scholarly journals Task Scheduling Problem Using Fuzzy Graph

2016 ◽  
Vol 1 (1-2) ◽  
pp. 16-20
Author(s):  
Vivek Raich ◽  
Shweta Rai
Keyword(s):  
Task Scheduling ◽  
Single Machine ◽  
Scheduling Problem ◽  
Scheduling Theory ◽  
Scheduling Problems ◽  
Fuzzy Graph ◽  
Minimum Value ◽  
Chromatic Sum ◽  
Completion Times

The concept of obtaining fuzzy sum of fuzzy colorings problem has a natural application in scheduling theory. The problem of scheduling N jobs on a single machine and obtain the minimum value of the job completion times is equivalent to finding the fuzzy chromatic sum of the fuzzy graph modeled for this problem. The aim of this paper is to solve task scheduling problems using fuzzy graph.

scholarly journals Task Scheduling Model of Double-Deep Multi-Tier Shuttle System

Processes ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 604 ◽  
Author(s):  
Yanyan Wang ◽  
Rongxu Zhang ◽  
Hui Liu ◽  
Xiaoqing Zhang ◽  
Ziwei Liu
Keyword(s):  
Task Scheduling ◽  
Production Scheduling ◽  
Current System ◽  
Storage System ◽  
Complex Structure ◽  
Vertical Movement ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
High Flexibility ◽  
Shuttle System

As a new type of part-to-picker storage system, the double-deep multi-tier shuttle system has been developed rapidly in the e-commerce industry because of its high flexibility, large storage capacity, and robustness. The system consists of a multi-tier shuttle sub-system that controls horizontal movement and a lift sub-system that manages vertical movement. The combination of shuttles and lifts, instead of a stacker crane in conventional automated storage and retrieval system, undertakes inbound/outbound tasks. Because of the complex structure and numerous equipment of the system, task scheduling has become a major difficulty in the outbound operation of the double-deep multi-tier shuttle system. Figuring out methods to improve the overall efficiency of task scheduling operations is the focus of current system application enterprises. This paper introduces the task scheduling problem for the shuttle system. Inspired from workshop production scheduling problems, we minimize the total time of a batch of retrieval tasks as the objective function, applying the modified Simulated Annealing Algorithms (SAAs) to solve the task scheduling problem. In conclusion, we verified the proposed model and the algorithm efficiency, using case studies.

scholarly journals Single Machine Predictive Scheduling Using Inserted Idle Times

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Hongli Zhu ◽  
Hong Zhou
Keyword(s):  
Single Machine ◽  
Completion Time ◽  
Primary Objective ◽  
Computational Experiments ◽  
Scheduling Problem ◽  
High Predictability ◽  
Surrogate Measure ◽  
Predictive Scheduling ◽  
Completion Times

A single machine predictive scheduling problem is considered. The primary objective is to minimize the total completion times. The predictability of the schedule is measured by the completion time deviations between the predictive schedule and realized schedule. The surrogate measure of predictability is chosen to evaluate the completion time deviations. Both of the primary objective and predictability are optimized. In order to absorb the effects of disruptions, the predictive schedule is generated by inserting idle times. Right-shift rescheduling method is used as the rescheduling strategy. Three methods are designed to construct predictive schedules. The computational experiments show that these algorithms provide high predictability with minor sacrifices in shop performance.

The Review of the Single Machine Scheduling Problem and its Solving Methods

2013 ◽  
Vol 411-414 ◽  
pp. 2081-2084 ◽  
Author(s):  
Ou Yang Quan ◽  
Hong Yun Xu
Keyword(s):  
Single Machine ◽  
Production Efficiency ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Efficiency And Effectiveness ◽  
History Of ◽  
Future Work ◽  
Difficult Subject

The research on the theory and method of single machine scheduling is a difficult subject, but it is very important for the companies to improve the production efficiency and effectiveness. The study on single machine scheduling problem has the history of 50 years, but there is still a gap between the classical scheduling theories and practical scheduling problems. According to this characteristic, the problems in practical scheduling area and the various factors need to be concerned are mentioned, and the main methods to solving the single machine scheduling problem and their applications are presented in details. Finally, the directions and suggestions of future work in single machine scheduling problem are summarized.

Minimizing Total Earliness and Total Tardiness on Single Machine with Release Dates

2011 ◽  
Vol 5 ◽  
pp. 30-43
Author(s):  
Elkanah Oyetunji ◽  
Ayodeji E. Oluleye
Keyword(s):  
Single Machine ◽  
Single Machine Scheduling ◽  
Total Tardiness ◽  
Release Dates ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Bicriteria Scheduling ◽  
Linear Composite ◽  
Effectiveness And Efficiency ◽  
Machine Scheduling Problems

This paper considers the bicriteria scheduling problem of minimizing the total earliness and the total tardiness on a single machine with release dates. In view of the fact that the problem has been characterized as NP-Hard, we propose two approximation algorithms (labeled as ETA1 and ETA2) for solving the problem. The proposed algorithms were compared with the MA heuristic selected from the literature. The two criteria (the total earliness and the total tardiness) were aggregated together into a linear composite objective function (LCOF). The performances of the algorithms were evaluated based on both effectiveness and efficiency. The algorithms were tested on a set of 1200 randomly generated single machine scheduling problems. Experimental results show that both the ETA1 and ETA2 algorithms outperformed (in terms of effectiveness and efficiency) the MA heuristic under all the considered problem sizes. Also, the ETA1 algorithm outperformed the ETA2 algorithm when the number of jobs (n) ranges between 20 and 500.

scholarly journals Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid

2011 ◽  
Vol 2011 ◽  
pp. 1-20 ◽  
Author(s):  
Ruey-Maw Chen ◽  
Chuin-Mu Wang
Keyword(s):  
Grid Computing ◽  
Task Scheduling ◽  
Project Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Resource Assignment ◽  
Grid Environment ◽  
Standard Particle Swarm Optimization ◽  
Communication Topology ◽  
Np Complete

The task scheduling problem has been widely studied for assigning resources to tasks in heterogeneous grid environment. Effective task scheduling is an important issue for the performance of grid computing. Meanwhile, the task scheduling problem is an NP-complete problem. Hence, this investigation introduces a named “standard“ particle swarm optimization (PSO) metaheuristic approach to efficiently solve the task scheduling problems in grid. Meanwhile, two promising heuristics based on multimode project scheduling are proposed to help in solving interesting scheduling problems. They are the best performance resource heuristic and the latest finish time heuristic. These two heuristics applied to the PSO scheme are for speeding up the search of the particle and improving the capability of finding a sound schedule. Moreover, both global communication topology and local ring communication topology are also investigated for efficient study of proposed scheme. Simulation results demonstrate that the proposed approach in this investigation can successfully solve the task-resource assignment problems in grid computing and similar scheduling problems.

scholarly journals Single-Machine Group Scheduling Problems with Deterioration to Minimize the Sum of Completion Times

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Yong He ◽  
Li Sun
Keyword(s):  
Single Machine ◽  
Group Scheduling ◽  
Scheduling Problems ◽  
Minimization Problems ◽  
Processing Times ◽  
Machine Group ◽  
Polynomially Solvable ◽  
Decreasing Functions ◽  
Completion Times ◽  
Sum Of Completion Times

We consider two single-machine group scheduling problems with deteriorating group setup and job processing times. That is, the job processing times and group setup times are linearly increasing (or decreasing) functions of their starting times. Jobs in each group have the same deteriorating rate. The objective of scheduling problems is to minimize the sum of completion times. We show that the sum of completion times minimization problems remains polynomially solvable under the agreeable conditions.

Genetic Algorithm for Single Machine Scheduling Problem with Setup Times

2013 ◽  
Vol 457-458 ◽  
pp. 1678-1681 ◽  
Author(s):  
Quan Ouyang ◽  
Hong Yun Xu
Keyword(s):  
Genetic Algorithm ◽  
Single Machine ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Setup Times ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Crossover Operator ◽  
Roulette Wheel ◽  
Selection Of

This paper describes a genetic algorithm to solve the single machine scheduling problem with setup times, which uses the fixed two point crossover operator (F2PX) to produce new offspring chromosomes and uses the roulette wheel method in the selection of the chromosome population. In order to avoid the premature convergence we use a neighborhood based mutation operator to conduct disturbance in our genetic algorithm. Through the application of this genetic algorithm in practical scheduling problems, the effect of the genetic algorithm proposed in this paper is remarkable.

Re-Entrant Production Scheduling Problem under Uncertainty Based on QPSO Algorithm

2011 ◽  
Vol 66-68 ◽  
pp. 1061-1066
Author(s):  
Feng Shan Pan ◽  
Chun Ming Ye ◽  
Ji Hua Zhou
Keyword(s):  
Production Scheduling ◽  
Product Life Cycle ◽  
Market Competition ◽  
Product Diversification ◽  
Theoretical Research ◽  
Scheduling Problem ◽  
Scheduling Theory ◽  
Scheduling Problems ◽  
The One ◽  
Construction Strategies

Production scheduling problem is the one of the most basic, important and difficult theoretical research in a manufacturing system. In the past decades of research, the classical scheduling theory has made signficant progress, but the actual scheduling problems are much more complicated than the classical theory. In order to study, the actual scheduling problems are often made much simpler. Therefore, it is difficult for the results of the classical scheduling theory to been put into practice. The considerable gap also exists between the classical scheduling theory and the actual scheduling problem. But with the increasingly fierce market competition, customers have become increasingly demanding product diversification, product life cycle is shorter and more sophisticated structure of the product makes the actual scheduling a large number of uncertainties, the traditional model has become difficult to obtain satisfactory results. This paper makes a analysis on the uncertainties of production scheduling,and tries to solve re-entrant production scheduling problem based on QPSO. It introduces the mathematical model and the solving process based on QPSO algorithm. Then it makes construction strategies to solve it. The paper simulates with Visual C. The results show that this algorithm is feasibility.

scholarly journals The Task Scheduling Problem: A NeuroGenetic Approach

2014 ◽  
Vol 12 (4) ◽  
pp. 327
Author(s):  
Anurag Agarwal ◽  
Selcuk Colak ◽  
Jason Deane ◽  
Terry Rakes
Keyword(s):  
Neural Network ◽  
Task Scheduling ◽  
Computational Time ◽  
Test Problems ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Network Approach ◽  
Neural Network Approach ◽  
One Machine ◽  
Better Than

This paper addresses the task scheduling problem which involves minimizing the makespan in scheduling n tasks on m machines (resources) where the tasks follow a precedence relation and preemption is not allowed. The machines (resources) are all identical and a task needs only one machine for processing. Like most scheduling problems, this one is NP-hard in nature, making it difficult to find exact solutions for larger problems in reasonable computational time. Heuristic and metaheuristic approaches are therefore needed to solve this type of problem. This paper proposes a metaheuristic approach - called NeuroGenetic - which is a combination of an augmented neural network and a genetic algorithm. The augmented neural network approach is itself a hybrid of a heuristic approach and a neural network approach. The NeuroGenetic approach is tested against some popular test problems from the literature, and the results indicate that the NeuroGenetic approach performs significantly better than either the augmented neural network or the genetic algorithms alone.

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