@conference {Amrani2016,
	title = {Generalization of capacitated p-median location problem: Modeling and resolution},
	booktitle = {Proceedings of the 3rd IEEE International Conference on Logistics Operations Management, GOL 2016},
	year = {2016},
	note = {cited By 0},
	abstract = {The capacitated p-median location problem (CPMP) is very famous in literature and widely used within industry scope. However, in some cases, this location problem variant has poor management of capacity resources. In fact, the capacity used by facilities is fixed and not dependent on customers{\textquoteright} demands. The budget constraint Multi-Capacitated Location Problem (MCLP), considered in that paper, is a generalization of the CPMP problem, it is characterized by allowing each facility to be open with different capacities. In this paper, we will discuss the mathematical modeling of the MCLP problem, then we suggest adapted solving methods. To do this, we propose to solve the MCLP problem using Branch and Cut method. This exact solving method well-known, will serve us to test and validate our new problem formulation. Then we will build one heuristic algorithm, well adapted to our problem, it will be called GCDF (Greatest Customer Demand First). For improving solution quality, the LNS method will complete the GCDF. Computational results are presented at the end using instances that we have created under some criteria of difficulties or adapted from those of p-median problems available in literature. The GCDF (GCDF improved) algorithm is fast and provides good results for most degree of difficulty instances, but it is unreliable for very specific cases. To remedy this problem, the method must start with a basic feasible solution determined by one of the reliable method such as Branch and Bound. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/GOL.2016.7731674},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85002428361\&doi=10.1109\%2fGOL.2016.7731674\&partnerID=40\&md5=22a51daec1a55776484f33f5ea5ed3a0},
	author = {Amrani, M.E.a and Benadada, Y.a and Gendron, B.b}
}
@conference {Razouk2016,
	title = {New approaches for solving the container stacking problem},
	booktitle = {Proceedings of the 3rd IEEE International Conference on Logistics Operations Management, GOL 2016},
	year = {2016},
	note = {cited By 0},
	abstract = {Containers shipment has grown very fast during the last ten years, in Tanger Med port for example, 3Millions containers (80\% in transshipment) TEUs (Twenty feet equivalent unit) has been changed during 2015 (Tanger Med Port authority {\textquoteright}TMPA{\textquoteright} study March{\textquoteright}2016 [1]), which correspond to an increase of 40\% comparing with the last study made in 2012. Thus, our study will deal with the port management and the improvement of the operations processes. The aim of this paper is to define a new strategies to solve the container stacking problem (CSP) using an approach of optimization. Thus we define a new MIP (Mathematical Integer Program) to deal with the operational tasks in a containers terminal. In which we optimize the number of the stacks used to store a given number of inbound containers and also we minimize the related cost of the traveling distance for inbound containers between the sea side and the yard side. This paper is organized as follow: we introduce and locate first our problem, then we present the literature review of the CSP. The problem definition and the MIP introduction will be the subject of the next section, and we finish by presenting the findings and the future perspectives. As a proposed resolution approach for our MIP, we propose a developed genetic algorithm strategy (DGAS) as a metaheuristic and the Branch \& Cut (B\&C) as an exact method. Our main objective is to avoid reshuffles and to find out the best yard configuration to store inbound containers. The DGAS will be applied to an existing instances in the literature, and the obtained numerical results is compared with the Cplex results (B\&C). The main inputs for our proposed framework are the height, weight, destination, type containers \& yard bays, and the expected departure time (EDT). Our objective at the end is to have an optimized guide to the planners to easily define the unloading plan and the storage position for each container, giving an initial stacking state and a container demand. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/GOL.2016.7731679},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85001976354\&doi=10.1109\%2fGOL.2016.7731679\&partnerID=40\&md5=423d4fb53e049a2ad56b99740b466821},
	author = {Razouk, C.a and Benadada, Y.a and Boukachour, J.b}
}
@conference {Ayadi2014148,
	title = {Evolutionary algorithm for a Green vehicle routing problem with multiple trips},
	booktitle = {Proceedings of 2nd IEEE International Conference on Logistics Operations Management, GOL 2014},
	year = {2014},
	note = {cited By 0},
	pages = {148-154},
	abstract = {This paper deals with a variant of vehicle routing problem where vehicles are allowed to take more than one route during the working day. The depreciation of the vehicle may be a bad investment for green transportation because it could generate more emissions. Hence, it is necessary to satisfy green transportation requirements by reducing the CO2 emissions from road transportation. The objective is to optimize the amount of greenhouse gas emissions. A restricted fleet size is used to serve demands, so the vehicles could exceed the time horizon. It is subject also to minimize the maximum overtime to find feasible solutions. A mathematical model has been proposed for the Green Vehicle Routing Problem with multiple trips (GVRPM). An evolutionary algorithm has been developed to solve it by combining a genetic algorithm with a local search procedure. The effectiveness of our approach is tested on a set of benchmarks. Comparing with existing algorithm, our approach shows competitive performance and contributes many new best solutions. {\textcopyright} 2014 IEEE.},
	doi = {10.1109/GOL.2014.6887432},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908638694\&doi=10.1109\%2fGOL.2014.6887432\&partnerID=40\&md5=0d81bed3c4e8a5f48a72694e68674134},
	author = {Ayadi, R.a and Elidrissi, A.E.b and Benadada, Y.a and El Hilali Alaoui, A.a}
}
