• A metaheuristic solution approach for the time-constrained project scheduling problem

      Verbeeck, Cédric; Van Peteghem, Vincent; Vanhoucke, Mario; Vansteenwegen, Pieter; Aghezzaf, El-Houssaine (OR Spectrum - Quantitative Approaches in Management, 2017)
      In this paper, a metaheuristic solution procedure for the Time-Constrained Project Scheduling Problem is proposed, in which additional resources can be temporarily allocated to meet a given deadline. The problem consists of determining a schedule such that the project is completed on time and that the total additional cost for the resources is minimized. For this problem, an artificial immune system is proposed, in which each solution is represented by a vector of activity start times. A local search procedure, which tries to shift cost causing activities, is applied to each population schedule. Computational experiments are applied to modified resource-constrained project scheduling problem benchmark instances and reveal promising results.
    • A normal approximation model for safety stock optimization in a two-echelon distribution system

      Desmet, Bram; Aghezzaf, El-Houssaine; Vanmaele, Hendrik (Journal of the Operational Research Society, 2010)
      This paper presents an approximation model for the retailer replenishment lead-times in a two-echelon distribution system, and discusses its implementation for safety stock optimization in a one-warehouse and N-identical retailers system. The model assumes normality of demand and nominal lead times. It takes into account not only the averages of these parameters but also their variances. This approximation model is first tested on a two-echelon, one-warehouse and N-identical retailers system using discrete event simulation. It is then applied to optimize the safety stock in a two-echelon distribution system of a European market leader in the production and distribution of air conditioning equipment. Results of this implementation are analysed and discussed in detail.
    • A data-driven framework for predicting weather impact on high-volume low-margin retail products

      Verstraete, Guylian; Aghezzaf, El-Houssaine; Desmet, Bram (Journal of Retailing and Consumer Services, 2019)
      Accurate demand forecasting is of critical importance to retail companies operating in high-volume low-margin industries. Inaccuracies in the forecasts lead either to stock-outs or to excess inventories, resulting in either lost sales or higher working capital, and for both cases in extra unnecessary costs. Prediction accuracy is essential to retail companies having a part of their product portfolio manufactured in low-cost countries and requiring long delivery times. It is rather vital when the demand for these goods is strongly weather dependent. The combination of long delivery times and weather dependence creates a business challenge, as the availability period of accurate weather information is much shorter than the lead time. In this paper we propose a methodology that handles the impact of both the short-term (with available weather data) and the long-term weather uncertainty on the forecast. For the former, the proposed framework is capable of automatically selecting the best prediction model. For latter, the framework fits a distribution on simulated and aggregated sales using the short-term regression model with historical weather data. This framework has been tested on a company's sales data and is proven to satisfactorily address the challenges that the company is facing.
    • Intelligent Techniques for Safety-Stock Optimization in Networked Manufacturing Systems

      Desmet, Bram; Aghezzaf, El-Houssaine; Vanmaele, Hendrik (2010)
    • A leading macroeconomic indicators' based framework to automatically generate tactical sales forecasts

      Verstraete, Gylian; Aghezzaf, El-Houssaine; Desmet, Bram (Computers and Industrial Engineering, 2020)
      Tactical sales forecasting is fundamental to production, transportation and personnel decisions at all levels of a supply chain. Traditional forecasting methods extrapolate historical sales information to predict future sales. As a result, these methods are not capable of anticipating macroeconomic changes in the business environment that often have a significant impact on the demand. To account for these macroeconomic changes, companies adjust either their statistical forecast manually or rely on an expert forecast. However, both approaches are notoriously biased and expensive. This paper investigates the use of leading macroeconomic indicators in the tactical sales forecasting process. A forecasting framework is established that automatically selects the relevant variables and predicts future sales. Next, the seasonal component is predicted by the seasonal naive method and the long-term trend using a LASSO regression method with macroeconomic indicators, while keeping the size of the indicator’s set as small as possible. Finally, the accuracy of the proposed framework is evaluated by quantifying the impact of each individual component. The carried out analysis has shown that the proposed framework achieves a reduction of 54.5% in mean absolute percentage error when compared to the naive forecasting method. Moreover, compared to the best performing conventional methods, a reduction of 25.6% is achieved in the tactical time window over three different real-life case studies from different geographical areas.
    • Safety Stock Optimization in Two-Echelon Assembly systems: Normal Approximation Models

      Desmet, Bram; Aghezzaf, El-Houssaine; Vanmaele, Hendrik (International Journal of Production Research, 2010)
      This paper tackles the problem of optimising safety stocks in a two-echelon assembly system. It presents and discusses several approximation models for the assembly lead-time under the assumption of normality of the assembly demand and normality of components’ nominal lead times. These approximation models are subsequently used to optimise safety stocks throughout a two-echelon assembly system. They are then tested on a particular two-echelon N-identical component assembly system. The obtained results are compared with the results of a discrete event simulation. Finally, it is shown that lead-times and safety stock results already obtained for a two-echelon distribution system can also be derived without difficulty from those of two-echelon assembly systems.