A Survey on Environmentally Friendly Vehicle Routing Problem and a Proposal of Its Classification

The growth of environmental awareness and more robust enforcement of numerous regulations to reduce greenhouse gas (GHG) emissions have directed efforts towards addressing current environmental challenges. Considering the Vehicle Routing Problem (VRP), one of the effective strategies to control greenhouse gas emissions is to convert the fossil fuel-powered fleet into Environmentally Friendly Vehicles (EFVs). Given the multitude of constraints and assumptions defined for different types of VRPs, as well as assumptions and operational constraints specific to each type of EFV, many variants of environmentally friendly VRPs (EF-VRP) have been introduced. In this paper, studies conducted on the subject of EF-VRP are reviewed, considering all the road transport EFV types and problem variants, and classifying and discussing with a single holistic vision. The aim of this paper is twofold. First, it determines a classification of EF-VRP studies based on different types of EFVs, i.e., Alternative-Fuel Vehicles (AFVs), Electric Vehicles (EVs) and Hybrid Vehicles (HVs). Second, it presents a comprehensive survey by considering each variant of the classification, technical constraints and solution methods arising in the literature. The results of this paper show that studies on EF-VRP are relatively novel and there is still room for large improvements in several areas. So, to determine future insights, for each classification of EF-VRP studies, the paper provides the literature gaps and future research needs

A novel robust chance constrained possibilistic programming model for disaster relief logistics under uncertainty

In this paper, a novel multi-objective robust possibilistic programming model is proposed, which simultaneously considers maximizing the distributive justice in relief distribution, minimizing the risk of relief distribution, and minimizing the total logistics costs. To effectively cope with the uncertainties of the after-disaster environment, the uncertain parameters of the proposed model are considered in the form of fuzzy trapezoidal numbers. The proposed model not only considers relief commodities priority and demand points priority in relief distribution, but also considers the difference between the pre-disaster and post-disaster supply abilities of the suppliers. In order to solve the proposed model, the LP-metric and the improved augmented ε-constraint methods are used. Second, a set of test problems are designed to evaluate the effectiveness of the proposed robust model against its equivalent deterministic form, which reveales the capabilities of the robust model. Finally, to illustrate the performance of the proposed robust model, a seismic region of northwestern Iran (East Azerbaijan) is selected as a case study to model its relief logistics in the face of future earthquakes. This investigation indicates the usefulness of the proposed model in the field of crisis

A New Robust Mathematical Model for the Multi-product Capacitated Single Allocation Hub Location Problem with Maximum Covering Radius

This paper presents a new robust mathematical model for the multi-product capacitated single allocation hub location problem with maximum covering radius. The objective function of the proposed model minimizes the cost of establishing hubs, the expected cost of preparing hubs for handling products, shipping and transportation in all scenarios, and the cost variations over different scenarios. In the proposed model, a single product of a single node cannot be allocated to more than one hub, but different products of one node can be allocated to different hubs. Also, a product can be allocated to a hub only if equipment related to that product is installed on that hub. Considering the NP-Hard complexity of this problem, a GA-based meta-heuristic algorithm is developed to solve the large scale variants of the problem. To evaluate the performance of the proposed algorithm, its results are compared with the results of exact method and simulated annealing algorithm. These results show the good performance of the proposed algorithm

A new robust model for inventory- locating of single-Periodical supply chain with three-echelon for small and medium business enterprises with uncertain demand

Today, field of production and service is faced with competition among the supply chains by changing the competition pattern of the independent companies. Most of the small and medium businesses (SMEs) still use traditional viewpoint for supply, production and distribution planning. It means, each of these SMEs plan their tasks independently, this will increase the total cost of the supply chain in many cases. In this study, a robust model of inventory-locating supply chain is proposed in three-level with uncertain demand. The model has been considered in single-period, multi-product state along with some transportation models with three levels of producers – distributors- retailers in certain and robust mode. Considering some transportation models along with robusting the model and the possibility of sending goods directly from the factory to the retailer is one of the innovations of this study. The objectives of the proposed model are to minimize the total cost of the three-leveled supply chain and to find the amount of safety stock. The certain model is solved by GAMS and the robust model is also solved by GAMS in single-objective mode and then transferred to the augmented ε-constraint method. The results have been discussed after solving the model

Generalized vehicle routing problem: Contemporary trends and research directions

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 definition of GVRP, similar problems, mathematical models, classification of different variants and solution methods developed for GVRPs, and practical implications. Finally, some useful suggestions 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

Multi-mode hybrid electric vehicle routing problem

Hybrid electric vehicles (HEVs) are environmental-friendly vehicles that use a combination of the electric engine and internal combustion engine in their propulsion systems to reduce the fuel consumption and emission. In this paper, we consider a fleet of HEVs in logistics operations and introduce the Hybrid Electric Vehicle Routing Problem (HEVRP). Since we allow HEVs to operate in different drive modes, we refer to this problem as the Multi-Mode HEVRP (MM-HEVRP). We first model the problem as a mixed-integer linear program, where the objective function minimizes the total cost of the distances traveled at different modes. Since the problem is not tractable, we develop a matheuristic approach to solve it. The proposed approach combines Variable Neighborhood Search with mathematical programming. We test the performance of the proposed approach by solving benchmark instances generated for the Hybrid Electric Vehicle-Traveling Salesman Problem (HEV-TSP) and comparing our results with those published in the literature. In addition, we generate new MM-HEVRP data by modifying HEV-TSP benchmark instances. We solve the small-size MM-HEVRP instances using CPLEX and compare our solutions with the optimal solutions. The numerical results show that the proposed matheuristic is able to achieve high-quality solutions with reasonable computation times. Furthermore, we address the large-size instances and present a sensitivity analysis to provide further insights