|Title:||Machine scheduling problems with a common due window|
Hong Kong Polytechnic University -- Dissertations
|Department:||Department of Management|
|Pages:||vi, 107 leaves : ill. ; 30 cm|
|Abstract:||The main purpose of this thesis is to study single and parallel machine problems with a common due window. The objective of the scheduling is to minimize a total penalty comprising a combination of the following performance measures: the weighted earliness and tardiness, the weighted number of early and tardy jobs, the weighted window location with tolerance and the weighted flowtime. The window location tolerance is a grace period for the latest due date so that the window location penalty is incurred only if the latest due date exceeds the tolerance. The flowtime criterion is incorporated into some problems since the total flowtime is closely related to the average in-process inventory. A review on previous literature shows that there is a lack of studies concerning due window machine scheduling problems which are becoming important and arise frequently in manufacturing systems nowadays. So a study devoted to this sort of problems is worthwhile. So far, in this area, no scheduling problems with window location tolerance and flowtime have been addressed by previous researchers, but these two factors are important and significant. Moreover, in the literature, the focus is mainly on the earliness-tardiness penalties where the penalties on the number of early-tardy jobs are ignored. Though the number of tardy jobs has been considered in the common due date earliness-tardiness problems by previous researchers, penalties for general earliness-tardiness and number of early-tardy jobs have not been combined in the performance measures previously. In this thesis, the computational complexity theory was used to analyze the complexity of the problems under study. The analysis revealed that most of the single and parallel machine scheduling problems are NP-complete in the ordinary sense. Some dominance properties were also developed which facilitated the construction of low complexity solution procedures. These solution procedures can be formulated as pseudo-polynomial time dynamic programming algorithms which provide exact solutions. Most of these dynamic programming algorithms are quite efficient to solve problems encountered in practice. Moreover, some special cases of single machine scheduling problems were shown to be polynomially solvable and hence can be efficiently solved from a theoretical point of view. The computational time complexities of the solution procedures for both the general problems and the special cases were also discussed. The main contributions of this thesis are: a systematic study of a relatively unexplored but important area of machine scheduling problems; a design of low complexity pseudo-polynomial or polynomial solution procedures for these problems; a demonstration of the hard problems to be NP-complete in the ordinary sense. Hence, the results of this thesis will help the advance of scheduling theory by providing a better understanding of the due window scheduling problems and serving as a basis for future scheduling research on due window.|
|Rights:||All rights reserved|
Files in This Item:
|b14422840.pdf||For All Users (off-campus access for PolyU Staff & Students only)||3.17 MB||Adobe PDF||View/Open|
As a bona fide Library user, I declare that:
- I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
- I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
- I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.
Please use this identifier to cite or link to this item: