Conclusion
This paper studied the problem of integrated production and logistics planning under the commit-to-deliver policy. This problem arises in many supply chains and is particularly relevant to the contract manufacturing industry, which deals with a group of customers and relies on third party services for delivering the orders. We have provided a framework (model and solution approaches) for a decision support tool that can assist manufacturers to better coordinate their production scheduling and shipping planning for improved overall performance. We studied a manufacturing setting with multiple unrelated parallel processors where orders have release dates, sequence dependent setup times and delivery due dates with non linear tardiness penalties. On the logistics side, we considered third party air and surface transportation options that provide a variety of pre-scheduled shipping options with different lead times and costs. While problem of integrating production and distribution has been studied before and its advantages were demonstrated in practice, this is the first research that considers the manufacturing scheduling in detail to allow for more realistic settings. We have modeled the problem as a mixed integer linear model. The problem is NP-hard; thus, we proposed a decomposition method based on successive sub problem solving method. We decomposed the problem to a set of sub problems for each processor. After transformation, we solve the sub problems with an approach based on traveling repairman with profit. An adaptation of variable target value method was presented for automatic parameter setting in our solution methodology. The performance of our methodology was tested against CPLEX optimum solution and linear relaxation lower bound trough a set of computational experiments. We demonstrate the effectiveness of our problem to achieve under 0.5 percent optimality gap against linear relaxation lower bound in average of less than 3 seconds. Moreover, we have showed that the algorithm is capable of solving larger problems efficiently and provide high quality solutions. In this research, we address the gap in the integrated production and logistics problem literature in addition to providing an effective solution method for the class of parallel machine scheduling problems with time-dependent cost functions.
We provided an effective modeling and solution approach for integrating production scheduling and logistics planning under commit-to-deliver policy for Manufactures.
This research can be further extended to account for the processor failures, delivery lead-time uncertainty, and shipping cost discounts due to consolidation. Moreover, this work can be further expanded to account for more detailed aspects of logistics and distributions systems especially important for the integrated production and logistics planning in global supply chains with flexible manufacturing facilities.