Job scheduling for a single machine (JSSM) remains a core challenge in manufacturing and service operations, where optimal job sequencing is essential to minimize flow time, reduce delays, prioritize high-value tasks, and enhance overall system efficiency. This study addresses JSSM by developing a hybrid solution aimed at balancing multiple performance objectives and minimizing overall processing time. Eight established scheduling rules were examined through a comprehensive simulation based on randomly generated scenarios, each defined by three parameters: processing time, customer weight, and job due date. Performance was evaluated using six key metrics: flow time, total delay, number of delayed jobs, maximum delay, average delay of delayed jobs, and average weight of delayed jobs. A multi-criteria decision-making (MCDM) framework was applied to identify the most effective scheduling rule. This framework combines two approaches: the Analytic Hierarchy Process (AHP), used to assign relative importance to each criterion, and the Evaluation based on Distance from Average Solution (EDAS) method, applied to rank the scheduling rules. AHP weights were determined by surveying expert assessments, whose averaged responses formed a consensus on priority ranking. Results indicate that the Earliest Due Date (EDD) rule consistently outperformed other rules, likely due to the high weighting of delay-sensitive criteria within the AHP, which positions EDD favourably in scenarios demanding stringent adherence to deadlines. Following this initial rule-based scheduling phase, an optimization stage was introduced, involving four Tabu Search (TS) techniques: job swapping, block swapping, job insertion, and block insertion. The TS optimization yielded marked improvements, particularly in scenarios with high job volumes, significantly reducing delays and improving performance metrics across all criteria. The adaptability of this hybrid MCDM framework is highlighted as a primary contribution, with demonstrated potential for broader application. By adjusting weights, criteria, or search parameters, the proposed method can be tailored to diverse real-time scheduling challenges across different sectors. This integration of rule-based scheduling with metaheuristic search underscores the efficacy of hybrid approaches for complex scheduling problems.
Suspension systems play a critical role in ensuring the safety, comfort, and stability of vehicles during the transportation of both passengers and goods. Among various suspension technologies, active or electronic suspensions have emerged as the most advanced due to their ability to dynamically adjust damping characteristics, thereby optimizing vehicle performance. This is typically achieved by modulating the pressure or flow of air or oil within the damper, or by altering its physical properties. To facilitate such dynamic adjustments, an effective control system is essential. Soft computing techniques, such as fuzzy logic controllers, are increasingly employed for their robustness and adaptability in providing the required control forces. In this study, the active suspension system was controlled via a fuzzy logic controller, with a piezoelectric actuator employed to generate the control force. A comparative analysis was conducted with traditional control methods, including the proportional-integral-derivative (PID) controller, to evaluate the performance of the fuzzy logic approach. Simulation results demonstrated that both control strategies were capable of achieving stable and smooth suspension behavior. However, fuzzy control was found to respond more quickly to dynamic changes, while the PID controller exhibited superior performance during the initial stages of vibration, offering enhanced safety during the commencement of transport. These findings underscore the potential of fuzzy logic control in optimizing the active suspension systems for improved vehicle dynamics and the safe transport of sensitive goods.
Open Access
News 
Journal Metrics
Journal Overview
Associated Journals