An augmented Tabu search algorithm for the green inventory-routing problem with time windows

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dc.authoridErfan Babaee Tirkolaee / 0000-0003-1664-9210
dc.authorscopusidErfan Babaee Tirkolaee / 57196032874
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017
dc.contributor.authorAlinaghian, Mahdi
dc.contributor.authorTirkolaee, Erfan Babaee
dc.contributor.authorDezaki, Zahra Kaviani
dc.contributor.authorHejazi, Seyed Reza
dc.contributor.authorDing, Weiping
dc.date.accessioned2020-11-30T12:11:51Z
dc.date.available2020-11-30T12:11:51Z
dc.date.issued2021en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractTransportation allocates a significant proportion of Gross Domestic Product (GDP) to each country, and it is one of the largest consumers of petroleum products. On the other hand, many efforts have been made recently to reduce Greenhouse Gas (GHG) emissions by vehicles through redesigning and planning transportation processes. This paper proposes a novel Mixed-Integer Linear Programming (MILP) mathematical model for Green Inventory-Routing Problem with Time Windows (GIRP-TW) using a piecewise linearization method. The objective is to minimize the total cost including fuel consumption cost, driver cost, inventory cost and usage cost of vehicles taking into account factors such as the volume of vehicle load, vehicle speed and road slope. To solve the problem, three meta-heuristic algorithms are designed including the original and augmented Tabu Search (TS) algorithms and Differential Evolution (DE) algorithm. In these algorithms, three heuristic methods of improved Clarke-Wright algorithm, improved Push-Forward Insertion Heuristic (PFIH) algorithm and heuristic speed optimization algorithm are also applied to deal with the routing structure of the problem. The performance of the proposed solution techniques is analyzed using some well-known test problems and algorithms in the literature. Furthermore, a statistical test is conducted to efficiently provide the required comparisons for large-sized problems. The obtained results demonstrate that the augmented TS algorithm is the best method to yield high-quality solutions. Finally, a sensitivity analysis is performed to investigate the variability of the objective function.en_US
dc.identifier.citationAlinaghian, M., Tirkolaee, E. B., Dezaki, Z. K., Hejazi, S. R., & Ding, W. (2020). An Augmented Tabu Search Algorithm for the Green Inventory-Routing Problem with Time Windows. Swarm and Evolutionary Computation, 100802.en_US
dc.identifier.doi10.1016/j.swevo.2020.100802en_US
dc.identifier.issn2210-6502en_US
dc.identifier.scopus2-s2.0-85096490073en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://www.doi.org/10.1016/j.swevo.2020.100802
dc.identifier.urihttps://hdl.handle.net/20.500.12713/1259
dc.identifier.volume60en_US
dc.identifier.wosWOS:000662077300020en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorTirkolaee, Erfan Babaee
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofSwarm and Evolutionary Computationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComprehensive Modal Emission Modelen_US
dc.subjectDifferential Evolutionen_US
dc.subjectFuel Consumptionen_US
dc.subjectInventory-Routing Problemen_US
dc.subjectTabu Searchen_US
dc.subjectTime Windowsen_US
dc.titleAn augmented Tabu search algorithm for the green inventory-routing problem with time windowsen_US
dc.typeArticleen_US

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