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Approaches to Solving of Permutation Flow Shop Scheduling Problem: An Exploration Study
Ivan Lazar
Research Journal of Applied Sciences , 2012, DOI: 10.3923/rjasci.2012.426.434
Abstract: Permutation flow shop scheduling problems present an important class of sequencing problems in the realm of production planning. The study briefly reviews typical categories of the PFSSPs in accordance with a defined classification scheme of the particular problems. Subsequently, a review of frequent approaches and methods for PFSSP solving is treated. A final section of the study summarizes findings of this review that is mapping the field over field over the last 25 years.
Solving the Flexible Job-Shop Scheduling Problem by a Genetic Algorithm  [PDF]
M. Zandieh,I. Mahdavi,A. Bagheri
Journal of Applied Sciences , 2008,
Abstract: A meta-heuristic approach for solving the flexible job-shop scheduling problem (FJSP) is presented in this study. This problem consists of two sub-problems, the routing problem and the sequencing problem and is among the hardest combinatorial optimization problems. We propose a Genetic Algorithm (GA) for the FJSP. Our algorithm uses several different rules for generating the initial population and several strategies for producing new population for next generation. Proposed GA is tested on benchmark problems and with due attention to the results of other meta-heuristics in this field, the results of GA show that our algorithm is effective and comparable to the other algorithms.
A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems  [PDF]
S. Narendhar,T. Amudha
Computer Science , 2012,
Abstract: Bio-Inspired computing is the subset of Nature-Inspired computing. Job Shop Scheduling Problem is categorized under popular scheduling problems. In this research work, Bacterial Foraging Optimization was hybridized with Ant Colony Optimization and a new technique Hybrid Bacterial Foraging Optimization for solving Job Shop Scheduling Problem was proposed. The optimal solutions obtained by proposed Hybrid Bacterial Foraging Optimization algorithms are much better when compared with the solutions obtained by Bacterial Foraging Optimization algorithm for well-known test problems of different sizes. From the implementation of this research work, it could be observed that the proposed Hybrid Bacterial Foraging Optimization was effective than Bacterial Foraging Optimization algorithm in solving Job Shop Scheduling Problems. Hybrid Bacterial Foraging Optimization is used to implement real world Job Shop Scheduling Problems.
Solving Fuzzy based Job Shop Scheduling Problems using Ga and Aco  [PDF]
P Surekha
Journal of Emerging Trends in Computing and Information Sciences , 2010,
Abstract: In this paper, we present a genetic algorithm and ant colony optimization algorithm for solving the Job-shop Scheduling Problem (JSSP). The genetic algorithm comprises of different stages like generating the initial population, selecting the individuals for reproduction and reproducing new individuals. Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ants, which is also used to solve this combinatorial optimization problem. In JSSP ants move from one machine (nest) to another machine (food source) depending upon the job flow, thereby optimizing the sequence of jobs. The sequence of jobs is scheduled using Fuzzy logic and optimizes using GA and ACO. The makespan, completion time, makespan efficiency, algorithmic efficiency and the elapsed time for the genetic algorithm and the ant colony algorithm are evaluated and compared. The improvement in the performance of the algorithms based on the computed parameters is also discussed in this paper. Computational results of these optimization algorithms are compared by analyzing the JSSP benchmark instances, FT10 and the ABZ10 problems.
Algorithm for Solving Job Shop Scheduling Problem Based on machine availability constraint
Kanate Ploydanai,,Anan Mungwattana
International Journal on Computer Science and Engineering , 2010,
Abstract: Typically, general job shop scheduling problems assume that working times of machines are equal, for instance eight hours a day. However, in real factories, these working times are different because the machines may have different processing speeds, or they may require maintenance. That is, one machine may need to be operated only half day whereas other machines may have to be operated for the entire day. So, each machine has its own working time window. In this paper, this type of problem is referred to as a job shop scheduling problembased on machine availability constraint which is more complex than typical job shop scheduling problems. In the previous research, this type of problem has been rarely investigated before. Thus a new algorithm is developed based on a non-delay scheduling heuristic by adding machine availability constraint to solve job shop scheduling problem with minimize makespan objective. The newly developed algorithm with the machine availability constraint assumption is more realistic. The study reveals the result of algorithm that consider machine availability constraint is better than the result of algorithm that ignores machine availability constraint when apply to the real problem.
Solving Flexible Job-Shop Scheduling Problem Using Gravitational Search Algorithm and Colored Petri Net
Behnam Barzegar,Homayun Motameni,Hossein Bozorgi
Journal of Applied Mathematics , 2012, DOI: 10.1155/2012/651310
Abstract: Scheduled production system leads to avoiding stock accumulations, losses reduction, decreasing or even eliminating idol machines, and effort to better benefitting from machines for on time responding customer orders and supplying requested materials in suitable time. In flexible job-shop scheduling production systems, we could reduce time and costs by transferring and delivering operations on existing machines, that is, among NP-hard problems. The scheduling objective minimizes the maximal completion time of all the operations, which is denoted by Makespan. Different methods and algorithms have been presented for solving this problem. Having a reasonable scheduled production system has significant influence on improving effectiveness and attaining to organization goals. In this paper, new algorithm were proposed for flexible job-shop scheduling problem systems (FJSSP-GSPN) that is based on gravitational search algorithm (GSA). In the proposed method, the flexible job-shop scheduling problem systems was modeled by color Petri net and CPN tool and then a scheduled job was programmed by GSA algorithm. The experimental results showed that the proposed method has reasonable performance in comparison with other algorithms.
On Solving Constraint Satisfaction Based Job-Shop Scheduling Problems
基于约束满足的Job-Shop调度问题求解方法研究

CHEN En-hong,XUE Han-hong,
陈恩红
,薛瀚宏

软件学报 , 1998,
Abstract: In this paper, the authors discuss how to solve a set of typical constraint satisfaction problems, Job-Shop scheduling problems. Based upon the depth-first search, formal strategies of enforcing consistency, selecting operation and selecting start time are given, heuristic strategies of enforcing consistency and incomplete back jumping are introduced to further enhance the efficiency in solving Job-Shop scheduling problems.
Solving the Job-Shop Scheduling Problem by Arena Simulation Software
Prof. Dr. Gamal M. Nawara,Eng. Wael S. Hassanein
International Journal of Engineering Innovations and Research , 2013,
Abstract: The job-Shop Scheduling problem (JSSP) attracted a lot of researchers from various research disciplines, mainly Operations Research, Management Science, Computer Science, and Manufacture Science for the last 50 years. JSSP is a typical NP-hard problem in the strong sense. Although the literature is full of researches concerning the JSSP, practitioners are not able to get benefit of the majority of these researches because of the assumptions which take the problem very far away from the real life JSSP. The aim of our research is to build a simulation model for the JSSP to be able to relax some of these assumptions to simulate the real life JSSP. We used discrete event simulation as it's suitable for the JSSP. We used Arena simulation software version 14 to build the model on a Dell Vostro PC (Intel Core(TM) i5–2400 CPU @ 3.10GHZ with 4 GB RAM).In this paper we will just show the basic model which is able to solve the famous benchmarks for the JSSP to prove that our model is ready for the real life JSSP. In the following papers we will show how to relax some of these assumptions one by one. The computational results for 9 benchmarks of different sizes showed that the proposed model is both effective and efficient. It gave good solutions in reasonable amounts of time.
Modified Adaptive Genetic Algorithms for Solving Job-shop Scheduling Problems
求解作业车间调度问题的改进自适应遗传算法

WANG Wan-liang,WU Qi-di,SONG Yi WT,
王万良

系统工程理论与实践 , 2004,
Abstract: Job-shop scheduling problem(JSP) is one of the most difficulty combinatorial optimization problems. It is widely applied to productive management of enterprise. It is one of the most important links on CIMS. This paper proposed improved adaptive genetic algorithms for solving job-shop scheduling problems according to the idea that the best individual on current generation should be kept to next generation, but the best individual should be crossed and mutated by some probability. The software package for these modified adaptive genetic algorithms are programmed and applied to solving job-shop scheduling problems. These modified methods increase the convergence rate. Especially, the crossover probability and mutation probability are given automatically in the search process. It is important in the engineering.
Using Fuzzy Scheduling System for Solving a Dynamic Job Shop Problem
用模糊调度系统求解动态Job Shop问题

WAN Guo,|hua,
万国华

系统工程理论与实践 , 2001,
Abstract: This paper concerns with the dynamic job shop scheduling problem to minimize total weighted tardiness is studied. A fuzzy system approach is proposed to dynamically guide the selection of dispatching rules for different problem instances by learning from fuzzy rules and previous solutions. Several experiments are designed and conducted in different scenarios to evaluate the effectiveness of the fuzzy scheduling system against the traditional dispatching rules. Preliminary experimental results indicate that the fuzzy scheduling system is efficient and effective.
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