An integrated lot-sizing and scheduling problem in a reconfigurable manufacturing system under workforce constraints

Nowadays, achieving higher levels of flexibility in manufacturing systems is necessary to maintain and enhance competitiveness. Accordingly, a new generation of production machine, namely Reconfigurable Machine Tool (RMT), has recently been introduced that can be effectively adapted to changes. Nevertheless, such systems are more worker-reliant, and neglecting workforce aspects results in suboptimal or even infeasible production schedules. In this regard, this study investigates an integrated lot-sizing and scheduling problem benefiting from RMTs under workforce constraints. First, a novel Mixed-Integer Linear Programming (MILP) model is provided to formulate the problem. Afterward, to confront the high complexity of the problem, an efficient Decomposition Heuristic (DH) empowered by a tailored feasibility cut is devised. A combination of MILP and Constraint Programming (CP) is employed in the DH to model the relevant master and sub-problems, respectively. Finally, the performance of the DH compared to the MILP model and an extended lower bound is evaluated. Moreover, the advantages of utilizing RMTs in the system are explored based on four defined key performance indicators.

Automated summary

In order to maintain and enhance competitiveness, achieving higher levels of flexibility in manufacturing systems has become necessary. This study investigates the integrated lot-sizing and scheduling problem under workforce constraints, using a new generation of production machine called Reconfigurable Machine Tool (RMT). A novel Mixed-Integer Linear Programming (MILP) model is formulated, and an efficient Decomposition Heuristic (DH) empowered by a tailored feasibility cut is devised to address the problem's high complexity.