Online Algorithms for the Interval Scheduling Problem in the Cloud: Affinity Pair Threshold based Approaches

2021 ◽  
pp. 1-1
Author(s):  
Panagiotis Oikonomou ◽  
Nikos Tziritas ◽  
Thanasis Loukopoulos ◽  
Georgios Theodoropoulos ◽  
Masatoshi Hanai ◽  
...  
Keyword(s):  
Online Algorithms ◽  
Scheduling Problem ◽  
Interval Scheduling

scholarly journals Dynamic algorithms for monotonic interval scheduling problem

2015 ◽  
Vol 562 ◽  
pp. 227-242 ◽  
Author(s):  
Alexander Gavruskin ◽  
Bakhadyr Khoussainov ◽  
Mikhail Kokho ◽  
Jiamou Liu
Keyword(s):  
Scheduling Problem ◽  
Interval Scheduling ◽  
Dynamic Algorithms

DECOMPOSITION ALGORITHMS FOR THE INTERVAL SCHEDULING PROBLEM

2010 ◽  
Vol 27 (04) ◽  
pp. 517-537 ◽  
Author(s):  
SHIDONG WANG ◽  
LI ZHENG ◽  
ZHIHAI ZHANG
Keyword(s):  
Large Scale ◽  
Heuristic Algorithms ◽  
Integer Program ◽  
Computational Effort ◽  
Small Scale ◽  
Test Problems ◽  
Decomposition Algorithms ◽  
Scheduling Problem ◽  
Interval Scheduling ◽  
Average Percentage

Scheduling track lines at a marshalling station where the objective is to determine the maximal weighted number of trains on the track lines can be modeled as an interval scheduling problem: each job has a fixed starting and finishing time and can only be carried out by an arbitrarily given subset of machines. This scheduling problem is formulated as an integer program, which is NP-Complete when the number of machines and jobs are unfixed and the computational effort to solve large scale test problems is prohibitively large. Heuristic algorithms (HAs) based on the decomposition of original problem have been developed and the benefits lie in both conceptual simplicity and computational efficiency. Genetic algorithm (GA) to address the scheduling problem is also proposed. Computational experiments on low and high utilization rates of machines are carried out to compare the performance of the proposed algorithms with Cplex. Computational results show that the HAs and GA perform well in most condition, especially HA2 with the maximum of average percentage deviation on average 3.5% less than the optimal solutions found by Cplex in small-scale problem. Our methodologies are capable of producing improved solutions to large-scale problems with reasonable computing resources, too.

OPTIMAL ONLINE ALGORITHMS ON TWO HIERARCHICAL MACHINES WITH RESOURCE AUGMENTATION

2012 ◽  
Vol 04 (01) ◽  
pp. 1250012
Author(s):  
YIWEI JIANG ◽  
AN ZHANG ◽  
JUELIANG HU
Keyword(s):  
Online Algorithms ◽  
The Other ◽  
Scheduling Problem ◽  
Resource Augmentation ◽  
Hierarchical Scheduling ◽  
Hierarchical Machines

This paper investigates an online hierarchical scheduling problem with resource augmentation, i.e., the resources of the online algorithms are different from those of the offline algorithms. The machines are provided with different capacity according to their hierarchies. One with the hierarchy 1 has a speed of s(q) in the online (offline) algorithms and can process all the jobs. The other with hierarchy 2 has a speed of 1 in the online/offline algorithms and can only process partial jobs. The objective is to minimize makespan. For any 0 < q, s < ∞, we present optimal online algorithms with parametric competitive ratios.

GENETIC ALGORITHMS AND REINFORCEMENT LEARNING FOR THE TACTICAL FIXED INTERVAL SCHEDULING PROBLEM

2001 ◽  
Vol 10 (01n02) ◽  
pp. 23-38 ◽  
Author(s):  
EUGENE SANTOS ◽  
XIAOMIN ZHONG
Keyword(s):  
Reinforcement Learning ◽  
Optimization Problems ◽  
Structural Characteristics ◽  
Fixed Interval ◽  
Learning System ◽  
Scheduling Problem ◽  
Interval Scheduling ◽  
Minimum Number ◽  
Identical Machine ◽  
Fixed Interval Scheduling

Discrete optimization problems are usually NP hard. The structural characteristics of these problems significantly dictate the solution landscape. In this paper, we explore a structure-based approach to solving these kinds of problems. We use a reinforcement learning system to adaptively learn the structural characteristics of the problem, hereby decomposing the problem into several subproblems. Based on these structural characteristics, we develop a Genetic Algorithm by using structural operations to recombine these subproblems together to solve the problem. The reinforcement learning system directs the GA. We test our algorithm on the Tactical Fixed Interval Scheduling Problem(TFISP) which is the problem of determining the minimum number of parallel non-identical machine such that a feasible schedule exists for a given set of jobs. This work continues our work in exploiting structure for optimization.

ONLINE ALGORITHMS FOR SCHEDULING WITH MACHINE ACTIVATION COST

2007 ◽  
Vol 24 (02) ◽  
pp. 263-277 ◽  
Author(s):  
YONG HE ◽  
SHUGUANG HAN ◽  
YIWEI JIANG
Keyword(s):  
Lower Bound ◽  
Online Algorithms ◽  
Competitive Ratio ◽  
Online Algorithm ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Scheduling Problem ◽  
Identical Machines ◽  
Parallel Machine Scheduling Problem

In this paper, we consider a variant of the classical parallel machine scheduling problem. For this problem, we are given m potential identical machines to non-preemptively process a sequence of independent jobs. Machines need to be activated before starting to process, and each machine activated incurs a fixed machine activation cost. No machines are initially activated, and when a job is revealed the algorithm has the option to activate new machines. The objective is to minimize the sum of the makespan and activation cost of machines. We first present two optimal online algorithms with competitive ratios of 3/2 and 5/3 for m = 2, 3 cases, respectively. Then we present an online algorithm with a competitive ratio of at most 2 for general m ≥ 4, while the lower bound is 1.88.

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