The continuous‐time single‐sourcing problem with capacity expansion opportunities

This paper develops a new model for allocating demand from retailers (or customers) to a set of production/storage facilities. A producer manufactures a product in multiple production facilities, and faces demand from a set of retailers. The objective is to decide which of the production facilities should satisfy each retailer's demand, in order minimize total production, inventory holding, and assignment costs (where the latter may include, for instance, variable production costs and transportation costs). Demand occurs continuously in time at a deterministic rate at each retailer, while each production facility faces fixed‐charge production costs and linear holding costs. We first consider an uncapacitated model, which we generalize to allow for production or storage capacities. We then explore situations with capacity expansion opportunities. Our solution approach employs a column generation procedure, as well as greedy and local improvement heuristic approaches. A broad class of randomly generated test problems demonstrates that these heuristics find high quality solutions for this large‐scale cross‐facility planning problem using a modest amount of computation time. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

A simple approximation for a multistage capacitated production-inventory system

We develop a simple approximation for multistage production-inventory systems with limited production capacity and variable demands. Each production stage follows a base-stock policy for echelon inventory, constrained by production capacity and the availability of upstream inventory. Our objective is to find base-stock levels that approximately minimize holding and backorder costs. The key step in our procedure approximates the distribution of echelon inventory by a sum of exponentials; the parameters of the exponentials are chosen to match asymptotically exact expressions. The computational requirements of the method are minimal. In a test bed of 72 problems, each with five production stages, the average relative error for our approximate optimization procedure is 1.9%. © 1996 John Wiley & Sons, Inc.     

Inventory cost impact of order processing priorities based on demand uncertainty

We evaluate an approach to decrease inventory costs at retail inventory locations that share a production facility. The retail locations sell the same product but differ in the variance of retail demand. Inventory policies at retail locations generate replenishment orders for the production facility. The production facility carries no finished goods inventory. Thus, production lead time for an order is the sojourn time in a single server queueing system. This lead time affects inventory costs at retail locations. We examine the impact of moving from a First Come First Served (FCFS) production rule for orders arriving at the production facility to a rule in which we provide non‐preemptive priority (PR) to orders from retail locations with higher demand uncertainty. We provide three approximations for the ratio of inventory costs under PR and FCFS and use them to identify conditions under which PR decreases retail inventory costs over FCFS. We then use a Direct Approach to establish conditions when PR decreases retail inventory costs over FCFS. We extend the results to orders from locations that differ in the mean and variance of demand uncertainty. The analysis suggests that tailoring lead times to product demand characteristics may decrease system inventory costs. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 376–390, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10016     

A dynamic,nonstationary inventory problem for a price/quantity setting firm

A dynamic and nonstationary model is formulated for a firm which attempts to minimize total expected costs over a finite planning horizon. The control variables are price and production. The price p and the demand ζ are linked through the relationship ζ = g(p) + η, where g(p) is the riskless demand curve and η is a random variable. The general model allows for proportional ordering costs, convex holding and stockout costs, downward sloping riskless demand curve, backlogging, partial backlogging, lost sales, partial spoilage of inventory, and two modes of collecting revenue. Sufficient conditions are developed for this problem to have an optimal policy which resembles the single critical number policy known from stochastic inventory theory. It is also shown what set of parameters will satisfy these sufficiency conditions.     

Optimal control of a production‐inventory system with both backorders and lost sales

We consider the optimal control of a production inventory‐system with a single product and two customer classes where items are produced one unit at a time. Upon arrival, customer orders can be fulfilled from existing inventory, if there is any, backordered, or rejected. The two classes are differentiated by their backorder and lost sales costs. At each decision epoch, we must determine whether or not to produce an item and if so, whether to use this item to increase inventory or to reduce backlog. At each decision epoch, we must also determine whether or not to satisfy demand from a particular class (should one arise), backorder it, or reject it. In doing so, we must balance inventory holding costs against the costs of backordering and lost sales. We formulate the problem as a Markov decision process and use it to characterize the structure of the optimal policy. We show that the optimal policy can be described by three state‐dependent thresholds: a production base‐stock level and two order‐admission levels, one for each class. The production base‐stock level determines when production takes place and how to allocate items that are produced. This base‐stock level also determines when orders from the class with the lower shortage costs (Class 2) are backordered and not fulfilled from inventory. The order‐admission levels determine when orders should be rejected. We show that the threshold levels are monotonic (either nonincreasing or nondecreasing) in the backorder level of Class 2. We also characterize analytically the sensitivity of these thresholds to the various cost parameters. Using numerical results, we compare the performance of the optimal policy against several heuristics and show that those that do not allow for the possibility of both backordering and rejecting orders can perform poorly.© 2010 Wiley Periodicals, Inc. Naval Research Logistics 2010     

Note: Dynamic lot sizing for a finite rate input process

The basic single-product dynamic lot-sizing problem involves determining the optimal batch production schedule to meet a deterministic, discrete-in-time, varying demand pattern subject to linear setup and stockholding costs. The most widely known procedure for deriving the optimal solution is the Wagner-Whitin algorithm, although many other approaches have subsequently been developed for tackling the same problem. The objective of this note is to show how these procedures can readily be adapted when the input is a finite rate production process. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 221–228, 1997     

Selection of the optimal setup policy

A model is developed taking into consideration all the costs (namely cost of sampling, cost of not detecting a change in the process, cost of a false indication of change, and the cost of readjusting detected changes) incurred when a production process, using an unscheduled setup policy, utilizes fraction-defective control charts to control current production. The model is based on the concept of the expected time between detection of changes calling for setups. It is shown that the combination of unscheduled setups and control charts can be utilized in an optimal way if those combinations of sample size, sampling interval, and extent of control limits from process average are used that provide the minimum expected total cost per unit of time. The costs of a production process that uses unscheduled setups in conjunction with the appropriate optimal control charts are compared to the costs of a production process that uses scheduled setups at optimum intervals in conjunction with its appropriate control charts. This comparison indicates the criteria for selecting production processes with scheduled setups using optimal setup intervals over unscheduled setups. Suggestions are made to evaluate the optimal process setup strategy and the accompanying optimal decision parameters, for any specific cost data, by use of computer enumeration. A numerical example for assumed cost and process data is provided.     

Estimating defence budget saving from disarmament: the United States’ case

Probing the technology in the production of US national defence by using a dynamic cost‐function model with adjustment costs, this paper evaluates the effect of reducing the level of national defence on the defence budget saving. Our inquiry involves estimating the defence production structure without output data for non‐market goods that are normally unavailable. Our findings include: (i) the United States behaves rationally to minimize cost in the production of national defence; (ii) the adjustment costs are larger in disarmament than in military build‐up; (iii) due to the adjustment costs peculiar to disarmament, the defence budget saving from disarmament appears small, but cutbacks allow great savings on the defence budget.     

Production-allocation scheduling and capacity expansion using network flows under uncertainty

This paper extends Connors and Zangwill's work in network flows under uncertainty to the convex costs case. In this paper the extended network flow under uncertainty algorithm is applied to compute N-period production and delivery schedules of a single commodity in a two-echelon production-inventory system with convex costs and low demand items. Given an initial production capacity for N periods, the optimal production and delivery schedules for the entire N periods are characterized by the flows through paths of minimal expected discounted cost in the network As a by-product of this algorithm the multi-period stochastic version of the parametric budget problem for the two-echelon production-inventory system is solved.     

Optimal capacity in a coordinated supply chain

We consider a supply chain in which a retailer faces a stochastic demand, incurs backorder and inventory holding costs and uses a periodic review system to place orders from a manufacturer. The manufacturer must fill the entire order. The manufacturer incurs costs of overtime and undertime if the order deviates from the planned production capacity. We determine the optimal capacity for the manufacturer in case there is no coordination with the retailer as well as in case there is full coordination with the retailer. When there is no coordination the optimal capacity for the manufacturer is found by solving a newsvendor problem. When there is coordination, we present a dynamic programming formulation and establish that the optimal ordering policy for the retailer is characterized by two parameters. The optimal coordinated capacity for the manufacturer can then be obtained by solving a nonlinear programming problem. We present an efficient exact algorithm and a heuristic algorithm for computing the manufacturer's capacity. We discuss the impact of coordination on the supply chain cost as well as on the manufacturer's capacity. We also identify the situations in which coordination is most beneficial. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

Learning and costs in airframe production: A multiple-output production function approach

This article examines the influence of both production rate and learning on airframe program costs. A dynamic multiple-output production model is developed and is used to observe the cost impact of changes in production rate and learning. Several simulations are performed to demonstrate the sensitivity of the optimal time path of cost to changes in the model parameters. The model is applied by estimating parameters from the F102 airframe program.     

Production/distribution system design with inventory considerations

This study addresses the design of a three‐stage production/distribution system where the first stage includes the set of established retailers and the second and third stages include the sets of potential distribution centers (DCs) and potential capacitated suppliers, respectively. In this problem, in addition to the fixed location/operating costs associated with locating DCs and suppliers, we consider the coordinated inventory replenishment decisions at the located DCs and retailers along with the appropriate inventory costs explicitly. In particular, we account for the replenishment and holding costs at the retailers and selected DCs, and the fixed plus distance‐based transportation costs between the selected plants and their assigned DCs, and between the selected DCs and their respective retailers, explicitly. The resulting formulation is a challenging mixed‐integer nonlinear programming model for which we propose efficient heuristic solution approaches. Our computational results demonstrate the performance of the heuristic approaches as well as the value of integrated decision‐making by verifying that significant cost savings are realizable when the inventory decisions and costs are incorporated in the production distribution system design. © 2012 Wiley Periodicals, Inc. Naval Research Logistics 59: 172–195, 2012     

Operations sequencing for a multi‐stage production inventory system

This article studies operations sequencing for a multi‐stage production inventory system with lead times under predictable (deterministic) yield losses and random demand. We consider various cases with either full or partial release of work‐in‐process inventories, for either pre‐operation or post‐operation cost structures, and under either the total discounted or average cost criteria. We derive necessary and sufficient criteria for the optimal sequence of operations in all cases. While the criteria differ in their specific forms, they all lead to the same principal: those operations with (1) lower yields, (2) lower processing costs, (3) longer lead times, and (4) lower inventory holding costs should be placed higher upstream in the system.Copyright © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 144–154, 2014     

Optimal production growth for the machine loading problem

This paper investigates a production growth logistics system for the machine loading problem (generalized transportation model), with a linear cost structure and minimum levels on total machine hours (resources) and product types (demands). An algorithm is provided for tracing the production growth path of this system, viz. in determining the optimal machine loading schedule of machines for product types, when the volumes of (i) total machine hours, and (ii) the total amount of product types are increased either individually for each total or simultaneously for both. Extensions of this methodology, when (i) the costs of production are convex and piecewise linear, and (ii) when the costs are nonconvex due to quantity discounts, and (iii) when there are upper bounds for productions are also discussed. Finally, a “goal-programming” production growth model where the specified demands are treated as just goals and not as absolute quantities to be satisfied is also considered.     

Production planning for multi-resource network systems

Production planning for large-scale production systems requiring the allocation of numerous resources is considered. It is demonstrated how the dynamic activity analysis developed by Shephard leads to linear programming solutions of production planning problems. Three types of planning problems are formulated: maximization of output levels for a given time horizon; minimization of production duration for given output histories; and minimization of production costs for given output histories.     

On the value of information in dynamic production/inventory problems under forecast evolution

In this article we explore how total system costs and inventory positions are affected when forecasts are incorporated explicitly in production/inventory systems. We assume that forecasts for demand of a certain item are available in each period, and they evolve from one period to the next in accordance with an additive evolution model. In order to analyze the effects of the forecasts on the production/inventory system we compare the optimal ordering policy and the expected costs of the model that keeps forecasts with that of a comparable standard inventory model. We show that under mild assumptions the former yields lower expected costs and inventory levels than the latter. © 1996 John Wiley & Sons, Inc.     

Lot sizing when yields increase during the production run

We investigate the problem of determining lot sizes for multiple items when the expected percentage of acceptable output increases with the duration of the production run, usually due to adjustments made during the early part of the production run. Such problems arise in metal stamping, textile finishing processes, and a variety of other industries. The goal is to minimize the total cost of production, inventory holding costs, and setup costs (where applicable). We develop a heuristic procedure based on a Lagrangian relaxation that differs from relaxations used in earlier studies. We use various properties of the objective function to guide the adjustment of the initial solution from the relaxation toward feasibility. Computational results indicate that, on the average, the heuristic produces solutions within 4.9% of the lower bound obtained from the Lagrangian relaxation. © 1996 John Wiley & Sons, Inc.     

Optimizing the quality control station configuration

We study unreliable serial production lines with known failure probabilities for each operation. Such a production line consists of a series of stations, existing machines, and optional quality control stations (QCSs). Our aim is to decide on the allocation of the QCSs within the assembly line, so as to maximize the expected profit of the system. In such a problem, the designer has to determine the QCS configuration and the production rate simultaneously. The profit maximization problem is approximated assuming exponentially distributed processing times, Poisson arrival process of jobs into the system, and the existing of holding costs. The novel feature of our model is the incorporation of holding costs that significantly complicated the problem. Our approximation approach uses a branch and bound strategy that employs our fast dynamic programming algorithm for minimizing the expected operational costs for a given production rate as a subroutine. Extensive numerical experiments are conducted to demonstrate the efficiency of the branch and bound procedure for solving large scale instances of the problem and for obtaining some qualitative insights.

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Assemble to order and assemble in advance in a single-period stochastic environment

Faced with stochastic demand, a firm may decide to assemble its products in advance or assemble them once actual demand is realized. In general, the production cost for items assembled in advance (AIA) is lower than for items assembled to order (ATO), because there is no need to expedite, and the production process can be planned and executed well in advance. On the other hand, items assembled in advance (AIA) for which there is no demand incur excessive and unnecessary assembly costs. The two policies, AIA and ATO, as well as a composite one, are compared and analyzed in light of these trade-offs. The composite model, which is shown as the dominating policy, is also extended to deal with the following two scenarios. The first assumes a loss of a fraction of the demand when demand cannot be satisfied from the shelf but rather through ATO. The second considers the effects of budget constraints on the total inventory cost. © 1995 John Wiley & Sons, Inc.     

MILITARY EXPENDITURE AND ECONOMIC GROWTH IN MIDDLE EASTERN COUNTRIES: A DYNAMIC PANEL DATA ANALYSIS

Defence expenditures have both costs and benefits to the economy. The costs of defence expenditures are mainly emphasized as opportunity costs. On the other hand, defence spending may have growth‐promoting potential benefits: a rise in defence spending may result in a higher aggregate demand, production and employment. This paper examines empirically the effects of military expenditures on economic growth for Middle Eastern countries and Turkey, for the time‐period 1989–1999. The relationship between military expenditure and economic growth is investigated by using cross‐section and dynamic panel estimation techniques. Empirical analysis indicates that military expenditure enhances economic growth in the Middle Eastern countries and Turkey as a whole.