Fuzzy Flow Shop Scheduling Problem Research Articles

Evaluating the performance of various Heuristic Algorithms to Solve Flow Shop Scheduling Problem with Fuzzy Processing Time

This paper evaluates the performance of selected heuristic algorithms, namely Palmer’s and Campbell, Dudek and Smith (CDS) algorithm, Nawaz-Enscore-Ham (NEH), and SAI algorithms for minimizing the makespan in fuzzy flow shop scheduling problem (FSSP) with triangular fuzzy processing times. The algorithms are tested on sixteen benchmark problems with size up to 10 machines and 20 jobs using a MATLAB program. Computational results show that, despite its simplicity, the FNEH algorithm can provide the best results compared to other algorithms, regardless of the size of the problem.

RETRACTED: New algorithm for two-machine fuzzy flow shop scheduling problem with trapezoidal fuzzy processing time

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.

Multi-stage hybrid evolutionary algorithm for multiobjective distributed fuzzy flow-shop scheduling problem.

In the current global cooperative production mode, the distributed fuzzy flow-shop scheduling problem (DFFSP) has attracted much attention because it takes the uncertain factors in the actual flow-shop scheduling problem into account. This paper investigates a multi-stage hybrid evolutionary algorithm with sequence difference-based differential evolution (MSHEA-SDDE) for the minimization of fuzzy completion time and fuzzy total flow time. MSHEA-SDDE balances the convergence and distribution performance of the algorithm at different stages. In the first stage, the hybrid sampling strategy makes the population rapidly converge toward the Pareto front (PF) in multiple directions. In the second stage, the sequence difference-based differential evolution (SDDE) is used to speed up the convergence speed to improve the convergence performance. In the last stage, the evolutional direction of SDDE is changed to guide individuals to search the local area of the PF, thereby further improving the convergence and distribution performance. The results of experiments show that the performance of MSHEA-SDDE is superior to the classical comparison algorithms in terms of solving the DFFSP.

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

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

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

Improved makespan of the branch and bound solution for a fuzzy flow-shop scheduling problem using the maximization operator

Improved makespan of the branch and bound solution for a fuzzy flow-shop scheduling problem using the maximization operator

Extension of fuzzy Johnson algorithm to three machines fuzzy flow-shop scheduling problem

Extension of fuzzy Johnson algorithm to three machines fuzzy flow-shop scheduling problem

Minimizing energy consumption and tardiness penalty for fuzzy flow shop scheduling with state-dependent setup time

This paper addresses a flow shop scheduling problem in a production system where the machine setup times depend on their prior states. State-dependent setup times exist widely in thermal facilities such as boilers and furnaces. The fuzzy set theory is introduced to describe the uncertainty of processing times and due dates in this study. The goal of the proposed fuzzy flow shop scheduling problem is to dispatch jobs to the machines and to determine the job sequence and state transition of each machine to minimize energy consumption and tardiness. To most efficiently determine the impact of uncertainty, the problem is formulated based on accurate operations of fuzzy numbers, which differ from approximate calculations in the existing literature on scheduling. To solve the problem, two common pattern matching schemes and heuristics are proposed to be combined with the classical genetic algorithm. Computational experiments show that the proposed GA performs better than the random key GA method, especially for large problems. The numerical results also provide practical implications for the proposed problem. The state-dependent setup time constraint significantly influences the scheduling results. In addition, the objective can be improved by reducing the uncertainty of processing times and due dates.

An Improved Multiobjective PSO for the Scheduling Problem of Panel Block Construction

Uncertainty is common in ship construction. However, few studies have focused on scheduling problems under uncertainty in shipbuilding. This paper formulates the scheduling problem of panel block construction as a multiobjective fuzzy flow shop scheduling problem (FSSP) with a fuzzy processing time, a fuzzy due date, and the just-in-time (JIT) concept. An improved multiobjective particle swarm optimization called MOPSO-M is developed to solve the scheduling problem. MOPSO-M utilizes a ranked-order-value rule to convert the continuous position of particles into the discrete permutations of jobs, and an available mapping is employed to obtain the precedence-based permutation of the jobs. In addition, to improve the performance of MOPSO-M, archive maintenance is combined with global best position selection, and mutation and a velocity constriction mechanism are introduced into the algorithm. The feasibility and effectiveness of MOPSO-M are assessed in comparison with general MOPSO and nondominated sorting genetic algorithm-II (NSGA-II).

An ant colony system for solving fuzzy flow shop scheduling problem

During the past years, the flow shop has been regarded by many researchers and some extensive investigations have been done on this respect. Flow Shop includes n works performed on m machines in a same sequence. It is very difficult in the real world to determine the exact process time of an operation on a machine. Therefore, we consider in this article the process time as trapezoidal fuzzy numbers. Our purpose is that we obtain a sequence of works using such fuzzy numbers in order to minimize maximum fuzzy time of completion entire jobs or fuzzy makespan. We offered an optimization algorithm of Ant Colony System (ACS) to solve this problem. Finally, we present computational results for explanation and comparison with other articles in future.

Modified genetic algorithms for solving fuzzy flow shop scheduling problems and their implementation with CUDA

In this paper we propose an improved algorithm to search optimal solutions to the flow shop scheduling problems with fuzzy processing times and fuzzy due dates. A longest common substring method is proposed to combine with the random key method. Numerical simulation shows that longest common substring method combined with rearranging mating method improves the search efficiency of genetic algorithm in this problem. For application in large-sized problems, we also enhance this modified algorithm by CUDA based parallel computation. Numerical experiments show that the performances of the CUDA program on GPU compare favorably to the traditional programs on CPU. Based on the modified algorithm invoking with CUDA scheme, we can search satisfied solutions to the fuzzy flow shop scheduling problems with high performance.

Evaluate the fuzzy completion times in the fuzzy flow shop scheduling problems using the virus-evolutionary genetic algorithms

Abstract The scheduling problem with fuzzy processing times is concerned in this paper. A computational procedure is proposed to obtain the approximated membership function of fuzzy completion time for each schedule. The possibility and necessity measures will be used to propose the ranking concept of fuzzy numbers. Under this ordering among fuzzy numbers, we plan to minimize the fuzzy makespan and total weighted fuzzy completion time. Two types of formulations will be proposed by considering the different directions of ordering among fuzzy numbers. We shall apply the modified virus-evolutionary genetic algorithms to search for the best schedules. Three numerical examples are also provided to clarify the discussions in this paper using the commercial software MATLAB.

A New Artificial Immune System Algorithm for Multiobjective Fuzzy Flow Shop

In this paper a new artificial immune system (AIS) algorithm is proposed to solve multi objective fuzzy flow shop scheduling problems. A new mutation operator is also described for this AIS. Fuzzy sets are used to model processing times and due dates. The objectives are to minimize the average tardiness and the number of tardy jobs. The developed new AIS algorithm is tested on real world data collected at an engine cylinder liner manufacturing process. The feasibility and effectiveness of the proposed AIS is demonstrated by comparing it with genetic algorithms. Computational results demonstrate that the proposed AIS algorithm is more effective meta-heuristic for multi objective flow shop scheduling problems with fuzzy processing time and due date.

A New Artificial Immune System Algorithm for Multiobjective Fuzzy Flow Shop Problems

In this paper a new artificial immune system (AIS) algorithm is proposed to solve multi objective fuzzy flow shop scheduling problems. A new mutation operator is also described for this AIS. Fuzzy sets are used to model processing times and due dates. The objectives are to minimize the average tardiness and the number of tardy jobs. The developed new AIS algorithm is tested on real world data collected at an engine cylinder liner manufacturing process. The feasibility and effectiveness of the proposed AIS is demonstrated by comparing it with genetic algorithms. Computational results demonstrate that the proposed AIS algorithm is more effective meta-heuristic for multi objective flow shop scheduling problems with fuzzy processing time and due date.

Genetic algorithms and neighborhood search algorithms for fuzzy flowshop scheduling problems

Abstract This paper examines two fuzzy flowshop scheduling problems with fuzzy due dates. The membership function of a fuzzy due date assigned to each job represents the grade of satisfaction of a decision maker for the completion time of that job. One fuzzy flowshop scheduling problem is to maximize the minimum grade of satisfaction over given jobs, and the other is to maximize the total grade of satisfaction. First, we investigate the relations between the fuzzy scheduling problems and conventional scheduling problems. We next apply a genetic algorithm and neighborhood search algorithms (multi-start descent, taboo search and simulated annealing) to the fuzzy flowshop scheduling problems in order to examine the ability of these algorithms to find near optimal solutions. By computer simulations, it is shown that the maximization problem of the total satisfaction grade is even more tractable by these algorithms than that of the minimum satisfaction grade. Then, we show how the performance of these algorithms can be improved for the latter problem by utilizing problem domain knowledge. Finally, high performance of a hybrid genetic algorithm with neighborhood search is demonstrated.

Local search algorithms for flow shop scheduling with fuzzy due-dates

This paper formulates a fuzzy flow shop scheduling problem where the due-date of each job is given as a fuzzy set. The membership function of the fuzzy due-date corresponds to the grade of satisfaction of a completion time. The objective function of the formulated problem is to maximize the minimum grade of satisfaction over given jobs. Several local search algorithms including multi-start descent, simulated annealing and taboo search algorithms are applied to the problem. The performance of each algorithm is compared with one another by computer simulations on randomly generated test problems. It is shown by simulation results that some algorithms do not work well for the fuzzy flow shop scheduling problem. Thus, a new approach is proposed by changing the objective function. The effectiveness of this approach is demonstrated by computer simulations.