scholarly journals Makespan Minimization in Job Shop Scheduling

2021 ◽  
Vol 11 (1) ◽  
pp. 228-230
Author(s):  
K. Sathya Sundari
Keyword(s):  
Completion Time ◽  
Job Shop ◽  
Short Term Memory ◽  
Job Shop Scheduling ◽  
Rollback Recovery ◽  
Makespan Minimization ◽  
Recovery Strategy ◽  
Short Term ◽  
Term Memory ◽  
Shop Scheduling

In industries, the completion time of job problems in the manufacturing unit has risen significantly. In several types of current study, the job's completion time, or makespan, is reduced by taking straight paths, which is time-consuming. In this paper, we used an Improved Ant Colony Optimization and Tabu Search (ACOTS) algorithm to solve this problem by precisely defining the fault occurrence location in order to rollback. We have used a short-term memory-based rollback recovery strategy to minimise the job's completion time by rolling back to its own short-term memory. The recent movements in Tabu quest are visited using short term memory. As compared to the ACO algorithm, our proposed ACOTS-Cmax solution is more efficient and takes less time to complete.

scholarly journals An Improved Ant Colony Optimized Tabu Search Algorithm for Makespan Improvement in Job Shop

2019 ◽  
Vol 9 (2) ◽  
pp. 670-674
Keyword(s):  
Tabu Search ◽  
Completion Time ◽  
Job Shop ◽  
Short Term Memory ◽  
Search Algorithm ◽  
Ant Colony ◽  
Rollback Recovery ◽  
Short Term ◽  
Term Memory ◽  
Recovery Technique

In industries, the completion time of job problems is increased drastically in the production unit. In many existing kinds of research, the completion time i.e. makespan of the job is minimized using straight paths which is time-consuming. In this paper, we addressed this problem using an Improved Ant Colony Optimization and Tabu Search (ACOTS) algorithm by identifying the fault occurrence position exactly to rollback. Also, we used a short term memory-based rollback recovery technique to roll back to its own short term memory to reduce the completion time of the job. Short term memory is used to visit the recent movements in Tabu search. Our proposed ACOTS-Cmax approach is efficient and consumed less completion time compared to the ACO algorithm

scholarly journals Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm

Mathematics ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 688 ◽  
Author(s):  
Fei Luan ◽  
Zongyan Cai ◽  
Shuqiang Wu ◽  
Shi Qiang Liu ◽  
Yixin He
Keyword(s):  
Completion Time ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Low Carbon ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Exploration And Exploitation ◽  
Shop Scheduling ◽  
Whale Optimization

The flexible job shop scheduling problem (FJSP) is a difficult discrete combinatorial optimization problem, which has been widely studied due to its theoretical and practical significance. However, previous researchers mostly emphasized on the production efficiency criteria such as completion time, workload, flow time, etc. Recently, with considerations of sustainable development, low-carbon scheduling problems have received more and more attention. In this paper, a low-carbon FJSP model is proposed to minimize the sum of completion time cost and energy consumption cost in the workshop. A new bio-inspired metaheuristic algorithm called discrete whale optimization algorithm (DWOA) is developed to solve the problem efficiently. In the proposed DWOA, an innovative encoding mechanism is employed to represent two sub-problems: Machine assignment and job sequencing. Then, a hybrid variable neighborhood search method is adapted to generate a high quality and diverse population. According to the discrete characteristics of the problem, the modified updating approaches based on the crossover operator are applied to replace the original updating method in the exploration and exploitation phase. Simultaneously, in order to balance the ability of exploration and exploitation in the process of evolution, six adjustment curves of a are used to adjust the transition between exploration and exploitation of the algorithm. Finally, some well-known benchmark instances are tested to verify the effectiveness of the proposed algorithms for the low-carbon FJSP.

scholarly journals A jssp solution for production planning optimization combining industrial engineering and evolutionary algorithms

2020 ◽  
pp. 58-58
Author(s):  
Saso Srsen ◽  
Marjan Mernik
Keyword(s):  
Completion Time ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Research Work ◽  
Industrial Applications ◽  
Industrial Engineering ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Planning Optimization

A Job Shop Scheduling Problem (JSSP), where p processes and n jobs should be processed on m machines so that the total completion time is minimal, is a well-known problem with many industrial applications. Many researchers focus on the JSSP classification and algorithms that address the different JSSP classes. In this research work, the production times, a very well-known theme covered in Industrial Engineering (IE), are integrated into an Evolutionary Algorithm (EA) to solve real-world JSSP problems. Since a drawback of classical IE is a manual determination of the technological times, an Internet of Things (IoT) architecture is proposed as a possible solution.

Petri Net and GA Based Method for JSP Optimization with Dual-Objective

2011 ◽  
Vol 121-126 ◽  
pp. 4547-4551
Author(s):  
Li Xin Qi ◽  
Ze Tao
Keyword(s):  
Petri Net ◽  
Supervisory Control ◽  
Completion Time ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Linear Constraint ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Scheduling Method

A new dual-objective scheduling method based on the controlled Petri net and GA is proposed to the job-shop scheduling problem (JSP) constrained by machines, workers. Firstly, a detailed analysis of supervisory control for Petri net with uncontrollable transitions, especially important, for OR-logics linear constraint, a new method for constructing a Petri net feedback controller based on monitor and inhibitor arcs is presented. The Petri net model is constructed based on above method in flexible JSP. Then, the genetic algorithm (GA) is applied based on the controlled Petri net model and Pareto. Function objectives of the proposed method are to minimize the completion time and the total expense of machines and workers. Finally, Scheduling example is employed to illustrate the effectiveness of the method.

Petri Net with Uncontrollable Transition and GA Based Approach for JSP

2011 ◽  
Vol 48-49 ◽  
pp. 824-829
Author(s):  
Tao Ze ◽  
Xiao Xia Liu
Keyword(s):  
Petri Net ◽  
Completion Time ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Scheduling Method

A new dual-objective scheduling method based on the controlled Petri net and GA is proposed to the job-shop scheduling problem (JSP) with urgent orders constrained by machines, workers. Firstly, a controller designed method for Petri net with uncontrollable transition is introduced, and based on the method, the Petri net model is constructed for urgent jobs in flexible job shop scheduling problem. Then, the genetic algorithm (GA) is applied based on the controlled Petri net model and Pareto. Function objectives of the proposed method are to minimize the completion time and the total expense of machines and workers. Finally, Scheduling example is employed to illustrate the effectiveness of the method.

Using mixed graph coloring to minimize total completion time in job shop scheduling

2006 ◽  
Vol 182 (2) ◽  
pp. 1137-1148 ◽  
Author(s):  
Fawaz S. Al-Anzi ◽  
Yuri N. Sotskov ◽  
Ali Allahverdi ◽  
George V. Andreev
Keyword(s):  
Graph Coloring ◽  
Completion Time ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Total Completion Time ◽  
Mixed Graph ◽  
Shop Scheduling
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