Vehicle Exhausts Emission Process Parametric Optimization and Selection using the Fuzzy-Knapsack Dynamic Programming-EDAS Method for Logistics Application | ||||
| The Egyptian International Journal of Engineering Sciences and Technology | ||||
| Volume 46, Issue 2, June 2024, Page 105-123 PDF (816.96 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/eijest.2023.218424.1236 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Sunday Ayoola Oke   1; Alexander Iwodi Agada2; Wasiu Oyediran Adedeji3; Kasali Aderinmoye Adedeji4; John Rajan 5; Elkanah Olaosebikan Oyetunji4
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| 1Department of Mechanical Engineering, Faculty of Engineering, University of Lagos | ||||
| 2Department of Mechanical Engineering, University of Lagos | ||||
| 3Department of Mechanical Engineering, Osun State University, Osogbo | ||||
| 4Department of Mechanical Engineering, Lagos State University, Epe | ||||
| 5Department of Manufacturing Engineering, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, India | ||||
| Abstract | ||||
| Vehicular exhaust emissions threaten health with the poisonous gas discharges attributed to health problems. Thus, there is an urgent need to reduce discharges from exhaust emissions during logistic services. Consequently, this study proposes a fuzzy (F)-0/1 knapsack dynamic programming (0/1 KDP)-EDAS method, shortened as the F-0/1KDP-EDAS method. The method minimizes the exhaust emissions from vehicles while identifying the most important parameter contributing to the emission process. Then, this study extracts the factor-level information from the process and applies the fuzzy extent and fuzzy geometric concepts in the perspectives of three decision-makers, notably the bottling manager, head of business operations and chief executive officers as evaluated by the researchers. The outcomes of both fuzzy concept applications are then integrated with the 0/1 KDP scheme to produce criteria weights that are used in the EDAS method to produce the final results. The outcome of the fuzzy synthetic method yielded 0.1621 for parameters A, B, C, E and F while parameter D was 0.8931. After applying the 0/1 KDP scheme, parameters A, C, D and F were assigned zero values indicating non-contributory characteristics to the optimization process while parameters B and E were assigned the values of 20.59 and 904.89 respectively. The integrated F-0/1KDP scheme yielded 0.0222 and 0.9778, respectively. The originality of this work is the introduction of the fuzzy-knapsack-EDAS method to control exhaust emissions from vehicles in logistic services. Policy makers and logistic managers may employ the findings reported here to revise the periodic standards set for vehicles in exhaust emissions. | ||||
| Keywords | ||||
| Knapsack; optimization; selection; logistics; emission; fuzzy | ||||
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