NNMA: An effective memetic algorithm for solving multiobjective permutation flow shop scheduling problems (original) (raw)
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An improved multiobjective memetic algorithm for permutation flow shop scheduling
This paper addresses a multiobjective scheduling problem in the permutation flow shop. The objectives are to minimize makespan and total flow time. The proposed approach is based on the framework of memetic algorithm, which is known as a hybrid of genetic algorithm and local search. The local search procedure is an iterative process repeating neighbor generation, neighbor evaluation, and neighbor selection. We take a problem-specific heuristic for neighbor generation and propose several strategies for neighbor evaluation and neighbor selection. Archive injection (adding non-dominated solutions to the population) is another issue under investigation. We examine the effects of the proposed strategies through experiments using forty widely used problem instances with different scales. We also evaluate the proposed approach by comparing it with other twenty-six ones in terms of three performance metrics. Our approach outperforms all benchmarks and updates a large portion of the sets of best known non-dominated solutions for large-scale instances.
Omega, 2014
The flow shop scheduling problem is finding a sequence given n jobs with same order at m machines according to certain performance measure(s). The job can be processed on at most one machine; meanwhile one machine can process at most one job. The most common objective for this problem is makespan. However, many real-world scheduling problems are multi-objective by nature. Over the years there have been several approaches used to deal with the multi-objective flow shop scheduling problems (MOFSP). Hence, in this study, we provide a brief literature review of the contributions to MOFSP and identify areas of opportunity for future research.
Computers & Operations Research, 2009
This paper proposes a hybrid metaheuristic for the minimization of makespan in permutation flow shop scheduling problems. The solution approach is robust, fast, and simply structured, and comprises three components: an initial population generation method based on a greedy randomized constructive heuristic, a genetic algorithm (GA) for solution evolution, and a variable neighbourhood search (VNS) to improve the population. The hybridization of a GA with VNS, combining the advantages of these two individual components, is the key innovative aspect of the approach. Computational experiments on benchmark data sets demonstrate that the proposed hybrid metaheuristic reaches high-quality solutions in short computational times. Furthermore, it requires very few user-defined parameters, rendering it applicable to real-life flow shop scheduling problems.
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...
Optimization of Permutation Flow Shop with Multi-Objective Criteria
The flowshop scheduling is concerned with allocation of available resources over a period of time with an optimum objective. The objective may be one or more like minimizing makespan, idle time and/or total flow time. Many researchers proposed various algorithms to achieve these objectives through an optimal sequence in a Permutation Flow Shop (PFS). For identifying an optimum sequence for 'n' jobs in 'm' machines, n! sequences are to be worked. Due to the limitation in computing capabilities, the new heuristics are required to identify an optimal sequence. This paper deals with the heuristic to get an optimal or near to optimal sequence in the PFS. An algorithm is newly proposed with an exponential function of mathematical and computational aspects to validate the optimal sequence. The makespan and total flow time of the newly proposed heuristic is compared with one of the well-known classical algorithm called Gupta algorithm. The various sizes of PFS problems are s...
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.
IEEE Transactions on Evolutionary Computation, 2003
This paper shows how the performance of evolutionary multiobjective optimization (EMO) algorithms can be improved by hybridization with local search. The main positive effect of the hybridization is the improvement in the convergence speed to the Pareto front. On the other hand, the main negative effect is the increase in the computation time per generation. Thus, the number of generations is decreased when the available computation time is limited. As a result, the global search ability of EMO algorithms is not fully utilized. These positive and negative effects are examined by computational experiments on multiobjective permutation flowshop scheduling problems. Results of our computational experiments clearly show the importance of striking a balance between genetic search and local search. In this paper, we first modify our former multiobjective genetic local search (MOGLS) algorithm by choosing only good individuals as initial solutions for local search and assigning an appropriate local search direction to each initial solution. Next, we demonstrate the importance of striking a balance between genetic search and local search through computational experiments. Then we compare the modified MOGLS with recently developed EMO algorithms: the strength Pareto evolutionary algorithm and revised nondominated sorting genetic algorithm. Finally, we demonstrate that a local search can be easily combined with those EMO algorithms for designing multiobjective memetic algorithms.
RAIRO - Operations Research
Flow shop scheduling is a type of scheduling where sequence follows for each job on a set of machines for processing. In practice, jobs in flow shops can arrive at irregular times, and the no-wait constraint allows the changes in the job order to flexibly manage such irregularity. The flexible flow shop scheduling problems with no-wait have mainly addressed for flow optimization on the shop floor in manufacturing, processing, and allied industries. The scope of this paper is to identify the literature available on permutation and non-permutation flow shop scheduling with no-wait constraint. This paper organizes scheduling problems based on performance measures of variability and shop environments. The extended summary of two/three-machine and m-machine problems has been compiled, including their objectives, algorithms, parametric considerations, and their findings. A systematic appearance of both conceptual and analytical results summarizes various advances of the no-wait constraint...
Parallel hybrid heuristics for the permutation flow shop problem
Annals of Operations Research, 2012
This paper addresses the Permutation Flowshop Problem with minimization of makespan, which is denoted by F |prmu|Cmax . In the permutational scenario, the sequence of jobs has to remain the same in all machines. The Flowshop Problem (FSP) is known to be NP-hard when more than three machines are considered. Thus, for medium and large scale instances, highquality heuristics are needed to find good solutions in reasonable time. We propose and analyse parallel hybrid search methods that fully use the computational power of current multi-core machines. The parallel methods combine a memetic algorithm (MA) and several iterated greedy algorithms (IG) running concurrently. Two test scenarios were included, with short and long CPU times. The tests were conducted on the set of benchmark instances introduced by Taillard in 1993, commonly used to assess the performance of new methods. Results indicate that the use of the MA to manage a pool of solutions is highly effective, allowing the improvement of the best known upper bound for one of the instances.
A local search heuristic with self-tuning parameter for permutation flow-shop scheduling problem
2009 IEEE Symposium on Computational Intelligence in Scheduling, 2009
In this paper, a new local search metaheuristic is proposed for the permutation flow-shop scheduling problem. In general, metaheuristics are widely used to solve this problem due to its NP-completeness. Although these heuristics are quite effective to solve the problem, they suffer from the need to optimize parameters. The proposed heuristic, named STLS, has a single self-tuning parameter which is calculated and updated dynamically based on both the response surface information of the problem field and the performance measure of the method throughout the search process. Especially, application simplicity of the algorithm is attractive for the users. Results of the experimental study show that STLS generates high quality solutions and outperforms the basic tabu search, simulated annealing, and record-to-record travel algorithms which are well-known local search based metaheuristics.