Fuzzy Flexible Job Shop Scheduling Research Articles

Fuzzy flexible job shop dynamic scheduling considering machine remaining processing capacity

Dynamic fuzzy flexible job shop scheduling (DFFJSP) is significant in the medical equipment industry. Generally the dynamic flexible job shop scheduling problem considers a single dynamic factor or ignores resources such as machine itself also plays a huge role in dealing dynamic issues. Thus fuzzy processing time and machine breakdowns are considered in this paper. In order to copy with two problems mentioned above, the model of DFFJSP containing two objectives of makespan and energy consumption is established. To solve this problem, the remaining processing capacity of broken machine is applied to weaken the impacts of machine breakdowns and a multi-objective evolutionary algorithm (NG-WOA) is proposed, which integrating NSGA-II and whale optimal algorithm (WOA). Furthermore, the joining of variable neighborhood search makes NG-WOA skip the local optimum solution in time. Through experiments, the validity of remaining processing capacity is verified in rescheduling scheme. Then NG-WOA algorithm is proven can find better solutions when compared with other two algorithms according to the distribution of pareto forefront solutions in benchmark examples. The results of this paper show that the remaining processing capacity has greater advantages in guaranteeing the stability of dynamic scheduling problems, because the machine’s processing capacity can be utilized fully compared to previous strategies for fault disturbances.

Multi-level guided evolution algorithm for solving fuzzy flexible job shop problem

Traditional flexible job shop scheduling problems (FJSP) are mostly limited to deterministic environments. In actual production, some uncertain factors lead to changes in job processing time. When solving multi-objective fuzzy flexible job shop scheduling problems (MOFFJSP), the existing evolutionary algorithms do not fully utilize information transfer between levels during the population evolution process. Therefore, a multi-level guided evolutionary (MLGE) optimization method is proposed to solve MOFFJSP. In the proposed MLGE method, decomposition and dominance techniques are combined to balance the diversity and convergence performance of the algorithm. Meanwhile, the idea of the Jaya algorithm approaching good individuals and distancing poor individuals is introduced. Combined with improved Jaya operator operations, it is used to guide individual evolution, preserving and inheriting solutions that perform well in terms of convergence and diversity. Finally, numerous experiments are carried out to evaluate the effectiveness of MLGE. The results show that MLGE can provide promising results for MOFFJSP. Additionally, it shows how MLGE outperforms other comparison algorithms in terms of convergence and variety of solutions.

A multi-objective fuzzy flexible job shop scheduling problem considering the maximization of processing quality

This paper analyzes practical production characteristics, including customer's stringent quality requirements and uncertain processing time in aircraft shaft parts manufacturing. Considering the above characteristics, we propose a multi-objective fuzzy aircraft shaft parts production scheduling problem considering the maximization of production quality. We define this problem as a multi-objective fuzzy flexible job shop scheduling problem (MO-fFJSP) with fuzzy processing time. To address this problem, we developed an improved multi-objective spider monkey optimization (IMOSMO) algorithm. IMOSMO integrates strategies such as genetic operators, variable neighborhood search and Pareto optimization theory on the framework of the conventional Spider Monkey Optimization (SMO) framework and discretize the continuous SMO algorithm to solve MO-fFJSP. To enhance the efficiency of the algorithm, we further adjust the sequence of the local leader learning phase and the global leader learning phase within the proposed IMOSMO framework. We conduct a comparative analysis between the performance of IMOSMO and NSGA-Ⅱ using 28 cases of varying scales. The computational results demonstrate the superiority of our algorithm over NSGA-Ⅱ in terms of both solution diversity and quality. Moreover, the performance of the proposed algorithm upgrades as the problem scale increases.

An enhanced memetic algorithm with hierarchical heuristic neighborhood search for type-2 green fuzzy flexible job shop scheduling

The green flexible job shop has received increasing attention due to the development of modern industry and the improvement of environmental protection awareness. Meanwhile, in customized manufacturing, the processing time cannot be exactly predicted. Therefore, it can be predicted by the type-2 fuzzy system, which can effectively characterize the uncertainty of processing time. This work aims to solve the green type-2 fuzzy flexible job shop problem (T2FJSP), considering minimizing total energy consumption (TEC) and the makespan simultaneously. Memetic algorithms are used for the T2FJSP, however, most of them do not allocate local search opportunities based on the quality of solutions, resulting in the waste of computing resources. In this paper, an enhanced memetic algorithm with hierarchical heuristic neighborhood search (EMAH) is proposed to overcome the shortcoming. EMAH has the following features: (i) a cooperative heuristic initialization is designed by combining six heuristics to generate high-quality solutions; (ii) a hierarchical heuristic neighborhood search strategy is proposed to better enhance the exploitation by allocating computing resources based on the quality of the solutions; and (iii) an energy-saving strategy is designed to decrease the makespan and TEC. The effectiveness of EMAH is verified by comparing it with the state-of-the-art algorithms in a T2FJSP benchmark. The results indicate the superiority of EMAH in solving the T2FJSP.

Q-learning based multi-objective immune algorithm for fuzzy flexible job shop scheduling problem considering dynamic disruptions

Confronted with complex industrial environments, dynamic disruptions like new job arrival and machine breakdown bring significant challenges to the robustness and stability of the manufacturing process, making the static production depart from the original scheduling scheme. To address this problem, a flexible job shop scheduling problem with fuzzy processing time, dynamic disruptions, and variable processing speeds is considered simultaneously. As well as three objectives of maximum completion time, total energy consumption, and average agreement index are demonstrated in this study. Then, a predictive-reactive dynamic/static rescheduling model is developed, where the off-line based mixed integer linear programming model and the on-line based rescheduling heuristics are proposed. Next, a multi-objective immune algorithm combined with a Q-learning algorithm (Q-MOIA) is developed. In the proposed algorithm, an active decoding heuristic based on the interval insertion mechanism is used to optimize the initial solutions. After that, the clonal selection-based immune algorithm and the Q-learning algorithm are adopted to improve the exploration and exploitation capabilities, respectively, where four objective-driven neighborhood structures are designed. Eventually, extensive computational experiments were conducted on 27 instances under static and dynamic scenarios to demonstrate the superiority and stability of the proposed predictive-reactive dynamic/static rescheduling model and the Q-MOIA. Comparative analysis with four state-of-the-art approaches revealed that proposed Q-MOIA outperformed in approximately 51.9, 66.7, and 83.3 % of the instances for the three multi-objective metrics.

An improved quantum particle swarm algorithm for solving multi-objective fuzzy flexible job shop scheduling problem

Due to the dynamic nature of work conditions in the manufacturing plant, it is difficult to obtain accurate information on process processing time and energy consumption, affecting the implementation of scheduling solutions. The fuzzy flexible job shop scheduling problem with uncertain production parameters has not yet been well studied. In this paper, a scheduling optimization model with the objectives of maximum completion time, production cost and delivery satisfaction loss is developed using fuzzy triangular numbers to characterize the time parameters, and an improved quantum particle swarm algorithm is proposed to solve it. The innovations of this paper lie in designing a neighborhood search strategy based on machine code variation for deep search; using cross-maintaining the diversity of elite individuals, and combining it with a simulated annealing strategy for local search. Based on giving full play to the global search capability of the quantum particle swarm algorithm, the comprehensive search capability of the algorithm is enhanced by improving the average optimal position of particles. In addition, a gray target decision model is introduced to make the optimal decision on the scheduling scheme by comprehensively considering the fuzzy production cost. Finally, simulation experiments are conducted for test and engineering cases and compared with various advanced algorithms. The experimental results show that the proposed algorithm significantly outperforms the compared ones regarding convergence speed and precision in optimal-searching. The method provides a more reliable solution to the problem and has some application value.

A teaching-learning-based optimization with feedback for L-R fuzzy flexible assembly job shop scheduling problem with batch splitting

A teaching-learning-based optimization with feedback for L-R fuzzy flexible assembly job shop scheduling problem with batch splitting

A hybrid evolutionary immune algorithm for fuzzy flexible job shop scheduling problem with variable processing speeds

In this study, a fuzzy flexible job shop scheduling problem with variable processing speeds is considered. To address this problem, a multi-objective hybrid evolutionary immune algorithm (HEIA) is proposed, where the fuzzy maximum completion time (makespan) and fuzzy total energy consumption are optimized simultaneously. In the proposed HEIA, a two left-shift heuristic based active decoding method is proposed to optimize the fuzzy makespan. Then, two hybrid evolutionary strategies are used to separately enhance the exploration ability and exploitation ability, where a reference point-based angle selection strategy is incorporated to optimize the search mechanism. For the first evolutionary strategy, a Pareto similar information-based crossover operator is adopted to improve population diversity. For the second evolutionary strategy, a deep local search mechanism and four objective-driven neighborhood structures are developed. Finally, five types of instances are generated to verify the effectiveness of the proposed HEIA.

An efficient hybrid optimization method for Fuzzy Flexible Job-Shop Scheduling Problem: Steady-state performance and analysis

Due to the uncertainty pervasive in manufacturing and production systems, a crisp processing time is no longer appropriate. Thus, the flexible job-shop scheduling issue comes with fuzzy processing times. Fuzzy numbers are used to depict the uncertainty associated with processing times. This paper proposes a Hybrid Fuzzy Flexible job-shop Scheduling Approach (HFFSA) for addressing the Fuzzy Flexible Job-Shop Scheduling Problem (FFJSSP). An encoding-and-decoding scheme is employed for initializing the population. HFFSA incorporates four operators to enhance the quality of solutions: the Different Positions Shuffling (DPS) operator, the Randomly Selected Positions Shuffling (RSPS) operator, the Block Shuffling (BS) operator and the Inversion Mutation (IM) operator. The DPS operator shuffles the different positions between a randomly generated solution and the current one. On the other hand, the RSPS shuffles random positions to maintain population diversity. The BS and IM work on a subset of positions from the current solution to obtain near-optimal solutions. Furthermore, we integrate the pair-wise local search strategy with HFFSA to improve the best solution. HFFSA is compared to twenty-five algorithms using Lei and remanufacturing benchmarks. Several statistical measures are employed, like the CPU time, best, average and worst fuzzy makespan values, boxplots and the Wilcoxon signed-rank test. The statistical analyses affirm the superiority of the proposed HFFSA.

Digital twin-oriented collaborative optimization of fuzzy flexible job shop scheduling under multiple uncertainties

Digital twin-oriented collaborative optimization of fuzzy flexible job shop scheduling under multiple uncertainties

Improved Multiverse Optimization Algorithm for Fuzzy Flexible Job-Shop Scheduling Problem

An improved multiverse optimization (IMVO) algorithm is proposed herein for the fuzzy flexible job-shop scheduling problem pertaining to the non-deterministic polynomial-time hard (NP-hard) problem. First, we designed a hybrid initialization method to improve the quality of the initial solution, and thereafter, introduced a self-crossing technique along with an insert-based heuristic algorithm to simulate the process of exchanging objects between black/white holes and wormholes, respectively. Second, a universe selection mechanism is proposed to reduce the possibility of the algorithm falling into a local optimum. Third, four kinds of neighborhood structures were designed to improve the local search ability of the algorithm. In the decoding operation, we adopted the shift-left strategy to completely utilize the idle time of the machine. Finally, numerous experiments were conducted on the three benchmark test sets of various types and sizes to investigate the performance of the proposed IMVO algorithm. The experimental results demonstrated the effectiveness of the algorithm, especially in large-scale instances, displaying a strong superiority with an average maximum enhancement efficiency of 50.44%.

A Global Neighborhood with Hill-Climbing Algorithm for Fuzzy Flexible Job Shop Scheduling Problem

The Flexible Job Shop Scheduling Problem (FJSSP) continues to be studied extensively to test new metaheuristics and because of its closeness to current production systems. A variant of the FJSSP uses fuzzy processing times instead of fixed times. This paper proposes a new algorithm for FJSSP with fuzzy processing times called the global neighborhood with hill-climbing algorithm (GN-HC). This algorithm performs solution exploration using simple operators concurrently for global search neighborhood handling. For local search, random restart hill-climbing is applied at each solution to find the best machine for each operation. For the selection of operations in hill climbing, a record of the operations defining the fuzzy makespan is employed to use them as a critical path. Finally, an estimation of the crisp makespan with the longest processing times in hill climbing is made to improve the speed of the GN-HC. The GN-HC is compared with other recently proposed methods recognized for their excellent performance, using 6 FJSSP instances with fuzzy times. The obtained results show satisfactory competitiveness for GN-HC compared to state-of-the-art algorithms. The GN-HC implementation was performed in Matlab and can be found on GitHub (check Data Availability Statement at the end of the paper).

Super Heuristic Genetic Algorithm for Fuzzy Flexible Job Shop Scheduling

Super Heuristic Genetic Algorithm for Fuzzy Flexible Job Shop Scheduling

Hybrid Memetic Algorithm to Solve Multiobjective Distributed Fuzzy Flexible Job Shop Scheduling Problem with Transfer

Most studies on distributed flexible job shop scheduling problem (DFJSP) assume that both processing time and transmission time are crisp values. However, due to the complexity of the factory processing environment, the processing information is uncertain. Therefore, we consider the uncertainty of processing environment, and for the first time propose a multiobjective distributed fuzzy flexible job shop scheduling problem with transfer (MO-DFFJSPT). To solve the MO-DFFJSPT, a hybrid decomposition variable neighborhood memetic algorithm (HDVMA) is proposed with the objectives of minimizing the makespan, maximum factory load, and total workload. In the proposed HDVMA, the well-designed encoding/decoding method and four initialization rules are used to generate the initial population, and several effective evolutionary operators are designed to update populations. Additionally, a weight vector is introduced to design high quality individual selection rules and acceptance criteria. Then, three excellent local search operators are designed for variable neighborhood search (VNS) to enhance its exploitation capability. Finally, a Taguchi experiment is designed to adjust the important parameters. Fifteen benchmarks are constructed, and the HDVMA is compared with four other famous algorithms on three metrics. The experimental results show that HDVMA is superior to the other four algorithms in terms of convergence and uniformity of non-dominated solution set distribution.

A reinforcement learning based RMOEA/D for bi-objective fuzzy flexible job shop scheduling

The flexible job shop scheduling problem (FJSP) is significant for realistic manufacturing. However, the job processing time usually is uncertain and changeable during manufacturing. This paper presents a multi-objective FJSP with fuzzy processing time (MOFFJSP) for optimizing the makespan and total machine workload as objectives. To solve the MOFFJSP, a MOEA/D based on reinforcement learning named RMOEA/D is proposed. RMOEA/D can be featured as: (i) an initial strategy with three rules is used to get a high-quality initial population; (ii) a parameter adaption strategy based on Q-learning is proposed to guide the population choose the best parameter to increase diversity; (iii) a variable neighborhood search based on reinforcement learning is designed to lead the solution to choose the right local search method; and (iv) an elite archive is used to improve the usage rate of the abandoned historical solution. RMOEA/D is compared with five well-known realted methods, i.e., MOEA/D, NSGA-II, MOEA/D-M2M, NSGA-III and IAIS on three benchmark suites. The results show that RMOEA/D outperforms these five state-of-art algorithms.

A Hybrid Cooperative Coevolution Algorithm for Fuzzy Flexible Job Shop Scheduling

Flexible scheduling is one of the most significant core techniques for intelligent manufacturing systems. Realization of an optimized schedule through flexible resources assignment is critical to the application and popularization of flexible scheduling, especially in uncertain manufacturing environments. In this paper, we consider flexible job shop scheduling with uncertain processing time represented by fuzzy numbers, which is named fuzzy flexible job shop scheduling. We propose an effective hybrid cooperative coevolution algorithm (hCEA) for the minimization of fuzzy makespan. The hCEA combines particle swarm optimization with the genetic algorithm to improve the convergence ability. A parameter self-adaptive strategy is applied to the problems with different scale effectively as well. Five benchmarks and three large-scale problems with fuzzy processing time are adopted to test the hCEA. Computational results show that the hCEA performs better than the existing methods from the literature.

Solving multi-objective fuzzy flexible job shop scheduling problem using MABC algorithm

Solving multi-objective fuzzy flexible job shop scheduling problem using MABC algorithm

Operating Room Surgery Scheduling with Fuzzy Surgery Durations Using a Metaheuristic Approach

Operating room (OR) surgery scheduling is a challenging combinatorial optimization problem that determines the operation start time of every surgery to be performed in different surgical groups, as well as the resources assigned to each surgery over a schedule period. One of the main challenges in health care systems is to deliver the highest quality of care at the lowest cost. In real-life situations, there is significant uncertainty in several of the activities involved in the delivery of surgical care, including the duration of the surgical procedures. This paper tackles the operating room surgery scheduling problem with uncertain surgery durations, where uncertainty in surgery durations is represented by means of fuzzy numbers. The problem can be considered as a Fuzzy Flexible Job-shop Scheduling Problem (FFJSP) due to similarities between operating room surgery scheduling with uncertain surgery durations and a multi-resource constraint flexible job-shop scheduling problem with uncertain processing times. This research handles both the advanced and allocation scheduling problems simultaneously and provides an Ant Colony Optimization (ACO) metaheuristic algorithm which utilized a two-level ant graph to integrate sequencing jobs and allocating resources at the same time. To assess the performance of the proposed method, a computational study on five test surgery cases is presented, considering both deterministic and fuzzy surgery durations to enhance the significance of the study. The results of this experiment demonstrated the effectiveness of the proposed metaheuristic algorithm.

A flower pollination algorithm for flexible job shop scheduling with fuzzy processing time

Considering the uncertainty in real manufacturing workshops, the fuzzy flexible job shop scheduling problem (fFJSP) is addressed, in which the triangular fuzzy number is used to represent the processing time. A discrete flower pollination algorithm (DFPA) is proposed in this paper to minimize the maximum fuzzy completion time. Flower pollination algorithm (FPA) is inspired by the pollination process of flowering plants, which realizes global search and local search by means of cross-pollination and self-pollination of flowers in nature. DFPA extends to FPA by introducing discrete operator during iterations. Simulation results on instances validate the effectiveness and feasibility of this algorithm compared with particle swarm optimization.

A hybrid multi-verse optimization for the fuzzy flexible job-shop scheduling problem

In this paper, a novel algorithm called hybrid multi-verse optimization (HMVO) is proposed to address the fuzzy flexible job-shop scheduling problem (FFJSP). Firstly, path relinking technique is introduced to mimic the process of swapping objects through the black/white holes. Secondly, a mixed phase which integrates insertion-based heuristic and path relinking technique is incorporated into the algorithm to enlarge the search space. Thirdly, pairwise-based local search is proposed as a hybrid strategy to improve the solution quality. Finally, extensive experiments are conducted on three benchmark sets to investigate the performance of the proposed HMVO. Computational results and comparisons with some existing meta-heuristics demonstrate the competitive efficiency and effectiveness of the proposed algorithm.