Scheduling economic lot sizes in deteriorating production systems

The paper considers the economic lot scheduling problem (ELSP) where production facility is assumed to deteriorate, owing to aging, with an increasing failure rate. The time to shift from an “in‐control” state to an “out‐of‐control” state is assumed to be normally distributed. The system is scheduled to be inspected at the end of each production lot. If the process is found to be in an “out‐of‐control” state, then corrective maintenance is performed to restore it to an “in‐control” state before the start of the next production run. Otherwise, preventive maintenance is carried out to enhance system reliability. The ELSP is formulated under the capacity constraint taking into account the quality related cost due to possible production of non‐conforming items, process inspection, and maintenance costs. In order to find a feasible production schedule, both the common cycle and time‐varying lot sizes approaches are utilized. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 650–661, 2003     

An approach to the allocation of common costs of multi-mission systems

Many Naval systems, as well as other military and civilian systems, generate multiple missions. An outstanding problem in cost analysis is how to allocate the costs of such missions so that their true costs can be determined and resource allocation optimized. This paper presents a simple approach to handling this problem for single systems. The approach is based on the theory of peak-load pricing as developed by Marcel Boiteux. The basic principle is that the long-run marginal cost of a mission must be equal to its “price.” The implication of this is that if missions can cover their own marginal costs, they should also be allocated some of the marginal common costs. The proportion of costs to be allocated is shown to a function of not only the mission-specific marginal costs and the common marginal costs, but also of the “mission price.” Thus, it is shown that measures of effectiveness must be developed for rational cost allocation. The measurement of effectiveness has long been an intractable problem, however. Therefore, several possible means of getting around this problem are presented in the development of the concept of relative mission prices.     

A cost analysis of sampling inspection under Military Standard 105D

Military Standard 105D has been almost universally adopted by government and private consumers for the lot-by-lot sampling inspection of product which may be inspected on a dichotomoun basis The plan specifies, for each lot size, a random sample size and set of acceptance numbers (maximum allowable number of defectives in each sample). The acceptance numbers are based upon the binomial distribution and depend upon the quality required by the purchaser. Where several consecutive lots are submitted, a shift to less severe (“reduced”) inspection or more severe (“tightened”) inspection is specified when the ongoing quality is very high or low. Further experience permits a return to normal sampling from either of these states This paper examines the long range costs of such a sampling scheme. The three inspection types are considered as three distinct Markov chains, with periodic transitions from chain to chain. The expected sample size and the expected proportion of rejected product are determined as a function of the two parameters under control of the manufacturer, lot size and product quality. Some numerical examples are given which illustrate how to compute the overall cost of sampling inspection. Suggestions are made concerning the choice of parameters to minimize this cost.     

Production to order and off‐line inspection when the production process is partially observable

This study combines inspection and lot‐sizing decisions. The issue is whether to INSPECT another unit or PRODUCE a new lot. A unit produced is either conforming or defective. Demand need to be satisfied in full, by conforming units only. The production process may switch from a “good” state to a “bad” state, at constant rate. The proportion of conforming units in the good state is higher than in the bad state. The true state is unobservable and can only be inferred from the quality of units inspected. We thus update, after each inspection, the probability that the unit, next candidate for inspection, was produced while the production process was in the good state. That “good‐state‐probability” is the basis for our decision to INSPECT or PRODUCE. We prove that the optimal policy has a simple form: INSPECT only if the good‐state‐probability exceeds a control limit. We provide a methodology to calculate the optimal lot size and the expected costs associated with INSPECT and PRODUCE. Surprisingly, we find that the control limit, as a function of the demand (and other problem parameters) is not necessarily monotone. Also, counter to intuition, it is possible that the optimal action is PRODUCE, after revealing a conforming unit. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Optimal job splitting on a multi‐slot machine with applications in the printing industry

In this article, we define a scheduling/packing problem called the Job Splitting Problem, motivated by the practices in the printing industry. There are n types of items to be produced on an m‐slot machine. A particular assignment of the types to the slots is called a “run” configuration and requires a setup cost. Once a run begins, the production continues according to that configuration and the “length” of the run represents the quantity produced in each slot during that run. For each unit of production in excess of demand, there is a waste cost. Our goal is to construct a production plan, i.e., a set of runs, such that the total setup and waste cost is minimized. We show that the problem is strongly NP‐hard and propose two integer programming formulations, several preprocessing steps, and two heuristics. We also provide a worst‐case bound for one of the heuristics. Extensive tests on real‐world and randomly generated instances show that the heuristics are both fast and effective, finding near‐optimal solutions. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

The determination of optimal base-stock inventory policy when the costs of under- and oversupply are uncertain

This article considers the determination of the optimal base-stock inventory policy for the newsboy inventory model when there is uncertainty about either or both of its basic cost inputs: either C_u, the marginal cost of an undersupply mistake, or C_o, the marginal cost of an oversupply mistake. Such uncertainties often arise in implementing the newsboy model, especially with respect to C_u, whose value depends mostly on the often-imponderable economic consequences of a lost sale or backorder. Given this uncertainty, we use decision theory to propose and analyze two measures of policy “goodness” and two base-stock selection criteria, which in combination provide four alternative “optimal” base-stock policies. Formulas and/or conditions defining each alternative policy are provided. Our empirical study indicates that the recommended policy can be quite sensitive to the measure/criterion chosen, and that the consequences of the wrong choice can be quite considerable.     

The threshold policy in the M/G/1 queue with server vacations

This article deals with the M/G/1 queue with server vacations in which the return of the server to service depends on the number of customers present in the system. The main goal is optimization, which is done under the average cost criterion in the multiple- and single-vacation models as well as for the “total cost for one busy cycle” criterion in the multiple-vacation case. Expressions that characterize the optimal number of customers, below which the server should not start a new service period, are exhibited for the various cases. It is found that under the average cost criterion, the expression may be universal in the sense that it may hold for a general class of problems including such that arise in production planning and inventory theory (for the particular cost structure discussed).     

The applicability of acceptance sampling with respect to process stability and control

This article examines the applicability of acceptance sampling and the effectiveness of Deming's kp rule in relation to the degree of process stability achieved through statistical process control techniques. A discrete-event simulation model is used to characterize the correlation between the number of defective units in a randomly drawn sample versus in the remainder of a lot, in response to a number of system and control chart parameters. The model reveals that such correlation is typically present when special causes of variation affect the production process from time to time, even though the process is tightly monitored through statistical process control. Comparison of these results to an analogous mixed binomial scenario reveals that the mixed binomial model overstates the correlation in question if the state of the process is not necessarily constant during lot production. A generalization of the kp analysis is presented that incorporates the possibility of dependence between a sample and the unsampled portion of the lot. This analysis demonstrates that acceptance sampling is generally ineffective for lots generated by a process subject to statistical process control, despite the fact that the number of defectives in the sample and in the remainder of the lot are not strictly independent. © 1994 John Wiley & Sons, Inc.     

Flow‐shop batch scheduling with identical processing‐time jobs

In many practical situations of production scheduling, it is either necessary or recommended to group a large number of jobs into a relatively small number of batches. A decision needs to be made regarding both the batching (i.e., determining the number and the size of the batches) and the sequencing (of batches and of jobs within batches). A setup cost is incurred whenever a batch begins processing on a given machine. This paper focuses on batch scheduling of identical processing‐time jobs, and machine‐ and sequence‐independent setup times on an m‐machine flow‐shop. The objective is to find an allocation to batches and their schedule in order to minimize flow‐time. We introduce a surprising and nonintuitive solution for the problem. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

Dynamic lot‐sizing model with production time windows

We consider a dynamic lot‐sizing model with production time windows where each of n demands has earliest and latest production due dates and it must be satisfied during the given time window. For the case of nonspeculative cost structure, an O(nlogn) time procedure is developed and it is shown to run in O(n) when demands come in the order of latest production due dates. When the cost structure is somewhat general fixed plus linear that allows speculative motive, an optimal procedure with O(T⁴) is proposed where T is the length of a planning horizon. Finally, for the most general concave production cost structure, an optimal procedure with O(T⁵) is designed. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Some recent findings in bayesian attribute single and double sampling

This article deals with several items, including theoretical and applied results. Specific topics include (1) a discrete, economically based, attributes acceptance sampling model and its adaptations, (2) relevant costs, (3) relevant prior distributions, (4) comparison of single- and double-sampling results, and (5) reasons for marginal implementation success following excellent implementation efforts. The basic model used is one developed by Guthrie and Johns; adaptations include provisions for fixed costs as well as modifications to permit double sampling. Optimization is exact, rather than approximate. Costs incorporated into the model are for sampling inspection, lot acceptance, and lot rejection. For each of these three categories a fixed cost is included as well as two variable costs, one for each item and the other for each defective item. Discrete prior distributions for the number of defectives in a lot are used exclusively. These include the mixed binomial and Polya distributions. Single- and double-sampling results are compared. Double sampling regularly performs at only slightly lower cost per lot than single sampling. Also, some cost and prior distribution sensitivity results are presented. Comments are provided regarding actual implementation experiences in industry. Practical deficiencies with the Bayesian approach are described, and a recommendation for future research is offered.     

Cyclical schedules for the joint replenishment problem with dynamic demands

The Joint Replenishment Problem (JRP) involves production planning for a family of items. The items have a coordinated cost structure whereby a major setup cost is incurred whenever any item in the family is produced, and an item-specific minor setup cost is incurred whenever that item is produced. This paper investigates the performance of two types of cyclical production schedules for the JRP with dynamic demands over a finite planning horizon. The cyclical schedules considered are: (1) general cyclical schedules—schedules where the number of periods between successive production runs for any item is constant over the planning horizon—and (2) power-of-two schedules—a subset of cyclical schedules for which the number of periods between successive setups must be a power of 2. The paper evaluates the additional cost incurred by requiring schedules to be cyclical, and identifies problem characteristics that have a significant effect on this additional cost. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 577–589, 1997.     

Simultaneous equation production functions (cobb-douglas type) for decisions pertaining to sea-based tactical air resources

This paper proposes the use of a set of simultaneous, modified Cobb-Douglas production functions to determine the “best” mix of aircraft inputs (men, spare parts, support equipment, etc.) to achieve certain levels of output. The simultaneous equations are “solved” by the use of proxy values in an iterative search process. Tests of this production function model indicate that real world data fits quite well (R² ranges from 0.884 to 0.951) and the residuals do not appear to have any significant bias. The aircraft production functions for several types of aircraft (F-4, A-7, A-6, A-4) have been maximized for a fixed budget and specific constraints. A potential increase in output of up to 10 percent appears possible. In general the marginal value of spare parts surpasses the other inputs.     

Quality control and the sales process

Product quality is emerging as a major strategic instrument for competition. The purpose of this article is to assess the effects of quality control on sales, and, vice versa, the effects of the sales process on quality control. A model relating quality control and the sales process (advertising, repeat purchase, and word-of-mouth effects) is developed to evaluate the above relationships. Two special cases, with degenerate and beta distribution for defect items in the production lot, are analyzed in detail. In the former case, analytical results for the optimal quality control schemes are obtained, whereas in the latter, efficient bounds are derived to search for the optimal scheme. It is shown, analytically and numerically, that the sales parameters have significant impact on whether more “stringent” or “tighter” quality control is warranted. Future research directions are also discussed.     

Degeneracy in inventory models

In order‐quantity reorder‐point formulations for inventory items where backordering is allowed, some of the more common ways to prevent excessive stockouts in an optimal solution are to impose either a cost per unit short, a cost per stockout occasion, or a target fill rate. We show that these popular formulations, both exact and approximate, can become “degenerate” even with quite plausible parameters. By degeneracy we mean any situation in which the formulation either cannot be solved, leads to nonsensical “optimal” solutions, or becomes equivalent to something substantially simpler. We explain the reasons for the degeneracies, yielding new insight into these models, and we provide practical advice for inventory managers. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 686–705, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10037     

Three ways of obtaining the average cost expression in a problem related to joint replenishment inventory control

This paper does not present a new result, rather it is meant to illustrate the choice of modelling procedures available to an analyst in a typical inventory control problem. The same “average cost per unit time” expression is developed by three quite different procedures. This variety of approaches, as well as the recounting of the author's chronological efforts to solve the problem, should be of interest to the reader. The specific inventory problem studied is one where the controller of an item is faced with random opportunities for replenishment at a reduced setup cost; the problem is an integral component of the broader problem of inventory control of a group of items whose replenishments are coordinated to reduce the costs of production, procurement, and/or transportation.     

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     

When is a base stock policy optimal in recovering disrupted cyclic schedules?

The extended economic lot scheduling problem (EELSP) is concerned with scheduling the production of a set of items in a single facility to minimize the long-run average holding, backlogging, and setup costs. Given an efficient cyclic production schedule for the EELSP, called the target schedule, we consider the problem of how to schedule production after a single schedule disruption. We propose a base stock policy, characterized by a base stock vector, that prescribes producing an item until its inventory level reaches the peak inventory of the target schedule corresponding to the item's position in the production sequence. We show that the base stock policy is always successful in recovering the target schedule. Moreover, the base stock policy recovers the target schedule at minimal excess over average cost whenever the backorder costs are proportional to the processing times. This condition holds, for example, when the value of the items is proportional to their processing times, and a common inventory carrying cost and a common service level is used for all the items. Alternatively, the proportionality condition holds if the inventory manager is willing to select the service levels from a certain set that is large enough to guarantee any minimal level of service, and then uses the imputed values for the backorder costs. When the proportionality condition holds we provide a closed-form expression for the total relevant excess over average cost of recovering the target schedule. We assess the performance of the base stock policy when the proportionality condition does not hold through a numerical study, and suggest some heuristic uses of the base stock policy. © 1994 John Wiley & Sons, Inc.     

A closed-from approach for multi-item inventory grouping

The problem of optimally coordinating the replenishments of the many items in stock with one another is dealt with in this article. Specifically, it considers this coordination based on classifying the items into a few groups with common order cycles for all the items in a particular group. Assuming that the cumulative distribution by value of the inventory can be characterized by a Pareto function of the type f(n) = n/(an + b), (a, b > 0), it establishes that the optimal boundaries of the groups can be obtained as closed-form expressions by solving a system of simultaneous equations. The composition of the successive groups thus obtained is found to equipartition the total cost and to follow geometric sequences in relation to the number of items, the value of items, and the lengths of the order cycles. Graphs have been proposed to aid the implementation of the grouping scheme. Simple iterative schemes are outlined within the framework of the Pareto function to handle other relevant costs.     

A semieconomic method for the determination of optimal lot sizes

The construction of lot sizes usually depends upon factors influencing homogeneity. When these factors are not a function of lot quantity, it is possible to determine an optimal lot size. The optimization process balances the cost of sampling against the expected cost of lot rejection for some specified procurement quantity. The rationale for balancing the two costs is contingent upon the fact that rejection criteria waivers frequently occur when the lot size is large. This concept implies that the lot size should be as small as possible, whereas the cost of sampling drives the lot size up. Hence, trade-offs may be made. The formulation is termed a semieconomic one because it combines a pure economic objective function with a pure statistical constraint. This constraint is necessary because the nature of the items under study dictates that the cost of accepting defective material cannot be explicitly stated. The paper presents the formulation, describes when it should be used, derives a good analytical approximation under certain assumptions and gives various ramifications when it is used.