Recently, the petroleum sector in Morocco has been liberalized which has a significant effect for petroleum product distributors. Since the beginning of December 2015, fuel prices are freely determined. This event presents many constraints affecting the balance of the sector plus the competition between its economic players. The lack of accompanying measures by the State makes this vital reform for public finances that stop subsidizing the price of gasoline vulnerable. As all fuel products are imported, we will be interested in the evolution by making forecasts of the price of fuels in the Moroccan market. In this context, our paper aims mainly to study the selling price of diesel and gasoline in order to provide precise forecasts to the company and to respect the permissible error margin of 3%. To this end, we worked with a widely used approach for price forecasting: artificial neural networks technique (radial basis function). Recently, it is suggested to work with artificial neural networks in forecasting field as an alternative to the traditional linear methods. We developed a radial basis function network to come up with conclusions in terms of the superiority in forecasting performance. Consequently, the radial basis function technique proved its strength manifested in the error that was further minimized: 1.95% instead of 2.85% for autoregressive integrated moving average (ARIMA) model used in our previous work. The error is further minimized by applying radial basis function technique.

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The need for a good forecast estimate is imperative for managing flows in a supply chain. For this, it is necessary to make forecasts and integrate them into the flow control models, in particular in contexts where demand is very variable. However, forecasts are never reliable, hence the need to give a measure of the quality of these forecasts, by giving a measure of the forecast uncertainty linked to the estimate made. Different forecasting models have been developed in the past, particularly in the statistical area. Before going to our application on real industrial cases which highlights a prospective study of demand forecasting and a comparative study of sales price forecasts, we begin, in the first section of this chapter, by presenting the forecasting models, as well as their validation and monitoring.

The attributes of the vehicle routing problem (VRP) are as many additional characteristics or constraints which aim to better take into account the specificities of real application cs. The variants of the VRP thus formed are the support of an extremely rich literature, comprising an immense variety of heuristics. This article constitutes an industrial application and an objective synthesis of successful and challenging heuristic concepts for time-windowed VRP problems. The purpose will be to minimize transport costs and determining the optimal number of trucks by applying a transport algorithm. The results show that the solution method should help to increase the competitiveness of transportation operations in this important economic sector.

The liberalization of the petroleum sector in Morocco has a significant effect for petroleum product distributors. Since the beginning of December 2015, fuel prices are freely determined. This event presents many constraints affecting the balance of the sector plus the competition among its economic players. As all fuel products are imported, we will be interested in the evolution by making forecasts of the price of fuels in the Moroccan market. In this context, our paper aims mainly to study the time series of diesel and gasoline in order to provide precise forecasts to the company and to respect the permissible error margin of 3%. To this end, the harmonic dynamic regression model through the proposed process approach yielded excellent forecasting results for the first quarter of 2017 with an average error margin of 1.617%. Compared to ARIMA model, the harmonic dynamic regression proves its strength manifested in the low rate of error. In addition, the assumption that the residuals are a Gaussian white noise has always been verified. The forecasts obtained are very crucial for managers to take good decisions at the strategic level.