Green Logistics Oriented Framework for the Integrated Scheduling of Production and Distribution Networks - A Case of the Batch Process Industry

Nowadays, most consumable goods are produced and transported in batches. Within the globalized environment, the flow of these batches is raising dramatically to satisfy the recurrent demands of the increasing population. Planning the flow of these batches from suppliers to customers, through dynamic logistics systems, has a high degree of uncertainties on supply chain related decisions. In order to respond effectively and efficiently to these uncertainties, the supply chain network has to be redesigned, considering the economic and environmental requirements. To handle these requirements sustainably, green logistics is a promising approach. However, there is a lack of green logistics models which integrate both the production and distribution decisions within the batch process industries. This research develops a green logistics oriented framework in the case of the batch process industry. The framework integrates the tactical and operational levels of planning and scheduling to generate the optimum production and distribution decisions. A two-stage stochastic programming model is formulated to design and manage batch supply chain. This is a mixed-integer linear program of the two-stage stochastic production-distribution model with economic-environmental objectives. The first stage is concerned with optimum schedules of the production and distribution of the required batches. The second stage subsequently generates the optimum delivering velocities for the optimal distribution routes which are resulted from the first stage. Carbon emissions under uncertainties are incorporated as a function of random delivery velocities at different distribution routes within the network of the supply chain. To examine the applicability of the developed framework, the model is verified and validated through four theoretical scenarios as well as two real world case studies of multi-national batch process industries. The results of the analysis provide some insights results into supply chain costs and emissions. Based on the results, savings of about 43 percent of the total related economic and environmental costs were achieved compared to the actual situation at the case study companies. Cost savings mean long-term profitability, which is essential to sustain a worldwide competitive advantage. Furthermore, the stochastic and expected value solutions are compared in several scenarios. The stochastic solutions are consistently better with respect to costs and emissions. Calculations indicate that up to 13 percent of total cost savings are achieved when a stochastic approach is used to solve the problem as opposed to an expected value approach. The proposed framework supports academic green logistics models and real world supply chain decision making in batch process industry. Building such a framework provides a practical tool which links being green and being economically successful