Abstract:
An increase in environmental issues has encouraged the consideration of various factors that influence the environment. In this regard, the green supply chain has attracted the attention of researchers because of its considerable impacts on the environment. This study, therefore, was an attempt to design a forward/revers logistics network by putting emphasis on some environmental issues like the quantity of CO2 emission in its model. In this logistics network, three objective functions including minimizing the total cost and quantity of CO2 emission as well as maximizing the satisfaction of customers are considered simultaneously. This persuaded the researchers to adopt multi-objective optimization methods. Thus, Non-dominated sorting genetic algorithms (NSGA-ӀӀ) and Multi-objective particle swarm optimization (MOPSO) are proposed to cope with the problem. Finally, the results of the experiments on several test problems are verified by GAMS software. They confirm the superiority of NSGA-ӀӀ over MOPSO in terms of all comparison metrics.
Machine summary:
They assumed demand and price to be stochastic by using mixed integer linear programming, studied forward/reverse logistics models which include multi-period, multi-echelon and vehicle routing, and considered particle swarm optimization and artificial immune system algorithms.
com The Comparison of Neural Networks’ Structures for Forecasting Ilham Slimani*, a, Ilhame El Farissi b and Said Achchaba a Al-Qualsadi Research and Development Team, National Higher School for Computer Science and System analysis (ENSIAS), Mohammed V University, Rabat, Morocco b Laboratory LSE2I, National School of Applied Sciences (ENSAO), Mohammed first University, Oujda, Morocco Abstract This paper considers the application of neural networks to demand forecasting in a simple supply chain composed of asingle retailer and his supplier with a game theoretic approach.
Neural networks; Artificial intelligence; Supply chain management; Information sharing; Demand forecasting; Game theory Keywords: .
For example, in sales, it is possible to predict the upcoming requests that provide a more accurate vision of production or stock level (Slimani, ElFarissi, & Achchab, 2015): (Borade and Bansod,2011) made a comparison of neural network forecasts on the basis of costs and profits in supply chain using three multi-criteria decision-making tools for evaluation.
(Doganis, Alexandridis, & Panagiotis, 2006) Present a complete framework that can be used for developing nonlinear time series sales forecasting models and apply a combinatory technique of two artificial intelligence methods, namely the RBF (Radial Basis Function) neural net architecture and a specifically designed genetic algorithm (GA) for forecasting sales data of fresh milk.
g. (Chang & Wang, 2006), (Gumus, Guneri, & Keles, 2009), (Wang, Chen, Wang, & Lin, 2006)).