scholarly journals A New Meta-Heuristic Algorithm for Solving the Flexible Dynamic Job-Shop Problem with Parallel Machines

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 165 ◽  
Author(s):  
Arun Sangaiah ◽  
Mohsen Suraki ◽  
Mehdi Sadeghilalimi ◽  
Seyed Bozorgi ◽  
Ali Hosseinabadi ◽  
...  
Keyword(s):  
Manufacturing Systems ◽  
Parallel Machines ◽  
Job Shop ◽  
Two Dimensional ◽  
Scheduling Problems ◽  
Operational Flexibility ◽  
Machine Failure ◽  
Different Dimensions ◽  
First Time ◽  
Dynamic Job Shop

In a real manufacturing environment, the set of tasks that should be scheduled is changing over the time, which means that scheduling problems are dynamic. Also, in order to adapt the manufacturing systems with fluctuations, such as machine failure and create bottleneck machines, various flexibilities are considered in this system. For the first time, in this research, we consider the operational flexibility and flexibility due to Parallel Machines (PM) with non-uniform speed in Dynamic Job Shop (DJS) and in the field of Flexible Dynamic Job-Shop with Parallel Machines (FDJSPM) model. After modeling the problem, an algorithm based on the principles of Genetic Algorithm (GA) with dynamic two-dimensional chromosomes is proposed. The results of proposed algorithm and comparison with meta-heuristic data in the literature indicate the improvement of solutions by 1.34 percent for different dimensions of the problem.

scholarly journals Utilizing Model Knowledge for Design Developed Genetic Algorithm to Solving Problem

2018 ◽  
Vol 3 (2) ◽  
pp. 172
Author(s):  
Hamidreza Salmani mojaveri
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Parallel Machines ◽  
Job Shop ◽  
Optimization Problems ◽  
Premature Convergence ◽  
Local Optimum ◽  
Scheduling Problems ◽  
Algorithm Implementation

One of the discussed topics in scheduling problems is Dynamic Flexible Job Shop with Parallel Machines (FDJSPM). Surveys show that this problem because of its concave and nonlinear nature usually has several local optimums. Some of the scheduling problems researchers think that genetic algorithms (GA) are appropriate approach to solve optimization problems of this kind. But researches show that one of the disadvantages of classical genetic algorithms is premature convergence and the probability of trap into the local optimum. Considering these facts, in present research, represented a developed genetic algorithm that its controlling parameters change during algorithm implementation and optimization process. This approach decreases the probability of premature convergence and trap into the local optimum. The several experiments were done show that the priority of proposed procedure of solving in field of the quality of obtained solution and convergence speed toward other present procedure.

An Optimization Model for Manufacturing Systems in an Uncertain Environment

2015 ◽  
Author(s):  
Michael Mitnovitsky ◽  
Miri Weiss Cohen ◽  
Moshe Shpitalni
Keyword(s):  
Manufacturing Systems ◽  
Job Shop ◽  
Adaptive Control System ◽  
Operational Flexibility ◽  
Agent Based ◽  
Machine Breakdowns ◽  
Processing Times ◽  
Dynamic Events ◽  
Increase Productivity ◽  
Flexible Job Shop Problem

This paper examines a flexible job shop problem that considers dynamic events, such as stochastic job arrivals, uncertain processing times, unexpected machine breakdowns and the possibility of processing flexibility. To achieve this goal, a new agent-based adaptive control system has been developed at the factory level, along with advanced decision-making strategies that provide responsive factories with adaptation and reconfiguration capabilities and advanced complementary scheduling abilities. The aim is to facilitate operational flexibility and increase productivity as well as offer strategic advantages such as analysis of factory development options by simulation. The feasibility of the proposed system is demonstrated by simulation under various experimental settings, among them shop utilization level, due date tightness and breakdown level.

scholarly journals Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions

2021 ◽  
Vol 11 (11) ◽  
pp. 4741
Author(s):  
Jacqueline Otala ◽  
Alden Minard ◽  
Golshan Madraki ◽  
Seyedamirabbas Mousavian
Keyword(s):  
Manufacturing Systems ◽  
Job Shop ◽  
Technological Development ◽  
Directed Graphs ◽  
Survey Study ◽  
Future Research ◽  
Scheduling Problems ◽  
Research Directions ◽  
Shop Scheduling ◽  
Future Research Directions

Graphs are powerful tools to model manufacturing systems and scheduling problems. The complexity of these systems and their scheduling problems has been substantially increased by the ongoing technological development. Thus, it is essential to generate sustainable graph-based modeling approaches to deal with these excessive complexities. Graphs employ nodes and edges to represent the relationships between jobs, machines, operations, etc. Despite the significant volume of publications applying graphs to shop scheduling problems, the literature lacks a comprehensive survey study. We proposed the first comprehensive review paper which (1) systematically studies the overview and the perspective of this field, (2) highlights the gaps and potential hotspots of the literature, and (3) suggests future research directions towards sustainable graphs modeling the new intelligent/complex systems. We carefully examined 143 peer-reviewed journal papers published from 2015 to 2020. About 70% of our dataset were published in top-ranked journals which confirms the validity of our data and can imply the importance of this field. After discussing our generic data collection methodology, we proposed categorizations over the properties of the scheduling problems and their solutions. Then, we discussed our novel categorization over the variety of graphs modeling scheduling problems. Finally, as the most important contribution, we generated a creative graph-based model from scratch to represent the gaps and hotspots of the literature accompanied with statistical analysis on our dataset. Our analysis showed a significant attention towards job shop systems (56%) and Un/Directed Graphs (52%) where edges can be either directed, or undirected, or both, Whereas 14% of our dataset applied only Undirected Graphs and 11% targeted hybrid systems, e.g., mixed shop, flexible, and cellular manufacturing systems, which shows potential future research directions.

scholarly journals Predictive Maintenance Scheduling with Failure Rate Described by Truncated Normal Distribution

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6787
Author(s):  
Iwona Paprocka ◽  
Wojciech M. Kempa ◽  
Grzegorz Ćwikła
Keyword(s):  
Normal Distribution ◽  
Job Shop ◽  
Estimation Method ◽  
Production Capacity ◽  
Estimation Methods ◽  
Continuous Control ◽  
Scheduling Problems ◽  
Unknown Parameters ◽  
Machine Failure ◽  
Truncated Normal

The method of risk assessment and planning of technical inspections of machines and optimization of production tasks is the main focus of this study. Any unpredicted failure resulted in the production plans no longer being valid, production processes needing to be rescheduled, costs of unused machine production capacity and losses due to the production of poor-quality products increase, as well as additional costs of human resources, equipment, and materials used during the maintenance. The method reflects the operation of the production system and the nature of the disturbances, allowing for the estimation of unknown parameters related to machine reliability. The machine failure frequency was described with the normal distribution truncated to the positive half of the axis. In production practice, this distribution is commonly used to describe the phenomenon of irregularities. The presented method was an extension of the Six Sigma concept for monitoring and continuous control in order to eliminate and prevent various inconsistencies in processes and resulting products. Reliability characteristics were used to develop predictive schedules. Schedules were assessed using the criteria of solution and quality robustness. Estimation methods of parameters describing disturbances were compared for different job shop scheduling problems. The estimation method based on a maximum likelihood approach allowed for more accurate prediction of scheduling problems. The paper presents a practical example of the application of the proposed method for electric steering gears.

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