Papers by Keyword: Flexible Job-Shop Scheduling

Abstract: The flexible job-shop scheduling algorithm with the high computational complexity is important in both fields of production management and combinatorial optimization. We developed an improved genetic approach for the flexible job-shop scheduling problem (FJSP), since it is quite difficult to achieve an optimal solution to this problem within reasonable computation time. A chromosome representation combines both routing and sequencing information is represented with genetic algorithms (GAs) commonly optimize the problem by minimizing an objective, makespan. Our algorithm selects the chromosome according to different objectives, and crossover each other to maintain the species diversity. New crossover and the mutation operators utilize the critical path with certain probability to modify assignment and sequence for preventing being trapped in a local optimum. The results obtained from the computational study have shown that the proposed algorithm is an effective approach for the FJSP.

Abstract: This paper presents a hybrid algorithm to address the flexible job-shop scheduling problem (FJSP). Based on Differential Evolution (DE), a global search method is introduced in the hybrid algorithm, where variations are made to the mutation and crossover operators in DE, according to the quantum rotation gate. And an Interchange-based local search method is further adopted in the proposed algorithm to gain a better performance. Experiments are performed to show the efficiency of the proposed algorithm.

Abstract: In order to optimize the multi-objective flexible job shop scheduling problem (FJSP) roundly, multi-objective mathematical model is established, including makespan, maximal workload, total workload and total tardiness. In this paper a dual coding method which takes operation arrangement and machine selection into account is employed, and designed new crossover and mutation methods, the infeasible solutions are avoided. In order to deal with the multi-objective optimization problem, a new non-dominated sorting method is introduced, which can get the Pareto optimal solutions quickly and correctly by dividing the whole population into three parts. Example shows that the algorithm is efficient.

Abstract: To solve the multi-objective flexible job shop scheduling problem, an improved non-dominated sorting genetic algorithm is proposed. Multi-objective mathematical model is established, four objectives, makespan, maximal workload, total workload and total tardiness are considered together. In this paper a dual coding method is employed, and infeasible solutions were avoided by new crossover and mutation methods. Pareto optimal set was taken to deal with multi-objective optimization problem, in order to reduce computational complexity, the non-dominated sorting method was improved. The niche technology is adopted to increase the diversity of solutions, and a new self adaptive mutation rate computing method is designed. The proposed algorithm is tested on some instances, and the computation results demonstrate the superiority of the algorithm.

Abstract: A mathematical model was constructed with two objectives. A two-stage hybrid algorithm was developed for solving this problem. At first, the man-hour optimization based on genetic algorithm and dynamic programming method, the model decomposes the flow shop into two layers: sub-layer and patrilineal layer. On the basis of the man-hour optimization，A simulated annealing genetic algorithm was proposed to optimize the sequence of operations. A new selection procedure was proposed and hybrid crossover operators and mutation operators were adopted. A benchmark problem solving result indicates that the proposed algorithm is effective.

Abstract: Scheduling for the flexible job shop is very important in fields of production management. To solve the multi–objective optimization in flexible job shop scheduling problem (FJSP), the FJSP multi-objective optimization model is constructed. The cost, quality and time are taken as the optimization objectives. An improved strength Pareto evolutionary algorithm (SPEA2+) is put forward to optimize the multi-objective optimization model parallelly. The algorithm uses a new model of a Multi-objective genetic algorithm that includes more effective crossover and could obtain diverse solutions in the objective and variable spaces to archive the Pareto optimal sets for FJSP multi-objective optimization. Then an approach based on fuzzy set theory was developed to extract one of the Pareto-optimal solutions as the best compromise one. The optimization results were compared with those obtained by NSGA-II and POS. At last, an instance of flexible job shop scheduling problem in automotive industry is given to illustrate that the proposed method can solve the multi-objective FJSP effectively.

Abstract: A hybrid genetic algorithm based on Pareto was proposed and applied to flexible job shop scheduling problem (FJSP) with multi-objective, and the multi-objective FJSP optimization model was built, where the make-span and the machine utilization rate were concerned. The algorithm embeds Pareto ranking strategy into Pareto competition method. The operation-based encoding and an active scheduling decoding method are employed. In order to promote solution diversity, the niche technology and many kinds of crossover operations are used. Pareto filter saves the optimum individual occurring in the course of evolution, which avoids losing the optimum solutions. Three simulation experiments are carried out to illustrate that the proposed method could solve multi-objective job shop scheduling problem effectively.

Abstract: Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where the same operation could be processed on more than one machine. It is quite difficult to achieve optimal or near-optimal solutions with single traditional optimization approach because the multi objective FJSP has the high computational complexity. An novel hybrid algorithm combined variable neighborhood search algorithm with genetic algorithm is proposed to solve the multi objective FJSP in this paper. An external memory is adopted to save and update the non-dominated solutions during the optimization process. To evaluate the performance of the proposed hybrid algorithm, benchmark problems are solved. Computational results show that the proposed algorithm is efficient and effective approach for the multi objective FJSP.

Abstract: Flexible job shop scheduling problem (FJSP) is an extended traditional job shop scheduling problem, which more approximates to real scheduling problems. This paper presents a multi-objective genetic algorithm (GA) based on immune and entropy principle to solve the multi-objective FJSP. In this improved multi-objective GA, the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Advanced crossover and mutation operators are proposed to adapt to this special chromosome structure. The proposed algorithm is evaluated on three representative instances and the computational results and comparison with some other approaches show that the proposed multi-objective algorithm is effective and potential.

Abstract: Classical flexible job-shop scheduling problem (FJSP) does not consider the transportation time of jobs movement among different machines, which reduces the potential significance of its practical applications. This paper defines an FJSP problem with transportation time incurred by movement of jobs with one Automated Guided Vehicle (AGV) and one Load/Unload (L/U) station. A filtered beam search (FBS) based meta-heuristic algorithm is presented to solve this problem. The detailed procedure of the algorithm is described, and an example is shown to illustrate the algorithm. Finally, preliminary experimental results with comparisons of other dispatching rules demonstrate the feasibility and effectiveness of the proposed algorithm for the FJSP with transportation time.