A Study Of Multi-plant Integrated Production-transportation Planning Based On Data Envelopment Analysis

Under today’s competitive environment, optimization models and algorithms, decision support systems and computerized analysis tools are effective examples of approaches taken by companies in an attempt to improve their operational performance. This all helps companies to remain competitive under the threat of increasing competition.An integrated production and transportation planning problem is considered in this paper:how to optimally determine aggregate production planning and transportation trips among distribution centers (DCs) and the corresponding transportation volumes, where production and transportation plan are considered simultaneously.Our model differs from previous works in the technique to characterize production function. We assume no a priori information on production technology. In particular, this paper introduces data envelopment analysis (DEA), a nonparametric method to describe production process, into integrated production-transportation problem, which is a different approach compared to the previous works in this field. There have been many papers covering the integrated production-transportation problem in a tactical level, some of which include the management of inventory especially in multi-period situations. However, most of them link the production process with a priori production relationship. DEA is the one of the best modeling tools for providing a satisfactory solution. By using "satisfactory solution", we imply that our model is based on limited information about production process that the decision maker (DM) could be able to secure.The second distinguishing aspect of our models is that we take the uncertainty in production process into consideration. In fact, uncertainty is a rule, rather than an exception in real life. Production relationships are often stochastic in nature. For example, in labor intensive industries, the products will be easily influenced by the people’s emotion. With the rise of mechanized production, the production process can be affected by blackouts, machine error or other uncontrollable factors. Our stochastic extension enhances the applicability of our models and narrows the gap between theory and practice by better reflecting the actual situation in production process.One step further, a numerical example is used to illustrate the models. In deterministic scenario, sensitivity analysis is used by changing the ratio of order of magnitude of input price to that of for transportation. While in the stochastic scenario3confidence levels are used to get better understanding of the models.