جستجوی مقالات مرتبط با کلیدواژه "job shop scheduling" در نشریات گروه "مواد و متالورژی"

In modern production environments where perishable products are manufactured in a job shop system, machine reliability is of utmost importance, and delays during job processing are not acceptable. Therefore, it becomes crucial to consider machines maintenance activities and set upper bounds for interruptions between job operations. This paper tackels the Flexible Job Shop Scheduling Problem taking into account these factors. The study is conducted in two phases. Initially, a novel Mixed-Integer Linear Programming (MILP) model is elaborated for the problem and juxtaposed with the Benders decomposition method to assess computational efficiency. Nevertheless, owing to the elevated complexity of the problem, attaining an optimal solution for instances of realistic size poses an exceptionally challenging task using exact methods. Thus, in the second stage, a Discrete Grey Wolf Optimizer (D-GWO) as an alternative approach to solve the problem is proposed. The performance of the extended algorithms is evaluated through numerical tests. The findings indicate that for small instances, the Benders decomposition method outperforms other approaches. Nevertheless, as the instances grow in size, the efficiency of exact methods diminishes, and the Discrete Grey Wolf Optimizer (D-GWO) performs better under such conditions. Overall, this study highlights the importance of considering machines maintenance activities and interruptions in scheduling of job shop for the production of perishable products. The proposed model and Benders decomposition method in small instances, and the metaheuristic algorithm in large instances provide viable solutions.

The present paper extends the idea of job shop scheduling problem with resting constraints to the train scheduling problem with the Muslim praying considerations. For this purpose, after proposing the new mathematical model, a heuristic algorithm based on the Electromagnetism-Like algorithm (EM) which is well adjusted to scheduling problems is employed to solve the large-size practical cases. The effectiveness of the proposed algorithm is then validated by comparing with optimum solution using small-size instances and simulated annealing algorithm, and Particle swarm Optimization (PSO) using medium and large-size instances. At the end, a practical case from Iranian railway network is studied and the results are reported. The results indicate that in the case of considering the Muslim praying constraint, the ratios of total tardiness of trains, and the total praying times are 14.5%, and 3.5%, respectively, while in the case of relaxing this constraint; the first ratio reduces to 12.3%. This result demonstrates that the proposed algorithm is able to schedule the praying times so that in many cases the trains with different directions meet each other during the praying times.

This paper presents a new multi objective job shop scheduling with sequence-dependent setup times. The objectives are to minimize the makespan and sum of the earliness and tardiness of jobs in a time window. A mixed integer programming model is developed for the given problem that belongs to NP-hard class. In this case, traditional approaches cannot reach to an optimal solution in a reasonable time. Thus, we propose an efficient multi-objective hybrid genetic algorithm.we assign fitness based dominance relation and weighted aggregate in the genetic algorithm and local search, respectively.We take a variable neighborhood search algorithm as a local improving procedure in the proposed algorithm to the best individuals in the population of GA every specific number generations. To prove the efficiency of our proposed HGA, a number of test problems are solved. Its reliability based on some comparison metrics is compared with a prominent multi-objective evolutionary algorithm, namely SPEA-II. The computational results show that the proposed HGA outperforms the SPEAII algorithm.