94,636 research outputs found

    Computing (R, S) policies with correlated demand

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    This paper considers the single-item single-stocking non-stationary stochastic lot-sizing problem under correlated demand. By operating under a nonstationary (R, S) policy, in which R denote the reorder period and S the associated order-up-to-level, we introduce a mixed integer linear programming (MILP) model which can be easily implemented by using off-theshelf optimisation software. Our modelling strategy can tackle a wide range of time-seriesbased demand processes, such as autoregressive (AR), moving average(MA), autoregressive moving average(ARMA), and autoregressive with autoregressive conditional heteroskedasticity process(AR-ARCH). In an extensive computational study, we compare the performance of our model against the optimal policy obtained via stochastic dynamic programming. Our results demonstrate that the optimality gap of our approach averages 2.28% and that computational performance is good

    Evolutionary multiobjective optimization of the multi-location transshipment problem

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    We consider a multi-location inventory system where inventory choices at each location are centrally coordinated. Lateral transshipments are allowed as recourse actions within the same echelon in the inventory system to reduce costs and improve service level. However, this transshipment process usually causes undesirable lead times. In this paper, we propose a multiobjective model of the multi-location transshipment problem which addresses optimizing three conflicting objectives: (1) minimizing the aggregate expected cost, (2) maximizing the expected fill rate, and (3) minimizing the expected transshipment lead times. We apply an evolutionary multiobjective optimization approach using the strength Pareto evolutionary algorithm (SPEA2), to approximate the optimal Pareto front. Simulation with a wide choice of model parameters shows the different trades-off between the conflicting objectives

    A Neuroevolutionary Approach to Stochastic Inventory Control in Multi-Echelon Systems

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    Stochastic inventory control in multi-echelon systems poses hard problems in optimisation under uncertainty. Stochastic programming can solve small instances optimally, and approximately solve larger instances via scenario reduction techniques, but it cannot handle arbitrary nonlinear constraints or other non-standard features. Simulation optimisation is an alternative approach that has recently been applied to such problems, using policies that require only a few decision variables to be determined. However, to find optimal or near-optimal solutions we must consider exponentially large scenario trees with a corresponding number of decision variables. We propose instead a neuroevolutionary approach: using an artificial neural network to compactly represent the scenario tree, and training the network by a simulation-based evolutionary algorithm. We show experimentally that this method can quickly find high-quality plans using networks of a very simple form

    The Q(s,S) control policy for the joint replenishment problem extended to the case of correlation among item-demands

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    We develop an algorithm to compute an optimal Q(s,S) policy for the joint replenishment problem when demands follow a compound correlated Poisson process. It is a non-trivial generalization of the work by Nielsen and Larsen (2005). We make some numerical analyses on two-item problems where we compare the optimal Q(s,S) policy to the optimal uncoordinated (s,S) policies. The results indicate that the more negative the correlation the less advantageous it is to coordinate. Therefore, in some cases the degree of correlation determines whether to apply the coordinated Q(s,S) policy or the uncoordinated (s,S) policies. Finally, we compare the Q(s,S) policy and the closely connected P(s,S) policy. Here we explain why the Q(s,S) policy is a better choice if item-demands are correlated.joint replenishment problem; compound correlated Poisson process

    Occurrence, morphology and growth of understory saplings in Swedish forests

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    Growing demands for a multipurpose forestry leads to increased use of silvicultural systems that avoid clear-cutting. Regeneration in such systems is based on establishment and ingrowth of new seedlings under a more or less closed canopy. At long-term forestry planning reliable ingrowth models are needed to predict the future wood production. The objectives of this thesis were to review the field of ingrowth in established stands, to develop a model for prediction of ingrowth for the planning system Heureka and to deepen the insight in the ingrowth process by a case study. The ingrowth model consisted of four parts, describing: Probability for occurrence of saplings (1-39 mm diameter at breast height (DBH)) on plots with r = 5 m. Number of saplings on stocked plots (plots with saplings of target species). Probability for ingrowth of a sapling over 39 mm DBH during a 5-year period. Diameter of ingrown trees at the end of the 5-year period. The model was based on data from permanent plots at the National Forest Inventory. Separate functions were developed for seven species and species groups. Picea abies saplings had the widest distribution and occurred on 58 % of 12 469 representative plots in established forests. Betula spp. saplings occurred on 50 % of the plots, while the occurrence of saplings of other species was less than 20 %. Sapling density on stocked plots was highest for Betula spp, in average 10 per plot. Average ingrowth rate was 14.6 stems per ha and year, and P. abies made up more than half of this. The ingrowth varied according to the different functions with age, density and species composition of the stand and the moisture and fertility of the site. Growth and morphology of young conifers was examined in a species experiment on a clearcut and in shelterwoods of three different densities (41 – 124 stems per hectare). The largest intra-specific differences between clearcut and shelterwood were found for Pinus spp, while moderate differences were found for Picea spp. For Pinus spp, stem height and diameter decreased, while the stem slenderness increased with increasing shelterwood density. Moreover, the number of branches per whorl and the crown ratio decreased with increasing shelterwood density. The proportion of biomass in roots, stem, branches and needles was analysed as a function of estimated irradiance transmission for each individual. The proportion of stem decreased and the proportion of branches increased with increasing irradiance for Pinus spp. No significant trends were found for Picea spp

    A discrete time Markov chain model for a periodic inventory system with one-way substitution.

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    This paper studies the optimal design of an inventory system with “one-way substitution”, in which a high-quality (and hence, more expensive) item fulfills its own demand and simultaneously acts as backup safety stock for the (cheaper) low-quality item. Through the use of a discrete time Markov model we analyze the effect of one-way substitution in a periodic inventory system with an (R,s,S) or (R,S) order policy, assuming backorders, zero replenishment leadtime and correlated demand. In more detail, the optimal inventory control parameters (S and s) are determined in view of minimizing the expected total cost per period (i.e. sum of inventory holding costs, purchasing costs, backorder costs and adjustment costs). Numerical results show that the one-way substitution strategy can outperform both the “no pooling” (only product-specific stock is held, and demand can never be rerouted to stock of a different item) and “full pooling” strategies (implying that demand for a particular product type is always rerouted to the stock of the flexible product, and no product-specific stock is held) − provided the mix of dedicated and flexible inputs is chosen adequately − even when the cost premium for flexibility is significant. Furthermore, we can observe that decreasing the demand correlation results in rerouting more demand to the flexible product and because of the risk-pooling effect reduces the optimal expected total cost.Inventory management; One-way substitution;
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