John Miltenburg
McMaster University
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Featured researches published by John Miltenburg.
Journal of Operations Management | 1991
John Miltenburg; Wenjiang Zhang
Abstract Nine algorithms, developed to solve the cell formation problem, are evaluated and compared for their ability to produce good solutions to large problems. A primary performance measure and two secondary performance measures are presented for gauging the quality of a solution to the cell formation problem. Eight problems from the open literature and 60 randomly generated, large problems are analyzed. The analysis shows that there is no solution algorithm that performs better on all performance measures for problems from both the literature and the randomly generated data sets. One algorithm, called the ISNC algorithm and based on clustering around seeds, performs significantly better than all other algorithms on the primary performance measure (and performs well on the secondary measures) for the randomly generated data set, and performs well for the problems from the literature. Therefore this algorithm appears to the most appropriate algorithm to use as a general purpose solution algorithm for CF problems having between 25 and 50 machines, 35 and 50 parts, and densities between 10 and 20%. (It may not be appropriate for larger problems, since its computational and space requirements appear to be high.) After the ISNC algorithm, there is no significant difference between most of the other algorithms. However. different kinds of solutions are developed by some of these other algorithms. The well-known SC/ROC and ROC/ROC algorithms develop solutions which consist of a small number of relatively large cells. The BEA algorithm develops solutions which consist of relatively small cells (where each machine in the cell is visited by most of the parts assigned to that cell) and one large cell for those parts not assigned to the small cells.
International Journal of Production Research | 1989
John Miltenburg; Gordon Sinnamon
Many companies are converting their mixed-model multi-level production systems to just-in-time systems. This requires reducing set-up times so that small-lot production can be run. In this paper a theoretical basis is developed for scheduling these systems, and scheduling algorithms and heuristics are developed. The only other known scheduling method (the goal-chasing method), developed and used by Toyota, is shown to be a special case of these heuristics.
International Journal of Production Economics | 2001
John Miltenburg
Abstract Most writers describe the U-shaped production line as the special type of cellular manufacturing used in just-in-time (JIT) production systems. JIT is defined to be an umbrella term for a number of techniques whose purpose is to improve product quality and cost by eliminating all waste in the production system. The U-line arranges machines around a U-shaped line in the order in which production operations are performed. Operators work inside the U-line. One operator supervises both the entrance and the exit of the line. Machine-work is separated from operator-work so that machines work independently as much as possible. Standard operation charts specify exactly how all work is done. U-lines may be simple or complex. U-lines are rebalanced periodically when production requirements change. The U-line satisfies the flow manufacturing principle. This requires operators to be multi-skilled to operate several different machines or processes. It also requires operators to work standing up and walking. When setup times are negligible, U-lines are operated as mixed-model lines where each station is able to produce any product in any cycle. When setup times are larger, multiple U-lines are formed and dedicated to different products. 114 US and Japanese U-lines are examined in this study. The average U-line has 10.2 machines and 3.4 operators. About one-quarter of all U-lines are manned by one operator and so run in chase mode. The reported benefits are impressive. Productivity improved by an average of 76%. WIP dropped by 86%. Leadtime shrunk by 75%. Defective rates dropped by 83%.
International Journal of Production Research | 1998
D. Sparling; John Miltenburg
Many manufacturers are switching their production lines from single product or batch production to mixed-model production, often as a consequence of implementing just-in-time (JIT) principles into their operations. In mixed-model production, different products or models are produced on the same line with the models interspersed throughout a production sequence. This helps manufacturers provide their customers with a variety of products in a timely and cost-effective manner. The mixed-model U-line balancing (MMULB) problem assigns the tasks required to produce all models to a minimum number of stations on a U-shaped line. U-lines are widely used and well-suited to mixed-model production. A model of the MMULB problem is developed in this paper. The problem is NP-hard. An approximate solution algorithm is presented, and an illustrative example is worked. Areas where more research is needed are identified.
International Journal of Flexible Manufacturing Systems | 2002
John Miltenburg
The production line considered in this paper is a U-shaped, mixed-model, asynchronous line where assembly and fabrication tasks are performed to produce a variety of different discrete products or models in a just-in-time (JIT) environment. Two important problems occur routinely with these lines. The first is the assignment of tasks to stations on the line—the line balancing problem—and the second is the selection of the sequence in which models will be produced—the model sequencing problem. The model sequence cannot be set independently of the line balance, because different models require different tasks and the same tasks have different completion times for different models, and, in the JIT environment, the model sequence cannot be set independently of the schedules of other lines and production facilities. JIT uses a pull rather than a push system of production control, which means that the model sequence at the U-shaped mixed-model final assembly line sets the schedules at the other production facilities. JIT requires these latter schedules to be “level” and this requirement imposes an additional constraint on the model sequence. The effect of these two conditions is to require that the line-balancing and model-sequencing problems be solved simultaneously. In this article, we model the joint problem and present a solution algorithm for solving instances of practical size.
European Journal of Operational Research | 1998
John Miltenburg
Abstract U-shaped production lines and facilities consisting of many such lines are important parts of modem manufacturing systems. The problem of balancing and rebalancing U-line facilities is studied in this paper. Like the traditional line balancing problem this problem is NP-hard. The objective is to assign tasks to a minimum number of regular, crossover, and multiline stations while satisfying cycle time, precedence, location, and station-type constraints. A secondary objective is to concentrate the idle time in one station so that improvement efforts can be focused there in accordance with modern just-in-time principles. A reaching dynamic programming algorithm is presented for determining optimal balances. It is effective for balancing and rebalancing facilities with any number of U-lines, provided that individual U-lines do not have more than 22 tasks and do not have wide, sparse precedence graphs.
Iie Transactions | 2001
John Miltenburg
Now-a-days shorter product life cycles and increased demands for customization make it difficult to produce some products on traditional production lines. Often the best that can be done is to produce them in batch flow systems that have been improved through the incorporation of line flow principles. This is one-piece flow manufacturing. Traditional cells with irregular material flows are replaced by U-shaped production lines within which flow is regular and paced by a cycle time and between which flow is controlled by pull signals. This tutorial examines the research literature on one-piece flow manufacturing. It begins with the decisions rules that determine when one-piece flow is appropriate. Next the unique elements of one-piece flow (takt time, standard work, flow manufacturing on U-shaped lines, pull production, and jidoka) are reviewed. Then the mathematical models that are used to design one-piece flow systems are examined. Finally areas where more research is needed are discussed.
International Journal of Production Research | 2009
John Miltenburg
Manufacturing strategy is a plan for moving a company from where it is to where it wants to be. Determining the best manufacturing strategy is not easy because of the wide range of choices and constraints a company faces. Manufacturing strategy frameworks or models are helpful because they identify the objects that comprise manufacturing strategy and organise these objects into a structure that enables a company to understand and use the objects to develop strategy. This paper examines a companys international manufacturing network. It identifies and examines six manufacturing strategy objects (generic international strategies, manufacturing networks, network manufacturing outputs, network levers, network capability, and factory types), linkages between objects, and the manufacturing strategy framework that follows from these objects and linkages. Then the paper applies the framework to the manufacturing networks of three companies in the global steel industry: Arcelor (Luxembourg), Mittal (India), and Dofasco (Canada).
Iie Transactions | 1992
John Miltenburg; Gordon Sinnamon
Abstract Many companies are using Just-In-Time (JIT) control systems in their mixed-model multi-level production facilities. When scheduling these facilities the most important objective is to keep a constant rate of usage for every part used by the system. In this paper a mathematical model for scheduling these facilities is studied and techniques for determining good schedules are developed. Procedures for solving very large problems are given. Handled by the Department of Scheduling, Planning and Control.
IEEE Transactions on Engineering Management | 2000
Chun Hung Cheng; John Miltenburg; Jaideep Motwani
Straight lines and U-lines are two commonly used layouts for production lines. To date, no research has studied the effect of these layouts on the quality of the products produced by the line. This paper examines U-shaped lines and straight lines from the viewpoint of their effect on quality, which is organized into Jurans quality planning, quality control, and quality improvement categories. Two or more quantitative measures are developed for each quality category, the effect of the shape of the line on the values of these measures is carefully analyzed, and the implications for problems of realistic size are discussed. The authors find that U-shaped lines outperform straight lines in all of the aspects of quality they examined.