A Multiobjective Stochastic Production-Distribution Planning Problem in an Uncertain Environment Considering Risk and Workers Productivity

• Published in: Mathematical Problems in Engineering 2011-01, Vol.2011 (2011), p.1784-1797-100
• Main Authors: Mirzapour Al-e-Hashem, S. M. J., Baboli, A., Sadjadi, S. J., Aryanezhad, M. B.
• Format: Article
• Language: English
• Subjects: Algorithms; Computer simulation; Decision making; Heuristic; Lead time; Linear programming; Marketing; Mathematical models; Mathematical problems; Monte Carlo methods; Multiple objective analysis; Operating costs; Optimization; Parameter robustness; Parameter uncertainty; Production costs; Productivity; Random variables; Stochastic models; Stochastic programming; Stochasticity; Studies; Supply chains
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2011/406398

A multi-objective two stage stochastic programming model is proposed to deal with a multi-period multi-product multi-site production-distribution planning problem for a midterm planning horizon. The presented model involves majority of supply chain cost parameters such as transportation cost, inventory holding cost, shortage cost, production cost. Moreover some respects as lead time, outsourcing, employment, dismissal, workers productivity and training are considered. Due to the uncertain nature of the supply chain, it is assumed that cost parameters and demand fluctuations are random variables and follow from a pre-defined probability distribution. To develop a robust stochastic model, an additional objective functions is added to the traditional production-distribution-planning problem. So, our multi-objective model includes (i) the minimization of the expected total cost of supply chain, (ii) the minimization of the variance of the total cost of supply chain and (iii) the maximization of the workers productivity through training courses that could be held during the planning horizon. Then, the proposed model is solved applying a hybrid algorithm that is a combination of Monte Carlo sampling method, modified ε-constraint method and L-shaped method. Finally, a numerical example is solved to demonstrate the validity of the model as well as the efficiency of the hybrid algorithm.