A new algorithm for online uniform-machine scheduling to minimize the makespan

Author name used in this publication: T.C.E. ChengAuthor name used in this publication: C. T. Ng2005-2006 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Групповые технологии и динамические оценки в задачах с неполной информацией

Секция 10. Теоретическая информатикаПредлагаются новые подходы и методы разработки и анализа алгоритмов для задач разбиения и упаковки с неполной информацией

ЗАДАЧА МИНИМИЗАЦИИ ВРЕМЕНИ ЗАВЕРШЕНИЯ ПРОЕКТА НА МНОГОПРОЦЕССОРНОЙ СИСТЕМЕ С РАЗЛИЧНЫМИ СКОРОСТЯМИ ПРОЦЕССОРОВ

Предлагается алгоритм решения задачи минимизации времени завершения проекта на много-процессорной системе с различными скоростями процессоров. Доказывается, что значение асимптотического коэффициента эффективности предлагаемого алгоритма не превосходит двух

Online scheduling on three uniform machines

AbstractThis paper investigates the online scheduling on three uniform machines problem. Denote by sj the speed of each machine, j=1,2,3. Assume 0<s1≤s2≤s3, and let s=s2/s1 and t=s3/s2 be two speed ratios. We show the greedy algorithm LS is an optimal online algorithm when the speed ratios (s,t)∈G1∪G2, where G1={(s,t)|1≤t<1+316,s≥3t5+2t−6t2} and G2={(s,t)|s(t−1)t≥1+s,s≥1,t≥1}. The competitive ratio of LS is 1+s+2sts+st when (s,t)∈G1 and 1+sst+1 when (s,t)∈G2. Moreover, for the general speed ratios, we show the competitive ratio of LS is no more than min{1+s+2sts+st,1+sst+1,1+s+3st1+s+st} and its overall competitive ratio is 2 which matches the overall lower bound of the problem

Worker scheduling with induced learning in a semi-on-line setting

Scheduling is a widely researched area with many interesting fields. The presented research deals with a maintenance area in which preventative maintenance and emergency jobs enter the system. Each job has varying processing time and must be scheduled. Through learning the operators are able to expand their knowledge which enables them to accomplish more tasks in a limited time. Two MINLP models have been presented, one for preventative maintenance jobs alone, and another including emergency jobs. The emergency model is semi-on-line as the arrival time is unknown. A corresponding heuristic method has also been developed to decrease the computational time of the MINLP models. The models and heuristic were tested in several areas to determine their flexibility. It has been demonstrated that the inclusion of learning has greatly improved the efficiency of the workers and of the system