optimization of a high-speed placement machine using tabu search algorithms
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Annals of Operations Research 96 (2000) 125147 125
Optimization of a high-speed placement machine usingtabu search algorithms
Peter Csaszar a,, Thomas M. Tirpak a and Peter C. Nelson ba Motorola Advanced Technology Center, Motorola, Inc., Schaumburg, IL 60196-1078, USA
E-mail: email@example.com Artificial Intelligence Laboratory, Department of Electrical Engineering and Computer Science
(M/C 154), University of Illinois at Chicago, 851 South Morgan Street, Chicago, IL 60607-7053, USA
Combinatorial optimization represents a wide range of real-life manufacturing optimiza-tion problems. Due to the high computational complexity, and the usually high number ofvariables, the solution of these problems imposes considerable challenges.
This paper presents a tabu search approach to a combinatorial optimization problem, inwhich the objective is to maximize the production throughput of a high-speed automatedplacement machine. Tabu search is a modern heuristic technique widely employed to copewith large search spaces, for which classical search methods would not provide satisfactorysolutions in a reasonable amount of time. The developed TS strategies are tailored to addressthe different issues caused by the modular structure of the machine.Keywords: combinatorial optimization, tabu search, manufacturing optimization, placementmachines
Electronics manufacturing companies face a highly competitive market, in whichadopting new technologies to cut production costs, while still maintaining high quality,becomes a crucial aspect in determining profitability. The majority of electronics prod-ucts involves printed wiring board (PWB) assembly, in which electronic componentsare placed onto circuit boards in an assembly line. The older through-hole method-ology has been essentially replaced by surface mount technology (SMT). Because ofthe reduced size of the electronic components or parts, increased assembly precisionbecame necessary. Studies conducted on SMT assembly lines  have shown that,considering the complete SMT production process, the bottleneck occurs most oftenin the placement phase. These facts explain why small parts placement has becomethe primary area for automation. Optimizing the operation of automated placementmachines is, therefore, a key issue in increasing the throughput and cost-effectivenessof the entire SMT production line.
This paper focuses on developing an optimization method for the family of mod-ular high-speed automated SMT placement machines. The formulated optimization Corresponding author.
J.C. Baltzer AG, Science Publishers
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problem can be shown to be NP-complete; therefore, a modern heuristic algorithm,namely tabu search (TS) was selected, with special strategies to address the particularstructure of the placement machines.
Section 2 gives an introduction to the optimization of chip shooters, and a reviewof related literature. Section 3 describes the particular model of placement machinefor our study, while section 4 presents the formulated optimization problem and itspartitioning into two subproblems. The search problem based on the optimizationproblem is established is section 5. Section 6 describes the TS optimizer, explainingthe development and mechanism of the proposed tabu strategies. The experimentalresults are provided in section 7. Finally, section 8 gives a brief conclusion, anddiscusses opportunities for improvement and future applications.
2. Problem statement
This section first gives an introduction to the structure of placement machines,then discusses how their productivity can be increased. This is followed by a summaryof publications in the field of manufacturing optimization, with special emphasis onchip shooters.
Many different models of automated placement machines have been developedfor the electronics industry to perform a variety of assembly operations that differ interms of board size, part geometry, production quantity/throughput requirements, etc.All machines, however, share a common structure with the following three major com-ponents. (For an exhaustive and detailed classification of contemporary chip shooterssee .)
A feeder array, which may be divided into separate feeder banks. The feeder arraymay be movable or fixed, and is equipped with different types of feeder devices(feeders for short). Each feeder holds a large number of identical electronic parts. A placing component, which contains a nozzle or a group of nozzles, capable of
picking a part from the feeder, and placing it on the PWB. The nozzles may beassembled on a rotating turret, or a positionable head. The characteristics of thehead also vary in terms of the number of installed nozzles (one or more), thepossibility of run-time nozzle changeover (supported or not), and the positioningmachinery (gantry, robotic arm, etc.). A vision system, which determines the picked parts orientation and exact position
on the nozzle. The inherent imprecision of the picking process can then be correctedwhen the part is placed. A board support mechanism, on which the boards are affixed during part place-
ment. This can be a worktable (fixed board), a conveyor (one-dimensional boardmovement) or an X-Y table (two-dimensional board movement).
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Machines can have multiple instances of the components listed above, providinga degree of parallelism to improve the production throughput. The precise structureof the machines in our study is detailed in section 3.
The primary goal of optimization for every machine is to minimize the cycletime, the time elapsed between two consecutive board completions. This can beachieved in the following ways:
Minimizing the time of each placement cycle, i.e., the time for picking a part fromthe feeder and placing it on the board. Minimizing the time required by board transport, fiducial mark checking and other
off-cycle activities. Minimizing the occurrence of occasional non-productive events taking a relatively
long time, such as feeder replenishment, nozzle changeover (if applicable), etc. Maximizing the machines parallel operation (if applicable).
A great amount of work has been done with simulation and optimization forautomated placement machines. The framework of the problem is well stated in thepapers by McGinnis et al.  and Ammons et al. . In their classical paper, Balland Magazine  developed an algorithm for optimizing the auto-insertion processof a single pick-up head placement machine with a stationary board and feeder. Theproblem they formulated to model the insertion operations is equivalent to the directedpostman problem. A plethora of different optimization approaches for different ma-chines has been developed. Leipala and Nevalainen  investigated a single headsequential placement machine with a moving feeder carriage and an X-Y table. Theyfound that the problem can be broken into two subproblems: an asymmetric travel-ing salesman problem (TSP) representing the insertion sequence optimization, and aquadratic assignment problem (QAP) regarding the feeder allocation. The heuristicapproach applied does not guarantee optimality, but fairly good suboptimal solutions.Crama et al.  worked on an entire line of placement machines operating concurrently,and found a hierarchical approach to the solution by dividing the whole problem intosimpler subproblems, which are solved separately. The machine in Grotzingers paper is a system with dual heads on a fixed length arm and two component deliverycarriers on either side of the worktable. Here the dual delivery system causes diffi-culties resulting in several nonlinearities in the formulation. The method presented inthe paper begins with linearization, after which the result is solved as a mixed integerprogramming problem.
Foulds and Hamacher  identified the optimal bin locations in order to determinethe best parts insertion sequence for the machine of their study. The bin locationassignment was formulated as a single-facility location problem. Different distancenorms, such as the Chebyshev distance, were used for the solution. After the feederassignment is identified, the insertion sequence is achieved by solving the formulatedTSP. In their work, Mauckner et al.  tackle the placement sequence generationwith an algorithm called clock heuristic, and provide its comprehensive analysis for
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three different distance metrics above. Sohn and Park  examined a multi-headturret placement machine. Their method first determined the locations of componentreels in the rack, followed by the placement sequence of components, such that aminimized total assembly time is achieved. Shih et al.  designed an expert systemfor finding the optimal placement sequence, feeder-, tooling- and nozzle-setup for agantry type machine. Their knowledge base contained information on the placementprocesses, machine specifications and components, the inference engine implementedforward chaining as the search strategy.
Contemporary general heuristic techniques have also been applied to attack theplacement machine optimization problem. Su and Shribari  dealt with a verysimilar machine to that of Shih et al. mentioned above, and produced an optimizercombining expert systems with artificial neural networks (ANNs). The ANNs wereinvolved in order to address the unstructured and ill-defined nature of the problem.An integer programming (IP) relaxation approach is presented by Kumar and Li .Relaxing the problem to linear programming (LP), and solving the LP with a com-mercially available software tool pro