多货栈及变质情形下两种可替代物品的经济订货批量模型

研究了多货栈及变质情形下两种可替代物品的经济订货批量问题.在计划期内,若某一种易变质物品发生缺货,则可以被另一种易变质物品以一定的替代率代替补充,不同物品有不同的变质率,且要决定租用货栈的数量.以库存系统的总费用最小为目标函数,分别对货栈容量无限与有限的情形建立模型,证明了最优策略存在的唯一性,并分别给出了求解最优订购策略的算法,最后通过一个算例验证了算法的最优性.     

基于二层信用策略的两货栈库存模型

假设供应商向零售商提供信用支付期的同时,零售商也向顾客提供信用支付期,研究了两货栈的变质物品库存模型,并讨论了模型最优解的唯一性,最后给出了最优订购策略的算法步骤与数值例子.     

基于信用支付和现金折扣的变质物品库存模型

本文在供应商提供给零售商定期信用支付和现金折扣情况下,研究了零售商的变质物品最优库存问题。基于信用支付和现金折扣的两种支付条件下,分四种情况建立库存模型,并给出了寻求变质物品最优订购周期和最优付款时间的有效算法。最后,给出算例以及最优解,以说明本模型及求解过程。     

延期支付条件下允许缺货的变质物品库存模型

在延期支付条件下,建立了缺货量部分拖后的变质物品库存模型,证明了最优解的存在性与唯一性,并给出确定最优订购策略的算法步骤,最后用数值例子验证了模型与算法的有效性.     

空中加油问题的递推模型与调度策略

首先对空中加油问题进行了分析,提取了相关性质,在此基础上建立了问题的递推模型.根据该模型,提出了一种启发式搜索算法.该算法计算复杂度低,适用性好.对应于辅机是否可以多次起飞,该算法分为两子算法.对这两种不同情况下的具体问题,设计了相关的优化函数.所有算法都在计算机中运行,并得到了相应结果.值得指出的是,提出的启发式搜索算法十分高效.对于问题1和问题2,该算法所得解是约束条件下的最优调度策略.对于问题3,问题4,问题5,该算法所得解逼近最优调度策略.     

融合差分进化与多策略的阿基米德优化算法

鉴于阿基米德优化算法存在易早熟,收敛慢等缺点,提出一种融合差分进化与多策略的阿基米德优化算法.首先,通过位置参数,随机选择两种混沌映射初始化种群来增强种群的多样性;其次,通过余弦控制因子的动态边界策略改进密度因子,来平衡算法的全局探索与局部开发能力;接着,融合差分进化算法,缩小最优位置的范围,以达到快速向最优位置靠拢的目的.最后,选取10个基准测试函数进行仿真实验,并对实验结果进行Wilcoxon秩和检验,结果表明所提算法性能优于对比算法.     

易变质产品的可替代库存模型的最优订购策略研究

研究了易变质产品的可替代库存模型.在有限计划期内,供应商面临两种不同产品的需求,当一种产品发生短缺时,另一种产品可以以一定的替代率代替短缺产品.通过分析系统的总成本函数的性质,提出了最优订购策略.最后通过算例验证了算法的最优性.     

库存系统中的时滞变质物品的EOQ模型

本文考虑时段性变质物品的库存问题 .给出了一订货就交货 ,不允许缺货的时滞变质物品的库存模型与最优库存策略 ,并证明了该模型不是那物品自始至终有变质性质的 EOQ模型的简单叠加 .     

考虑时值及通货膨胀率的多阶段变质性物品最优库存模型

本文考虑了时值及通货膨胀率下，部分短缺量拖后的变质性物品最优订购问题。在假定变质率为常数和短缺期间损失率与实际缺货量成正比的前提下，给出了寻找最优订购策略的算法，并且证明了在该策略下费用函数取得最小值。最后给出数字实例以说明本模型及求解过程。     

基于Hurwicz准则的不确定养老金最优投资策略问题

本文提出一种新的养老金最优投资策略模型,研究了带有不确定工资过程的DC型养老金最优投资策略问题.以二次损失函数的Hurwicz加权平均值最小化为目标,针对两类相对财富过程,给出了养老金最优投资策略的显式表达式.最后,通过数值分析,研究了模型参数对最优投资策略的影响.     

A multi-agent inventory problem with general transportation costs

This paper addresses the cost allocation problem that arises from an inventory system with multiple item and several agents that place joint orders according to an EOQ policy. In this setting, the cost per a new order has two components: a fixed cost and a variable cost. We assume that the variable part is given by a general function, not necessarily additive. We obtain the optimal policy and we evaluate some proposals of allocation rule for the ordering costs.     

Optimal control of an inventory-production system with state-dependent random yield

In this paper we study a single stage, periodic-review inventory problem for a single item with stochastic demand. The inventory manager determines order sizes according to an order-up-to logic and observes a random yield due to quality problems in the production. We distinguish between two different states of the production process combined with different probabilities to produce a defective unit. In order to improve the production process, periodic inspections are conducted and in case of a failure the machine is repaired. Approximations are developed to evaluate the average cost for a given order-up-to level and a given inspection interval and we illustrate the existence of optimal policy parameters. The approximations are tested in a simulation study and reveal an excellent performance as they lead to near optimal policy parameters. Moreover, we decompose the problem and test different methods to compute the policy parameters either sequentially or separately. Our results show that a joint optimization of the inventory and maintenance policy leads to a better system performance and reduced costs.     

一类最优EOQ模型的进一步扩展

对一类经济批量订购模型作如下进一步扩展:第一,允许短缺,短缺量部分拖后供给,且短缺期间损失率与实际缺货量成正比;第二,订购费用是可变的,且线性依赖于订购量.在此假定下,研究了有限计划时间水平及常数变质率下,部分短缺量拖后的变质性物品多阶段库存问题,给出了寻找最优订购策略的算法,证明了所给最优策略的存在唯一性及在该策略下费用函数取得最小值.最后给出应用实例.     

A scheduling policy for adjusting economic lot quantities to a feasible solution

A heuristic scheduling policy is introduced for a multi-item, single-machine production facility. The scheduling policy uses the presumed optimal order quantities derived from solving an Economic Lot Size Problem and checks that the quantities obtain a feasible production schedule according to current inventory levels and expected demand rates. If not, the scheduling policy modifies the order quantities to achieve a possible solution without shortages. The scheduling policy is inspired by modification of the similar heuristic Dynamic Cycle Lengths Policy by Leachman and Gascon from 1988, 1991. The main characteristics of this scheduling policy are successive batches of the same item are treated explicitly, due to that it is quite possible that one item be manufactured several times before one other item is manufactured once more; the batches are ordered in increasing run-out time; if the existing situation creates stock-outs with ordinary order quantities, then the order quantities are decreased with a common scaling factor to try to prevent inventory shortages; in case the decrease of the order quantities changes expected run-out times, the batches are reordered after new run-out times; no filling up to an explicit inventory level is done, the filling up is done by the desirable order quantity; to prevent possible excess inventory the policy suggests time periods where no production should be performed. The scheduling policy contains no economical evaluation; this is supposed to be done when the order quantities are calculated, the policy prevents shortages and excess inventory. A numerical example illustrates the suggested scheduling policy. Finally, it is discussed as to how the policy can also take into account stochastic behaviour of the demand rates and compensate the schedule by applying appropriate safety times.     

Optimizing a dynamic order-picking process

This research studies the problem of batching orders in a dynamic, finite-horizon environment to minimize order tardiness and overtime costs of the pickers. The problem introduces the following trade-off: at every period, the picker has to decide whether to go on a tour and pick the accumulated orders, or to wait for more orders to arrive. By waiting, the picker risks higher tardiness of existing orders on the account of lower tardiness of future orders. We use a Markov decision process (MDP) based approach to set an optimal decision making policy. In order to evaluate the potential improvement of the proposed approach in practice, we compare the optimal policy with two naïve heuristics: (1) “Go on tour immediately after an order arrives”, and, (2) “Wait as long as the current orders can be picked and supplied on time”. The optimal policy shows a considerable improvement over the naïve heuristics, in the range of 7–99%, where the specific values depend on the picking process parameters. We have found that one measure, the slack percentage of the picking process, associated with the difference between the promised lead time and the single item picking time, predicts quite accurately the cost reduction generated by the optimal policy. Since relatively small-scale problems could be solved by the optimal algorithm, a heuristic was developed, based on the structure and properties of the optimal solutions. Numerical results show that the proposed heuristic, MDP-H, outperforms the naïve heuristics in all experiments. As compared to the optimal solution, MDP-H provides close to optimal results for a slack of up to 40%.     

A stocking policy for spare part provisioning under age based preventive replacement

A new policy, called stocking policy for ease of reference, has been advanced for joint optimization of age replacement and spare provisioning. It combines age replacement policy with continuous review (s, S) type inventory policy, where s is the stock reorder level and S is the maximum stock level. The policy is generally applicable to any operating situation having either a single item or a number of identical items. A simulation model has been developed to determine the optimal values of the decision variables by minimizing the total cost of replacement and inventory. The behaviour of the stocking policy has been studied for a number of case problems specifically constructed by 5-factor second order rotatory design and the effects of different cost elements and item failure characteristics have been highlighted. For all case problems, optimal (s, S) policies to-support the Barlow-Proschan age policy have also been determined. Simulation results clearly indicate that the optimal stocking policy is, in general, more cost-effective than the Barlow-Proschan policy.     

Optimal mapping in direct mapped cache environments

In this paper we study positioning strategies for improving the performance of a memory system with a direct mapped cache. A positioning technique determines for every program item, (instruction or data), its address in main memory.Assuming the Independent Reference Model, we break the general positioning problem into two, the collision minimization and the grouping problems and show optimal algorithms for both problems. Using these algorithms we derive an optimal algorithm for the general positioning problem.Since the optimal positioning is of very special structure we consider other, less restricted, positionings. We show that the quality of a class of natural assignments that distribute the items almost arbitrarily is good as long as the optimal hit ratio is sufficiently large. Another possible requirement is that the items should be distributed as evenly as possible. We find an optimal assignment for the special case of the pair assignment.In addition we look at the expected performance gain of two frequently suggested cache features. The cache bypass feature supports the access of items in memory without loading the item into the cache. We show an assignment with best possible hit ratio. Also it is shown that a cache which employs a random assignment policy, i.e., the assignment of an item is determined randomly, does not improve the expected hit ratio.     

An Alternative Procedure for Determining the Optimal Policy for an Inventory Item Having Linear Trend in Demand

In this paper we develop an iterative procedure for determining the optimal replenishment policy for an item having linear trend in demand. Shortages are permitted for the inventory item and can be backordered. Our optimal procedure is easier to apply than an earlier solution method reported in inventory literature with linearly time-varying demand and shortages. Two examples are included to illustrate the iterative procedure.