A New GT Heuristic for Solving Multi Objective Job Shop Scheduling Problems

2014 ◽  
Vol 591 ◽  
pp. 184-188
Author(s):  
D. Lakshmipathy ◽  
M. Chandrasekaran ◽  
T. Balamurugan ◽  
P. Sriramya
Keyword(s):  
Production Scheduling ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Heuristic Method ◽  
Flow Time ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Practical Applications ◽  
Shop Scheduling ◽  
Mean Flow Time

The n-job, m-machine Job shop scheduling (JSP) problem is one of the general production scheduling problems in manufacturing system. Scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using reasonable resources in many cases. In this paper, optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered. New Game theory based heuristic method (GT) is used for finding optimal makespan, mean flow time, mean tardiness values of different size problems. The results show that the GT Heuristic is an efficient and effective method that gives better results than Genetic Algorithm (GA). The proposed GT Heuristic is a good problem-solving technique for job shop scheduling problem with multi criteria.

scholarly journals AIS-SFHM APPROACH FOR OPTIMIZATION OF MULTI OBJECTIVE JOB SHOP PROBLEMS

2017 ◽  
Vol 13 (7) ◽  
pp. 6363-6368
Author(s):  
Chandrasekaran Manoharan
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Approach ◽  
Flow Time ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Mean Flow ◽  
Shop Scheduling ◽  
Multi Objective

The n-job, m-machine Job shop scheduling (JSP) problem is one of the general production scheduling problems. The JSP problem is a scheduling problem, where a set of ‘n’ jobs must be processed or assembled on a set of ‘m’ dedicated machines. Each job consists of a specific set of operations, which have to be processed according to a given technical precedence order. Job shop scheduling problem is a NP-hard combinatorial optimization problem.  In this paper, optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered. The hybrid approach of Sheep Flocks Heredity Model Algorithm (SFHM) is used for finding optimal makespan, mean flow time, mean tardiness. The hybrid SFHM approach is tested with multi objective job shop scheduling problems. Initial sequences are generated with Artificial Immune System (AIS) algorithm and results are refined using SFHM algorithm. The results show that the hybrid SFHM algorithm is an efficient and effective algorithm that gives better results than SFHM Algorithm, Genetic Algorithm (GA). The proposed hybrid SFHM algorithm is a good problem-solving technique for job shop scheduling problem with multi criteria.

Optimization of Multi Objective Job Shop Scheduling Problems Using Firefly Algorithm

2014 ◽  
Vol 591 ◽  
pp. 157-162 ◽  
Author(s):  
K.C. Udaiyakumar ◽  
M. Chandrasekaran
Keyword(s):  
Firefly Algorithm ◽  
Job Shop ◽  
Heuristic Algorithms ◽  
Job Shop Scheduling ◽  
Optimization Techniques ◽  
Computational Time ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Practical Applications ◽  
Mean Flow Time

Scheduling is the allocation of resources over time to carry out a collection of tasks assigned in any field of engineering and non engineering. Majority of JSSP are categorized into non deterministic (NP) hard problem because of its complexity. Scheduling are generally solved by using heuristics to obtain optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using available resources in many cases. Many researchers attempted to solve the problem by applying various optimization techniques. While using traditional methods they observed huge difficulty in solving high complex problems and meta-heuristic algorithms were proved most efficient algorithms to solve various JSSP so far. The objective of this paper i) to make use of a newly developed meta heuristic called Firefly algorithm (FA) because of inspiration on Firefly and its characteristic. ii) To find the combined objective function by determining optimal make span, mean flow time and tardiness of different size problems (using Lawrence 1-40 problems) as a bench marking dataset and to find the actual computational time. Iii) to prove that the proposed FFA algorithm is a good problem solving technique for JSSP with multi criteria.

scholarly journals Pengembangan Algoritma Non Delay Pada Kasus Penjadwalan Non-Permutation Hybrid Flowshop Untuk Minimasi Mean Flowtime

2018 ◽  
Vol 19 (2) ◽  
pp. 148
Author(s):  
Siti Muhimatul Khoiroh
Keyword(s):  
Production Scheduling ◽  
Manufacturing Industry ◽  
Success Factors ◽  
Flow Shop ◽  
Flow Time ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Mean Flow Time ◽  
Permutation Flow Shop

Production scheduling is one of the key success factors in the production process. Scheduling approach with Non-Permutation flow shop is a generalization of the traditional scheduling problems Permutation flow shop for the manufacturing industry to allow changing the job on different machines with the flexibility of combinations. This research tries to develop a heuristic approach that is non-delay algorithm by comparing Shortest Processing Time (SPT) and Largest Remaining Time (LRT) in the case of non-permutation flow shop to produce minimum mean flow time ratio. The result of simulation shows that the SPT algorithm gives less mean flow time value compared to LRT algorithm which means that SPT algorithm is better than LRT in case of non-permutation hybrid flow shop.

Using the Memetic Algorithm for Multi Objective Job Shop Scheduling Problems

2012 ◽  
Vol 544 ◽  
pp. 245-250 ◽  
Author(s):  
Guo Hui Zhang
Keyword(s):  
Search Strategy ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Memetic Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Shop Scheduling ◽  
Multi Objective

The multi objective job shop scheduling problem is well known as one of the most complex optimization problems due to its very large search space and many constraint between machines and jobs. In this paper, an evolutionary approach of the memetic algorithm is used to solve the multi objective job shop scheduling problems. Memetic algorithm is a hybrid evolutionary algorithm that combines the global search strategy and local search strategy. The objectives of minimizing makespan and mean flow time are considered while satisfying a number of hard constraints. The computational results demonstrate the proposed MA is significantly superior to the other reported approaches in the literature.

Improved Adaptive Genetic Algorithms for Job Shop Scheduling Problems

2010 ◽  
Vol 97-101 ◽  
pp. 2473-2476 ◽  
Author(s):  
Mei Hong Liu ◽  
Xiong Feng Peng
Keyword(s):  
Genetic Algorithms ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Mathematical Function ◽  
Scheduling Problems ◽  
Practical Applications ◽  
Shop Scheduling ◽  
Adaptive Genetic Algorithms ◽  
Mutation Probability ◽  
Crossover Probability

In this paper, the adaptability of the genetic algorithm (GA) is considered. Two improved adaptive genetic algorithms (AGA) which are called Ch-AGA and Th-AGA for short are proposed based on the previous AGA. The crossover probability and the mutation probability of the Ch-AGA and the Th-AGA are non-linear changed between some a certain region, and adopted the mathematical function of chx and thx respectively. The two improved adaptive genetic algorithms are used to solve the classical job shop scheduling problems and the results indicate that the algorithms are more effective and more efficient than previous AGA, and should be used in practical applications.

scholarly journals On Neighborhood Structures and Repair Techniques for Blocking Job Shop Scheduling Problems

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 242 ◽  
Author(s):  
Julia Lange ◽  
Frank Werner
Keyword(s):  
Simulated Annealing Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Heuristic Method ◽  
General Purpose ◽  
Future Research ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Feasible Solutions ◽  
The One

The job shop scheduling problem with blocking constraints and total tardiness minimization represents a challenging combinatorial optimization problem of high relevance in production planning and logistics. Since general-purpose solution approaches struggle with finding even feasible solutions, a permutation-based heuristic method is proposed here, and the applicability of basic scheduling-tailored mechanisms is discussed. The problem is tackled by a local search framework, which relies on interchange- and shift-based operators. Redundancy and feasibility issues require advanced transformation and repairing schemes. An analysis of the embedded neighborhoods shows beneficial modes of implementation on the one hand and structural difficulties caused by the blocking constraints on the other hand. The applied simulated annealing algorithm generates good solutions for a wide set of benchmark instances. The computational results especially highlight the capability of the permutation-based method in constructing feasible schedules of valuable quality for instances of critical size and support future research on hybrid solution techniques.

A Genetic Algorithm for Hybrid Job-Shop Scheduling Problems with Minimizing the Makespan or Mean Flow Time

2018 ◽  
Vol 17 (04) ◽  
pp. 461-486
Author(s):  
Omid Gholami ◽  
Yuri N. Sotskov ◽  
Frank Werner
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Heuristic Algorithms ◽  
Flow Time ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Average Percentage ◽  
Type Formula ◽  
Mean Flow Time ◽  
Specific Subset

We address a generalization of the classical job-shop problem which is called a hybrid job-shop problem. The criteria under consideration are the minimization of the makespan and mean flow time. In the hybrid job-shop, machines of type [Formula: see text] are available for processing the specific subset [Formula: see text] of the given operations. Each set [Formula: see text] may be partitioned into subsets for their processing on the machines of type [Formula: see text]. Solving the hybrid job-shop problem implies the solution of two subproblems: an assignment of all operations from the set [Formula: see text] to the machines of type [Formula: see text] and finding optimal sequences of the operations for their processing on each machine. In this paper, a genetic algorithm is developed to solve these two subproblems simultaneously. For solving the subproblems, a special chromosome is used in the genetic algorithm based on a mixed graph model. We compare our genetic algorithms with a branch-and-bound algorithm and three other recent heuristic algorithms from the literature. Computational results for benchmark instances with 10 jobs and up to 50 machines show that the proposed genetic algorithm is rather efficient for both criteria. Compared with the other heuristics, the new algorithm gives most often an optimal solution and the average percentage deviation from the optimal function value is about 4%.

Solving Multi Objective Job Shop Scheduling Problems Using Artificial Immune System Shifting Bottleneck Approach

2015 ◽  
Vol 766-767 ◽  
pp. 1209-1213 ◽  
Author(s):  
S. Gopinath ◽  
C. Arumugam ◽  
Tom Page ◽  
M. Chandrasekaran
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flow Time ◽  
Scheduling Problems ◽  
Artificial Immune ◽  
Shifting Bottleneck ◽  
Shop Scheduling ◽  
Multi Objective

Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using reasonable resources in many cases. Scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered in this work. The Artificial Immune System Shifting Bottleneck Approach is used for finding optimal makespan, mean flow time, mean tardiness values of two benchmark problems. In this Artificial Immune System Shifting Bottleneck Approach (AISSB), initial sequences are generated with Artificial Immune System Algorithm (AIS) and Shifting Bottleneck Algorithm (SB) is used for finding final solutions. The results show that the AISSB Approach is effective algorithm that gives better results than literature results. The proposed AISSB Approach is an efficient problem-solving technique for multi objective job shop scheduling problem.

scholarly journals Energy efficiency, robustness, and makespan optimality in job-shop scheduling problems

2015 ◽  
Vol 30 (3) ◽  
pp. 300-312 ◽  
Author(s):  
Miguel A. Salido ◽  
Joan Escamilla ◽  
Federico Barber ◽  
Adriana Giret ◽  
Dunbing Tang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Production Scheduling ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Efficient Solutions ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Planning And Scheduling ◽  
Shop Scheduling

AbstractMany real-world problems are known as planning and scheduling problems, where resources must be allocated so as to optimize overall performance objectives. The traditional scheduling models consider performance indicators such as processing time, cost, and quality as optimization objectives. However, most of them do not take into account energy consumption and robustness. We focus our attention in a job-shop scheduling problem where machines can work at different speeds. It represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The main goal of the paper is focused on the analysis of three important objectives (energy efficiency, robustness, and makespan) and the relationship among them. We present some analytical formulas to estimate the ratio/relationship between these parameters. It can be observed that there exists a clear relationship between robustness and energy efficiency and a clear trade-off between robustness/energy efficiency and makespan. It represents an advance in the state of the art of production scheduling, so obtaining energy-efficient solutions also supposes obtaining robust solutions, and vice versa.

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