Optimal Algorithm of the M 3 Scheduling Problem

ICTE 2015 ◽  
2015 ◽  
Author(s):  
Wei Wang ◽  
Haifeng Yan
Keyword(s):  
Optimal Algorithm ◽  
Scheduling Problem

An Optimal Algorithm for Scheduling with Variable Job Processing Times and Rejection

2015 ◽  
Vol 775 ◽  
pp. 449-452
Author(s):  
Ji Bo Wang ◽  
Chou Jung Hsu
Keyword(s):  
Objective Function ◽  
Polynomial Time ◽  
Single Machine ◽  
Processing Time ◽  
Optimal Algorithm ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Scheduling Problem ◽  
Processing Times ◽  
Rejection Penalty

This paper studies a single machine scheduling problem with rejection. Each job has a variable processing time and a rejection penalty. The objective function is to minimize the sum of the makespan of the accepted jobs and the total rejection penalty of the rejected jobs. We show that the problem can be solved in polynomial time.

Research on Dynamic Scheduling of Job-Shop Production with the Ant Colony Optimal Algorithm

2007 ◽  
Vol 10-12 ◽  
pp. 109-113
Author(s):  
Yong Xian Liu ◽  
J. Xiong ◽  
B.M. Sun
Keyword(s):  
Production Management ◽  
Job Shop ◽  
Dynamic Scheduling ◽  
Job Shop Scheduling ◽  
Optimal Algorithm ◽  
Optimal Solution ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Multi Agent ◽  
High Computational Complexity

Job-shop dynamic scheduling is an important subject in the fields of production management and combinatorial optimization. It is usually hard to achieve the optimal solution with classical methods due to the high computational complexity of the problem. A solution of job-shop scheduling problem based on multi-agent is presented for the comparability between the dynamic scheduling problem of job-shop production and the TSP problem. The dynamic scheduling of job-shop production is designed according to the pattern of TSP problem which can be applied with ACO. By the application case, the ACO is the new method to solve the dynamic scheduling of job-shop production.

BICRITERIA SCHEDULING ON SINGLE-MACHINE WITH INVENTORY OPERATIONS

2009 ◽  
Vol 01 (02) ◽  
pp. 227-234
Author(s):  
BAOQIANG FAN ◽  
RONGJUN CHEN ◽  
GUOCHUN TANG
Keyword(s):  
Single Machine ◽  
Approximation Scheme ◽  
Optimal Algorithm ◽  
Single Machine Scheduling ◽  
Scheduling Problem ◽  
Bicriteria Scheduling ◽  
Processing Times ◽  
Polynomial Formula ◽  
Tardy Jobs ◽  
Special Case

In this paper, we consider the single machine scheduling problem with inventory operations. The objective is to minimize makespan subject to the constraint that the total number of tardy jobs is minimum. We show the problem is strongly NP-hard. A polynomial [Formula: see text]-approximation scheme for the problem is presented, where m is defined as the total job's processing times ∑ pj divided by the capacity c of the storage, and an optimal algorithm for a special case of the problem, in which each job is one unit in size.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

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