Production/inventory systems with a stochastic production rate under a continuous review policy

A single production facility is dedicated to producing one product with completed units going directly into inventory. The unit production time is a random variable. The demand for the product is given by a Poisson process and is supplied directly from inventory when available, or is backordered until it is produced by the production facility. Relevant costs are a linear inventory holding cost, a linear backorder cost, and a fixed setup cost for initiating a production run. The objective is to find a control policy that minimizes the expected cost per time unit.The problem may be modeled as an M/G/1 queueing system, for which the optimal decision policy is a two-critical-number policy. Cost expressions are derived as functions of the policy parameters, and based on convexity properties of these cost expressions, an efficient search procedure is proposed for finding the optimal policy. Computational test results demonstrating the efficiency of the search procedure and the behavior of the optimal policy are presented.     

A heuristic for determination of optimal capacity of central storage

A system of one machine used to produce m products in batches and a central storage used for storing raw materials and finished products is considered. The system maintains enough finished products to assure no stockout will happen. The machine has a finite production rate greater than or equal to the demand rate for each product, and thus operates with periodic start-ups and shut-downs. For special case when set-up for each product requires time and every product is produced in every cycle, the best policy given limited storage space is determined, and total production cost as a function of set-up costs, carrying cost, storage cost, and material handling cost is presented. A heuristic is developed that determines “optimal feasible cycle time” such that production scheduling remains feasible, the capacity of the central storage is not exceeded, and the overall costs are minimized.     

Optimal (s,S) policies with delivery time guarantees for manufacturing systems with early set-up

We consider optimal (s, S) policies with delivery time guarantees for production planning in one-machine manufacturing systems with early set-up. The machine produces one type of product and delivery time guarantee is offered to the customers for each unit of ordered product. The inter-arrival time of the demand and the processing time for one unit of product are assumed to be exponentially distributed. In a (s, S) policy, the machine will shut down when an inventory level of S is attained and once the inventory level drops to s, the machine will re-start. A set-up time is required for the machine. We model the set-up by the exponential distribution. We obtained an analytical form of the steady state probability distribution for the inventory levels derived. The average profit of the system can be written in terms of this probability distribution. Hence the optimal (s, S) policy can be obtained by varying different possible values of s and S.     

Economic lot sizing in a production system with random demand

An extended economic production quantity model that copes with random demand is developed in this paper. A unique feature of the proposed study is the consideration of transient shortage during the production stage, which has not been explicitly analysed in existing literature. The considered costs include set-up cost for the batch production, inventory carrying cost during the production and depletion stages in one replenishment cycle, and shortage cost when demand cannot be satisfied from the shop floor immediately. Based on renewal reward process, a per-unit-time expected cost model is developed and analysed. Under some mild condition, it can be shown that the approximate cost function is convex. Computational experiments have demonstrated that the average reduction in total cost is significant when the proposed lot sizing policy is compared with those with deterministic demand.     

制造系统的递阶滚动优化调度模型

研究的对象是只有一台不可靠（failure-prone)机器的非完全柔性制造系统，该系统能生产多种产品，但在同一时刻只能生产一种产品，并且当机器由生产一种产品向生产另一种产品切换时，需要考虑setup时间及其成本，待决策变量是setup序列及产品生产率，本文基于非完全柔性制造系统的特点，引入递阶层控的思想，采用新的递阶结构框架和阈值控制策略，对问题进行分解，建立了考虑setup时间及成本的递阶流率控制最优化调度模型，并给出了递阶的滚动优化算法，仿真结果表明，这种调度策略更易于工程实现。     

Lot size reorder point inventory model with controllable lead time and set-up cost

This paper deals with the lead time and set-up cost reductions problem on the modified lot size reorder point inventory model in which the production process is imperfect. We consider that the lead time can be shortened at an extra crashing cost, which depends on the length of lead time to be reduced and the ordering lot size. The option of investing in reducing set-up cost is also included. Two commonly used investment cost functional forms, logarithmic and power, are employed for set-up cost reduction. We assume that the stochastic demand during lead time follows a Normal distribution. The objective is simultaneously to optimize the lot size, reorder point, set-up cost and lead time. An algorithm of finding the optimal solution is developed, and two numerical examples are given to illustrate the results.     

Grouping in decomposition method for multi-item capacitated lot-sizing problem with immediate lost sales and joint and item-dependent setup cost

This article examines a dynamic and discrete multi-item capacitated lot-sizing problem in a completely deterministic production or procurement environment with limited production/procurement capacity where lost sales (the loss of customer demand) are permitted. There is no inventory space capacity and the production activity incurs a fixed charge linear cost function. Similarly, the inventory holding cost and the cost of lost demand are both associated with a linear no-fixed charge function. For the sake of simplicity, a unit of each item is assumed to consume one unit of production/procurement capacity. We analyse a different version of setup costs incurred by a production or procurement activity in a given period of the planning horizon. In this version, called the joint and item-dependent setup cost, an additional item-dependent setup cost is incurred separately for each produced or ordered item on top of the joint setup cost.     

Hierarchical production and setup scheduling in stochasticmanufacturing systems

This paper is concerned with an asymptotic analysis of hierarchical production and setup scheduling in a stochastic manufacturing system consisting of a single failure-prone machine and facing constant demands for a number of products. At any given time the system can only produce one type of product, and the system requires a setup if production is to be switched from one type of product to another. A setup may involve setup time or setup cost or both. The objective of the problem is to minimize the total costs of setup, production, and surplus. The control variables are a sequence of setups and a production plan. An asymptotic analysis with respect to increasing rates of change in machine states gives rise to a deterministic limiting optimal control problem in which there is a control variable associated with each of the machine states and the production rate is obtained by weighting these controls with the stationary probabilities of the corresponding states. Asymptotic optimal controls for the original problem from optimal or near-optimal controls for the limiting problem are constructed     

A production inventory model with fuzzy random demand and with flexibility and reliability considerations

The classical inventory control models assume that items are produced by perfectly reliable production process with a fixed set-up cost. While the reliability of the production process cannot be increased without a price, its set-up cost can be reduced with investment in flexibility improvement. In this paper, a production inventory model with flexibility and reliability (of production process) consideration is developed in an imprecise and uncertain mixed environment. The aim of this paper is to introduce demand as a fuzzy random variable in an imperfect production process. Here, the set-up cost and the reliability of the production process along with the production period are the decision variables. Due to fuzzy-randomness of the demand, expected average profit of the model is a fuzzy quantity and its graded mean integration value (GMIV) is optimized using unconstraint signomial geometric programming to determine optimal decision for the decision maker (DM). A numerical example has been considered to illustrate the model.     

Scheduling concurrent production over a finite planning horizon: polynomially solvable cases

The paper analyzes a manufacturing system made up of one workstation which is able to produce concurrently a number of product types with controllable production rates in response to time-dependent product demands. Given a finite planning horizon, the objective is to minimize production cost, which is incurred when the workstation is not idle and inventory and backlog costs, which are incurred when the meeting of demand results in inventory surpluses and shortages. With the aid of the maximum principle, optimal production regimes are derived and continuous-time scheduling is reduced to a combinatorial problem of sequencing and timing the regimes. The problem is proved to be polynomially solvable if demand does not exceed the capacity of the workstation or it is steadily pressing and the costs are “agreeable”.

Scope and purpose

Efficient utilization of modern flexible manufacturing systems is heavily dependent on proper scheduling of products throughout the available facilities. Scheduling of a workstation which produces concurrently a number of product types with controllable production rates in response to continuous, time-dependent demand is under consideration. Similar to the systems considered by many authors in recent years, a buffer with unlimited capacity is placed after the workstation for each product type. The objective is to minimize inventory storage, backlog and production costs over a finite planning horizon. Numerical approaches are commonly used to approximate the optimal solution for similar problems. The key contribution of this work is that the continuous-time scheduling problem is reduced to a combinatorial problem, exactly solvable in polynomial time if demand does not exceed the capacity of the workstation or the manufacturing system is organized such that the early production and storage of a product to reduce later backlogs are justified.     

Production control of a manufacturing system with multiple machinestates

The production control of a single-product manufacturing system with arbitrary number of machine states (failure modes) is discussed. The objective is to find a production policy that would meet the demand for the product with minimum average inventory or backlog cost. The optimal production policy has a special structure and is called a hedging-point policy. If the hedging points are known, the optimal production rate is readily specified. Assuming a set of tentative hedging points, the simple structure of the optimal policy is utilized to find the steady-state probability distribution of the surplus (inventory or backlog). Once this function is determined, the average surplus cost is easily calculated in terms of the values of the hedging points. The average cost is then minimized to find the optimum hedging points     

Modelling the effect of demand variations on the performance of a production system

Modelling the effect of demand variations on a production system manufacturing multiple products is discussed. The various system costs involved in the production system, namely set-up cost and inventory cost incurred due to change in demands for the products with respect to products and planning periods are estimated. A statistical modelling is presented for determining the production capacity and inventory level requirement to satisfy the customer to a certain level decided by the management. Two important factors, (i) number of types of products and (ii) multiple planning horizons are considered to identify the costs as well as the production capacity and inventory level requirements. A statistical method, analysis of variance (ANOVA) is used to study the variations in the demands and costs involved. Finally, an example is presented to explain the application and the behaviour of the statistical model.     

Joint management of finished goods inventory and demand process for a make-to-stock product: a computational approach

In this paper, we consider the joint management of finished goods inventory and demand for a product in a make-to-stock production system. The production process is random with controllable mean rate, and the demand process is stochastic with changeable mean rate dependent on the sale price being high or low. The management issue is how to dynamically adjust the production rate and the sale price to maximize the long run total discounted profit. We show that: 1) the optimal management of the finished goods inventory follows a base stock policy: when the inventory is above certain base stock level, the production is halted; otherwise the maximum production rate is deployed to raise the inventory to the base stock level; and 2) the optimal management of the demand process follows a price switch threshold policy: when the inventory is above the threshold, the low sale price is chosen to sell the product; and below it the high price is chosen to reduce the demand. We provide an algorithm to compute the base stock level and price switch threshold. Extension to multiple price choices is given with proofs highlighted.     

按单装配系统中组件生产和库存分配控制策略研究

针对由两种组件、三类顾客需求组成的按单装配系统, 本文研究了其中的组件生产控制与库存分配问题. 在各类顾客需 求是泊松到达过程, 各种组件加工时间服从指数分布的假设下, 我们运用马尔科夫决策理论建立了无限期折扣总成本模型, 根据Lippman转换得到了相应归一化后的离散最优方程, 在此基础之上分析了生产和库存分配联合最优控制策略的结构性质. 本文证明了最优策略是依赖于系统状态的动态策略. 组件的最优生产策略是动态基库存策略, 其中基库存水平是关于系统中其他组件库存水平的非减函数. 而最优的分配策略是动态的阈值策略, 对于只需一种组件构成的顾客需求, 组件的分配阈值是系统中另一组件库存水平的增函数; 而对于同时需要两种组件组成的顾客需求, 其各组件的分配阈值是另一组件库存水平的减函数. 最后通过数值试验给出了各个参数对联合最优控制策略的影响, 并得到了相应的管理启示.     

价格波动下的生产-库存控制研究

研究原材料价格波动下多级生产-库存系统的控制问题.所有的原材料价格、半成品加工成本、成品的生产成本、库存费用率和产品的需求率都随时间变动,为此,分析了最优采购、加工、生产决策的必要和充分条件,得到了在某些假设条件下的最优生产-库存策略为JIT(Just-in-time)采购、加工、生产策略,或者为在最开始阶段以最大能力进行采购、加工、生产活动的Bang-Bang策略.     

基于二阶段时间延迟的多产品生产系统生产与维修联合优化研究

针对多产品生产部件串联系统的生产和维修问题进行了研究,提出了基于二阶段时间延迟的联合优化模型。首先,基于生产周期分段理论,将整个周期等分成若干单位时间段,生产与维修共用每段时间,且若干时间段后采取一次预防维修。其次,考虑生产系统的实际生产时间、可用生产时间和维修耗费时间,建立了生产计划与维修计划总成本模型。其中,维修计划考虑缺陷和故障维修费用、维修检查费用,以及非正常状态下设备运行可能产生的不合格产品损失费用;生产计划考虑生产成本、库存成本、延期未交货成本和维修停机后恢复生产的设备启动成本。最后,通过算例分析,计算最优预防维修周期和各单位时间段各产品产量,验证了模型的有效性。     

Optimal price and order size for a reseller under partial backordering

The problem of determining the optimal price and lot size for a reseller is considered in this paper. It is assumed that demand can be backlogged and that the selling price is constant within the inventory cycle. The backlogging phenomenon is modeled without using the backorder cost and the lost sale cost since these costs are not easy to estimate in practice. The case in which the selling price is fixed and therefore, demand is a known constant is also considered. Given the new way of modeling the backlogging phenomenon, the results for the case of constant demand are developed. Analysis is also presented for the reselling situation in which a nonperishable product is sold.Scope and purposePerishable products constitute a sizable component of inventories. A common question in a reselling situation involving a perishable (or a nonperishable) product is: What should be the size of the replenishment? If demand for the product is sensitive to price, then another question is: What should be the selling price? Although the ability to vary price within an inventory cycle is important, in many cases, the reseller may opt for a policy of constant selling price for administrative convenience. In this paper the pricing and/or lot sizing problem faced by a reseller is modeled assuming a general deterioration rate and a general demand function. The model allows for backlogging of demand. When a product is highly perishable, the reseller may need to backlog demand to contain costs due to deterioration. In this sense, perishability and backlogging are complementary conditions. Given that the problem entails revenue and costs, a natural objective function for the model is profit per period. The conventional approach to modeling the backlogging phenomenon requires the use of the backorder cost and the lost sale cost. These costs, however, are difficult to estimate in practice. A new approach is used in which customers are considered impatient. Hence the fraction of demand that gets backlogged at a given point in time is a decreasing function of waiting time. First the subproblem in which price is fixed is solved to determine the optimal inventory policy. The subproblem represents the important case in which the reseller has no flexibility to change the selling price. Then a procedure is developed for determining the optimal quantity and the selling price for the broader problem. The procedure can be implemented on a spreadsheet.     

Optimal ordering decision for deteriorating items with expiration date and uncertain lead time

The ordering policy for the retailers and the suppliers is a function of deterioration, product expiration date, the supplier’s uncertain lead time, available capital constraint and the retailer’s seasonal pattern demand. We develop a deteriorating inventory replenishment model of the system and present an algorithm to derive the retailer’s optimal replenishment cycle, shortage period, order quantity and the supplier’s managing cost. Numerical example and sensitivity analysis are given to illustrate the model.     

A mathematical model on EPQ for stochastic demand in an imperfect production system

In the paper, we develop an EPQ (economic production quantity) inventory model to determine the optimal buffer inventory for stochastic demand in the market during preventive maintenance or repair of a manufacturing facility with an EPQ (economic production quantity) model in an imperfect production system. Preventive maintenance, an essential element of the just-in-time structure, may cause shortage which is reduced by buffer inventory. The products are sold with the free minimal repair warranty (FRW) policy. The production system may undergo “out-of-control” state from “in-control” state, after a certain time that follows a probability density function. The defective (non-conforming) items in “in-control” or “out-of-control” state are reworked at a cost just after the regular production time. Finally, an expected cost function regarding the inventory cost, unit production cost, preventive maintenance cost and shortage cost is minimized analytically. We develop another case where the buffer inventory as well as the production rate are decision variables and the expected unit cost considering the above cost functions is optimized also. The numerical examples are provided to illustrate the behaviour and application of the model. Sensitivity analysis of the model with respect to key parameters of the system is carried out.     

An interactive game for production and financial planning

This paper describes a computerized interactive game for use by students of business administration. The problem situation involves the need to determine an aggregate production plan for a small manufacturing firm which faces highly seasonal demand for its product. The student user is required to make decisions regarding planned manpower and production levels in future periods. In order to assist him in making these decisions, functional relationships describing pertinent production costs (wages, cost of hiring and firing, cost of carrying inventory) and financial costs (the cost of borrowing money to support production) are made available to the student decision maker.