Application of NSGA-II and fuzzy TOPSIS to time–cost–quality trade-off resource leveling for scheduling an agricultural water supply project

Sadeghi, Roya; Heidari, Ali; Zahedi, Farbod; Khordehbinan, M. W.; Khalilzadeh, Mohammad

doi:10.1007/s13762-022-04743-2

Cited by 9 publications

(6 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rate of achievement to two objectives simultaneously (RAS) is used as another factor for making comparisons. The RAS equation is as follows (Jolai et al., 2013; Sadeghi et al ., 2023):In which Fi=min{f1i,f2i}. The less value the better (Jolai et al., 2013; Kebriyaii et al ., 2021).…”

Section: Computational Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid metaheuristic approach for solving a bi-objective capacitated electric vehicle routing problem with time windows and partial recharging

Zahedi

Kia

Khalilzadeh³

2023

JAMR

Self Cite

View full text Add to dashboard Cite

PurposeThe vehicle routing problem (VRP) has been widely investigated during last decades to reduce logistics costs and improve service level. In addition, many researchers have realized the importance of green logistic system design in decreasing environmental pollution and achieving sustainable development.Design/methodology/approachIn this paper, a bi-objective mathematical model is developed for the capacitated electric VRP with time windows and partial recharge. The first objective deals with minimizing the route to reduce the costs related to vehicles, while the second objective minimizes the delay of arrival vehicles to depots based on the soft time window. A hybrid metaheuristic algorithm including non-dominated sorting genetic algorithm (NSGA-II) and teaching-learning-based optimization (TLBO), called NSGA-II-TLBO, is proposed for solving this problem. The Taguchi method is used to adjust the parameters of algorithms. Several numerical instances in different sizes are solved and the performance of the proposed algorithm is compared to NSGA-II and multi-objective simulated annealing (MOSA) as two well-known algorithms based on the five indexes including time, mean ideal distance (MID), diversity, spacing and the Rate of Achievement to two objectives Simultaneously (RAS).FindingsThe results demonstrate that the hybrid algorithm outperforms terms of spacing and RAS indexes with p-value <0.04. However, MOSA and NSGA-II algorithms have better performance in terms of central processing unit (CPU) time index. In addition, there is no meaningful difference between the algorithms in terms of MID and diversity indexes. Finally, the impacts of changing the parameters of the model on the results are investigated by performing sensitivity analysis.Originality/valueIn this research, an environment-friendly transportation system is addressed by presenting a bi-objective mathematical model for the routing problem of an electric capacitated vehicle considering the time windows with the possibility of recharging.

show abstract

Section: Computational Resultsmentioning

confidence: 99%

“…(5) The rate of achievement to two objectives simultaneously (RASÞ The rate of achievement to two objectives simultaneously (RAS) is used as another factor for making comparisons. The RAS equation is as follows (Jolai et al, 2013;Sadeghi et al, 2023):…”

Section: The Algorithm Performance Criteriamentioning

confidence: 99%

A hybrid metaheuristic approach for solving a bi-objective capacitated electric vehicle routing problem with time windows and partial recharging

Zahedi

Kia

Khalilzadeh³

2023

JAMR

Self Cite

View full text Add to dashboard Cite

show abstract

“…If α ≥ P − value, the hypothesis H 0 (no meaningful difference between the means of the algorithms) will be rejected; otherwise, the hypothesis H 0 will not be rejected. For this purpose, α = 0.05 [79][80][81].…”

Section: Discussionmentioning

confidence: 99%

“…The first category deals with the convergence and quality of the solutions, and the second category focuses on the diversification of the solutions in the solution space. In this study, four indexes of CPU time, Mean Ideal Distance (MID), Multi Objective Coefficient of Variation (MOCV), and Hypervolume Indicator (HV) are employed to compare the performance of the MOGWO, MOSA, MOPSO, and NSGA-II-TLBO meta-heuristic algorithms [79,80].…”

Section: The Criteria For the Performance Comparison Of The Meta-heur...mentioning

confidence: 99%

A Sustainable Multi-Objective Model for Capacitated-Electric-Vehicle-Routing-Problem Considering Hard and Soft Time Windows as Well as Partial Recharging

Azadi,

Khalilzadeh,

Antucheviciene

et al. 2024

Biomimetics

Self Cite

View full text Add to dashboard Cite

Due to the high pollution of the transportation sector, nowadays the role of electric vehicles has been noticed more and more by governments, organizations, and environmentally friendly people. On the other hand, the problem of electric vehicle routing (EVRP) has been widely studied in recent years. This paper deals with an extended version of EVRP, in which electric vehicles (EVs) deliver goods to customers. The limited battery capacity of EVs causes their operational domains to be less than those of gasoline vehicles. For this purpose, several charging stations are considered in this study for EVs. In addition, depending on the operational domain, a full charge may not be needed, which reduces the operation time. Therefore, partial recharging is also taken into account in the present research. This problem is formulated as a multi-objective integer linear programming model, whose objective functions include economic, environmental, and social aspects. Then, the preemptive fuzzy goal programming method (PFGP) is exploited as an exact method to solve small-sized problems. Also, two hybrid meta-heuristic algorithms inspired by nature, including MOSA, MOGWO, MOPSO, and NSGAII_TLBO, are utilized to solve large-sized problems. The results obtained from solving the numerous test problems demonstrate that the hybrid meta-heuristic algorithm can provide efficient solutions in terms of quality and non-dominated solutions in all test problems. In addition, the performance of the algorithms was compared in terms of four indexes: time, MID, MOCV, and HV. Moreover, statistical analysis is performed to investigate whether there is a significant difference between the performance of the algorithms. The results indicate that the MOSA algorithm performs better in terms of the time index. On the other hand, the NSGA-II-TLBO algorithm outperforms in terms of the MID, MOCV, and HV indexes.

show abstract

“…NSGA-II algorithm [26], as a multiobjective optimization algorithm, can directly optimize and solve multi-objective problems with good optimization effect and fast optimization speed [27]. NSGA-II algorithm has been widely used in aerospace [28], machine design [29], reservoir optimization scheduling [30], resource scheduling [31], power systems [32], and many other fields. For example, in reservoir scheduling, Chang [33] et al applied the NSGA-II algorithm to the reservoir group optimal scheduling problem and tested the feasibility and effectiveness of the algorithm in the multi-objective optimal scheduling of reservoirs.…”

Section: Introductionmentioning

confidence: 99%

Research on the Carbon Reduction Technology Path of the Iron and Steel Industry Based on a Multi-Objective Genetic Algorithm

Xie,

Ma,

Wang

et al. 2024

Sustainability

View full text Add to dashboard Cite

This paper establishes a multi-objective optimization model based on an improved NSGA-II algorithm, aiming to study the carbon reduction technology path of specific enterprises in the steel industry under the background of China’s dual-carbon goal and fill the research gap in the carbon reduction technology path of steel enterprises, which has certain guiding significance for the realization of China’s dual-carbon goal and the low-carbon development of steel enterprises. Firstly, through the analysis of the list of extreme energy efficiency technologies in the steel industry and the main process flow of steel industry production, the multi-objective optimization model is constructed from the two objective dimensions of maximum CO2 emission reduction and maximum enterprise economic benefit. Then the improved NSGA-II algorithm is used to solve the model. And the empirical analysis of a Hebei iron and steel enterprise, based on the technology application of enterprises before the release of the technology list, the technology path of enterprises to reduce carbon is predicted. The actual application data of the enterprise is used for verification and analysis, and suggestions on the technical path for the future low-carbon development of the enterprise are provided. The experimental results show that: (1) The optimal solution set of Pareto is consistent with the practical application of enterprises, and the constructed model is accurate and efficient, which can be used for the research of carbon reduction technology path. (2) When introducing technology, enterprises can give priority to the solution of common set technology based on their own needs.

show abstract

Application of NSGA-II and fuzzy TOPSIS to time–cost–quality trade-off resource leveling for scheduling an agricultural water supply project

Cited by 9 publications

References 43 publications

A hybrid metaheuristic approach for solving a bi-objective capacitated electric vehicle routing problem with time windows and partial recharging

A hybrid metaheuristic approach for solving a bi-objective capacitated electric vehicle routing problem with time windows and partial recharging

A Sustainable Multi-Objective Model for Capacitated-Electric-Vehicle-Routing-Problem Considering Hard and Soft Time Windows as Well as Partial Recharging

Research on the Carbon Reduction Technology Path of the Iron and Steel Industry Based on a Multi-Objective Genetic Algorithm

Contact Info

Product

Resources

About