We study the scheduling problem of minimising weighted completion times on parallel identical batching machines with dynamic job arrivals and incompatible job families. Each job is associated with a family, weight (priority), release time, and size. Batching machines can process simultaneously up to a specified total size of the jobs of a particular family. The scheduling problem can be represented by. We present a mathematical model and heuristic algorithms employing different local search procedures individually and sequentially under a variable neighbourhood search scheme. We have shown that among local searches, repositioning the batches instead of jobs yields better results. The best-performing heuristic algorithm is capable of generating solutions within 0.6% of the best overall heuristic solution for each instance in a reasonable amount of time. When this heuristic is compared against the mathematical model, solutions that are 3.7% above optimal on average in the 15-job problem instances are possible.
- batch scheduling
- mathematical modelling
- variable neighbourhood search
ASJC Scopus subject areas
- Strategy and Management
- Management Science and Operations Research
- Industrial and Manufacturing Engineering