Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in Small and Medium Enterprises

Scheduling problems are naturally dynamic. Increasing flexibility will help solve bottleneck issues, increase production, and improve performance and competitive advantage of Small Medium Enterprises (SMEs). Maximum make span, as well as the average workflow time and latency time of parts are considered the objectives of scheduling, which are compatible with the philosophy of on‐time production and supply chain management goals. In this study, these objectives were selected to optimize the resource utilization, minimize inventory turnover, and improve commitment to customers; simultaneously controlling these objectives improved system performance. In the job‐shop scheduling problem considered in this paper, the three objectives were to find the best total weight of the objectives, maximize the number of reserved jobs and improve job‐shop performance. To realize these targets, a multi‐parametric objective function was introduced with dynamic and flexible parameters. The other key accomplishment is the development of a new method called TIME_GELS that uses the gravitational emulation local search algorithm (GELS) for solving the multi‐objective flexible dynamic job‐shop scheduling problem. The proposed algorithm used two of the four parameters, namely velocity and gravity. The searching agents in this algorithm are a set of masses that interact with each other based on Newton’s laws of gravity and motion. The results of the proposed method are presented for slight, mediocre and complete flexibility stages. These provided average improvements of 6.61, 6.5 and 6.54 %. The results supported the efficiency of the proposed method for solving the job‐shop scheduling problem particularly in improving SME’s productivity.