Robust Production Plan with Periodic Order Quantity under Uncertain Cumulative Demands

International audienceIn this paper, we are interested in a production planning process in collaborative supply chains. More precisely, we consider supply chains, where actors use Manufacturing Resource Planning process (MRPII). Moreover, these actors collaborate by sharing procurement plans.We focus on a supplier, who applies the Periodic Order Quantity (POQ) rule to plan a production integrating the uncertain procurement plan sent by her/his customer. The uncertainty of the procurement plan is expressed by closed intervals on the cumulative demands. In order to choose a robust production plan, under the interval uncertainty representation, the min-max criterion is applied. We propose algorithms for determining the set of possible costs of a given production plan - due to the uncertainty on the cumulative demands.We then construct algorithms for computing a robust production plan with respect to the min-max criterion: the algorithm based on iterative adding constraints and the polynomial algorithms under certain realistic assumptions

Paints supply chain optimisation

Imperial Users onl

TANK SIZING AND OPTIMIZING LOOP PLACEMENT IN A BRANCHED WATER DISTRIBUTION SYSTEM

More than eighteen percent of the world’s population lives without reliable access to clean water, forced to walk long distances to get small amounts of contaminated surface water. Carrying heavy loads of water long distances and ingesting contaminated water can lead to long-term health problems and even death. These problems affect the most vulnerable populations, women, children, and the elderly, more than anyone else. Water access is one of the most pressing issues in development today. Boajibu, a small village in Sierra Leone, where the author served in Peace Corps for two years, lacks access to clean water. Construction of a water distribution system was halted when a civil war broke out in 1992 and has not been continued since. The community currently relies on hand-dug and borehole wells that can become dirty during the dry season, which forces people to drink contaminated water or to travel a far distance to collect clean water. This report is intended to provide a design the system as it was meant to be built. The water system design was completed based on the taps present, interviews with local community leaders, local surveying, and points taken with a GPS. The design is a gravity-fed branched water system, supplied by a natural spring on a hill adjacent to Boajibu. The system’s source is a natural spring on a hill above Boajibu, but the flow rate of the spring is unknown. There has to be enough flow from the spring over a 24-hour period to meet the demands of the users on a daily basis, or what is called providing continuous flow. If the spring has less than this amount of flow, the system must provide intermittent flow, flow that is restricted to a few hours a day. A minimum flow rate of 2.1 liters per second was found to be necessary to provide continuous flow to the users of Boajibu. If this flow is not met, intermittent flow can be provided to the users. In order to aid the construction of a distribution system in the absence of someone with formal engineering training, a table was created detailing water storage tank sizing based on possible source flow rates. A builder can interpolate using the source flow rate found to get the tank size from the table. However, any flow rate below 2.1 liters per second cannot be used in the table. In this case, the builder should size the tank such that it can take in the water that will be supplied overnight, as all the water will be drained during the day because the users will demand more than the spring can supply through the night. In the developing world, there is often a problem collecting enough money to fund large infrastructure projects, such as a water distribution system. Often there is only enough money to add only one or two loops to a water distribution system. It is helpful to know where these one or two loops can be most effectively placed in the system. Various possible loops were designated for the Boajibu water distribution system and the Adaptive Greedy Heuristic Loop Addition Selection Algorithm (AGHLASA) was used to rank the effectiveness of the possible loops to construct. Loop 1 which was furthest upstream was selected because it benefitted the most people for the least cost. While loops which were further downstream were found to be less effective because they would benefit fewer people. Further studies should be conducted on the water use habits of the people of Boajibu to more accurately predict the demands that will be placed on the system. Further population surveying should also be conducted to predict population change over time so that the appropriate capacity can be built into the system to accommodate future growth. The flow at the spring should be measured using a V-notch weir and the system adjusted accordingly. Future studies can be completed adjusting the loop ranking method so that two users who may be using the water system for different lengths of time are not counted the same and vulnerable users are weighted more heavily than more robust users

A tri-level optimization model for inventory control with uncertain demand and lead time

We propose an inventory control model for an uncapacitated warehouse in a manufacturing facility under demand and lead time uncertainty. The objective is to make ordering decisions to minimize the total system cost. We introduce a two-stage tri-level optimization model with a rolling horizon to address the uncertain demand and lead time regardless of their underlying distributions. In addition, an exact algorithm is designed to solve the model. We compare this model in a case study with three decision-making strategies: optimistic, moderate, and pessimistic. Our computational results suggest that the performances of these models are either consistently inferior or highly sensitive to cost parameters (such as holding cost and shortage cost), whereas the new tri-level optimization model almost always results in the lowest total cost in all parameter settings

On-line lot-sizing with perceptrons

x+167hlm.;24c

Robust Filters for Intensive Care Monitoring: Beyond the Running Median

Current alarm systems on intensive care units create a very high rate of false positive alarms because most of them simply compare the physiological measurements to fixed thresholds. An improvement can be expected when the actual measurements are replaced by smoothed estimates of the underlying signal. However, classical filtering procedures are not appropriate for signal extraction as standard assumptions, like stationarity, do no hold here: the measured time series often show long periods without change, but also upward or downward trends, sudden shifts and numerous large measurement artefacts. Alternative approaches are needed to extract the relevant information from the data, i.e. the underlying signal of the monitored variables and the relevant patterns of change, like abrupt shifts and trends. This article reviews recent research on filter based online signal extraction methods which are designed for application in intensive care. --