The just-in-time job-shop scheduling problem with distinct due-dates for operations

In the just-in-time job-shop scheduling (JIT–JSS) problem every operation has a distinct due-date, and earliness and tardiness penalties. Any deviation from the due-date incurs penalties. The objective of JIT–JSS is to obtain a schedule, i.e., the completion time for performing the operations, with the smallest total (weighted) earliness and tardiness penalties. This paper presents a matheuristic algorithm for the JIT–JSS problem, which operates by decomposing the problem into smaller sub-problems, optimizing the sub-problems and delivering the optimal schedule for the problem. By solving a set of 72 benchmark instances ranging from 10 to 20 jobs and 20 to 200 operations we show that the proposed algorithm outperforms the state-of-the-art methods and the solver CPLEX, and obtains new best solutions for nearly 56% of the instances, including for 79% of the large instances with 20 jobs.