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Abstract
Scheduling in open shop problems is a important scheduling problem and universal and it is widely used in industry.Open shop scheduling problem is the part of NP-Hard problem. The open shop scheduling problem solution space is considerably larger than our Job shop scheduling problem and it seems that the books and articles that have less attention. Classical methods for obtaining the optimal solution for this problem is the high time complexity and in some cases it is impossible so to solve these problems, most heuristic method is used. The goal in open shop scheduling problem is getting a possible combination of a machine and job orders determined that makespan be in lowest time possible. Among the articles on solving open shop problems, no one considered machine maintenance parameter; whereas, factories machinery will damage during operation due to various reasons which result in extensive loss such as, waste of time and extra cost for re-operating halfway procedure. In this thesis for solve open shop scheduling problem among the articles on solving open shop problems, no one considered machine maintenance parameter; whereas, factories machinery will damage during operation due to various reasons which result in extensive loss such as, waste of time and extra cost for re-operating halfway procedure. In this thesis a new method to solve the open shop scheduling problem is presented by using genetic algorithms that it also considers the machines maintenance. The proposed algorithm uses different operators with more targeted selection of chromosomes for the performance of the algorithm is trying and Experimental results show the more performance of the proposed algorithm is compared with other algorithms.
Keywords: Open Shop Sheduling Problem, Genetic Algorithm, Machine Maintenance, Makespan
1 Open Shop Sheduling Problem (OSSP)
2 NP-Hard
3 Job Shop Sheduling Problem
4 Makespan
5 Machine Maintenance
6 Process
7 Heuristic Search
8 Genetic Algorithm
9 Machine Maintenance
10 Multi Generation
11 Job
12 NP-Hard
13 Optimization
14 TPM
15 Decision-making
16 CPU
17 Preemption
18 Combinatorial Optimization
19 Work Station
20 Preemption
21 Comprehensive Quality
22 Tardiness
23 Work Station
24 Genetic Algorithm – GA
25 Fitness Function
26 Resource Allocation
27 Earliest Start Time
28 Completion Time
29 Processing Time
30 Release Date
31 Due Date
32 Weight
33 Makespan
34 Single Machine
35 Earliest Due Date (EDD)
36 Earliest Due Date (EDD)
37 Shortest Processing Time (SPT)
38 Parallel Machine
39 Flow Shop
40 Unified Sequence
41 re-entrant Shop
42 Job Shop
43 Flexible Job Shop
44 Priority rules
45Combinational Optimization
46 Constraints Analysis
47 Ready-time
48 Due-time
49 Interrupt
50 Gantt Chart
51 Open Shop
52 time value of money
53 Dependent Shop
54 Batch Processing
55 Material-Handing Constraint
56 Sequence-Dependent Setup Time
57 Maximum Lateness
58 The Number of Trady Jobs
59 Total Tardiness
60 Work-in-Process Costs
61 Genetic Algorithm
62 John Holland
63 Heuristic Search
64 Evolutionary Algorithm
65 opulation
66 Fitness Function
67 Initial Population
68 Gene
69 Chromosome
70 Population
71 Binary encoding
72 Permutation Encoding
73 Value Encoding
74 Tree Encoding
75 Genetic Programming
76 Selection Operator
77 Roulette Wheel Selection
78 John Holland
79 Rank Selection
80 Elitist Selection
81 Tournament Selection
82 Tournament size
83 Crossover
84 Single-Point Crossover
85 Two-Point Crossover
86 Mutation
87 Inversion Operator
88 Deletion and Duplication Operator
89 Deletion and Regeneration Operator
90 Convergence
91 Heuristic Search
92 Extended Timed-Place Petri Nets
93 Timed Petri Nets
94 Colored Petri Nets
95 Tournament Selection
96 Single-Point Crossover
97 Genetic Algorithm-Ant Colony Optimization
98 Parallel Machines Scheduling
99 Fuzzy Logic
100 Hybrid Genetic Algorithm
101 Genetic Algorithm with Dominant Genes
102 Time – Cost Trade Off
103 Age
104 Process
105 Crossover Operator
106 Maintenance Machine in Open Shop scheduling problem with Multi generation Genetic Algorithm
107 Maintenance Machine in Open Shop scheduling problem with Genetic Algorithm
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