in this paper, for the first time in the literature, we integrated production scheduling decisions and WIPs planning decisions in a distributed environment. We study the distributed and flexible job shop scheduling problem (DFJSP) which involves the scheduling of jobs (products) in a distributed manufacturing environment, under the assumption that the shop floor of each factory/cell is configured as a flexible job shop. It is also assumed that the work-in-process (WIP) parts can be bought from the market instead of manufacturing them in-house, and they also can be sold in the market instead of PROCESSING their remaining operations and selling the end products. Moreover, the PROCESSING TIMES of the operations can be decreased by paying a cost. However, there are a lower limit and an upper limit for the PROCESSING time of each operation. We formulate this general problem as a mixed integer linear programming (MILP) model. A fast heuristic algorithm is also developed to obtain good solutions in very short time. The algorithm is tested on some problem instances in order to evaluate its performance. Computational results show that the proposed heuristic is a computationally efficient and practical approach.

Scheduling for flexible flowshop environments is generally limited by resources such as manpower and machines. However, the majority of efforts tackle machines as the only constrained resource. This paper aims to investigate the problem of scheduling in flexible flowshop environments considering different skills as human resource constraints to minimize the total completion time. In this way, a mathematical model of complex integer linear programming is presented for solving small-sized problems in a reasonable computational time. In addition, due to the NP-hard nature of the problem, a whale hybrid optimization algorithm is tuned to solve the problem in large-sized dimensions. In order to evaluate the performance of the proposed optimization algorithm, the results are compared with five known optimization algorithms in the research background. All evaluations and results show the good performance of the whale hybrid algorithm. Especially, the final solution of the proposed algorithm shows a 0.75% deviation of the best solution in solving different instances on large-scale sizes. However, the genetic algorithm, memetic global and local search algorithm, and hybrid salp swarm algorithm are in the next ranks with 3.31, 3.52, and 4.02 percent respectively. In addition, proper discussions and managerial insights are provided for the relevant managers.

In this paper, the flexible job shop scheduling problem with machine flexibility and controllable process TIMES is studied. The main idea is that the PROCESSING TIMES of operations may be controlled by consumptions of additional resources. The purpose of this study is to find the best trade-off between PROCESSING cost and delay cost in order to minimize the total costs. The proposed model, flexible job shop scheduling with controllable PROCESSING TIMES (FJCPT), is formulated as an integer non-linear programming (INLP) model and then it is converted into an integer linear programming (ILP) model. Due to NP-hardness of FJCPT, conventional analytic optimization methods are not efficient. Hence, in order to solve the problem, a Scatter Search (SS), as an efficient metaheuristic method, is developed. To show the effectiveness of the proposed method, numerical experiments are conducted. The efficiency of the proposed algorithm is compared with that of a genetic algorithm (GA) available in the literature for solving FJSP problem. The results showed that the proposed SS provide better solutions than the existing GA.

This paper presents a mathematical model for a flow shop scheduling problem consisting ofm machine and n jobs with fuzzy PROCESSING TIMES that can be estimated as independent stochastic or fuzzy numbers. In the traditional flow shop scheduling problem, the typical objective is to minimize the makespan). However, two significant criteria for each schedule in stochastic models are: expectable makespan and the probability of minimizing the makespan. These criteria can be considered for fuzzy problems as well. In this paper, we propose a solution for the fuzzy model by the use of fuzzy logic based on developing the model presented by MacCahon [18].

Flexible job shop scheduling has drawn the attention of many researchers. This paper examines the flexible job shop scheduling problem, considering sequence dependent PROCESSING TIMES and assembly case. The aim is to determine the operations assignment to a set of machines and to identify their PROCESSING priority to minimize total completion time of the final products. Meanwhile and since the problem is not studied in the subject literature; the mathematical model of the problem is developed, initially. Due to the NP-hardness, being the problem, three meta-heuristic algorithms, including genetic algorithms, simulated annealing and a hybrid algorithm that combines the two previous algorithms is presented to solve the problem. Finally, a comparison is made among these algorithms. The computational results show that the hybrid algorithm gives a better performance than the others.

This paper presents a predictive robust and stable approach for a two-machine flow shop scheduling problem with machine disruption and uncertain job PROCESSING time. Indeed, a general approach is proposed that can be used for robustness and stability optimization in an m-machine flow shop or job shop scheduling problem. The robustness measure is the total expected realized completion time. The expected sum of squared aberration between each jobs’ completion time in the realized and initial schedules is the stability measure. We proposed and compared two methods to deal with such an NP-hard problem; a method based on decomposing the problem into sub-problem and solving each sub-problem, and a theorem-based method. The extensive computational results indicated that the second method has a better performance in terms of robustness and stability, especially in large-sized problems. In other words, the second method is preferable because of the better manufacturer responsiveness to the customer and the production staff satisfaction enhancement.

In this paper, an alternative approach in operational modal analysis is presented, utilizing image PROCESSING technique and transmissibility functions. Imaging sensors do not impose additional mass on the structure due to their non-contact nature, while transmissibility functions, independent of excitation type, can directly extract mode shapes. The innovation of this research lies in combining these two techniques to record dynamic responses and identify modal properties. To capture the temporal response history from video signals, the block-matching method with sub-pixel accuracy was employed. Validation was conducted by recording the response of the tip of a cantilevered steel beam subjected to impact excitation, using a high-speed camera and a laser vibrometer, simultaneously. The RMSE plots in the time domain and the PSD in the frequency domain indicate high accuracy of this method. Using this approach, the displacement time histories of various points on the structure were extracted from the video signals, and the modal properties, including natural frequencies, damping ratios, and mode shapes, were identified using the transmissibility matrix method. The results obtained from the proposed method were compared with the stochastic subspace identification (SSI) method and analytical solutions. The findings reveal the accuracy of the modal identification approach introduced in this article. The highest relative error in estimating the natural frequencies of the first and second modes, compared to the values from the laser method, are 0.19% and 0.13%, respectively, and in comparison to the analytical values, they are 0.34% and 1.5%, respectively.

Scheduling theory and permutation therein are two important subjects in discrete operation research. In this paper, a new heuristic algorithm is proposed for solving permutation flow shop problem by using regulations of columnar entries in the PROCESSING TIMES matrix. There are  jobs to be processed on  machines with deterministic PROCESSING TIMES and the object is obtaining the minimum of the total time to complete the schedule (makespan). This is not solvable in polynomial time. First, an initial suitable sequence of jobs is determined similar to many heuristics. For this, the matrix  is made such that every determines the measure of the fitness for the location of the th old row in the th new position. Thereafter, the Bellman Esogbue Nabeshima theorem is used. The presented algorithm is compared with the NEH (the best well-known existing method). This comparison is made by the Taillard’s standard test problems. Computational results demonstrate that the heuristic algorithm is better than some of the proposed heuristics known so far and it is superior with respect to others in a number of Taillard instances. As a result, it is almost as good as NEH and is very promising for the problem. On the basis of the structure of the proposed algorithm, it can perform a role as meta-heuristic.