Product Pricing in a Sustainable Supply Chain in the Conditions of Market Boom and Bust

In this paper, the problem of product pricing in a sustainable supply chain consisting of several production centers, several distribution centers, and customers in two direct and indirect channels is modeled. this model aims to achieve more profit in a sustainable supply chain by complying with environmental and social considerations. in order to solve the model, a hybrid of genetic algorithm (ga) and grey wolf optimizer (gwo) called genetic grey wolf optimizer (ggwo) was developed, with the ability to search the problem-solving space based on the simultaneous use of the operators of the two algorithms. analysis of the numerical example with ggwo and its comparison with other solution methods indicate that pricing in direct and indirect channels is strongly related to the price elasticity coefficient of the substitute product and the price elasticity of demand so that the market boom and bust of the depends on the proper determination of these model parameters. it is also observed that in the conditions if market boom, demand increased in both direct and indirect channels and that product prices decreased in both channels. therefore, the marginal profit increased due to the sale of more products. the ggwo algorithm has faster convergence compared to ga and ggwo, while the solution time in this method is higher than other solution methods. analyzing different numerical examples suggest that the efficiency of the used algorithms, in particular ggwo, is higher than exact methods.