Demand management in Multi-Stage Distribution Chain

In this paper we discuss demand management problems in a multi-stage distribution chain.We focus on distribution chains where demand processes have high variability due to a few large customer orders.We give a possible explanation, and suggest two simple procedures that help to smooth demand.It is shown that these procedures yield stock reductions of 40%-50% in practical situations.The quantitative results are based on the analysis of the underlying model related to the two procedures proposed, called large order overflow, applicable if the supplying organization executes a multi-stage distribution chain, and delivery splitting, applicable to any situation.

The significance of glucosinolates for sulfur storage in Brassicaceae seedlings

Brassica juncea seedlings contained a twofold higher glucosinolate content than B. rapa and these secondary sulfur compounds accounted for up to 30% of the organic sulfur fraction. The glucosinolate content was not affected by H2S and SO2 exposure, demonstrating that these sulfur compounds did not form a sink for excessive atmospheric supplied sulfur. Upon sulfate deprivation, the foliarly absorbed H2S and SO2 replaced sulfate as the sulfur source for growth of B. juncea and B. rapa seedlings. The glucosinolate content was decreased in sulfate-deprived plants, though its proportion of organic sulfur fraction was higher than that of sulfate-sufficient plants, both in absence and presence of H2S and SO2. The significance of myrosinase in the in situ turnover in these secondary sulfur compounds needs to be questioned, since there was no direct co-regulation between the content of glucosinolates and the transcript level and activity of myrosinase. Evidently, glucosinolates cannot be considered as sulfur storage compounds upon exposure to excessive atmospheric sulfur and are unlikely to be involved in the re-distribution of sulfur in B. juncea and B. rapa seedlings upon sulfate deprivation

On the (R,s,Q) Inventory Model when Demand is Modelled as a Compound Process

In this paper we present an approximation method to compute the reorder point s in a (R; s; Q) inventory model with a service level restriction, where demand is modelled as a compound Bernoulli process, that is, with a xed probability there is positive demand during a time unit, otherwise demand is zero.The demand size and replenishment leadtime are stochastic variables.It is shown that this kind of mod- elling is especially suitable for intermittent demand.Furthermore, an approximation for the expected average physical stock is derived.The quality of both the reorder point determination as well as the approximation for the expected average physical stock turn out to be excellent, as is veri ed by discrete event simulation.

The Value of Information in an (R,s,Q) Inventory Model

In this paper we compare three methods for the determination of the reorder point s in an (R; s; Q) inventory model subject to a service level constraint. The three methods di er in the modelling assumptions of the demand process which in turn leads to three di erent approximations for the distribution function of the demand during the lead time.The rst model is most common in the literature, and assumes that the time axis is divided in time units (e.g. days).It is assumed that the demands per time unit are independent and identically distributed random variables.The second model monitors the customers individually.In this model it is assumed that the demand process is a compound renewal process, and that the distribution function of the interarrival times as well as that of the demand per customer are approximated by the rst two moments of the associated random variable.The third method directly collects information about the demand during the lead time plus undershoot, avoiding convolutions of stochastic random variables and residual lifetime distributions.Consequently, the three methods require di erent types of information for the calculation of the reorder point in an operational setting.The purpose of this paper is to derive insights into the value of information; therefore it compares the target service level with the actual service level associated with the calculated reorder point.It will be shown that the performance of the rst model (discrete time model) depends on the coe cient ofvariation of the interarrival times. Furthermore, because we use asymptotic relations in the compound renewal model, we derive some bounds for the input parameters within which this model applies. Finally we show that the aggregated information model is superior to the other two models.

Development of a mud transport model for the Scheldt estuary in the framework of LTV

In 2006, a work plan was conceived for the development of a mud transport model for the Scheldt estuary in the framework of LTV (Long Term Vision) (Winterwerp and De Kok, 2006). The purpose of this model is to support managers of the Scheldt estuary with the solution of a number of managerial issues. Also in 2006, the first two phases were initiated. The present report discusses the activities that have been carried out during the first half of 2007, i.e. Further improvement of the hydrodynamic and mud transport model.At a technical level, all model improvements scheduled for 2007 have been implemented. The most important developments are: longer hydrodynamic simulation period (up to one year), more accurate concentration boundary conditions, variable wave effects and biological effects.The hydrodynamic simulation yields realistic values for water levels and salinities, although it is expected that the modelled velocities will be too high. Three actions are identified that can enhance the simulated hydrodynamics:The high fresh water inflow event in the beginning of March can be modelled more accurately by adding more data points in the time series of fresh water inflow to increase the volume of fresh water contained in the peak.The time series of fresh water inflow of the Bathse Spuikanaal has to be added in the modelA different set of boundary conditions could yield better results for water levels.Regarding the mud transport simulations, the following is concluded:A minor shift of two dumping locations near Antwerp much improves the proper modelling of the ETMNew concentration boundary conditions at sea result in more realistic SPM concentrations at seaThe difference between simulations with 5 and 10 horizontal layers is only minorVariable waves temporarily enhance the concentration in the western part of the Western Scheldt during stormsThe biological impact on large-scale SPM concentrations in the Scheldt estuary appears to be minorThe SPM levels appear to be rather sensitive to the volume of harbour situation and dumpin

A dynamic programming approach to multi-objective time-dependent capacitated single vehicle routing problems with time windows

A single vehicle performs several tours to serve a set of geographically dis- persed customers. The vehicle has a finite capacity and is only available for a limited amount of time. Moreover, tours' duration is restricted (e.g. due to quality or security issues). Because of road congestion, travel times are time-dependent: depending on the departure time at a customer, a different travel time is incurred. Furthermore, all customers need to get delivered in their specicified time windows. Contrary to most of the literature, we con- sider a multi-objective cost function: simultaneously minimizing the total time traveled including waiting times at customers due to time windows, and maximizing the total demand fulfilled. Efficient dynamic programming algorithms are developed to compute the Pareto set of routes, assuming a specific structure for time windows and travel time profiles

Multidepot distribution planning at logistics service provider Nabuurs B.V.

Distribution networks of many logistics service providers have evolved from single-depot to complex, dynamic multidepot networks. In a single-depot network, the deliveries from each depot are planned for that depot only, and drivers return to the starting depot to pick up each new order. In a multidepot network, the deliveries from multiple depots can be planned simultaneously; therefore, a logistics service provider can efficiently combine its resources, thus reducing its labor and transport costs. However, an increasing emphasis on reliability, customization, and flexibility is affecting the logistics structures. This paper describes the shift from single-depot planning to multidepot planning for Nabuurs B.V., a large Benelux logistics service provider that implemented a centralized, automated multidepot planning process throughout its organization. We developed a simulation model to evaluate system performance and to address performance challenges. In this paper, we discuss the results of extensive simulation tests and the specific recommendations that Nabuurs B.V. management implemented

The Value of Information in an (R,s,Q) Inventory Model

In this paper we compare three methods for the determination of the reorder point s in an (R; s; Q) inventory model subject to a service level constraint. The three methods di er in the modelling assumptions of the demand process which in turn leads to three di erent approximations for the distribution function of the demand during the lead time.The rst model is most common in the literature, and assumes that the time axis is divided in time units (e.g. days).It is assumed that the demands per time unit are independent and identically distributed random variables.The second model monitors the customers individually.In this model it is assumed that the demand process is a compound renewal process, and that the distribution function of the interarrival times as well as that of the demand per customer are approximated by the rst two moments of the associated random variable.The third method directly collects information about the demand during the lead time plus undershoot, avoiding convolutions of stochastic random variables and residual lifetime distributions.Consequently, the three methods require di erent types of information for the calculation of the reorder point in an operational setting.The purpose of this paper is to derive insights into the value of information; therefore it compares the target service level with the actual service level associated with the calculated reorder point.It will be shown that the performance of the rst model (discrete time model) depends on the coe cient ofvariation of the interarrival times. Furthermore, because we use asymptotic relations in the compound renewal model, we derive some bounds for the input parameters within which this model applies. Finally we show that the aggregated information model is superior to the other two models.inventory models;information