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Taking into consideration the requirements of real-time embedded systems, the processor scheduler must guarantee a constant scheduling frequency, providing determinism and predictability of tasks execution. The purpose of this study is to implement the nMPRA (multi pipeline register architecture) processor into field-programmable gate array, and to integrate the already existing scheduling methods, thus providing a preemptive schedulability analysis of the proposed architecture based on the pipeline assembly line and hardware scheduler. This study describes a hardware implementation of the real-time scheduler named nHSE (hardware scheduler engine for n tasks) and presents the results obtained using the appropriate schedulability methods used in real-time environments. The scheduling and task switch operations are the main source of non-determinism, being successfully dealt with real-time nMPRA concept, in order to improve the system's functionality. Some mechanisms used for synchronisation and inter-task communication are also taken into consideration.

The classical distributed production scheduling problem (DPSP) assumes that factories are identical, and each factory is composed of just some machines. Inspired by the fact that manufacturers these days typically work across different factories, and each of these factories normally has some workshops, we study an important extension of the DPSP with different factories and workshops (DPFW), where jobs can be processed and transferred between the factories, workshops and machines. To the best of our knowledge, this is the very first time distributed production scheduling with different factories and workshops is studied. We propose a novel memetic algorithm (MA) to solve this DPFW, aiming to minimize the makespan and total energy consumption. The proposed MA is incorporated with a well-designed chromosome encoding method and a balance-transfer initialization method to generate a good initial population. An effective local search operator is also presented to improve the MA’s convergence speed and fully exploit its solution space. A total of 50 DPFW benchmark instances are used to evaluate the performance of our MA. Computational experiments carried out confirm that the MA is able to easily obtain better solutions for the majority of the tested problem instances compared to three other well-known algorithms, demonstrating its superior performance over these algorithms in terms of solution quality. Our proposed method and the results presented here may be helpful for production managers who work with distributed manufacturing systems in scheduling their production activities by considering different factories and workshops. With this DPFW, imbalanced resource loads and unexpected bottlenecks, which regularly arise in traditional DPSP models, can be easily avoided.

Automated guided vehicles (AGVs) are key equipment in automated container terminals (ACTs), and their operational efficiency can be impacted by conflicts and battery swapping. Additionally, AGVs have bidirectional transportation capabilities, allowing them to move in the opposite direction without turning around, which helps reduce transportation time. This paper aims at the problem of AGV scheduling and bidirectional conflict-free routing with battery swapping in automated terminals. A bi-level mixed integer programming (MIP) model is proposed, taking into account task assignment, bidirectional conflict-free routing, and battery swapping. The upper model focuses on container task assignment and AGV battery swapping planning, while the lower model ensures conflict-free movement of AGVs. A double-threshold battery swapping strategy is introduced, allowing AGVs to utilize waiting time for loading for battery swapping. An improved differential evolution variable neighborhood search (IDE-VNS) algorithm is developed to solve the bi-level MIP model, aiming to minimize the completion time of all jobs. Experimental results demonstrate that compared to the differential evolution (DE) algorithm and the genetic algorithm (GA), the IDE-VNS algorithm reduces fitness values by 44.49% and 45.22%, though it does increase computation time by 56.28% and 62.03%, respectively. Bidirectional transportation reduces the fitness value by an average of 10.97% when the container scale is small. As the container scale increases, the fitness value of bidirectional transportation gradually approaches that of unidirectional transportation. The results further show that the double-threshold battery swapping strategy enhances AGV utilization and reduces the fitness value.

The incentive for many developments and scientific progresses within the field of scheduling has emerged from industrial environments, and naturally, it could be utilized in expressing the scheduling concepts regarding terms used in the industry. Generally speaking, scheduling problems are known as limited optimization issues through which decisions related to the machines’ assignment and works processing sequence are probed. Thus, following a review of the related literature, the major goal of this research is to design a mathematical model and to solve it through a meta-heuristic for no-wait flow shop scheduling problem using different machines for the purpose of minimizing the time required to complete the work using whale and ant colony optimization (ACO) algorithms in Sanat-Gostar-e-Hamgam Shoe Company. The ACO and whale algorithm methods are used to compare and predict scheduling activities in manufacturing line of shoe industry. The results showed an ACO algorithm with two stages in mean ideal distance (MID) end amounting to 76.65 and 77.38, respectively. Also, regarding the amounts of standard error mean squares, it could be claimed that the model designed using the improved whale algorithm has a better prediction, and the minimum time required to complete works using the whale algorithm is estimated to be equal to 86.1071. This could lead to an optimal state in achieving the predetermined goals.

Cloud manufacturing as an emerging trend has benefited from information technologies such as cloud computing to achieve a customer-oriented paradigm. Over time, factory machines tend to deteriorate slowly, and maintenance planning is implemented to ensure that the machines remain in acceptable condition. When it comes to managing production and maintenance in a system, it’s important to consider them simultaneously. This study presents three models that integrate subtask scheduling and logistics with maintenance policies for three types of task structures (sequential, loop, and parallel) on a cloud platform. These models aim to reduce costs imposed on the cloud manufacturing system, including subtask implementation, logistics between factories in different geographical locations, logistics to the delivery point, preventive maintenance, minimal repairs, and earliness/tardiness. Due to the complexity of the models, a genetic algorithm is developed to solve them. To demonstrate the importance of the main characteristics of the models, three similar models are proposed, in each of which one of the features is removed. Moreover, a sensitivity analysis is conducted to design effective guidelines for cloud manufacturing managers.

Scheduling issues are typically classified as constrained optimization problems that examine the allocation of machines and the sequence in which tasks are processed. Regarding the existence of one machine, identification of works processing sequence forms a complete time schedule. Therefore, following a review of previous works, the goal of the present study is designing a mathematical model for open shop scheduling (OSS) problems using different machines aiming at minimizing the maximum time required to complete the works using an artificial neural network (ANN) and genetic algorithm (GA). The research data were driven from a Shoe company carried out between the years 2019 and 2020. The GA and ANN methodologies were employed to analyze and forecast the scheduling of activities within the shoe manufacturing sector. The findings indicated that the probability associated with the third population of the GA was 0.15. Furthermore, an examination of the average values of standard error revealed that the neural network model outperformed in terms of predictive accuracy. The estimated minimum time necessary for task completion, as determined by the neural network, was calculated to be 0.96699, facilitating an optimal condition for meeting the established objectives.

The teaching–learning-based optimisation (TLBO) algorithm is a population-based metaheuristic inspired on the teaching–learning process observed in a classroom. It has been successfully used in a wide range of applications. In this study, the authors present a variant version of TLBO. In the proposed version, different weights are assigned to students during the student phase, with higher weights being assigned to students with better solutions. Three different approaches to assign weights are investigated. Numerical experiments with benchmark instances of the flow-shop and the job-shop scheduling problems are carried out to investigate the performance of the proposed approaches. They compare the proposed approaches with the original TLBO algorithm and with two variants of TLBOs proposed in the literature in terms of solution quality, convergence speed and simulation time. The results obtained by the application of a Friedman statistical test showed that the proposed approaches outperformed the original version of TLBO in terms of convergence, with no significant losses in the average makespan. The additional simulation time required by the proposed approaches is small. The best performance was achieved with the approach of assigning a fixed weight to half the students with the best solutions and assigning zero to other students.

This paper considers a single-machine scheduling problem with past-sequence-dependent delivery times and the truncated sum-of-processing-times-based learning effect. The goal is to minimize the total costs that comprise the number of early jobs, the number of tardy jobs and due date. The due date is a decision variable. There will be corresponding penalties for jobs that are not completed on time. Under the common due date, slack due date and different due date, we prove that these problems are polynomial time solvable. Three polynomial time algorithms are proposed to obtain the optimal sequence.

This paper considers the single machine scheduling problem with three different due dates in which the actual processing time of the job is a simple deterioration function of the starting time. The goal is to minimize the total costs that contain the earliness, tardiness and due date. We prove that these problems are polynomial time solvable, and we propose the corresponding algorithms to obtain the optimal sequence and due date.

This article investigates single-machine group scheduling integrated with resource allocation under different due-window (DIFDW) assignment. Three distinct scenarios are examined: one with constant processing times, one with a linear resource consumption function, and one with a convex resource consumption function. The objective is to minimize the total cost comprising the maximum earliness/tardiness penalties, the due-window starting time cost, the due-window size cost, and the resource consumption cost. For each problem variant, we analyze the structural properties of optimal solutions and develop corresponding solution algorithms: a polynomial-time optimal algorithm for the case with constant processing times, heuristic algorithms for problems involving linear and convex resource allocation, and the branch-and-bound algorithm for obtaining exact solutions. Numerical experiments are conducted to evaluate the performance of the proposed algorithms.