Zero-order production planning models with stochastic demand and workload-dependent lead times

Abstract

We present three different formulations of a simple production planning problem that treat workload-dependent lead times, limited capacity and stochastic demand in an integrated fashion. We compare chance-constrained models, two-stage stochastic programming and robust optimisation using computational experiments. Our results show that the robust optimisation approach is promising, but all the different models face different but challenging issues in addressing this complex problem. We also conclude that successful approximations to this difficult problem with the potential for practical implementation can be developed

A metamodel-based Monte Carlo simulation approach for responsive production planning of manufacturing systems

“…These techniques tend to be computationally less demanding than stochastic programming, but also represent uncertainty and its consequences in different ways. Aouam and Uzsoy [28,29] compare a number of these models in the context of a very simple single-stage production-inventory system under stochastic demand, and find that they need to be parameterized with care to yield desirable results.…”

Improving production planning through finite-capacity MRP

“…However, when used in field applications, MRP shows some weak points: it ignores facilities capacity constraints and assumes fixed lead-times (Rossi and Pero 2011;Jodlbauer and Reitner 2012;Sun et al 2012;Dolgui et al 2013;Aouam and Uzsoy 2015). Either shortcoming above leads to infeasible production schedules, fluctuating workloads over time and significant users' effort to adjust the plans.…”

Heuristic Algorithm for a WIP Projection Problem at Finite Capacity in Semiconductor Manufacturing

“…constraints (13) are not satisfied. In this case, the toolset is unable to process all its affected steps during the considered period so its loading should be balanced over subsequent periods.…”

“…Indeed, it is proven that MRP method can be inefficient and may produce unrealistic production schedules when used in field applications. It ignores capacity constraints and assumes fixed cycle times ( [10], [11], [12], [13]). However, in semiconductor facilities, cycle times depend on many factors, such as machine utilization rate, lot size, inventory and dispatching rules, and are thus variable.…”