Simulating alternative production policies with sequence-dependent costs

This article studies various sequencing and inventory rules in a manufacturing environment with nonlinear technological coefficients and stochastic demand. Multiple products require setup on a single machine and setup time and setup cost decrease with repeated setups. Furthermore, setup operations for different products have common components and an item can benefit from the setup operation of another item. The single-level, multi-item lot size model is used to model the production environment. The learning curve is used to represent this decrease in setup time with repeated setups. The learning transmission between items affects the scheduling of the products and the resulting model considers simultaneous decisions about lot sizing and sequencing in a nonlinear formulation. The problem is formulated and various production policies are simulated. Two sequencing rules and four inventory rules are examined. A simulation experiment of 6400 runs is used to compare the schedules produced by simple policies and those produced by more involved ones. A statistical analysis of the simulation results indicates that the simple rules perform equally well and in some cases better than the computationally harder rules.