Optimization of Permutation Flow Shop with Multi-Objective Criteria (original) (raw)
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A Novel Hybrid Algorithm for Permutation Flow Shop Scheduling
In the present scenario the recent engineering and industrial built-up units are facing hodgepodge of problems in a lot of aspects such as machining time, electricity, man power, raw material and customer's constraints. The job-shop scheduling is one of the most significant industrial behaviours, particularly in manufacturing planning. This paper proposes the permutation flow shop sequencing problem with the objective of makespan minimization using the new modified proposed method of johnson's algorithm as well as the gupta's heuristic algorithm. This paper involves the determination of the order of processing of n jobs in m machines. Although since the problem is known to be np-hard for three or more machines, that produces near optimal solution of the given problem. The proposed method is very simple and easy to understand followed by a numerical illustration is given.
International Journal of Applied Information Systems, 2013
Flow Shop Scheduling has been an interesting field of research for over six decades. They are easy to formulate, yet difficult to solve. In a shop, there are 'm' machines arranged in series to process a set of 'n' jobs having different processing times. Each job has to pass through each machine, in order. The problem is to find a sequence of jobs to be processed in all the machines, so that a given performance parameter is optimized. The total number of schedules is (n!) m . If the order of machines is not to be changed, the problem is simplified, and the overall number of solutions is reduced to n!. This problem is referred to a permutation flow shop scheduling problem, or PFSP in short. Starting from two machines, 'n' jobs, various Heuristics have been proposed over the years. After the invention of meta heuristics and evolutionary algorithms, and increased computational capabilities available today, finding optimal/ near optimal solutions become comparatively easier. In this paper, a few heuristic algorithms have been analyzed for makespan criterion considering machine idle time and processing time, by comparing the results with the well known CDS algorithm. Benchmark problems proposed by Taillard and Ruben Ruiz are used for the performance analysis.
Scheduling of Permutation Flow Shops Using a New Hybrid Algorithm
2018
In the current situation, modern engineering and industrial built-up units are encountering a jumble of issues in a variety of areas, including machining time, electricity, manpower, raw materials, and client restraints. One of the most important industrial behaviors, particularly in manufacturing planning, is job-shop scheduling. This study provides a new updated suggested approach of johnson's algorithm as well as the gupta's heuristic algorithm to solve the permutation flow shop sequencing problem with the goal of making the makespan as little as possible. This work is about determining the processing order of n tasks in m machines. Although, because the problem is np-hard for three or more computers, this results in a near-optimal solution to the given issue. The suggested approach is straightforward and easy to comprehend, and it is accompanied with a numerical example.
A Simple and Effective Approach for Tackling the Permutation Flow Shop Scheduling Problem
Mathematics, 2021
In this research, a new approach for tackling the permutation flow shop scheduling problem (PFSSP) is proposed. This algorithm is based on the steps of the elitism continuous genetic algorithm improved by two strategies and used the largest rank value (LRV) rule to transform the continuous values into discrete ones for enabling of solving the combinatorial PFSSP. The first strategy is combining the arithmetic crossover with the uniform crossover to give the algorithm a high capability on exploitation in addition to reducing stuck into local minima. The second one is re-initializing an individual selected randomly from the population to increase the exploration for avoiding stuck into local minima. Afterward, those two strategies are combined with the proposed algorithm to produce an improved one known as the improved efficient genetic algorithm (IEGA). To increase the exploitation capability of the IEGA, it is hybridized a local search strategy in a version abbreviated as HIEGA. HIE...
An improvement heuristic for permutation flow shop scheduling
International Journal of Process Management and Benchmarking, 2019
An improvement heuristic algorithm is proposed in this paper for solving flow shop scheduling problem (F m / prmu / C max). To test its efficiency, firstly the performance of the proposed algorithm is done against the six heuristics existing in the literature including the best NEH heuristic on 120 Taillard benchmarks. Further, this set of 120 Taillard instances is increased to 266 benchmark problem instances which include Carlier's, some Reeves and some new hard VRF instances from Vallada et al. (2015). On these instances, the performance of the proposed algorithm is tested against the best NEH and the famous CDS heuristic with the best known upper bounds. Further, a brief analysis of the other heuristics and metaheuristics existing in literature is done on Taillard problem instances. The proposed heuristic outperforms all the heuristics reported in this paper.
A new heuristic for minimizing total completion time objective in permutation flow shop scheduling
The International Journal of Advanced Manufacturing Technology, 2011
A constructive heuristic for the permutation flow shop scheduling problem with the objective of minimizing total completion time is proposed in this paper. It is constructed using a population-based technique and also the insertion rule similar to that presented in the Nawaz-Enscore-Ham (Omega 11:91-95, 1983) heuristic for the makespan criterion. We show, through computational results, that the proposed heuristic performs better than the heuristic of Woo and Yim (Comput Oper Res 25:175-182, 1998) for small and large problem sizes and the heuristic of Framinan and Leisten (Omega 31:311-317, 2003) for small and medium problem sizes. However, the relative performance of the heuristic of Framinan and Leisten improves compared to the proposed method when the job size increases. The time complexity of the proposed method has been shown to be less than those required by the existing heuristics. The average CPU time of the proposed heuristic is significantly less than the heuristic of Framinan and Leisten for large jobs.
An Optimization Algorithm for Optimal Problem of Permutation Flow Shop Scheduling
International Journal of Computer Applications, 2017
Nowadays the permutation flow shop scheduling problems become one of the most important problems in scheduling field. In this paper whale optimization algorithm was modified for solving PFSP. WOA is new meta-heuristic was proposed by Sayedali and Andrew in 2016 that was inspired from the nature of humpback whales movements in hunting prey. The modification is depending on two stages: firstly; WOA algorithm is converted to discrete algorithm to deal with PFSP; secondly; the mutation permutation strategy was used to improve the results of WOA. The modified algorithm is implemented on MATLAB workspace. The modified algorithm is tested with various benchmark datasets available for flow shop scheduling. The statistical results prove that the modified algorithm (MWOA) is competent and efficient for solving flow shop problems.
Improved solution for minimizing makespan in permutation flow shop
Journal of Industrial and Production Engineering, 2019
Flow shop is one of the scheduling systems in which the jobs are processed on different machines in a fixed order so as to optimize certain scheduling criteria. The general flow shop problem is shown to be NP-complete. In this paper, a simple constructive heuristic algorithm is proposed for optimizing flow shop scheduling problem with the objective to minimize the makespan. Experimental results show the superiority of the proposed algorithm over wellknown heuristics when tested on existing benchmark problems in scheduling literature. Based upon the concept of CL-heuristic [a high-performing constructive heuristic for minimizing makespan in permutation flowshops, Journal of Industrial and Production Engineering, 2013; 30(6): 355-362], we propose a new tie-breaking rule in the proposed approach. An illustrative example is given in the paper to explain the working of the proposed procedure. Statistical tests are performed to establish the effectiveness of the proposed heuristic.
A heuristic to minimize total flow time in permutation flow shop☆
Omega, 2009
In this paper, we address an n-job, m-machine permutation flow shop scheduling problem for the objective of minimizing the total flow time. We propose a modification of the best-known method of Framinan and Leisten [An efficient constructive heuristic for flowtime minimization in permutation flow shops. Omega 2003;31:311-7] for this problem. We show, through computational experimentation, that this modification significantly improves its performance while not affecting its time-complexity.
Solving Permutation Flow Shop Scheduling Problem with Sequence-Independent Setup Time
Journal of Applied Mathematics
In this paper, we study the resolution of a permutation flow shop problem with sequence-independent setup time. The objective is to minimize the maximum of job completion time, also called the makespan. In this contribution, we propose three methods of resolution, a mixed-integer linear programming (MILP) model; two heuristics, the first based on Johnson’s rule and the second based on the NEH algorithm; and finally two metaheuristics, the iterative local search algorithm and the iterated greedy algorithm. A set of test problems is simulated numerically to validate the effectiveness of our resolution approaches. For relatively small-size problems, it has been revealed that the adapted NEH heuristic has the best performance than that of the Johnson-based heuristic. For the relatively medium and large problems, the comparative study between the two metaheuristics based on the exploration of the neighborhood shows that the iterated greedy algorithm records the best performances.