Optimizing the Raw Material Supply Chain of the Wood Biomass Power Generation Industry for Different Stakeholders’ Benefits: An Analysis of Inner Mongolia, China

Abstract

A large number of sand shrubs have been planted in western China, especially in Inner Mongolia. Sand shrubs produce a large amount of stump residue, and wood biomass power generation enterprises that use stump residue as raw materials have emerged in Wushen Banner and other areas. In this paper, the Mixed Integer Linear Programming (MILP) model is used to optimize the raw material supply chain of forest biomass power generation enterprises. Optimizations with different objectives represent the choices of different stakeholders. The optimization results are listed as follows. (1) The self-issuance behavior of enterprises is inconsistent with the enterprise behavior required by social planners; (2) When social planners only pay attention to environmental benefits, the utilization rate of raw materials in towns located far from a power plant will be greatly reduced, which is not conducive for the reuse of stump residue; (3) When social planners consider economic, environmental, and social benefits simultaneously, the utilization rate of raw materials in each town will be significantly improved, resources will be effectively utilized, and certain economic benefits will be realized; (4) It is possible to reduce the difficulty of achieving optimization goals by promoting industrial development and encouraging technological progress.

Multi-Oligopoly Sequential Pricing Mechanisms and Their Game Analysis in Raw Material Supply Chains

“…Wang et al [5] developed a bi-objective mixed-integer nonlinear programming model to optimize the supply chain network consisting of raw material suppliers, final product manufacturers, and distribution centers. Bai et al [6] used the mixed-integer linear programming (MILP) model to optimize the raw material supply chain of forest biomass power generation enterprises. Razmi et al [7] developed a dynamic mixed-integer linear programming (DMILP) model to optimize a seasonal raw material supply chain network by considering a multilevel supply chain with multiple products and multiple time periods.…”