sell their capacity and knowledge to produce a variety of parts for a number of customers. Oftentimes, they do not own the parts, specialized tooling, fixtures and molds, and have a limited control on parts design and characteristics. In spite of this, they must offer a highly flexible, but still competitive manufacturing environment. 239
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The physical configuration of the dynamic cellular manufacturing system is prone to change in time. Its configuration aims at minimizing total marginal cost of material handling (parts and machines) over a certain horizon. THE BENCH TEST The bench test is written in Microsoft Visual Foxpro Version 3.00 and uses a relational database composed of more than 20 tables. At some points, an integer programming model is generated and passed to LINDO (Simplex algorithm). The output generated by LINDO contains the new cellular configuration that is recuperated and added to the database. The jobs are released in the new cellular system according with some sequencing rules. The rate of introduction of the parts within each cell is determined by the bottleneck workstation in that cell. After a certain time, a module determines the job order that has been completed and those that are only partly completed. Then, it calculates the planned load on machines, considers the list of jobs that have not yet been introduced, performs a rough cut capacity planning for the next planning horizon, generates the IP model and submits it to L1NDO. LINDO returns a new cellular configuration and so on. The job orders database contains enough job orders to load the system for more then 10 planning horizons. The rough cut capacity planning module determines what are the job orders that can be introduced within the planning horizon subject to machines disponibility and a global target load. Sequencing and scheduling consider the job orders size, the transfer lot size, the processing times and the due date. The authors have used the integer programming model developed by Rheault [RHE95] to determine the dynamic cellular configuration that best fits theenterprise requirements for a certain horizon. The model minimizes the total marginal cost of material handling (parts and machines) over a certain horizon; that includes the cost for reconfiguring the cells over time. It is believed that the Dynamic Cellular Manufacturing System is more efficient in turbulent environment than a classical job shop configuration or a classical cellular manufacturing system. The authors have done the following hypothesis. Setup time and machine reconfiguration time are negligeable. Within zones, the proximity of machines permit a unitary transfer lot size whereas between zones, the transfer lot size depends on the size of the container (bac or pallet). A CASE STUDY The system has 15 machines grouped in 5 distinct zones. Figure #1 illustrates the initial layout and the machines/parts matrix. The initial configuration was obtained with the IP model but in this case, machine grouping appears to be identical to the one obtained with the clustering algorithm of Kusiak [KUS87]. This layout is used as the initial DCMS and the classical (static) cellular system. Various performance measures will be calculated for the mutating DCMS (10 reconfigurations) and for the static cellular system. The size of the job orders varies from 100 to 1 500 parts. When parts are moved from one zone to another, the lot sizing is 25 parts ; it is unitary within a given zone. The number of machines visited is either 2 or 3 for each product. Processing times is uniformly distributed and varies from 3 to 10 minutes. One uses the rectilinear distance between zones and a material handling cost of 0.25S/feet. There are two horizons : the planning horizon and the revised horizon. The planning horizon is considered to determine the new cellular configuration. Production planning is performed for the whole planning horizon. In a typical DCMS, this horizon could be anywhere from two to eight weeks. The revised horizon can be equal to the planning horizon but finishes usually before the end of the planning horizon. During the planning horizon, it is most likely that some kind o f unexpected events will occur. In that case, the production will not perform as expected. This is why one has to revise the production plan. In this study, the planning horizon is set to two weeks (5 000 Min) and the revised horizon is set to 1 week (2 500 Min).
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