Simulating alternative production policies with sequence-dependent costs

This article studies various sequencing and inventory rules in a manufactur ing environment with nonlinear technological coefficients and stochastic de mand. Multiple products require setup on a single machine and setup time an d setup cost decrease with repeat ed 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 us ed to represent this decrease in setup time with repeated setups. The learn ing transmission between items affects the scheduling of the products and t he resulting model considers simultaneous decisions about lot sizing and se quencing 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 invol ved ones. A statistical analysis of the simulation results indicates that t he simple rules perform equally well and in some cases better than the comp utationally harder rules.