Flow shop scheduling with multiple objectiveof minimizing makespan and total flow time (original) (raw)
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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.
Modified Heuristics for Scheduling in Flow Shop to Minimize Makespan
Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), 2021
The NP-completeness of flow shops scheduling problems has been discussed for many years. Hence many heuristics have been proposed to obtain solutions of good quality with a small computational effort. The CDS (Campbell et al) and NEH (Nawaz, Enscore and Ham) heuristics are efficient among meta-heuristics such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).
Comparative study for Utilization of machines in the Flow-Shop Scheduling Problems
Scheduling is the procedure of generating the schedule which is a physical document and generally informs the happening of things and demonstrate a plan for the timing of certain activities. The flow shop problem is one of the most widely studied classical scheduling problems and reflects real operation of several industries. The aim of the present work is to evaluate the performance of four methods when it is used to solve flow shop scheduling problems with minimization makespan. The four heuristics methods are Johnson, Palmer, CDS and Gupta methods. In this work, an attempt has been made to solve the flow shop scheduling problem for comparative study for utilization of machines in the flow-shop scheduling problems among pervious methods. A simulation study has been made to evaluate the performance of the four method under consideration based on two performance measures makespan and utilization of machine , the results has been proved that the Palmer and CDS heuristic methods show the minimum value of average of makespan and average utilization of machine when it compared with other heuristic methods.
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.
Alternative Heuristic Algorithm for Flow Shop Scheduling Problem
Innovative Methods and Approaches, 2012
In this chapter an alternative heuristic algorithm is proposed that is assumed for a deterministic flow shop scheduling problem. The algorithm is addressed to an m-machine and n-job permutation flow shop scheduling problem for the objective of minimizing the make-span when idle time is allowed on machines. This chapter is composed in a way that the different scheduling approaches to solve flow shop scheduling problems are benchmarked. In order to compare the proposed algorithm against the benchmarked, selected heuristic techniques and genetic algorithm have been used. In realistic situation, the proposed algorithm can be used as it is without any modification and come out with acceptable results.
IAEME PUBLICATION, 2020
In this paper the authors investigate the problem of minimizing the Makespan using Heuristic approach. The author compared the result with the existing algorithms namely Palmer’s Heuristic(a slope order index), CDS Heuristic, NEH Heuristic algorithm, Gupta heuristic, RA (rapid access) Heuristic found in the literature and it was found that our algorithm perform superior than the other algorithms found in the literature
Review on Job-Shop and Flow-Shop Scheduling Using
Journal of Mechanical Engineering, 2011
Scheduling is widely studied and complex combinatorial optimization problems. A vast amount of research has been performed in this particular area to effectively schedule jobs for various objectives. The multi-criteria scheduling problem is one of the main research subjects in the field of modern manufacturing where most of them are considered as NP-hard. This paper discusses the more recent literature on scheduling using multi criteria decision making (MCDM). This article addresses both job-shop and flow-shop scheduling problem.DOI: http://dx.doi.org/10.3329/jme.v41i2.7508
FLOW SHOP SCHEDULING USING GENETIC ALGORITHM: HISTORICAL REVIEW AND CATEGORIZATION OF PROCEDURES
The primary objective of flow shop scheduling is to obtain the best sequence which optimizes various objectives such as makespan, total flow time, total tardiness, or number of tardy jobs, etc. Due to the combinatorial nature of the flow shop problem (FSP) there is a lot of artificial intelligence methods proposed to solve it. The Genetic Algorithm (GA), one of these methods, is considered a valuable search algorithm capable of finding a reasonable solution in a short computational time. GA operators, (selection, crossover and mutation process), will give different forms. The different forms of crossover and mutation process in GA method can be combined to give various GAs that can be impact on the quality of the solution. In this paper we present a comprehensive review of different GAs built up-to-date for solving the flow shop scheduling problems. Also, we show the suitable default GA parameters mentioned in literature at different problem size.
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.
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.