Robust Energy-Aware Scheduling for SMEs with Aging Machines and Time-Dependent Tariffs

نوع: Type: Thesis

مقطع: Segment: masters

عنوان: Title: Robust Energy-Aware Scheduling for SMEs with Aging Machines and Time-Dependent Tariffs

ارائه دهنده: Provider: Majid Nateghi

اساتید راهنما: Supervisors: Amirsaman Kheirkhah Ph.D.

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: Mahdi Karimi - Safoura Famil Alamdar

زمان و تاریخ ارائه: Time and date of presentation: 2026

مکان ارائه: Place of presentation: 62

چکیده: Abstract: In small and medium-sized manufacturing environments, the complexity of scheduling processes, resource constraints, processing time variability, machine deterioration, and variability in operator performance pose significant challenges to achieving efficient and stable schedules. This challenge becomes more economically significant when electricity costs are time-dependent, as the timing of operations affects not only the completion time but also energy costs. Accordingly, this study develops a multi-objective robust model for the Flexible Job Shop Scheduling Problem (FJSP), in which completion time and operating cost, including energy costs and penalties, are simultaneously optimized. Uncertainty in processing times, machine efficiency, operator performance and error rates, time-of-use electricity tariffs, and disruptions caused by machine breakdowns are incorporated into the decision-making process. To model uncertainty, a prefix-based robust approach based on the Bertsimas–Sim framework is employed, with the level of conservatism controlled through the uncertainty budget and maximum deviation. To solve the proposed model, three memetic metaheuristic algorithms, namely NSGA-II, SPEA2, and MOEA/D, are developed, and their performance on problems of different sizes is evaluated using numerical performance measures, hypervolume indicators, convergence analysis, Pareto front analysis, and statistical tests. Furthermore, the validity of the model for small-sized instances is verified using the exact ε-constraint method, and the performance of the solution approaches is compared with that of a benchmark study. The stability of the resulting schedules against machine breakdowns is also evaluated using Monte Carlo simulation, while sensitivity analyses are conducted to investigate the effects of uncertainty, energy tariffs, machine efficiency, operator performance, and managerial parameters. The results indicate that none of the algorithms demonstrates absolute superiority across all problem sizes and evaluation criteria, and that the selection of the solution method depends on the problem structure and the decision-maker’s priorities. The sensitivity analysis further reveals that increasing the level of uncertainty and the magnitude of processing time deviations leads to increases in both completion time and operating cost. Moreover, the magnitude of deviation has a considerable impact on cost, particularly idle energy cost, under the investigated scenarios. The results also show that changes in the structure of tariff periods can have a greater impact on scheduling decisions than changes in the absolute price levels alone. Improvements in machine efficiency and operator performance lead to reductions in both completion time and operating cost, whereas a moderate increase in operator error rates exhibits a relatively smaller effect. The results of disruption simulations further confirm the effectiveness of the robust approach in mitigating the effects of machine breakdowns and enhancing schedule stability. The proposed framework can be used as a decision-support tool for flexible job shop scheduling by providing a balance among completion time, energy cost, and schedule stability.