Yazar "Alinaghian, Mahdi" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim
    Öğe
    An augmented Tabu search algorithm for the green inventory-routing problem with time windows
    (Elsevier B.V., 2021) Alinaghian, Mahdi; Tirkolaee, Erfan Babaee; Dezaki, Zahra Kaviani; Hejazi, Seyed Reza; Ding, Weiping
    Transportation 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.
  • Küçük Resim Yok
    Öğe
    Generalized vehicle routing problem: Contemporary trends and research directions
    (Cell Press, 2023) Jolfaei, Ali Aghadavoudi; Alinaghian, Mahdi; Bahrami, Roghayeh; Tirkolaee, Erfan Babaee
    Generalized Vehicle Routing Problem (GVRP) is a challenging operational research problem which has been widely studied for nearly two decades. In this problem, it is assumed that graph nodes are grouped into a number of clusters, and serving any node of a cluster eliminates the need to visit the other nodes of that cluster. The general objective of this problem is to find the set of nodes to visit and determine the service sequence to minimize the total traveling cost. In addition to these general conditions, GVRP can be formulated with different assumptions and constraints to practically create different sub-types and variants. This paper aims to provide a comprehensive survey of the GVRP literature and explore its various dimensions. It first encompasses the defi-nition of GVRP, similar problems, mathematical models, classification of different variants and solution methods developed for GVRPs, and practical implications. Finally, some useful sugges-tions are discussed to extend the problem. For this review study, Google Scholar, Scopus, Science Direct, Emerald, Springer, and Elsevier databases were searched for keywords, and 160 potential articles were extracted, and eventually, 45 articles were judged to be relevant.
  • Küçük Resim
    Öğe
    The time-dependent multi-depot fleet size and mix green vehicles routeing problem: improved adaptive large neighbourhood search
    (TAYLOR & FRANCIS LTD, 2021) Alinaghian, Mahdi; Jamshidian, Maryam; Tirkolaee, Erfan Babaee
    This study presents a mathematical model for the multi-depot Time-Dependent Fleet Size and Mix Green Vehicles Routeing Problem (TD-FSMGVRP). The objective function of the developed model is to minimize the total cost including vehicles' fixed cost, drivers' cost, fuel costs, and costs of Greenhouse Gas (GHG) emission. Fleet composition, load, vehicle speed, road slope, and traffic are considered as factors affecting the produced pollution. Considering the NP-Hard complexity of this problem, an Improved Adaptive Large Neighbourhood Search (IALNS) algorithm is designed to treat the problem efficiently. The performance of the proposed algorithm is enhanced using the Taguchi design method. Finally, Adaptive Large Neighbourhood Search (ALNS) algorithm and Variable Neighbourhood Search (VNS) algorithm are considered as two well-known algorithms to test the efficiency of the IALNS algorithm using benchmark problems. Furthermore, a statistical test is conducted to efficiently provide the required comparisons for large-sized problems. It is revealed that the proposed IALNS has a superior performance and can appropriately tackle the problem. Finally, the impacts of the proposed model on cost-saving are evaluated using the proposed IALNS algorithm.