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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    International journal of flexible manufacturing systems 6 (1994), S. 5-31 
    ISSN: 1572-9370
    Keywords: printed circuit board assembly ; surface mount technology ; quadratic integer programming ; dynamic programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract Populating printed circuit boards is one of the most costly and time-consuming steps in electronics assembly. At the beginning of each work order, three decisions are required: (1) a sequence must be specified for placing the individual components on the board; (2) tape reels must be assigned to positions on the magazine rack; and (3) a retrieval plan must be determined should the same component type be assigned to more than one magazine slot. Collectively, these problems can be modeled as a nonlinear integer program. In this paper, we develop a series of algorithms for solving each using an iterative two step approach. Initially, a placement sequence is generated with a weighted, nearest neighbor traveling salesman problem (TSP) heuristic; the two remaining problems are then formulated as a quadratic integer program and solved with a Lagrangian relaxation scheme. As a final step, the current magazine assignments are used to update the placement sequence, and the entire process is repeated. Our ability to deal, at least in part, with simultaneous machine operations represents the major contribution of this work. The methodology was simulated for a set of boards obtained from Texas Instruments and theoretically compared with a heuristic currently in use.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Journal of global optimization 3 (1993), S. 289-309 
    ISSN: 1573-2916
    Keywords: Single machine scheduling ; dynamic programming ; greedy heuristics ; bicriteria optimization ; branch and bound
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper considers the problem of schedulingn jobs on a single machine to minimize the total cost incurred by their respective flow time and earliness penalties. It is assumed that each job has a due date that must be met, and that preemptions are not allowed. The problem is formulated as a dynamic program (DP) and solved with a reaching algorithm that exploits a series of dominance properties and efficiently generated bounds. A major factor underlying the effectiveness of the approach is the use of a greedy randomized adaptive search procedure (GRASP) to construct high quality feasible solutions. These solutions serve as upper bounds on the optimum, and permit a predominant portion of the state space to be fathomed during the DP recursion. To evaluate the performance of the algorithm, an experimental design involving over 240 randomly generated problems was followed. The test results indicate that problems with up to 30 jobs can be readily solved on a microcomputer in less than 12 minutes. This represents a significant improvement over previously reported results for both dynamic programming and mixed integer linear programming approaches.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Journal of global optimization 6 (1995), S. 109-133 
    ISSN: 1573-2916
    Keywords: Combinatorial optimization ; search heuristic ; GRASP ; computer implementation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Today, a variety of heuristic approaches are available to the operations research practitioner. One methodology that has a strong intuitive appeal, a prominent empirical track record, and is trivial to efficiently implement on parallel processors is GRASP (Greedy Randomized Adaptive Search Procedures). GRASP is an iterative randomized sampling technique in which each iteration provides a solution to the problem at hand. The incumbent solution over all GRASP iterations is kept as the final result. There are two phases within each GRASP iteration: the first intelligently constructs an initial solution via an adaptive randomized greedy function; the second applies a local search procedure to the constructed solution in hope of finding an improvement. In this paper, we define the various components comprising a GRASP and demonstrate, step by step, how to develop such heuristics for combinatorial optimization problems. Intuitive justifications for the observed empirical behavior of the methodology are discussed. The paper concludes with a brief literature review of GRASP implementations and mentions two industrial applications.
    Type of Medium: Electronic Resource
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