Multi-objective Flow Shop Scheduling 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.

Multi-objective flow shop scheduling system based on wireless network genetic algorithm from perspective of artificial intelligence

Multi-objective flow shop scheduling system based on wireless network genetic algorithm from perspective of artificial intelligence

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.

An energy-efficient multi-objective permutation flow shop scheduling problem using an improved hybrid cuckoo search algorithm

The flow shop scheduling problem has been widely studied in recent years, but the research on multi-objective flow shop scheduling with green indicators is still relatively limited. It is urgent to strengthen the research on effective methods to solve such interesting problems. To consider the economic and environmental factors simultaneously, the paper investigates the multi-objective permutation flow shop scheduling problems (MOPFSP) which minimizes the makespan and total carbon emissions. Since MOPFSP is proved to be a NP-hard problem for more than two machines. A hybrid cuckoo search algorithm (HCSA) is proposed to solve the problems. Firstly, a largest-order-value method is proposed to enhance the performance of HCS algorithm in the solution space of MOPFSP. Then, an adaptive factor of step size is designed to control the search scopes in the evolution phases. Finally, a multi-neighborhood local search rule is addressed in order to find the optimal sub-regions obtained by the HCSA. Numerical experiments show that HCSA can solve MOPFSP efficiently.

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

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.

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 decomposition based multiobjective genetic algorithm with adaptive multipopulation strategy for flowshop scheduling problem

Recently, the solution algorithm for multiobjective scheduling problems has gained more and more concerns from the community of operational research since many real-world scheduling problems usually involve multiple objectives. In this paper, a new evolutionary multiobjective optimization (EMO) algorithm, which is termed as decomposition based multiobjective genetic algorithm with adaptive multipopulation strategy (DMOGA-AMP), is proposed to addressmultiobjective permutation flowshop scheduling problems (PFSPs). In the proposed DMOGA-AMP algorithm, a subproblem decomposition scheme is employed to decompose a multiobjective PFSP into a number of scalar optimization subproblems and then introduce the decomposed subproblems into the running course of algorithm in an adaptive fashion, while a subpopulation construction method is employed to construct multiple subpopulations adaptively to optimize their corresponding subproblems in parallel. In addition, several special strategies on genetic operations, i.e., selection, crossover, mutation and elitism, are also designed to improve the performance of DMOGA-AMP for the investigated problem. Based on a set of test instances of multiobjective PFSP, experiments are carried out to investigate the performance of DMOGA-AMP in comparison with several state-of-the-art EMO algorithms. The experimental results show the better performance of the proposed DMOGA-AMP algorithm in multiobjective flowshop scheduling.

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 two-stage multiprocessor flow shop scheduling – a subgroup particle swarm optimisation approach

Flow shop production system – compared to other economically important production systems – is popular in real manufacturing environments. This study focuses on the flow shop with multiprocessor scheduling problem (FSMP), and develops an improved particle swarm optimisation heuristic to solve it. Additionally, this study designs an integer programming model to perform effectiveness and robustness testing on the proposed heuristic. Experimental results demonstrate a 10% to 50% improvement in the effectiveness of the proposed heuristic in small-scale problem tests, and a 10% to 40% improvement in the robustness of the heuristic in large-scale problem tests, indicating extremely satisfactory performance.