Managing information and supplies inventory operations in a manufacturing environment. Part 2: An order-timing and sizing algorithm

We develop a new, flexible independent demand forecasting-optimisation algorithm, and apply it to nine difficult-to-manage maintenance and repair products at the AREVA nuclear fuel rod manufacturing facility. The algorithm results in a 27% reduction in inventory holding and ordering costs relative to AREVA's baseline ERP method. This is in addition to improving the line item fill rates from 96 to 98%. This new algorithm is more flexible than the baseline method in that (1) our forecast error distribution is not assumed to be normal—we automatically find the best-fitting distribution from a large family of distributions, (2) we jointly optimise the order quantity and reorder point by using an optimisation routine that is embedded in a simulation methodology. Our algorithm can therefore handle a non-stationary demand process during the planning horizon, and (3) we dynamically select the best time series forecaster for demand based on the most recent history. This flexibility drove the performance improvements. Our algorithm can be easily adapted to any independent demand situation across any industry's supply chain.     

Reliability-based maintenance and job scheduling for identical parallel machines

Quality has an important role in manufacturing, and on the other hand, machine condition has a significant effect on quality. Based on this fact, all manufacturers integrate the production scheduling with maintenance activities to keep the machines in perfect conditions. In this paper, we propose a mixed integer nonlinear model to optimise the quality cost, maintenance cost, earliness–tardiness cost and interruption cost simultaneously. We assume that if machines work in undesirable conditions, their quality is reduced, resulting in quality cost. On the other hand, if the machines are repaired to decrease the quality cost, maintenance cost and other cost such as earliness–tardiness cost and interruption cost are imposed to the manufacturer. Several numerical instances are implemented by the proposed model to show the model effectiveness to obtain the best maintenance and production scheduling with minimum quality cost.     

Optimal variability sensitive condition-based maintenance with a Cox PH model

Condition based maintenance (CBM) is an important maintenance strategy in practice. In this paper, we propose a CBM method to effectively incorporate system health observations into maintenance decision making to minimise the total maintenance cost and cost variability. In this method, the system degradation process is described by a Cox PH model and the proposed framework includes simulation of failure process and maintenance policy optimisation using adaptive nested partition with sequential selection (ANP-SS) method, which can adaptively select or create the most promising region of candidates to improve the efficiency. Different from existing CBM strategies, the proposed method relaxes some restrictions on the system degradation model and taking the cost variation as one of the optimisation objectives. A real industry case study is used to demonstrate the effectiveness of our framework.     

Divergent production scheduling with multi-process routes and common inventory

This research considers a scheduling problem in a divergent production system (DPS) where a single input item is converted into multiple output items. Therefore, the number of finished products is much larger than the number of input items. This paper addresses two important challenges in a real-life DPS problem faced by an aluminium manufacturing company. One challenge is that one product can be produced following different process routes that may have slightly different capabilities and capacities. The other is that the total inventory capacity is very limited in the company in the sense that a fixed number of inventory spaces are commonly shared by raw materials, WIP (work-in-process) items and finished products. This paper proposes a two-step approach to solving this problem. In the first step, an integer programming (IP) model is developed to plan the type and quantity of operations. In the second step, a particle swarm optimisation (PSO) is proposed to schedule the operations determined in the first step. The computational results based on actual production data have shown that the proposed two-step solution is appropriate and advantageous for the DPS scheduling problem in the company.