A MILP model for single machine family scheduling with sequence-dependent batch setup and controllable processing times
For researchers in scheduling, this work provides a mathematical programming approach to a specific scheduling problem, but it is incremental as it builds on existing MILP and dynamic programming methods.
This paper proposes MILP models for single machine family scheduling with sequence-dependent batch setup and controllable processing times, and compares their performance with optimal control strategies from dynamic programming. The best model uses two sets of binary variables for job positioning and sequencing.
A mathematical programming model for a class of single machine family scheduling problem is described in this technical report, with the aim of comparing the performance in solving the scheduling problem by means of mathematical programming with the performance obtained when using optimal control strategies, that can be derived from the application of a dynamic programming-based methodology proposed by the Author. The scheduling problem is characterized by the presence of sequence-dependent batch setup and controllable processing times; moreover, the generalized due-date model is adopted in the problem. Three mixed-integer linear programming (MILP) models are proposed. The best one, from the performance point of view, is a model which makes use of two sets of binary variables: the former to define the relative position of jobs and the latter to define the exact sequence of jobs. In addition, one of the model exploits a stage-based state space representation which can be adopted to define the dynamics of the system.