Supply Chain and Logistics Management, Multicriteria Decision Making, Operations Research, Meta-Heuristics.
Hajlaoui, S., Halouani, N., Chabchoub, H., (2020), "An implementation of the hesitant fuzzy linguistic ordered interaction distance operators in a multi-attribute group decision making problem", ,
Production and Operations Management (U, G), Supply Chain and Logistics Management (U, G), Quality and Operations Management (G), Modeling and Decision (U), Advanced Topics in Mathematical Programming (G), Quality Management (U), Quantitative Business Analysis (U), Research Methodology (G).
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all.
This person’s work contributes towards the following SDG(s):
Published in: Journal of Ambient Intelligence and Humanized Computing
Jul 08, 2019
Municipal solid waste (MSW) is considered as one of the primary factors that contribute greatly to the rising of climate change and global warming affecting sustainable development in many different ways. It is indeed necessary to investigate an efficient computerized method for the optimization of MSW collection that minimizes the environmental and other factors according to a given waste collection scenario. In this paper, we propose a heuristic-based smart routing algorithm for MSW collection and implement it by Python scripts in ArcGIS to calculate optimal solutions of the model including routes and total travelling distances and operational time of vehicles. The algorithm will be validated on a case study of Sfax city which is the second largest and among the most polluted cities in Tunisia. A novel optimization model for the MSW collection in Sfax is designed and given to the algorithm for calculation. The achieved results are then compared with those of the current real scenario as well as evaluated by a multi-criteria decision aid method namely PROMETHEE in terms of environment and economic criteria.
Published in: Soft Computing
Jun 03, 2019
This paper deals with an optimization problem encountered in the field of transport of goods and services, namely the K-traveling repairman problem (K-TRP). This problem is a generalization of the metric traveling repairman problem (TRP) which is also known as the deliveryman problem and the minimum latency problem. The K-TRP and the related problems can be considered as “customer-centric” routing problems because the objectif function consists in minimize the sum of the waiting times of customers rather than the vehicles travel time. These problems are also considered as problems with “cumulative costs.” In this paper, we propose a quantum particle swarm optimization (QPSO) method to solve the K-TRP. In order to avoid the violations of problem constraints, the proposed approach also incorporates a heuristic repair operator that uses problem-specific knowledge instead of the penalty function technique commonly used for constrained problem. To the best of our knowledge, this study is the first to report on the application of the QPSO method to the K-TRP. Experimental results obtained on sets of the Capacitated Vehicle Routing Problem test instances, of up to 100 customers, available in the literature clearly demonstrate the competitiveness of the proposed method compared to the commercial MIP solver CPLEX 12.5 of IBM-ILOG and the state-of-the-art heuristic methods. The results also demonstrate that the proposed approach was able to reach more optimal solutions and to improve 5 best known solutions in a short and reasonable computation time.
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