Multi-objective Flow Shop Scheduling Problem Research Articles

Research on multi-objective flow shop scheduling optimization in supply chain environment based on Fuzzy Relevance Entropy Method

For the multi-objective flow shop scheduling problem in the supply chain environment, this paper proposes the Fuzzy Relevance Entropy method (FREM) to solve the adaptive value assignment problem in the multi-objective optimization process of the supply chain environment by combining Fuzzy Information Entropy Theory (FIET) and Degree of Membership Function (DMF). Firstly, the uncertainty of each sub-objective of the ideal solution and Pareto solution of the objective is extracted using the Degree of Membership Function. Secondly, each solution is mapped into an affiliation degree fuzzy set and the information contained in the fuzzy set is reprocessed using Fuzzy Information Entropy Theory. Finally, the amount of information contained in the ideal solution solved by the Pareto method is used to guide the evolution of the Particle Swarm Optimization (PSO) algorithm, thus avoiding the traditional multi-objective optimization process of assigning weights to solve the fitness link. This paper combines both the Fuzzy Relevance Entropy method and the Stochastic Weight method with Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms to address the five-objective flow shop scheduling problem in the supply chain environment. Experimental results demonstrate that the proposed Fuzzy Relevance Entropy method effectively solves the multi-objective flow shop scheduling problem in the supply chain environment and achieves better optimization results compared to the Stochastic Weight method.

Hybrid evolutionary algorithm with sequence difference-based differential evolution for multiobjective fuzzy flow-shop scheduling problem

Hybrid evolutionary algorithm with sequence difference-based differential evolution for multiobjective fuzzy flow-shop scheduling problem

A new approach to solving multiobjective flowshop scheduling problems: A multi-MOORA-based genetic algorithm

Flowshop scheduling problems constitute a type of problem that is frequently discussed in the literature, where a wide variety of methods are developed for their solution. Although the problem used to be set as a single purpose, it became necessary to expect more than one objective to be evaluated together with increasing customer expectation and competition, after which studies started to be carried out under the title of multiobjective flowshop scheduling. With the increase in the number of workbenches and jobs, the difficulty level of the problem increases in a nonlinear way, and the solution becomes more difficult. This study proposes a new hybrid algorithm by combining genetic algorithms, which are metaheuristic methods, and the Multi-MOORA method, which is a multicriterion decision-making method, for the solution of multiobjective flowshop scheduling problems. The study evaluates and tries to optimize the performance criteria of maximum completion time, average flow time, maximum late finishing, average tardiness, and the number of late (tardy) jobs. The proposed algorithm is compared to the standard multiobjective genetic algorithm (MOGA), and the Multi-MOORA-based genetic algorithm (MBGA) shows better results.

Performance Comparison of Three Hybridization Categories to Solve Multi-Objective Flow Shop Scheduling Problem

The industries must preserve a rate of constant productivity; however, weaknesses appear at the level of production system which engenders high manufacturing costs. Scheduling is considered the most significant issue in the production system, the solution to that problem need complex methods to solve it. The goal of this paper is to establish three hybridization categories of the evolutionary methods ABC and PSO to solve multi-objective flow shop scheduling problem: Synchronous parallel hybridization using the weighted sum method of the fitness function, sequential hybridization using or not using the weighted sum method of the fitness function, and asynchronous parallel hybridization using the weighted sum method of the fitness function. Then to test these methods in an automotive multi-objective flow shop and to perform an in-depth comparison for verifying how the multi hybridization and the hybridization categories influence the resolution of multi-objective flow shop scheduling problems. The results are consistent with other studies that have shown that the multi hybridization improve the effectiveness of the algorithm.

Solving multi-objective flexible flow-shop scheduling problem using teaching-learning-based optimization embedded with maximum deviation theory

Solving multi-objective flexible flow-shop scheduling problem using teaching-learning-based optimization embedded with maximum deviation theory

A discrete multi-objective fireworks algorithm for flowshop scheduling with sequence-dependent setup times

Multi-objective flow shop scheduling problem with sequence-dependent setup times (MOFSP-SDST) is a class of important production scheduling problem with strong industry background. In this paper, a MOFSP-SDST mathematic model with the objectives of total production cost, makespan, mean flow time and mean idle time of machines is developed. To solve this multi-objective model, a novel multi-objective approach based on fuzzy correlation entropy analysis is proposed firstly. In this multi-objective approach, two types of objective function value sequences, namely the referenced function value sequence and comparable function value sequence, are constructed and mapped into two types of fuzzy sets by a modified relative membership function. The fuzzy correlation entropy coefficient between the two types of fuzzy sets is used to select better solutions in a multi-objective problem. A discrete multi-objective fireworks algorithm (DMOFWA) is proposed to address the MOFSP-SDST. In the DMOFWA, a new multi-objective approach is adopted to handle the multiple objectives and guide the search of the algorithm. Two kinds of machine learning strategies are adopted, namely opposition-based learning (OBL) and clustering analysis (CA). The OBL is employed to learn from the current search space and improve the exploration ability of DMOFWA, and the CA based on fuzzy correlation entropy coefficient is proposed to cluster firework individuals. Computational and statistical results show that the novel multi-objective approach, OBL and CA strategies can effectively improve the performance of DMOFWA. Furthermore, the results indicate that DMOFWA performs better than four state-of-the-art comparison algorithms.

Multi-objective flow shop scheduling with limited buffers using hybrid self-adaptive differential evolution

In this paper, a self-adaptive differential evolution (DE) algorithm is designed to solve multi-objective flow shop scheduling problems with limited buffers (FSSPwLB). The makespan and the largest job delay are treated as two separate objectives which are optimized simultaneously. To improve the performance of the proposed algorithm and eliminate the difficulty of setting parameters, an adaptive mechanism is designed and incorporated into DE. Moreover, various local search and hybrid meta-heuristic methods are presented and compared to improve the convergence. Through the analysis of the experimental results, the proposed algorithm is able to tackle the FSSPwLB problems effectively by generating superior and stable scheduling strategies.

Metaheuristics for solving a multi-objective flow shop scheduling problem with sequence-dependent setup times

Industries such as textiles, paints, chemicals, paper, drugs and pharmaceuticals operate as flow shops with sequence-dependent setup times (SDST). The sequence-dependent setup environment is characterised by the dependence of the setup time on the current job and also on the previous job processed on that machine. To further complicate the problem, in most real-life scenarios, decision-makers have to optimise more than one performance measure while scheduling jobs on machines. This work considers such a multi-objective SDST flow shop environment. The objectives considered in the present study are minimisation of makespan and minimisation of mean tardiness. Four metaheuristics, viz. non-dominated sorting genetic algorithm (NSGA) II, hybrid NSGA II, discrete particle swarm optimisation and hybrid discrete particle swarm optimisation, belonging to the category of intelligent optimisation techniques, are developed to obtain a set of Pareto-optimal solutions. The proposed metaheuristics are applied on benchmark SDST flow shop problems and their performance compared using different measures. Analysis of the results reveals that hybrid NSGA II outperforms the other three algorithms for all problem sizes considered in the present research. The results also indicate that hybridisation of the metaheuristics with variable neighbourhood search improves their performance.

Hybrid genetic algorithm for multi-objective flow shop scheduling problem with sequence dependent setup time: parameter design using Taguchi's robust design method

This paper determines optimal parameters of the proposed hybrid genetic algorithm for solving the multi-objective flow shop scheduling problem with sequence dependent setup time. The objectives considered in this study are minimisation of makespan and mean tardiness. In order to achieve these objectives, genetic algorithm is used in combination with a local search method to obtain Pareto-optimal solutions. The best parameters of the proposed algorithm are determined using the Taguchi's robust design method and the concept of utility index value. The set of parameters corresponding to the highest utility value is selected as the optimal parameters for the proposed algorithm. The analysis of results reveals that crossover type is the most influential parameter. The other parameters in the order of importance are mutation probability, crossover probability, mutation type and initial population. The hybrid genetic algorithm is applied to the benchmark problems of flow shop scheduling with sequence dependent setup time.

Hybrid genetic algorithm for multi-objective flow shop scheduling problem with sequence dependent setup time: parameter design using Taguchi's robust design method

Hybrid genetic algorithm for multi-objective flow shop scheduling problem with sequence dependent setup time: parameter design using Taguchi's robust design method

A hybrid genetic-gravitational search algorithm for a multi-objective flow shop scheduling problem

A hybrid genetic-gravitational search algorithm for a multi-objective flow shop scheduling problem

Genetic Algorithm and Based Particle Swarm Optimization Comparison for Solving a Flow-Shop Multiobjective Scheduling Problem in Pharmaceutical Industries

This paper deals with the comparison between two non-conventional optimization methods which are genetic algorithms and a new based particle swarm optimization one for multiobjective flow-shop scheduling problem resolution, in pharmaceutical industries. Results obtained by the comparison of these methods show the efficiency of the proposed based particle swarm optimization method.

A fitness assignment strategy based on the grey and entropy parallel analysis and its application to MOEA

In this paper, grey and entropy parallel analysis (GEPA) is presented as a new fitness-assignment strategy for solving multi-objective optimization problems. An evolutionary algorithm based on GEPA is proposed, and the grey and entropy parallel relational grade (GEPRG) is used as the fitness value to guide the development of the evolutionary algorithm. Under the analysis of the existing research work, the multi-objective flow shop scheduling problem is chosen as the application object and a flow shop scheduling model with five objectives is established. GEPA_GA, the GA based on GEPA, is described. To verify the performance of the proposed algorithm, GEPA_GA, together with the GA based on the random weighting method (RW_GA), NSGA-II and the GA based on g-dominance (g_GA), are used to optimize the multi-objective flow shop scheduling problem. The experimental data are analyzed by the statistical analysis method, the Kruskal–Wallis test, and three evaluation metrics. The influences of the five grey relational operators and the distinguishing coefficient on the algorithm performance are also studied. Experiments shows that the results obtained by GEPA_GA are better than those of RW_GA, NSGA-II and g_GA even under the situation that the combination of operator and distinguishing coefficient is not the best. It is proven that GEPA_GA works well in solving the multi-objective flow shop scheduling optimization problem, and GEPA is a promising strategy for solving multi-objective optimization problems.

A hybrid fruit fly optimisation algorithm to solve the flow shop scheduling problems with multi-objectives

Scheduling is one of the most important decision making processes in production and operations management. Different scheduling environments are addressed by the researchers in the literature. In this paper, flow shop scheduling problems are studied. Many researchers tackled with single objective flow shop scheduling problems with makespan objective only. However, scheduling problems consist of many conflict objectives. Hence, in this paper an attempt is made to solve the multi-objective flow shop scheduling problems. The objective is to minimise the weighted sum of makespan, mean flow time, earliness and tardiness. The flow shop scheduling problems have been proved to be NP-hard. Hence, the problems cannot be solved by the exact algorithms. In this paper, a relatively new meta-heuristic algorithm called as hybrid fruit fly optimisation algorithm is proposed to solve the multi-objective flow shop scheduling problems. Two constructive heuristics and a dispatching rule are incorporated with the fruit fly optimisation algorithm and hence the solution quality is improved. The proposed algorithm is tested on random problem instances and the results are compared with many other meta-heuristic algorithms. The results show that the proposed algorithm is more effective and better than other meta-heuristic algorithms.

Modelling and scheduling multi-objective flow shop problems with interfering jobs

This paper studies multi-objective flow shop scheduling problems with interfering jobs. That is, there are two sets of jobs and each of which has its own objective. Some jobs are scheduled so as to minimize makespan while the others are to minimize total tardiness. In this case, the problem was mathematically modeled by a mixed integer linear program. Then, a novel biogeography-based optimization was developed to solve the problem. To evaluate the algorithm, its performance was compared with three well-known algorithms in the literature. The results of the present study show that the proposed algorithm outperforms the other tested algorithms.

A Pareto block-based estimation and distribution algorithm for multi-objective permutation flow shop scheduling problem

Multi-objective flow shop scheduling plays a key role in real-life scheduling problem which attract the researcher attention. The primary concern is to find the best sequence for flow shop scheduling problem. Estimation of Distribution Algorithms (EDAs) has gained sufficient attention from the researchers and it provides prominent results as an alternate of traditional evolutionary algorithms. In this paper, we propose the pareto optimal block-based EDA using bivariate model for multi-objective flow shop scheduling problem. We apply a bivariate probabilistic model to generate block which have the better diversity. We employ the non-dominated sorting technique to filter the solutions. To check the performance of proposed approach, we test it on the benchmark problems available in OR-library and then we compare it with non-dominated sorting genetic algorithm-II (NSGA-II). Computational results show that pareto optimal BBEDA provides better result and better convergence than NSGA-II.

Multi-objective flow shop scheduling problem with stochastic parameters: fuzzy goal programming approach

Flow shop scheduling problem with stochastic parameters is dealt with in this paper. A multi-objective mixed integer linear programming model is proposed in this concern which can generate non-permutation schedules. To provide a more realistic model, process time and release time are considered stochastic variables with normal distribution. The objective functions are minimising three performance measures including maximum completion time (Makespan), total flow time and total tardiness. Chance constrained programming (CCP) approach and fuzzy goal programming (FGP) are applied to deal with the stochastic parameters and multi-objective function. Due to the complexity of the problem, we have implemented an adapted genetic algorithm to solve large-sized problem. According to the computational experiments, the GA can reach good-quality solutions in reasonable computational time, and can be used to solve large scale problems effectively.

Hybrid sliding level Taguchi-based particle swarm optimization for flowshop scheduling problems

A hybrid sliding level Taguchi-based particle swarm optimization (HSLTPSO) algorithm is proposed for solving multi-objective flowshop scheduling problems (FSPs). The proposed HSLTPSO integrates particle swarm optimization, sliding level Taguchi-based crossover, and elitist preservation strategy. The novel contribution of the proposed HSLTPSO is the use of a PSO to explore the optimal feasible region in macro-space, the use of a systematic reasoning mechanism of the sliding level Taguchi-based crossover to exploit the better solution in micro-space, and the use of the elitist preservation strategy to retain the best particles of multi-objective population for next iteration. The sliding level Taguchi-based crossover is embedded in the PSO to find the best solutions and consequently enhance the PSO. Using the systematic reasoning way of the Taguchi-based crossover with considering the influence of tuning factors α, β and γ is presented in this study to solve the conflicting problem of non-feasible solutions and to find the better particles. As a result, it exhibits a significant improvement in Pareto best solutions of the FSP. By combining the advantages of exploration and exploitation, from the computational experiments of the six test problems, the HSLTPSO provides better results compared to the existing methods reported in the literature when solving multi-objective FSPs. Therefore, the HSLTPSO is an effective approach in solving multi-objective FSPs.

An improved NSGA-II algorithm for multi-objective lot-streaming flow shop scheduling problem

Crossover and mutation operators in NSGA-II are random and aimless, and encounter difficulties in generating offspring with high quality. Aiming to overcoming these drawbacks, we proposed an improved NSGA-II algorithm (INSGA-II) and applied it to solve the lot-streaming flow shop scheduling problem with four criteria. We first presented four variants of NEH heuristic to generate the initial population, and then incorporated the estimation of distribution algorithm and a mutation operator based on insertion and swap into NSGA-II to replace traditional crossover and mutation operators. Last but not least, we performed a simple and efficient restarting strategy on the population when the diversity of the population is smaller than a given threshold. We conducted a serial of experiments, and the experimental results demonstrate that the proposed algorithm outperforms the comparative algorithms.

Hybrid Taguchi-Based Particle Swarm Optimization for Flowshop Scheduling Problem

A hybrid Taguchi-based particle swarm optimization (HTPSO) method is developed for solving multi-objective flowshop scheduling problems (FSPs). Search performance is improved using Taguchi-based crossover to avoid scheduling conflicts. Instead of the conventional approach to selecting dynamic weights randomly, which ignores very small weight values for the objective, a fuzzy inference system is used. A numerical example is given to demonstrate the application of the proposed HTPSO and its good performance. The numerical results show that the HTPSO effectively enhances particle swarm optimization. The improvement achieved by the HTPSO also exceeds that obtained by existing methods for finding Pareto optimum solutions for FSPs. Therefore, the proposed HTPSO method effectively solves multi-objective FSPs.