Using an the intelligent self-modifier of probability of section approach to study the revenue influence of the pricing scheme of recyclable items in a green vehicle routing problem. (April 2018)

• Record Type: Journal Article
• Authors: Ghezavati, VR; Sahihi, A; Barzegar, A
• Other Names: Mustafee Navonil guest-editor.; Mittal Saurabh guest-editor.; Diallo Saikou guest-editor.; Zacharewicz Gregory guest-editor.
• Is Part Of: Simulation. Volume 94:Number 4(2018)
• Page Start: 359
• Page End: 372
• Publication Date: 2018-04
• Subjects: Transportation and traffic -- vehicle routing -- operations research -- soft computing -- modeling and simulation environments; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549717714332
• Languages: English
• ISSNs: 0037-5497

Abstract

In this article, a hybrid meta-heuristic algorithm is applied to solve a green vehicle routing problem with respect to economic aspects. In this research, a transportation model will be studied in which the fleet operates with eco-friendly fuels in order to collect used products in different nodes. By implementing value-added processes, the firm can sell products and gain profit. However, using alternative fuels causes some limitations because of lack of alternative fuel stations. These limitations usually affect the travel distance range of vehicles and, consecutively, route selection to serve desired customers. A proper formulation for this type of problem could be applicable to manage imposed costs of transportation pertaining to alternative fuels and related issues. To reach this goal, the proposed model represents the revenue and purchasing price of used products in the output. These results are attained by using an improved Simulated Annealing (SA) algorithm. The self-modifier of probability of section approach (SMPSA) featured with a SA algorithm can solve the model in less time compared with the classic SA algorithm. In addition, a heuristic algorithm is used to generate each initial solution with higher quality. Finally, the results and running time of the proposed algorithm are compared with the exact method and the SA algorithm without the SMPSA. Then the results are discussed.