Green Procurement Relationships Development under Carbon Emissions Regulations: A Bi-Level Programming Approach

Abstract

A multi-period Stackelberg game is adopted to study a green procurement relationship between manufacturers and suppliers in a supply chain. The manufacturers are considered as leaders, while the suppliers are modelled as followers in this Stackelberg game. Accordingly, a mixed binary linear bi-level programming model is developed to elaborate the game in consideration of carbon tax scheme. The upper level (the leader) aims at selecting a proper number of suitable suppliers to provide heterogeneous raw materials at the lowest operational cost. The objective of the lower level (the follower) is to find optimal purchasing quantities of raw materials. In addition, two lemmas are introduced to transform the mixed linear bi-level programming model into a single level linear programming model. The numerical example illustrates that: (1) the manufacturer prefers to adopt the multiple sourcing strategy due to the flexibility; (2) keeping stable supplies and large order volumes could effectively reduce carbon emissions for the suppliers and make the supply chain greener.

Integrated Optimization of Sustainable Transportation and Inventory with Multiplayer Dynamic Game under Carbon Tax Policy

“…Many studies have investigated the significant impact of a carbon tax on supply chain performance. Bao and Zhang [22] developed a mixed linear programming model to explore sustainable procurement relationships in a supply chain under a carbon tax scheme. Fahimnia et al [4] proposed a tactical supply-planning model that optimizes carbon emissions and economic targets under a carbon tax.…”

Optimized Configuration of Manufacturing Resources for Middle and Lower Batch Customization Enterprises in Cloud Manufacturing Environment

“…e GA searches for the optimal solution in the global scope through coding, selection, intersection, and mutation, which can effectively solve the problem of largescale nonlinear optimization [23,24]. Aiming at the characteristics of the optimization problem of manufacturing resource configuration, this paper uses a hybrid genetic algorithm embedded in the bi-level programming principle to solve it [25,26]. e solution process of the algorithm is as follows.…”