Integrated optimization of inventory, replenishment, and vehicle routing for a sustainable supply chain utilizing a novel hybrid algorithm with carbon emission regulation

Abstract

An integrated approach of making supply chain decisions has been proven to be more cost-effective compared to the strategy of taking supply chain decisions sequentially. It has influenced researchers to break the norm and focus on integrated supply chain decision-making. Considering this an integrated sustainable supply chain network for a single vendor and multiple buyers is formulated along with trade credit policy. The vendor offers a trade credit policy to its buyers. Emissions generated from production, vehicle routing, and storage are taken into account. To minimize carbon emissions, a cap & trade policy is applied. The buyers are served by a fleet of vehicles available at the vendor location. Considering the aforementioned scenario, a mathematical formulation is developed so as to minimize the overall total cost subject to a set of constraints. The integrated decisions regarding production, inventory management, vehicle routing, and carbon emissions are taken. Besides this, a unique metaheuristic technique is also suggested to solve the proposed problem. The solutions obtained through the process of well-known genetic algorithm are further improved during the ruin and recreate phase of the hybridized algorithm named as GA-RR. An in-depth empirical examination of GA-RR algorithm is also done through a quantitative analysis of exploration and exploitation. To better illustrate the proposed mathematical problem, two numerical examples are also demonstrated. It is followed by an extensive sensitivity analysis of the various input parameters so as to highlight important results. It can be concluded that GA-RR outperforms other existing metaheuristic techniques for the proposed mathematical problem.