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In contrast to traditional job-shop scheduling problems, various complex constraints must be considered in distributed manufacturing environments; therefore, developing a novel scheduling solution is necessary. This paper proposes a hybrid genetic algorithm (HGA) for solving the distributed and flexible job-shop scheduling problem (DFJSP). Compared with previous studies on HGAs, the HGA approach proposed in this study uses the Taguchi method to optimize the parameters of a genetic algorithm (GA). Furthermore, a novel encoding mechanism is proposed to solve invalid job assignments, where a GA is employed to solve complex flexible job-shop scheduling problems (FJSPs). In addition, various crossover and mutation operators are adopted for increasing the probability of finding the optimal solution and diversity of chromosomes and for refining a makespan solution. To evaluate the performance of the proposed approach, three classic DFJSP benchmarks and three virtual DFJSPs were adapted from classical FJSP benchmarks. The experimental results indicate that the proposed approach is considerably robust, outperforming previous algorithms after 50 runs.

The Equilibrium Optimizer (EO) algorithm is a new meta-heuristic algorithm that uses an equilibrium pool and candidates to update particles (solutions). EO algorithm not only has strong exploitation and exploration capabilities but also avoids falling into the local optimum. The reason why EO has these advantages is because of the existence of “generation rate”. This paper proposes an Enhanced Equilibrium Optimizer (EEO) Algorithm based on three communication strategies to solve the Job Shop Scheduling Problem (JSSP). To prove the accuracy of the algorithm, this paper uses 28 benchmark functions for testing. At the same time, the Enhanced Equilibrium Optimizer (EEO1, EEO2, EEO3) Algorithms are compared with the existing optimization methods, including Grey Wolf Optimizer (GWO), Multi-Version Optimizer (MVO), Differential Evolution (DE), Whale Optimization Algorithm (WOA). Experiments show that the EO algorithm is significantly better than GWO, MVO, DE, WOA. EO algorithm is mainly used to optimize continuous problems, but JSSP is a discrete application, so the standard equilibrium optimizer algorithm needs to be discretized. This paper extends the enhanced equilibrium optimizer algorithm and adds discretization processing to JSSP. The algorithm is also applied for the job shop scheduling problem by discretization and is compared with the three improvement methods of EEO. Experimental results prove that the algorithm has made significant improvements in solving JSSP.

For the past decades, practitioners and researchers have been fascinated by the job-shop scheduling problems (JSSP) and have proposed many pristine meta-heuristic algorithms to solve them. JSSP is an NP-hard problem and a combinatorial optimization problem. This paper proposes a highly efficient and superior performance strategy for the artificial algae algorithm (AAA) integrated with the differential evolution (DE), denoted AAADE, to solve JSSP. The new movement algae colonies using DE operators are introduced to the proposed hybrid artificial algae algorithm and DE (AAADE). To improve AAA’s intensification ability, the movement using the DE mutation is implemented into the AAA. In the new hybrid method, the DE crossover can update its position based on both movements (helical and DE movements) to increase randomization. Two categories of problems verify the efficiency and validity of the proposed hybrid algorithm, AAADE, namely, CEC 2014 benchmark functions and different job-shop scheduling problems. The AAADE results are compared with other algorithms in the literature. Hence, comparisons numerical experiments validated and verified the quality of the proposed algorithm. Experimental results validate the effectiveness of the proposed hybrid method in producing excellent solutions that are promising and competitive to the state-of-the-art heuristic-based algorithms reported in the literature in most of the benchmark functions in CEC’14 and JSSP.

Since production efficiency and costs are directly affected by the ways in which jobs are scheduled, scholars have advanced a number of meta-heuristic algorithms to solve the job shop scheduling problem (JSSP). Although this JSSP is widely accepted as a computationally intractable NP-hard problem in combinatorial optimization, its solution is essential in manufacturing. This study proposes performance-driven meta-heuristic switching approaches that utilize the capabilities of multi-operator differential evolution (MODE) and particle swarm optimization (PSO) in a single algorithmic framework. The performance-driven switching mechanism is introduced to switch the population from an under-performing algorithm to other possibilities. A mixed selection strategy is employed to ensure the diversity and quality of the initial population, whereas a diversity check mechanism maintains population diversity over the generations. Moreover, a Tabu search (TS) inspired local search technique is implemented to enhance the proposed algorithm’s exploitation capability, avoiding being trapped in the local optima. Finally, this study presents two mixed population structure-based hybrid evolutionary algorithms (HEAs), such as a predictive sequence HEA (sHEA) and a random sequence HEA (rHEA), and one bi-population inspired HEA, called bHEA. The comparative impacts of these varied population structure-based approaches are assessed by solving 5 categories of the standard JSSP instances (i.e., FT, LA, ORB, ABZ and TA). The performance of these hybridized approaches (i.e., sHEA, rHEA and bHEA) is compared and contrasted with its constituent algorithms (MODE, PSO and TS) to validate the hybridization’s effectiveness. The statistical analysis shows that sHEA ranked first with mean value 1.84 compared to rHEA (1.96) and bHEA (2.21). Moreover, the proposed sHEA is compared with 26 existing algorithms and ranked first with a mean value 5.09 compared to the near-best algorithms. Thus, the simulation results and statistical analysis prove the supremacy of the sHEA.

To solve the problem of inserting new job into flexible job-shops, this paper proposes a dynamic self-learning artificial bee colony (DSLABC) optimization algorithm to solve dynamic flexible job-shop scheduling problem (DFJSP). Through the reasonable arrangement of the processing sequence of the jobs and the corresponding relationship between the operations and the machines, the makespan can be shortened, the economic benefit of the job-shop and the utilization rate of the processing machine can be improved. Firstly, the Q-learning algorithm and the traditional artificial bee colony (ABC) algorithm are combined to form the self-learning artificial bee colony (SLABC) algorithm. Using the learning characteristics of the Q-learning algorithm, the update dimension of each iteration of the ABC algorithm can be dynamically adjusted, which improves the convergence accuracy of the ABC algorithm. Secondly, the specific method of dynamic scheduling is determined, and the DSLABC algorithm is proposed. When a new job is inserted, the new job and the operations that have not started processing will be rescheduled. Finally, through solving the Brandimarte instances, it is proved that the convergence accuracy of the SLABC algorithm is higher than that of other optimization algorithms, and the effectiveness of the DSLABC algorithm is demonstrated by solving a specific example with a new job inserted.

This article aims to review the industrial applications of AI-based intelligent system algorithms in the manufacturing sector to find the latest methods used for sustainability and optimisation. In contrast to previous review articles that broadly summarised existing methods, this paper specifically emphasises the most recent techniques, providing a systematic and structured evaluation of their practical applications within the sector. The primary objective of this study is to review the applications of intelligent system algorithms, including metaheuristics, evolutionary algorithms, and learning-based methods within the manufacturing sector, particularly through the lens of optimisation of workflow in the production lines, specifically Job Shop Scheduling Problems (JSSPs). It critically evaluates various algorithms for solving JSSPs, with a particular focus on Flexible Job Shop Scheduling Problems (FJSPs), a more advanced form of JSSPs. The manufacturing process consists of several intricate operations that must be meticulously planned and scheduled to be executed effectively. In this regard, Production scheduling aims to find the best possible schedule to maximise one or more performance parameters. An integral part of production scheduling is JSSP in both traditional and smart manufacturing; however, this research focuses on this concept in general, which pertains to industrial system scheduling and concerns the aim of maximising operational efficiency by reducing production time and costs. A common feature among research studies on optimisation is the lack of consistent and more effective solution algorithms that minimise time and energy consumption, thus accelerating optimisation with minimal resources.

The paper deals with a production scheduling process, which is a problematic and it requires considering a lot of various factors while making the decision. Due to the specificity of the production system analysed in the practical example, the production scheduling problem was classified as a Job-shop Scheduling Problem (JSP). The production scheduling process, especially in the case of JSP, involves the analysis of a variety of data simultaneously and is well known as NP-hard problem. The research was performed in partnership with a company from the automotive industry. The production scheduling process is a task that is usually performed by process engineers. Thus, it can often be affected by mistakes of human nature e.g. habits, differences in experience and knowledge of engineers (their know-how), etc. The usage of heuristic algorithms was proposed as the solution. The chosen methods are genetic and greedy algorithms, as both of them are suitable to resolve a problem that requires analysing a lot of data. The paper presents both approaches: practical and theoretical aspects of the usefulness and effectiveness of genetic and greedy algorithms in a production scheduling process.

The flexible job shop scheduling problem (FJSP) becomes significantly more complex when real-world factors such as due dates, sequence-dependent setup times, and processing times are considered as multiple criteria. This study presents a hybrid scheduling approach that combines a genetic algorithm (GA) and variable neighborhood search (VNS), where several dispatching rules are used to create the initial population and improve exploration. The multiple objectives are to minimize makespan, total tardiness, and total setup time while improving overall production efficiency. To test the proposed approach, standard FJSP datasets were extended with due dates and setup times for two different environments. Due dates were generated using the Total Work Content (TWK) method. This study also introduces a dynamic scheduling framework that addresses dynamic events such as machine breakdowns and new job arrivals. A rescheduling strategy was developed to maintain optimal solutions in dynamic situations. Experimental results show that the proposed hybrid framework consistently performs better than other methods in static scheduling and maintains high performance under dynamic conditions. The proposed method achieved 6.5% and 2.59% improvement over the baseline GA in two different environments. The results confirm that the proposed strategies effectively address complex, multi-constraint scheduling problems relevant to Industry 4.0 and smart manufacturing environments.

In this paper, an improved Beluga Whale Optimization algorithm based on data mining and scheduling rules with AdaBoost(IBWO-DDR-AdaBoost) rule heuristic scheduling method for solving job-shop scheduling problems (JSP) is proposed, in which data mining-extracted dispatching rules are incorporated into the heuristic algorithm to guide the optimization process. Firstly, an AdaBoost-CART integrated learning algorithm is introduced to evolve dispatching knowledge from historical data and convert it into effective dispatching rules. Secondly, in order to address the issues of local optimality and slow convergence speed faced by the beluga whale optimization algorithm (BWO) when solving JSP, this study presents an improved beluga whale optimization algorithm (IBWO) that incorporates two enhancement mechanisms: a neighborhood search strategy based on greedy thinking and genetic operators. These enhancements aim to improve both the efficiency and quality of reconciliation in scheduling, ultimately leading to better scheduling schemes. Furthermore, the extracted scheduling rules obtained through the AdaBoost-CART integrated learning algorithm are embedded into the improved beluga optimization algorithm, enabling real-time solution updates for optimized schedules. Finally, extensive simulation tests are conducted on JSP benchmark examples of varying scales with minimizing maximum completion time as the objective function for schedule optimization. The simulation results demonstrate the significant advantages of our proposed IBWO-DDR-AdaBoost rule heuristic scheduling method in terms of accuracy, performance optimization, and convergence speed.

Job shop scheduling problem (JSSP) is essential in the production, which can significantly improve production efficiency. Dynamic events such as machine breakdown and job rework frequently occur in smart manufacturing, making the dynamic job shop scheduling problem (DJSSP) methods urgently needed. Existing rule-based and meta-heuristic methods cannot cope with dynamic events in DJSSPs of different sizes in real time. This paper proposes an end-to-end transformer-based deep learning method named spatial pyramid pooling-based transformer (SPP-Transformer), which shows strong generalizability and can be applied to different-sized DJSSPs. The feature extraction module extracts the production environment features that are further compressed into fixed-length vectors by the feature compression module. Then, the action selection module selects the simple priority rule in real time. The experimental results show that the makespan of SPP-Transformer is 11.67% smaller than the average makespan of dispatching rules, meta-heuristic methods, and RL methods, proving that SPP-Transformer realizes effective dynamic scheduling without training different models for different DJSSPs. To the best of our knowledge, SPP-Transformer is the first application of an end-to-end transformer in DJSSP, which not only improves the productivity of industrial scheduling but also provides a paradigm for future research on deep learning in DJSSP.