Deep reinforcement learning (DRL) has shown great potential for sequential decision-making, including early developments in inventory control. Yet, the abundance of choices that come with designing a DRL algorithm, combined with the intense computational effort to tune and evaluate each choice,...

Forecasting spare parts demand is notoriously difficult, as demand is typically intermittent and lumpy. Specialized methods such as that by Croston are available, but these are not based on the repair operations that cause the intermittency and lumpiness of demand. In this paper, we do propose a...

Purpose: The purpose of this paper is to show that intentional demand forecast bias can improve warehouse capacity planning and labour efficiency. It presents an empirical methodology to detect and implement forecast bias. Design/methodology/approach: A forecast model integrates historical...

Asphalt roads are gradually deteriorating over time and need road mending to remain in a good state. Inspections are performed to obtain information about the current state of the road; when the damage achieves the established standards, the road is repaired through a maintenance action that is...

In this chapter we discuss a tactical optimisation problem that arises in a multistage distribution system where customer orders can be delivered from any stockpoint. A simple rule to allocate orders to locations is a break quantity rule, which routes large orders to higher-stage stockpoints and...

This paper discusses several strategies for the maintenance of light standards, where each light standard consists of n independent and identical lamps screwed on a chandelier. The lamps are subject to stochastic failures, and must be correctively replaced if the number of failed lamps reaches a...

This article studies specific aspects of the joint replenishment problem in a realsupply chain setting. Particularly we analyze the effect on inventory performance of havingminimum order quantities for the different products in the joint order, given a complextransportation cost structure. The...

Response Surface Methodology (RSM) is a tool that was introduced in the early 50's by Box and Wilson (1951). It is a collection of mathematical and statistical techniques useful for the approximation and optimization of stochastic models. Applications of RSM can be found in e.g. chemical,...