Technical note – operational statistics: Properties and the risk‐averse case

Consider a repeated newsvendor problem for managing the inventory of perishable products. When the parameter of the demand distribution is unknown, it has been shown that the traditional separated estimation and optimization (SEO) approach could lead to suboptimality. To address this issue, an integrated approach called operational statistics (OS) was developed by Chu et al., Oper Res Lett 36 (2008) 110–116. In this note, we first study the properties of this approach and compare its performance with that of the traditional SEO approach. It is shown that OS is consistent and superior to SEO. The benefit of using OS is larger when the demand variability is higher. We then generalize OS to the risk‐averse case under the conditional value‐at‐risk (CVaR) criterion. To model risk from both demand sampling and future demand uncertainty, we introduce a new criterion, called the total CVaR, and find the optimal OS under this new criterion. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 206–214, 2015     

A note on the optimality of index priority rules for search and sequencing problems

We show that the linear objective function of a search problem can be generalized to a power function and/or a logarithmic function and still be minimized by an index priority rule. We prove our result by solving the differential equation resulting from the required invariance condition, therefore, we also prove that any other generalization of this linear objective function will not lead to an index priority rule. We also demonstrate the full equivalence between two related search problems in the sense that a solution to either one can be used to solve the other one and vice versa. Finally, we show that the linear function is the only function leading to an index priority rule for the single‐machine makespan minimization problem with deteriorating jobs and an additive job deterioration function. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Incorporation dynamic aspects and uncertainty in 1‐median location problems

In this paper we present several 1‐median formulations on a tree network which incorporate dynamic evolution and/or uncertainty of node demands and transportation costs over a planning horizon. Dynamic evolution is modeled using linear demand functions for the nodes and linear length functions for the edges. Uncertainty is modeled with the use of multiple scenarios, where a scenario is a complete specification of the uncertain node demands and/or edge lengths. We formulate our objective using minimax regret like criteria. We use two different criteria, namely, robust deviation and relative robustness. We discuss what motivated the introduction of these objectives, as well as their relation to existing literature and decision making practices. For all of the models presented, we provide low‐order polynomial time algorithms. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 147–168, 1999     

Stochastic allocation of inspection capacity to competitive processes

Optimal allocation and control of limited inspection capacity for multiple production processes are considered. The production processes, which operate independently but share inspection capacity, are subject to random failures and are partially observed through inspection. This study proposes an approach of stochastic allocation, using a Markov decision process, to minimize expected total discounted cost over an infinite time horizon. Both an optimal model and a disaggregate approximation model are introduced. The study provides some structural results and establishes that the control policy is of a threshold type. Numerical experiments demonstrate a significantly decreased amount of computational time required for the disaggregate approach when compared to the optimal solution, while generating very good control policies. © 2002 John Wiley & Sons, Inc. Naval Research Logistics, 49: 78–94, 2002; DOI 10.1002/nav.1049     

Estimation of a hidden service distribution of an M/G/∞ system

The maximum likelihood estimator of the service distribution function of an M/G/∞ service system is obtained based on output time observations. This estimator is useful when observation of the service time of each customer could introduce bias or may be impossible. The maximum likelihood estimator is compared to the estimator proposed by Mark Brown, [2]. Relative to each other, Brown's estimator is useful in light traffic while the maximum likelihood estimator is applicble in heavy trafic. Both estimators are compared to the empirical distribution function based on a sample of service times and are found to have drawbacks although each estimator may have applications in special circumstances.