scholarly journals The Optimality Region for a Single-Machine Scheduling Problem with Bounded Durations of the Jobs and the Total Completion Time Objective

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 382 ◽  
Author(s):  
Yuri N. Sotskov ◽  
Natalja G. Egorova

We study a single-machine scheduling problem to minimize the total completion time of the given set of jobs, which have to be processed without job preemptions. The lower and upper bounds on the job duration is the only information that is available before scheduling. Exact values of the job durations remain unknown until the completion of the jobs. We use the optimality region for the job permutation as an optimality measure of the optimal schedule. We investigate properties of the optimality region and derive O ( n ) -algorithm for calculating a quasi-perimeter of the optimality set (i.e., the sum of lengths of the optimality segments for n given jobs). We develop a fast algorithm for finding a job permutation having the largest quasi-perimeter of the optimality set. The computational results in constructing such permutations show that they are close to the optimal ones, which can be constructed for the factual durations of all given jobs.

2011 ◽  
Vol 28 (03) ◽  
pp. 419-429 ◽  
Author(s):  
CHUAN-LI ZHAO ◽  
HENG-YONG TANG

This paper considers single machine scheduling problems with linear deteriorating jobs under predictive disruption. In this model, the actual processing time of a job is a increasing linear function of its starting time; and machine is subject to an availability constraint. We assume that an optimal schedule can be obtained by using some algorithms if machine is available at all time. Because of the machine disruption, the original schedule may become infeasible or too far from optimal. We want to create the new schedule that takes into account both the original objective function and a measure of deviation from the original schedule. We consider two versions of the problem. In the first one, the objective is weighted sum of total completion time and total tardiness while in the second one, the objective is weighted sum of total completion time and total earliness. We first prove some properties of the optimal schedule then dynamic programming algorithms are proposed, respectively.


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