metaheuristic algorithm

Maintenance is a significant cost factor in supply chains and production systems. Intelligent maintenance systems are gaining global interest with the rise of IT-based technologies. This study focuses on a new reliability-centered maintenance (RCM) method, where an intelligent system decides on repair or replacement based on system reliability in the practical flexible job shop scheduling problem (FJSP). The research introduces a balance by integrating industrial units' green production and energy consumption with traditional objectives like production costs and system reliability in a multi-objective framework. Another key aspect is the consideration of less developed regions as a social factor in the energy calculation model, resembling the Ministry of Energy's computational models. The reliability model is tailored for a complex system with multiple machines having time-dependent lifespans and repair probabilities, where operation times, maintenance times, and post-repair reliability levels are all stochastic. Metaheuristic algorithms combined with simulation-based optimization are used to solve the model. Statistical and non-statistical methods are used to depict the performance of the algorithms. The study shows that these algorithms effectively solve complex multi-objective stochastic problems and can be considered as a decision support system (DSS) for software developers working on real-world applications.

In light of recent advancements in the modern world, multivariate-multistage quality control patterns are increasingly recognized as vital and indispensable in manufacturing industries. This study delves into the significance and necessity of multivariate-multistage quality control in manufacturing, specifically focusing on motor oil production. As a foundational factor, motor oil quality considerably influences engine performance, lifespan, customer satisfaction, and market positioning.

This research employs deep learning algorithms for monitoring and fault detection in quality components. The primary rationale for opting for deep learning algorithms over conventional statistical methods is the non-normal distribution of data and the large sample sizes, which can lead to inaccurate estimations and unstable analyses. Conversely, the unique capabilities of deep learning algorithms in handling complex data and extracting meaningful features from extensive motor oil production data justify their selection. To bolster accuracy and effective quality control, a combination of deep learning algorithms is utilized, including Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and hybrid models such as LSTM-CNN, as well as Residual Networks (ResNet) with Dense Networks (DenseNet). The LSTM-CNN algorithm is applied to control numerical quality variables and identify temporal and sequential patterns in the data. Meanwhile, ResNet-DenseNet manages and analyzes visual data with non-uniform and intricate distributions.

By integrating LSTM networks, CNNs, and residual connections, these algorithms excel at extracting meaningful features and capturing complex relationships within the data. This enhances performance and efficiency in quality control processes and facilitates intelligent decision-making. Such an approach is adept at uncovering latent patterns and intricate relationships between variables and quality attributes, enhancing quality control procedures and intelligent decision-making. The amalgamation of these algorithmic capabilities enhances the efficacy of quality control processes, outperforming single-algorithm approaches. Additionally, the Bee Colony Clonal Algorithm (BCC) is employed to fine-tune the parameters of the LSTM-CNN and ResNet-DenseNet algorithms. This hybrid approach harnesses the Artificial Bee Colony (ABC) and Genetic Algorithm (GA) strengths, markedly improving the performance of deep learning algorithms in quality control and reducing the time required to achieve desired outcomes. To illustrate the practical applicability of the proposed algorithms, a case study in the motor oil production industry is examined. The proposed LSTM-CNN hybrid algorithm in fault detection demonstrated superior results compared to standalone CNN and LSTM algorithms, achieving performance improvements of approximately 15% and 8%, respectively. Furthermore, the proposed ResNet-DenseNet hybrid algorithm exhibited higher accuracy in visual components, enhancing performance by approximately 10% and 15% compared to ResNet and DenseNet algorithms, respectively.

From both academic and practical standpoints, this research scrutinizes deep learning algorithms' influence on enhancing motor oil quality and efficiency. Advanced data analysis methods, particularly hybrid deep learning algorithms, are employed to identify quality patterns in production data.

Emphasizing the importance of competition and cooperation in supply chains promote the revitalization of game theory as a convenient tool for analyzing interactions, as well as how it affects the different levels of cooperation and coordination in a supply chain. Therefore, using the game theory concept, and based on a side-contract of partnership in profits and marketing costs, in which profit is shared between all the chain levels’ members and marketing expenses is shared between producer and retailer, a cooperation model has been designed with the aim of maximizing the supply chain profit.In this research for the first time in fuzzy enviormet and with considering discount costs, a mathematical modeling is proposed in which to get closer to real conditions, marketing cost is considered as a triangular fuzzy number. The proposed model has been solved using Genetic algorithm (GA), simulated annealing algorithm (SA) and a hybrid algorithm based on GA-SA, for some random examples, and the model has been validated using GAMS software.

Distributing the production activities among the supply chain facilities with regard to the considered criteria can have a significant impact on the productive management. In this paper, a comprehensive mathematical model for reentrant permutation flow shop scheduling via considering a preventive maintenance and distributed jobs on different facilities is proposed. The uncertainty of the time of preventive maintenance operation is handled using robust optimization technique based on the uncertainty budget approach. Job assignment to production facilities and job scheduling are determined in the proposed model by considering multiple objectives include Cmax minimization, production cost minimization, and average tardiness. Due to the NP-hard nature of the proposed flow shop scheduling problem, a new hybrid meta-heuristic based on the novel adaptive large neighborhood search and the simulated annealing is adopted. The obtained results from an extensive numerical experimentation indicate the efficiency of the proposed model and solution algorithm to tackle the proposed problem. In certain manufacturing industries, it has been observed that the classical assumption of flow shop scheduling, stating that each job visits each machine exactly once, is occasionally violated. The prime example can be noticed in the high-tech industries, i. e. semiconductor wafer fabrication in which the operation processes of the jobs are performed by re-visiting some workstations (Gupta & Sivakumar, 2006). The scheduling problem of this nature of processing is categorized as a distinct flow shop with reentrant line configuration, called reentrant flow shop scheduling (RFS) (Katragjini et. al. , 2015). The significance of RFS is the processing layers l. Each layer begins from the  first workstation and completes on the last workstation. It means that once a job finished a layer of a set of operations, it will repeat its process to the next layer starting on the first workstation until all operations are completed. The RFS scheduling has been an active research area and attracted a considerable attention since the past decade due to the development and improvement of high-tech industry. The complexity of RFS cannot be circumvented since it involves more operations than the classical flow shop. Moreover, the cyclic operations where the jobs with higher layers may overlap other jobs in the same work station are essential to be considered. As a result, these complexities have triggered the development of the efficient scheduling approaches to improve the system performance. Various researchers surveyed the scheduling techniques in semiconductor manufacturing and providing the global view on reentrant scheduling problems. Another form of RFS is reentrant permutation flow shop (RPFS) where at each level no passing is permitted, that is, not only the machine sequence the same for all jobs, but also the job sequence is the same for each machine (Rifai et al. , 2016).    Despite the enormous literature on the RFS, most studies -if not all- base their research on the assumption that the process only involves a single production line. Some studies exploredthe problem on hybrid RFS where the production stages have more than one machines available to process the jobs. Nevertheless, hybrid RFS is based on the single production line. Nowadays, single factory firms are less common, with multi-plant companies and supply chains taking a more important role in practice. Several literatures mentioned that multiple production lines with more than one production center, named as distributed manufacturing system, enables companies to achieve higher product quality, lower production costs and lower management risks. However, existing studies focused more on the economic field anddistributed finite capacity scheduling is seldom tackled. In this section, a novel hybrid meta-heuristic via considering the specific assumptions of the flow shop problem as a NP-hard problem is proposed. The proposed solution algorithm incorporates adaptive large neighborhood search and the simulated annealing algorithms. Various new construction and deconstruction neighborhood structures are applied in the proposed adaptive large neighborhood search algorithm. Details of the proposed algorithm is presented in Fig. 1. The results of the proposed solution algorithm assessment are presented based on the two common performance assessment criteria which are proposed in the literature after 10 times runs of the applied solution algorithms. These criteria are the average number of obtained Pareto solution at each iteration of  the algorithm and average number of Pareto solutions which are not dominated by solutions from  other compared algorithms. In addition, computational time is considered as a third criteria for performance assessment of the proposed solution algorithm (See Table 1). Obtained results indicate the superiority of the  proposed solution algorithm.   In this study, a comprehensive optimization model for an extended reentrant permutation flow shop scheduling via considering a preventive maintenance and distributed jobs on different facilities is proposed. To enhance the applicability of the proposed model, uncertainty of the time of preventive maintenance operation is handled using robust optimization technique based on the uncertainty budget approach. In the proposed mathematical model, multiple objectives include Cmax minimization, production cost minimization, and average tardiness are considered. The aim of the proposed model is to determine the job assignment to production facilities and job scheduling. A new hybrid meta-heuristic based on the novel adaptive large neighborhood search and the simulated annealing is applied as a consequence of the NP-hard nature of the proposed flow shop scheduling problem, . The obtained results from an extensive numerical experimentation indicate the efficiency of the proposed model and solution algorithm to tackle the proposed problem.

The goal of supply chain management is to enhance various functions of different parts and levels of a supply chain to obtain the maximum possible profit. But this goal is not completely achievable due to the fact that there are differences between mentioned parts and levels, in their attitude towards the goals. These differences, for example pricing, stocking, and the costs related to parts and levels, will gradually result in a deduction in strength and competitiveness in system. In this study, multi-echelon supply chain has been investigated using game theory approach and considering the dependence of demand to selling price, fuzzy marketing costs, and discounts for all units. The problem has been modeled assuming that there’s no cooperation between different levels. Also, Stackelberg model assumptions have been taken into account, in which each level, with respect to market conditions, can undertake the leadership task. The aim of this problem is to determine the best decision of each player to obtain the optimal order quantity, a shortage for manufacturer and the selling price of each player, and to maximize incomes, to minimize costs and in general, to maximize possible profit for all players participating in the chain. GAMS softwares and metaheuristic algorithms have been used to solve the problem. Finally, the profit for supply chain members’ in different leadership conditions have been analyzed by generating different examples.

The dramatic increase in number and turnover of the projects of organizations on one hand and the Aggravation of environmental concerns on the other hand, lead to Increasing attention to environmental concerns in the field of project management. Adding this factor to the other customary factors that have an impact on project scheduling is a reasonable approach toward evaluation and control of destructive environmental effects. To this end, environmental impacts have been considered as a novel factor in the time-cost trade off problem and a new mathematical model, which includes time, cost and environmental impacts simultaneously, has been proposed in this article. Due to its NP- hardness, two metaheuristic algorithms, namely MOPSO and MOFA, combined with a heuristic algorithm were coded in MATLAB software. The heuristic algorithm’s function is to transform infeasible solutions to feasible ones. The results of implementing the aforementioned model and algorithms in a drilling project indicate that project managers can choose between different amounts of time, cost and environmental impacts. Moreover, they can control environmental impacts of a given project as well. Furthermore, the values of Pareto answers criteria demonstrated that MOPSO algorithm outperforms MOFA algorithm in this project.