Dehua Xu

Traditional scheduling assumes that production can continue around the clock unless machines are not available. This is not without question for labor‐intensive industries where operations are performed manually. Motivated by operation management problems arising in the assembly stage of aeroengines and in the paint‐spraying stage of wooden furniture, we consider a scheduling problem with day shifts and breaks. We transform the problem into a new bin‐packing problem and present some theoretical analyses. To meet the needs of practical applications, we propose a binary integer programming (BIP) formulation and a hybrid genetic algorithm (HGA) that is based on five heuristic algorithms and two postoptimization procedures. Computational results indicate that the BIP formulation is suitable for some data sets while the HGA is the most promising in that it saves about 37% of day shifts compared to the best of the five heuristic algorithms in the context of scheduling.

We show by counterexample that the binary integer quadratic programming model proposed by Kaabi and Harrath [2019. “Scheduling on Uniform Parallel Machines with Periodic Unavailability Constraints.” International Journal of Production Research 57: 216–227] is incorrect. We fix this model and propose two linear ones.