@conference {Berahhou2022,
	title = {Dynamic Vehicle Routing Problem with Simultaneous Delivery and Pickup, Overtime and Inventory Restriction: Golden Ball Algorithm},
	booktitle = {2022 IEEE 6th International Conference on Logistics Operations Management, GOL 2022},
	year = {2022},
	note = {cited By 0},
	abstract = {This article studies an important and special variant of VRP. The Dynamic Vehicle Routing Problem with Simultaneous Delivery and Pickup, Overtime and Inventory Restriction (DVRPSDP-OT-IR) and propose an improved Golden Ball Algorithm (GBA) based on soccer concepts to solve the problem. A literature review of the GBA is given. A mathematical model has been proposed for the problem and validated by Cplex solver. A detailed description of the steps of GBA is presented. {\textcopyright} 2022 IEEE.},
	keywords = {DVRP, Dynamic vehicle routing problems, Golden ball algorithm, Inventory restriction, Literature reviews, Overtime, Pickups, Routing algorithms, Vehicle routing, VRPSDP},
	doi = {10.1109/GOL53975.2022.9820268},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135211662\&doi=10.1109\%2fGOL53975.2022.9820268\&partnerID=40\&md5=bee6326e667ddaeea4fde243d4e97f4a},
	author = {Berahhou, A. and Benadada, Y.}
}
@article {Annouch2021607,
	title = {An Adaptive Genetic Algorithm for a New Variant of the Gas Cylinders Open Split Delivery and Pickup with Two-dimensional Loading Constraints},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {12},
	number = {1},
	year = {2021},
	note = {cited By 1},
	pages = {607-619},
	abstract = {This paper studies a combination of two well-known problems in distribution logistics, which are the truck loading problem and the vehicle routing problem. In our context, a customer daily demand exceeds the truck capacity. As a result, the demand has to be split into several routes. In addition, it is required to assign customers to depots, which means that each customer is visited just once by any truck in the fleet. Moreover, we take into consideration a customer time windows. The studied problem can be defined as a Multi-depots open split delivery and pickup vehicle routing problem with two-dimensional loading constraints and time windows (2L-MD-OSPDTW). A mathematical formulation of the problem is proposed as a mixed-integer linear programming model. Then, a set of four class instances is used in a way that reflects the real-life case study. Furthermore, a genetic algorithm is proposed to solve a large scale dataset. Finally, preliminary results are reported and show that the MILP performs very well for small test instances while the genetic algorithm can be efficiently used to solve the problem for a widereaching test instances. {\textcopyright} 2021. All rights reserved.},
	keywords = {Adaptive genetic algorithms, Constraint programming, Fleet operations, Genetic algorithms, Integer programming, Large dataset, Loading constraints, Multi depots, Pickups, Routing algorithms, Sales, Split delivery, Split pickups, Test instances, Time windows, Trucks, Two-dimensional, Two-dimensional loading, Vehicle routing, Vehicle Routing Problems},
	doi = {10.14569/IJACSA.2021.0120170},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100379461\&doi=10.14569\%2fIJACSA.2021.0120170\&partnerID=40\&md5=7e2a6546b01de8c70b8cca5545b09a4e},
	author = {Annouch, A. and Bellabdaoui, A.}
}
@article {10906318420150210,
	title = {An Energy-Efficient Clustering Routing Algorithm Based on Geographic Position and Residual Energy for Wireless Sensor Network.},
	journal = {Journal of Computer Networks \& Communications},
	volume = {2015},
	year = {2015},
	pages = {1 - 11},
	abstract = {Recently wireless sensor network (WSN) has become one of the most interesting networking technologies, since it can be deployed without communication infrastructures. A sensor network is composed of a large number of sensor nodes; these nodes are responsible for supervision of the physical phenomenon and transmission of the periodical results to the base station. Therefore, improving the energy efficiency and maximizing the networking lifetime are the major challenges in this kind of networks. To deal with this, a hierarchical clustering scheme, called Location-Energy Spectral Cluster Algorithm (LESCA), is proposed in this paper. LESCA determines automatically the number of clusters in a network. It is based on spectral classification and considers both the residual energy and some properties of nodes. In fact, our approach uses the K-ways algorithm and proposes new features of the network nodes such as average energy, distance to BS, and distance to clusters centers in order to deter},
	keywords = {Data transmission systems, Energy consumption, Routing algorithms, Wireless sensor networks, Wireless sensor nodes},
	issn = {20907141},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=109063184\&site=ehost-live},
	author = {Jorio, Ali and El Fkihi, Sanaa and Elbhiri, Brahim and Aboutajdine, Driss}
}
