Abstract
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.
Highlights
One of the problems that could be addressed in the scope of supply chain management is production-distribution planning which is an operational activity that does a plan for the production process, to give an idea to management as to what quantity of materials and other
We formulate the proposed model as a multiobjective robust stochastic mixed-integer nonlinear programming problem, after linearization, it is solved by using a hybrid algorithm that is a combination of the extended Monte Carlo sampling method, modified ε-constraint technique type of the a posteriori methods which is a new version of the traditional famous multicriteria decision making method ε-constraint for solving multiobjective problems with conflicting objectives simultaneously and the L-shaped method which is on of the efficient heuristic method to solve two-stage stochastic optimization problems. This formulation takes into account the expected total cost of supply chain, and the risk reflected by the variability of the total cost
In this paper a multiobjective two-stage stochastic programming model is developed to deal with production-distribution planning in an uncertain supply chain considering workers productivity
Summary
One of the problems that could be addressed in the scope of supply chain management is production-distribution planning which is an operational activity that does a plan for the production process, to give an idea to management as to what quantity of materials and other. We formulate the proposed model as a multiobjective robust stochastic mixed-integer nonlinear programming problem, after linearization, it is solved by using a hybrid algorithm that is a combination of the extended Monte Carlo sampling method, modified ε-constraint technique type of the a posteriori methods which is a new version of the traditional famous multicriteria decision making method ε-constraint for solving multiobjective problems with conflicting objectives simultaneously and the L-shaped method which is on of the efficient heuristic method to solve two-stage stochastic optimization problems This formulation takes into account the expected total cost of supply chain, and the risk reflected by the variability of the total cost.
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