A Lagrangian heuristic algorithm for sustainable supply chain network considering CO2 emission. (10th October 2020) - Zheng, Meimei; Li, Wei; Liu, Ye; Liu, Xiao
Abstract: Manufacturing companies encounter pressure related to not only cost and service level but also certain limitations based on the constraints of energy saving and emission reduction. This study investigates production planning for a sustainable supply chain, for which factors such as the limits for CO2 emission, stochastic demands, service level, and inventory capacities are considered. We build a mixed-integer programming model and propose a Lagrangian relaxation (LR) algorithm to solve the large-scale production planning problem in a cost-efficient way. Based on the historical data of HY Automobile Co. in China and 120 randomly generated instances, we find that the LR algorithm can generate near-optimal solutions with less than a 1% difference as compared to CPLEX solutions. The algorithm can find a solution within 5 min for a case with a large problem size, whereas CPLEX cannot find the solution in an hour. At the same service level, the cost decreases with the initial inventory capacity and CO2 emission limit and increases with the demand and number of the types of products. The cost gap that is caused by increasing these parameters becomes larger when the service level is higher. This finding indicates that the parameters' values are important to the company when a high service level is required. Graphical abstract: Image 1 Highlights : ∙A production planning model considering emission, stochastic demands, and service level is formulated for a sustainable supply chain ∙A Lagrangian relaxation algorithm is proposed to solve the problem in a cost-efficient way ∙A real case study is conducted to demonstrate the effectiveness of the proposed algorithm ∙The solutions generated by the proposed algorithm and CPLEX are compared ∙Sensitivity analysis is conducted to show the trade-off between the cost and service level