Concavity and monotonicity properties in a groundwater management model

We consider a discrete‐time groundwater model in which the cost of pumping takes a slightly different form to that which has been traditional in the research literature to date. This enables us to prove that (a) the optimal pumping quantity is nondecreasing in the ground water stock, (b) the stock level remaining after each period's pumping is also nondecreasing in the groundwater stock, (c) the optimal decision is determined by maximizing a concave function, and finally (d) the optimal pumping quantity is nonincreasing in the number of periods to go. We show that (a)–(c), while intuitive, do not hold under traditional modeling assumptions. We also explain the connections between our results and similar ones for some classic problems of operations research. © 2011 Wiley Periodicals, Inc. Naval Research Logistics 00: 000–000, 2011     

A multilevel passenger screening problem for aviation security

Passenger prescreening is a critical component of aviation security systems. This paper introduces the Multilevel Allocation Problem (MAP), which models the screening of passengers and baggage in a multilevel aviation security system. A passenger is screened by one of several classes, each of which corresponds to a set of procedures using security screening devices, where passengers are differentiated by their perceived risk levels. Each class is defined in terms of its fixed cost (the overhead costs), its marginal cost (the additional cost to screen a passenger), and its security level. The objective of MAP is to assign each passenger to a class such that the total security is maximized subject to passenger assignments and budget constraints. This paper shows that MAP is NP‐hard and introduces a Greedy heuristic that obtains approximate solutions to MAP that use no more than two classes. Examples are constructed using data extracted from the Official Airline Guide. Analysis of the examples suggests that fewer security classes for passenger screening may be more effective and that using passenger risk information can lead to more effective security screening strategies. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

Easily computed approximations for (s,S) inventory system operating characteristics

The operating characteristics of (s,S) inventory systems are often difficult to compute, making systems design and sensitivity analysis tedious and expensive undertakings. This article presents a methodology for simplified sensitivity analysis, and derives approximate expressions for operating characteristics of a simple (s,S) inventory system. The operating characteristics under consideration are the expected values of total cost per period, holding cost per period, replenishment cost per period, backlog cost per period, and backlog frequency. The approximations are obtained by using least-squares regression to fit simple functions to the operating characteristics of a large number of inventory items with diverse parameter settings. Accuracy to within a few percent of actual values is typical for most approximations. Potential uses of the approximations are illustrated for several idealized design problems, including consolidating demand from several locations, and tradeoffs for increasing service or reducing replenishment delivery lead time.     

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.     

Present practices and future plans for MIL-STD-781

MIL-STD-781 specifies reliability acceptance test procedures based on both fixed-length tests and probability ratio sequential tests. The assumption underlying MIL-STD-781 is a constant mean time between failures (MTBF) and typical practice applies MIL-STD-781 to electrical, electronic, and mechanical equipment. This article discusses some of the difficulties that have prompted the C and D revisions of MIL-STD-781. In addition, it discusses the relationship of MIL-STD-781 with MIL-STD-1635(EC) which deals with reliability growth testing. The article concludes with a discussion of needed research in reliability growth testing, in support of MIL-STD-781, and in the area of stress and its impact of MTBF.     

Mexico: a model for success

In Mexico, a 40 year period of political stability and economic advancement, hailed for its high rates of growth in income per capita, rapid urbanization, and impressive gains in indicators of health and education, seemed to come to a halt in the early 1980s. Since the early 1970s, fertility has declined sharply in chronological association with a new population policy and the implementation of a national family planning program. If in 1940 there was no apparent reason for the Mexican state to have much interest in limiting fertility, such was no longer the case by 1970. The General Law of Population that had been passed in 1947 was laced with the expansionist ideology that dominated demographic issues for more that a century; its pro-natalism had been reinforced by health regulations prohibiting the sale and use of contraceptives and by a penal code that made abortion a crime. Between 1970 and 1981 the total fertility rate fell by about 39%. Since 1975, change in contraceptive practice accounts for the bulk of the measured fertility decline. Between 1976 and 1982 there was a 66% increase in contraceptive prevalence. The government's involvement in family planning activities helped to: 1) develop an effective contraceptive distribution system; 2) circulate extensive information, education, and communication publicizing fertility and images of the small family; and 3) mobilize health practitioners in public institutions to counsel and persuade their clients to accept and practice contraception. The emerging debate over population policy in Brazil may well prefigure debates in other Latin American countries; the recent democratization in Brazil is the vocalization of a demand from women's groups and the left for government provided family planning services. Overall, Mexico's willingness to take the long view tackle the birth rate issue head on is likely to remain an exception in Latin America.     

Minimal forecast horizon procedures for dynamic lot size models

This article presents new results which should be useful in finding production decisions while solving the dynamic lot sizing problem of Wagner–Whitin on a rolling horizon basis. In a rolling horizon environment, managers obtain decisions for the first period (or the first few periods) by looking at the forecasts for several periods. This article develops procedures to find optimal decisions for any specified number of initial periods (called planning horizon in the article) by using the forecast data for the minimum possible number of future periods. Computational results comparing these procedures with the other procedures reported in the literature are very encouraging.     

A forward network simplex algorithm for solving multiperiod network flow problems

An optimization model which is frequently used to assist decision makers in the areas of resource scheduling, planning, and distribution is the minimum cost multiperiod network flow problem. This model describes network structure decision-making problems over time. Such problems arise in the areas of production/distribution systems, economic planning, communication systems, material handling systems, traffic systems, railway systems, building evacuation systems, energy systems, as well as in many others. Although existing network solution techniques are efficient, there are still limitations to the size of problems that can be solved. To date, only a few researchers have taken the multiperiod structure into consideration in devising efficient solution methods. Standard network codes are usually used because of their availability and perceived efficiency. In this paper we discuss the development, implementation, and computational testing of a new technique, the forward network simplex method, for solving linear, minimum cost, multiperiod network flow problems. The forward network simplex method is a forward algorithm which exploits the natural decomposition of multiperiod network problems by limiting its pivoting activity. A forward algorithm is an approach to solving dynamic problems by solving successively longer finite subproblems, terminating when a stopping rule can be invoked or a decision horizon found. Such procedures are available for a large number of special structure models. Here we describe the specialization of the forward simplex method of Aronson, Morton, and Thompson to solving multiperiod network network flow problems. Computational results indicate that both the solution time and pivot count are linear in the number of periods. For standard network optimization codes, which do not exploit the multiperiod structure, the pivot count is linear in the number of periods; however, the solution time is quadratic.