Flexible flow shop scheduling problem to minimize makespan with renewable resources

Abbaszadeh, Nariman; Asadi-Gangraj, Ebrahim; Emami, Saeed

doi:10.24200/sci.2019.53600.3325

Cited by 8 publications

(7 citation statements)

References 38 publications

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“…Therefore, a Particle Swarm Optimization (PSO) algorithm, as well as a hybrid PSO and SA algorithm named SA-PSO, are developed to solve the model. Through numerical experiments on randomly generated test problems, the authors demonstrate that the hybrid SA-PSO algorithm outperforms the PSO, especially for large-size test problems [19].…”

Section: Literature Surveymentioning

confidence: 99%

Application of simulated annealing algorithm for multi criteria operation planning in flexible manufacturing systems

2023

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This paper considers a multi-objective model in which operation planning and tool assignment are simultaneously in a Flexible Manufacturing System (FMS). In this regard, the main characteristics of FMS have been analyzed. Then, a comprehensive model, including major system parameters and cost components, has been designed and presented. The proposed model contains cost factors, including machining cost, earliness or tardiness penalties, tool and part movement or switch costs, and idle time costs of tools and machines. Then, a multi-objective model for the problem has been proposed, in which the relative importance of each cost through weighting these costs based on the decision-making goals and the sum of the mentioned costs have been considered simultaneously. Based on the complex nature of the problem, standard solution techniques have yet to be employed. Therefore, to reduce computational times, the Simulated Annealing (SA) algorithm has been used for about 30 minutes (10,000 movements). The total production costs have been decreased from 7,000 to 4333 units using the SA algorithm. Based on the results, a 38% reduction in total production costs has been achieved. Computational results revealed that the proposed method is quite e cient in the multi-objective optimization of FMS within a short computational time.

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Section: Literature Surveymentioning

confidence: 99%

Application of simulated annealing algorithm for multi criteria operation planning in flexible manufacturing systems

2023

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“…The authors recommended the inclusion of due dates and the consideration of objective functions such as maximum lateness, total tardiness, and the number of tardy jobs in future models. Abbaszadeh et al (2021) tackled the Flexible Flow Shop (FFS) scheduling problem with UPMSPs and a renewable resource shared among the stages. They employed a combination of Simulated Annealing (SA) and PSO algorithms, referred to as SA-PSO, to solve the problem.…”

Section: Hybrid Based Optimization Algorithms For Upmspmentioning

confidence: 99%

Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A Concise Overview With a Proof-of-Concept Study

Ezugwu

2024

IEEE Access

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Sustainable development has emerged as a global priority, and industries are increasingly striving to align their operations with sustainable practices. Parallel machine scheduling (PMS) is a critical aspect of production planning that directly impacts resource utilization and operational efficiency. In this paper, we investigate the application of metaheuristic optimization algorithms to address the unrelated parallel machine scheduling problem (UPMSP) through the lens of sustainable development goals (SDGs). The primary objective of this study is to explore how metaheuristic optimization algorithms can contribute to achieving sustainable development goals in the context of UPMSP. We examine a range of metaheuristic algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and more, and assess their effectiveness in optimizing the scheduling problem. The evaluation of the algorithms focuses on their ability to improve the optimization of job-to-machine assignments, enabling industries to efficiently minimize the overall makespan of scheduling tasks. This, in turn, leads to waste reduction and enhanced energy efficiency. To conduct a comprehensive analysis, we consider UPMSP instances that incorporate sustainability-related constraints and objectives. We assess the algorithms' performance in terms of solution quality, convergence speed, robustness, and scalability, while also examining their implications for sustainable resource allocation and environmental stewardship. The findings of this study provide insights into the efficacy of metaheuristic optimization algorithms for addressing UPMSP with a focus on sustainable development goals. By leveraging these algorithms, industries can optimize scheduling decisions to minimize waste and enhance energy efficiency. The practical implications of this research are valuable for decision-makers, production planners, and researchers seeking to achieve sustainable development goals in the context of unrelated parallel machine scheduling. By embracing metaheuristic optimization algorithms, businesses can optimize their scheduling processes while aligning with sustainable principles, leading to improved operational efficiency, cost savings, and a positive contribution to the global sustainability agenda.INDEX TERMS sustainable development goals, metaheuristic optimization algorithms, unrelated parallel machine scheduling, resource utilization, energy consumption, environmental impact.

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“…Other metaheuristic algorithms, such as the Simulated Annealing (SA) algorithm or Particle Swarm Optimization (PSO), may also be used to solve the model. Interested readers are referred to Abbaszadeh et al [40] for more details about these algorithms.…”

Section: Table 2: Description Of Model Parameters and Decision Variablesmentioning

confidence: 99%

Constructing a Sensitive Control Chart to Monitor Process Mean using Optimal Filter: Time-Frequency Analysis Approach

2023

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Control charts are one of the critical tools for process monitoring. Test statistics are computed as a function of sample means in control charts for monitoring the process mean. In this study, these functions are modeled by filters. These filters have essential properties in both time and frequency domains. In previous studies, only their properties in the time domain have been considered. Thus, the resulting filters have sub-optimal performance. This study investigates the optimal design of these filters for monitoring the process mean. The behavior of these filters is analyzed not only in the time domain but also in the frequency domain. Properties such as stability are considered in designing this filter. An optimization model is designed and solved using a Genetic Algorithm to minimize the Average Run Length. The proposed optimal filter is compared with other control charts using simulation studies. Results showed the high speed of the proposed filter in detecting shifts in the process mean. The proposed optimal filter is also used to monitor the oil price of the OPEC basket. The results showed that shifts were detected at the right time using the proposed optimal filter.

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Flexible flow shop scheduling problem to minimize makespan with renewable resources

Cited by 8 publications

References 38 publications

Application of simulated annealing algorithm for multi criteria operation planning in flexible manufacturing systems

Application of simulated annealing algorithm for multi criteria operation planning in flexible manufacturing systems

Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A Concise Overview With a Proof-of-Concept Study

Constructing a Sensitive Control Chart to Monitor Process Mean using Optimal Filter: Time-Frequency Analysis Approach

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