Metaheuristic Algorithms for the Optimization of Integrated Production Scheduling and Vehicle Routing Problems in Supply Chains

Abstract

This paper examines the challenge of integrated production and distribution, aiming to deliver products to customers precisely on time. Customers, situated within the transportation network, have predefined requirements regarding demand volume and time frames. In the first phase (F1), the problem of planning and allocation of resources is presented as FJSP, while the second phase (F2) addresses the vehicle routing problem as CVRPTW. The first phase, F1, aims to optimize manufacturing processes by appropriately scheduling production tasks to maximize productivity and minimize the time of task execution on machines. Phase 2, F2, encompasses the process of distribution to customers, seeking to minimize the number of vehicles, delivery time, and overall distance travelled. As both problems are among the most challenging in combinatorial optimization, integrating these phases into a single supply chain system poses a significant challenge in problem-solving. A mathematical formulation has been developed to include planning and task allocation in production, as well as vehicle routing, to obtain an optimal solution to the integrated problem. The input data used in the observed case study represent real data in both the first and second phases, forming one integrated supply chain system. Experimental results support the applied methodology.