Variable neighborhood-based Cuckoo Search for production routing with time window and setup times

Major corporations compete over the strengths of their supply chains. Integrating production and distribution operations helps improve supply chain connectedness and responsiveness beyond the standalone optimization norms. This study proposes an original Mixed-Integer Linear Programming (MILP) formulation for the Production scheduling-based Routing Problem with Time Window and Setup Times (PRP-TWST). For this purpose, the identical parallel machine scheduling is integrated with the vehicle routing problem. Considering the highly intractable solution spaces of the integrated problem, hybrid metaheuristics based on the Variable Neighborhood Search (VNS), Particle Swarm Optimization (PSO), and Cuckoo Search (CS) algorithms are developed to solve the PRP-TWST problem. Extensive numerical experiments are conducted to evaluate the effectiveness of the developed algorithms considering the total delay time as the objective function. The results are supportive of the VNS-based CS algorithm’s effectiveness; the developed metaheuristics can be considered strong benchmarks for further developments in the field. This study is concluded by suggesting directions for modeling and managing integrated operations in the supply chain context. •Production scheduling and vehicle routing decisions are integrated.•Setup time and time-window constraints are included in a new mathematical formulation.•Two well-known metaheuristics are improved for more effective optimization of the problem.