Job Shop Scheduling with Limited-Capacity Buffers using Constraint Programming and Genetic Algorithms

This article aims to propose a new approach for solving production planning and scheduling in the process industries, in such a way to be adaptable to any manufacturing plant and exploring the use of innovative AI-style technologies. The main contributions of the work are: (i) the design of a specific data format to describe any manufacturing plant (including resources, layout and production recipes), being the input of the method; and (ii) the consideration of limited-capacity production lines with intermediate and final buffers in the optimization. The method involves two stages: the first one corresponds to a deterministic optimization algorithm based on Constraint Programming modelling to solve the JSSP in an ideal scenario with no storage limitation; while the second one is a Genetic Algorithm that only comes into play when the solutions obtained from the first one are infeasible for the available storage, so it is a complementary layer to try to solve the mismatches stochastically. Copyright (c) 2023 The Authors.