Short-term retrenchment planning in hierarchical manpower systems

We address the issue of short-term retrenchment planning required of organizations that are phasing down their manpower levels at rates faster than are allowed by natural attrition. Specifically, the problem we study is as follows: given the initial and target grade populations in a hierarchical manpower system at the end of a finite time horizon and the per-period rate of natural attrition for each grade, find a stationary manpower policy that minimizes the maximum per-period rate of retrenchment across all the grades over all stationary policies that yield the target grade populations at the end of the horizon. Because the problem is a nonconvex, nonseparable, nonlinear program, we develop a heuristic in which the promotion proportions of all the grades are successively fixed, starting from the lowest grade. We prove optimality of the heuristic policy in three nontrivial situations. In a computational experiment, in 135 out of 150 randomly generated instances (i.e., in 90% of the cases), the heuristic yielded a solution that was as good or better than that yielded by a benchmark computer program that solves the present problem as a nonlinear program. Further, the average computational time under the heuristic was an order of magnitude less than that under the program. © 1995 John Wiley & Sons, Inc.     

A suboptimality bound for permutation policies in single machine stochastic scheduling

A general class of single machine stochastic scheduling problems incorporating precedence constraints is modelled as a family of competing Markov decision processes. A bound on the optimal return yields a suboptimality bound for permutation policies. This in turn leads to a generalised “used better than new” principle as a (highly intuitive) sufficient condition for the optimality of a permutation policy in the class of all (preemptive) policies. © 1995 John Wiley & Sons, Inc.     

The impact of exponential smoothing forecasts on the bullwhip effect

An important phenomenon often observed in supply chain management, known as the bullwhip effect, implies that demand variability increases as one moves up the supply chain, i.e., as one moves away from customer demand. In this paper we quantify this effect for simple, two‐stage, supply chains consisting of a single retailer and a single manufacturer. We demonstrate that the use of an exponential smoothing forecast by the retailer can cause the bullwhip effect and contrast these results with the increase in variability due to the use of a moving average forecast. We consider two types of demand processes, a correlated demand process and a demand process with a linear trend. We then discuss several important managerial insights that can be drawn from this research. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 269–286, 2000     

Optimizing natural gas pipeline transmission with nonuniform elevation: A new initialization approach

In this paper, we develop an iterative piecewise linear approximation approach with a novel initialization method to solve natural gas pipeline transmission problems with the nonuniform network elevation. Previous approaches, such as energy minimization methods, cannot be applied to solve problems with the nonuniform network elevation because they exclude pressure range constraints, and thus provide solutions far from optimum. We propose a new initialization model that considers pressure range constraints and improves the optimality of the solutions and the computational efficiency. Furthermore, we extend the energy minimization methods and provide the necessary conditions under which the extended methods operate in networks with the nonuniform elevation. We test the performances of the methods with previously reported pipeline networks from the literature, with the open data set GasLib, and with our industrial collaborator. The initialization approach is shown to be more efficient than the method with fixed initial breakpoints. The newly proposed initialization approach generates solutions with a higher accuracy than the extended energy minimization methods, especially in large‐size networks. The proposed method has been applied to natural gas transmission planning by the China National Petroleum Corporation and has brought a direct profit increase of 330 million U.S. dollars in 2015‐2017.     

Mixed rules in multi‐issue allocation situations

Multi‐issue allocation situations study problems where an estate must be divided among a group of agents. The claim of each agent is a vector specifying the amount claimed by each agent on each issue. We present a two‐stage rule. First, we divide the estate among the issues following the constrained equal awards rule. Second, the amount assigned to each issue is divided among the agents in proportion to their demands on this issue. We apply the rule to two real‐world problems: the distribution of natural resources between countries and the distribution of budget for education and research between universities.     

Explicit modeling of detection within a stochastic duel

Many mathematical models have been formulated to describe combat between two weapon systems. However, until recently duel models did not explicitly represent target detection within a duel, leading to the necessity for the development of new model for each tactical situation. An earlier article by two of the authors described a duel between weapons with constant firing times and explicit modeling of detection. This article enhances the study of this form of duel between weapons by introducing a variable parameter for firing times. This enhancement removes the discontinuities evident during parametric analysis of the earlier model and hence provides a more coherent model of this combat situation. © 1993 John Wiley & Sons, Inc.     

Timing replacement decisions under discontinuous technological change

We consider the problem of deciding whether to keep a piece of equipment or replace it with a more advanced technology in an environment of technological change. Our model assumes that the costs associated with the presently available technology and future technologies are known, but that the appearance times of future technologies are uncertain. We develop a procedure for computing the optimal keep-or-replace decision that iteratively incorporates a technological forecast. For a certain class of situations, we show that our approach requires the minimum possible amount of forecasted data.     

Integrating multiple criteria decision making models into the decision support system framework for marketing decisions

A large number of multiple criteria decision making (MCDM) models have evolved over the past decade. This field now seems to have reached a stage of maturity. However, the managerial community has not yet extensively adopted these models in solving practical decision problems. The present article focuses on integrating the MCDM models within the decision support system (DSS) framework to encourage greater use of these models. A DSS framework and the criteria used for the choice of a model is discussed. Based on these criteria MCDM models generally used in the marketing field are evaluated. The possibility of using a mixture of MCDM models within the DSS framework is also explored. Following this, the role of the MCDM models in DSS is delineated. It is argued that, within the problem-solving process, the confluence of MCDM models and DSS plays a vital role in developing high-quality solutions.     

Stochastic linear fractional programming with the ratio of independent Cauchy variates

In this article is studied a stochastic linear fractional programming problem, in which the parameters of both the numerator and the denominator are assumed to be mutually independent Cauchy variates. The deterministic equivalent of the problem is obtained and is shown to be a linear fractional program. A numerical example is also added for illustration.     

Estimation of the common scale parameter of two pareto distributions in censored samples

The problem of estimation of the common scale parameter of two Pareto distributions with unknown and unequal shape parameters in censored samples is considered. The uniformly minimum variance unbiased estimator (UMVUE) is given along with the UMVUE of its variance.