Scheduling a set of jobs with convex piecewise linear cost functions on a single-batch-processing machine

Abstract

We investigate a general single-batch-processing machine scheduling problem of minimizing the total costs of all jobs with general convex piecewise linear cost functions, where the processing time, due dates, and sizes of jobs are non-uniform. Since the studied problem is NP-hard and the objective is irregular, we adopt a practical 3-step method that starts from a specific job sequence, finds the optimal and near-optimal schedules of the given job sequence, and then iteratively improves the schedules. For the restricted problem where the job sequence is given, we propose a dynamic programming algorithm to obtain the optimal schedule. To further reduce the running time, we also devise a span-limit tree search (SLTS) approach to find the near-optimal schedule. We then design a local search method to improve the solutions solved by SLTS. Finally, we design a branch and bound algorithm with a lower bound method for small-sized instances. All proposed methods are based on our analysis of the mathematical properties of the scheduling problem, and extensive numerical experiments are conducted to demonstrate their effectiveness and efficiency.