Job shop scheduling with combinatorial priority rules in a furniture company

This paper analyzed a job shop scheduling problem with combinatorial priority rule from a case study of a furniture company, whose product – cupboard – has the highest volume and revenue while it is struggling with tardiness at about 10%. The goal of this scheduling is to minimize the mean flow time, mean tardiness time and the proportion of tardy jobs and compare its performance to one traditional priority rule – Longest Processing Time first. As results, the combinatorial priority rules in scheduling would bring a positive outcome with reducing mean flow time and around 3.45% for proportion of tardy jobs. Moreover, the most significant improvement is true for mean tardiness time, which is reduced by over 3 times compared to Longest Processing Time first alternative. This combinatorial priority rule is recommended to schedule in flexibly ways according to the customer’s demand. It should be considered to use as the initial alternative for the improvement search algorithms to find out better solutions.