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Customer Order Scheduling in a General Job Shop Environment *
Author(s) -
Blocher James D.,
Chhajed Dilip,
Leung Mark
Publication year - 1998
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1998.tb00883.x
Subject(s) - tardiness , univariate , job shop , computer science , order (exchange) , operations research , flow shop scheduling , scheduling (production processes) , due date , process (computing) , multivariate statistics , job shop scheduling , operations management , industrial engineering , business , machine learning , economics , mathematics , engineering , schedule , operating system , finance
The primary objective of this study is to examine the performance of order‐based dispatching rules in a general job shop, where the environmental factors are shop utilization and due date tightness. An order is defined as a collection of jobs that are shipped as a group—an order—to the customer, only on completion of the last job of the order. We specifically compare dispatching rules from past job‐based studies to some rules adapted to encompass order characteristics. Standard flow time and tardiness measures are used, but in addition, we introduce measures that combine average performance with variation in an attempt to capture the performance of a majority of the orders processed in the shop. Of the 16 dispatching rules tested, our results show that four of the simple rules dominate the others. We also found that order‐based rules perform better than their job‐based counterparts. The study makes use of multivariate statistical analysis, in addition to the usual univariate tests, which can provide additional insight to managers using multiple criteria in their decision process.