@article {ElBouyahyiouy2021471,
	title = {A mixed-integer linear programming model for the selective full-truckload multi-depot vehicle routing problem with time windows},
	journal = {Decision Science Letters},
	volume = {10},
	number = {4},
	year = {2021},
	note = {cited By 0},
	pages = {471-486},
	abstract = {This article has studied a full truckload transportation problem in the context of an empty return scenario, particularly an order selection and vehicle routing problem with full truckload, multiple depots and time windows (SFTMDVRPTW). The aim is to develop a solution where a set of truck routes serves a subset of selected transportation demands from a number of full truckload orders to maximize the total profit obtained from those orders. Each truck route is a chain of selected demands to serve, originating at a departure point and terminating at an arriving point of trucks in a way that respects the constraints of availability and time windows. It is not mandatory to serve all orders, and only the profitable ones are selected. In this study, we have formulated the SFTMDVRPTW as a mixed-integer linear programming (MILP) model. Finally, Computational results are conducted on a new data set that contains thirty randomly generated problem instances ranging from 16 to 30 orders using the CPLEX software. The findings prove that our model has provided good solutions in a reasonable time. {\textcopyright} 2021 by the authors; licensee Growing Science, Canada.},
	doi = {10.5267/j.dsl.2021.7.002},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112687868\&doi=10.5267\%2fj.dsl.2021.7.002\&partnerID=40\&md5=4d2359491776b4902b7e1f3c680db9fc},
	author = {El Bouyahyiouy, K. and Bellabdaoui, A.}
}
@conference {ElBouyahyiouy2017142,
	title = {An ant colony optimization algorithm for solving the full truckload vehicle routing problem with profit},
	booktitle = {2017 International Colloquium on Logistics and Supply Chain Management: Competitiveness and Innovation in Automobile and Aeronautics Industries, LOGISTIQUA 2017},
	year = {2017},
	note = {cited By 0},
	pages = {142-147},
	abstract = {This paper proposes an ant colony optimization (ACO) to solve the full-truckload selective multi-depot vehicle routing problem under time windows constraints (denoted by FT-SMDVRPTW). The objective is to construct a solution composed of a set of routes associated with the trucks, aiming at maximizing the total profit. Each order is a pickup and delivery order associated with an origin, a destination, two time windows, and a price for serving the order paid by its corresponding shipper. Each route is a sequence of selected orders to serve so that the operational constraints are respected. Our problem appears clearly when the vehicles return back. It is not obligatory to serve all orders. The motivation of this study is to solve this problem by using an ant colony optimization metaheuristic, called ant colony system, which was originally implemented for solving the basic vehicle routing problem (VRP). We modify the algorithm to incorporate a robust optimization methodology, so that the full truckload can be handled. Finally, we give a numerical example on a randomly generated instance to illustrate our approach. {\textcopyright} 2017 IEEE.},
	doi = {10.1109/LOGISTIQUA.2017.7962888},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026628672\&doi=10.1109\%2fLOGISTIQUA.2017.7962888\&partnerID=40\&md5=88873263b0addef95537c20e4fe48482},
	author = {El Bouyahyiouy, K. and Bellabdaoui, A.}
}
@article {Abbassi2017210,
	title = {A hybrid algorithm for vehicle routing problem with time windows and target time},
	journal = {Journal of Theoretical and Applied Information Technology},
	volume = {95},
	number = {1},
	year = {2017},
	note = {cited By 1},
	pages = {210-219},
	abstract = {The routing of a fleet of vehicles to service a set of customers is important in the field of goods distribution. Vehicle routing problem with time windows (VRPTW) is a well-known combinatorial problem. This article aims at studying the vehicle routing problem with time windows and target time (VRPTWTT). VRPTWTT involves the routing of a set of vehicles with limited capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows as well as a target time. There are penalties associated with servicing either earlier or later than this target servicing time. The goal is to minimize the costs of transport and penalties of delay and ahead of time. Although VRPTWTT is a new variant of the VRP with time windows, the problem is not easy to solve, and it is also NP-hard. To solve the VRPTWTT, we propose a hybrid method combining Neighborhood search with Ant Colony Optimization Algorithm (ACO). Furthermore, when ACO is close to current optimal solution, neighborhood search is used to maintain the diversity of ACO and explore new solutions. First, we present a description of the hybrid method followed by computational results and the conclusion. {\textcopyright} 2005 - 2017 JATIT \& LLS. All rights reserved.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010188946\&partnerID=40\&md5=ab0a6c69de17f1da6b246486211ec193},
	author = {Abbassi, A. and El Bouyahyiouy, K. and El Hilali Alaoui, A. and Bellabdaoui, A.}
}
@conference {ElBouyahyiouy2016,
	title = {A new crossover to solve the full truckload vehicle routing problem using genetic algorithm},
	booktitle = {Proceedings of the 3rd IEEE International Conference on Logistics Operations Management, GOL 2016},
	year = {2016},
	note = {cited By 0},
	abstract = {This paper considers the full-truckload selective multi-depot vehicle routing problem under time windows constraints (denoted by FT-SMDVRPTW), which is a generalization of the vehicle routing problem (VRP). Our objective function is to maximize the total profit that the vehicle generates during its trip. In this study, we{\textquoteright}ll present a review of literature about full truckload vehicle routing; we{\textquoteright}ll define the FT-SMDVRPTW that will be resolved via using genetic algorithm. A new complex two-part chromosome is used to represent the solution to our problem. Through a selection based on the elitist method and roulette method, an improved crossover operator called selected two-part chromosome crossover (S-TCX), and swap mutation operator new individuals are generated. Finally, we give a numerical example on a randomly generated instance to illustrate our approach. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/GOL.2016.7731675},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85002262313\&doi=10.1109\%2fGOL.2016.7731675\&partnerID=40\&md5=1920c06e1091fbb7d7c43bf67e4cd65b},
	author = {El Bouyahyiouy, K. and Bellabdaoui, A.}
}
