The Application of Single-Pass Heuristics for U-Lines

U-lines have been adopted in many manufacturing settings as part of JIT implementation. In this paper, we examine the applicability of existing straight-line heuristics for obtaining a balance on a U-line. We modify 13 single-pass heuristics and study the effectiveness of various heuristics under different problem conditions. An extensive computational study is carried out to help identify the best heuristics. In addition, we compare recent U-line procedures with a single-pass heuristic using some literature problems. Based on a single-pass heuristic, we compare the configurations of a straight- and U-line

SOLUTIONS FOR THE CONSTRAINED DYNAMIC PLANT LAYOUT PROBLEM

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Critical Chain Analysis Using Project Management Software

Post print deposited 05/12/2016. Link to publisher's version http://ezproxy.lib.ucalgary.ca/login?url=http://search.proquest.com.ezproxy.lib.ucalgary.ca/docview/199916784?accountid=9838Previously in this journal, Umble and Umble (2000) discussed Goldratt’s “critical chain” (Goldratt 1997) for project scheduling. In this article, we show that explicitly defining the critical chain in a resource constrained project schedule will help avoid errors in slack calculation. Because these slacks can help identify the feeding buffers in critical chain analysis and prepare the backward schedules for the activities, it is important to identify them correctly.Ye

Notes: The Dynamics of Plant Layout

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The Dynamic Plant Layout Problem: Incorporating Rolling Horizons and Forecast Uncertainty

To the best of our knowledge, research in the Dynamic Plant Layout Problem (DPLP) assumes that the planning horizon is fixed and that material flows are known with certainty. But in practice, many companies use rolling planning horizons. Further, they have to deal with the effect of uncertainty in material flow forecasts. This paper investigates the performance of algorithms under fixed and rolling horizons, under different shifting costs and flow variability, and under forecast uncertainty. Nearly 1800 problems were run using different algorithms. The results show that algorithms that dominated under fixed horizons may not work as well under rolling horizons. Also it is difficult to identify an algorithm that performs well under all situations. Thus the development of efficient and effective heuristics might be useful in solving the rolling horizon problem. It also appears that increasing the planning horizon under rolling plans does not offer any advantage. Further forecast uncertainty may not significantly affect the performance of algorithms and in some cases may be beneficial

MANUFACTURING CELL FORMATION USING SIMILARITY COEFFICIENTS AND PAIR-WISE INTERCHANGE: FORMULATION AND COMPARISON

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Spreadsheet Optimization: A Support Tool for the Theory of Constraints

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An improved algorithm for solving a multi-period facility location problem

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DPLP Balakrishnan Dataset

The folder has been updated with 2010 files that explains the format of the data set. The 2008 and 2010 datasets itself are identical.Ye