Simulation and modelling of hybrid heuristics distribution algorithm on flow shop scheduling problem to optimize makespan in an Indian manufacturing industry (original) (raw)
Related papers
2015
Among typical production scheduling problems, job shop scheduling is one of the strong NP-complete combinatorial optimization problems. Using an enhanced genetic algorithm, an effective crossover operation for real coded job-based representation can be used to guarantee the feasibility of solutions, which are decoded into active schedules during the search process. This paper attempts to assign an optimum job sequence on a machine considering the objective of minimizing total makespan time in a flow shop environment based on the philosophy of Genetic Algorithms. The setup time or changeover time is taken as the main factor involved in the makespan time where setups are sequence dependent. For analyzing the results simulations are performed with various population sizes and crossover probabilities.
This paper deals with the development and analysis of hybrid genetic algorithms for flow shop scheduling problems with sequence dependent setup time. A constructive heuristic called setup ranking algorithm is used for generating the initial population for genetic algorithm. Different variations of genetic algorithm are developed by using combinations of types of initial populations and types of crossover operators. For the purpose of experimentation, 27 group problems are generated with ten instances in each group for flow shop scheduling problems with sequence dependent setup time. An existing constructive algorithm is used for comparing the performance of the algorithms. A full factorial experiment is carried out on the problem instances developed. The best settings of genetic algorithm parameters are identified for each of the groups of problems. The analysis reveals the superior performance of hybrid genetic algorithms for all the problem groups.
ICMIEE-PI-14016310 000 Minimization of Makespan in Flow Shop Scheduling Using Heuristics
2014
Production scheduling is one of the most significant issue in production and operations in any manufacturing system that has significant impact on cost reduction and increased productivity. Improper scheduling causes idle time for machines and hampers productivity that may cause an increased price of the product. So the main objective of this study is to minimize the makespan or total completion time. To do this study we have collected our data from Hatil complex limited, Mirpur, Dhaka, Bangladesh. This study presents Palmer’s heuristic, CDS heuristic, NEH algorithm for solving the flow shop scheduling problem to minimize the makespan. NEH yields more elaborate results as compared to Palmer and CDS heuristic. Grant chart is used to verify the effectiveness of heuristics. By applying these three techniques we have gotten an optimal result for each case. The use of these techniques makes it possible to generate a schedule that minimizes the makespan.
A Genetic Algorithm for Flow Shop Scheduling with Assembly Operations to Minimize Makespan
Journal Of The Institution Of Engineers (india): Series C, 2014
Manufacturing systems, in which, several parts are processed through machining workstations and later assembled to form final products, is common. Though scheduling of such problems are solved using heuristics, available solution approaches can provide solution for only moderate sized problems due to large computation time required. In this work, scheduling approach is developed for such flow-shop manufacturing system having machining workstations followed by assembly workstations. The initial schedule is generated using Disjunctive method and genetic algorithm (GA) is applied further for generating schedule for large sized problems. GA is found to give near optimal solution based on the deviation of makespan from lower bound. The lower bound of makespan of such problem is estimated and percent deviation of makespan from lower bounds is used as a performance measure to evaluate the schedules. Computational experiments are conducted on problems developed using fractional factorial orthogonal array, varying the number of parts per product, number of products, and number of workstations (ranging upto 1,520 number of operations). A statistical analysis indicated the significance of all the three factors considered. It is concluded that GA method can obtain optimal makespan.
A heuristic algorithm for scheduling in a flow shop environment to minimize makespan
Scheduling 'n' jobs on 'm' machines in a flow shop is NP-hard problem and places itself at prominent place in the area of production scheduling. The essence of any scheduling algorithm is to minimize the makespan in a flowshop environment. In this paper an attempt has been made to develop a heuristic algorithm, based on the reduced weightage of machines at each stage to generate different combination of 'm-1' sequences. The proposed heuristic has been tested on several benchmark problems of Taillard (1993) [Taillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64, 278-285.]. The performance of the proposed heuristic is compared with three well-known heuristics, namely Palmer's heuristic, Campbell's CDS heuristic, and Dannenbring's rapid access heuristic. Results are evaluated with the best-known upper-bound solutions and found better than the above three.
Minimizing makespan for a no-wait flowshop using genetic algorithm
Sadhana, 2012
This research paper addresses the scheduling problems with the primary objective of minimizing the makespan in a flow shop with 'N' jobs through 'M' machines. The EPDT (Heuristic approach) and BAT (Meta-Heuristic approach) heuristics are proposed to solve the flow shop scheduling problem in a modern manufacturing environment. These two algorithms are applied along with the Genetic Algorithm (GA) for the further improvement of results in achieving the minimal makespan. The performances of these newer heuristics are evaluated by solving the Taillard benchmark problems in MATLAB environment with various sizes of problems. The proposed GA applied EPDT heuristic and GA applied BAT meta-heuristic for the flow shop problems have been found very effective in solving scheduling problems and finding a better sequence which can reduce the makespan to a great extent. The improvement of EPDT and BAT were obtained by applying the GA yields superior results as well as these results also very close to upper bound than NEH results. The results of the heuristics are tested statistically by ANOVA and it shows that the GA applied heuristics gives a quality solution.
Minimization of Makespan in Job Shop Scheduling with Heuristic and Genetic Algorithms
International Journal of Darshan Institute on Engineering Research & Emerging Technology, 2018
As the performance of manufacturing system is directly related to the optimum utilization of time and resources, optimal scheduling of work activities plays an important role. Job shop scheduling is an optimization problem, in which a set of jobs has to be processed on a set of machines such that some specific objective functions are to be minimized. Here an effort has been made to minimize the makespan and average job flow time of the job shop scheduling problem of following two natures: 5-job 3-machine problem and 10-job 5-machine problem. Data for these problems were collected from a small scale industry. This paper describes the steps in well-known heuristics such as Palmer's, Campbell Dudek and Smith (CDS) & Nawaz Enscore and Ham (NEH) and a simple algorithm for proposed Genetic Algorithm. This paper investigates the present scheduling system followed by the industry and presents the solutions for the problems considered through heuristics and proposed Genetic Algorithm. Then the comparisons were made among the results obtained through proposed genetic algorithm, heuristics and LEKIN scheduling software to suggest an optimal sequence for the problems considered.
Improved heuristically guided genetic algorithm for the flow shop scheduling problem
International Journal of Services and Operations Management, 2007
This paper deals with the problem of scheduling on makespan criterion in the flow shop environment. We have presented a new heuristic genetic algorithm (NGA) that combines the good features of both the genetic algorithms and heuristic search. The NGA is run on a large number of problems and its performance is compared with that of the Standard Genetic Algorithm (SGA) and the well-known Nawaz-Enscore-Ham (NEH) heuristic. The NGA is seen to perform better in almost all instances. The complexity of the NGA is found to be better than that of the SGA. The NGA also performs superior results when compared with the simulated annealing from the literature.
A novel approach for no-wait flow-shop scheduling problem with hybrid genetic algorithm (HGA
No-wait flowshop scheduling problem (NWFSSP) is a constrained for flowshop scheduling problem that exists widely in manufacturing field. Such problems are NP-hard and hence the optimal solutions are not guaranteed. In this paper, a hybrid genetic algorithm (HGA) methodology is proposed to solve the no-wait flowshop scheduling problem with the objective of minimizing the makespan, for a number of jobs to be processed on a number of machines using a spreadsheet based genetic algorithm. This technique hybridizes the genetic algorithm and a novel local search scheme to generate population of initial chromosomes and also improve the operators of genetic algorithm (GA). The present approach is compared with existing literature for the efficiency of this method which performs better working solutions. Additionally, it is also shown that any objective function can be minimized with the same model without changing the logic of GA routine.
The journal of Mathematic and Computer Science
In This paper a two stages Hybrid Flow Shop (HFS) problem with sequence dependent set up times is considered in which the preemption is also allowed. The objective is to minimize the weighted sum of completion time and maximum tardiness. Since this problem is categorized as an NP-hard one, meta-heuristic algorithms can be utilized to obtain high quality solutions in a reasonable amount of time. In this paper a Genetic algorithm (GA) approach is used and for parameter tuning the Response Surface Method (RSM) is applied to increase the performance of the algorithm. Computational results show the high performance of the proposed algorithm to solve the generated problems.