فهرست مطالب نعیمه باقری راد

In this paper, the problem of real-time scheduling of multi-factury production network in the smart manufacturing system with job shop environment is studied. In this smart manufacturing system, a number of independently owned factories are joined together to form a multi-agent production network, which is also called a virtual production network. In such a network, each factory focuses on its interests and communicates with each other by sharing information such as machine breakdowns and job transfers. Therefore, it can be stated that studying the problem of distributed scheduling in the environment of smart factories is important and will have a significant effect on obtaining desirable and ideal results. At first, a bi-objective mixed integer linear programming model is presented; then an approach to solve the dynamic real-time scheduling problem is proposed. Considering the successful applications of the Lagrangian relaxation algorithm in solving scheduling problems, in this research, the improved Lagrangian relaxation algorithm is used to solve the problem. To examine the performance of the proposed algorithm, its results were compared with solving the original model that was solved by the augmented epsilon constraint method. The obtained results showed that the proposed Lagrangian relaxation algorithm has a better performance than the augmented epsilon constraint method

In this research, an integrated scheduling problem of job shop systems with an assembly stage and transportation to minimize the total tardiness time is studied. In this problem, the parts are processed in a job shop system and then assembled in the assembly stage. Ultimately, the products are shipped in packages to customers. Setup time is assumed to depend on sequence. At first, a mixed-integer linear model is developed. Since the problem is NP-hard, a hybrid imperialist competitive and simulated annealing (ICA-SA) algorithm is proposed to solve the problems with the medium and large sizes. To validate the performance of the proposed algorithm, results are compared to an imperialist competitive algorithm and a hybrid imperialist competitive and tabu search (ICA-TS) algorithm. Analysis of variance random block design is used to compare the results of the algorithms. P-values of algorithms and blocks in this test are smaller than the significance level of 0.05. The computational results show that the proposed hybrid algorithm achieves better performance than the imperialist competitive algorithm and hybrid imperialist competitive and tabu search.

In this paper, a hybrid model of fuzzy multi-criteria decision making is presented for the cases a large number of criteria, relationships or affiliation exists are between them. In this model, the DEMATEL method is used to determine the relationships between criteria and analytic network process (ANP) method to identify the weight of each criteria and the VIKOR method for optimizing the multi-criteria of ranking the best supplier. In this study, in order to cover the cases of decisions with ambiguous scenarios, rather than using absolute numbers, the linguistic variables are used. The proposed hybrid model can direct managers and experts organizations In order to improve their choices, especially when there are numerous criteria and there is dependence between them under conditions of uncertainty.The proposed method, reduces number of pairwise comparison matrix and volume of calculations and also increases calculation speed and the complexity of the problem is reduced. In order to showing the solving process a numerical example is presented. The computational results show that proposed hybrid method, in addition to ranking alternatives calculates the final weight of each criterion.

In this paper, a flexible job shop scheduling problem (FJSP) with assembly operations and sequence dependent setup time is studied. In this problem, each product is produced from assembling a set of several different parts. At first, the parts are processed in a flexible job shop system. Setup time is needed when a machine starts processing the parts or it changes items. Then in the second stage, the parts are assembled and products are produced. The assembly operation cannot be started for a product until the set of parts are completed in machining operations. In this paper, we presented a mathematical model for a flexible job shop scheduling problem with assembly operations and sequence dependent setup time. The objective is to minimize the completion time of all products (makespan). Since the problem is NP-hard, one particle swarm optimization (PSO) algorithm and two hybrid metaheuristic algorithms based on particle swarm optimization are proposed. The proposed hybrid algorithms are called, respectively, hybrid particle swarm optimization with a variable neighborhood search algorithm (HPSOVNS) and hybrid particle swarm optimization with a simulated annealing algorithm (HPSOSA). In these hybrid algorithms, we used particle swarm optimization (PSO) algorithm for global exploration at search space and variable neighborhood search (VNS)/ simulated annealing (SA) algorithm for local search at around solutions obtained in the each iteration. In order to evaluate and validate the performance of the proposed algorithms, we are designed numerical experiments and results are compared with hybrid genetic algorithm and tabu search (HGATS) presented by Li and Gao. For this purpose, the proposed mathematical model is coded in GAMS software and the proposed metaheuristic algorithms are coded in MATLAB software. For obtaining better and more sustainable results of the metaheuristic algorithms, Minitab software was used to design the experiments and assign the best level to the size of problems. For the problems in the small size, the optimal solution is obtained by GAMS software. Then a randomized complete block design considered to compare the ability of algorithms at finding the best solution for medium and large problems. Computational results revealed that for medium and large problems the HPSOVNS algorithm outperforms the HPSOSA, PSO and HGATS algorithms.