Alan W. Johnson

The Theory and Practice of Simulated Annealing

Simulated annealing is a popular local search meta-heuristic used to address discrete and, to a lesser extent, continuous optimization problems. The key feature of simulated annealing is that it provides a means to escape local optima by allowing hill-climbing moves (i.e., moves which worsen the objective function value) in hopes of finding a global optimum. A brief history of simulated annealing is presented, including a review of its application to discrete and continuous optimization problems. Convergence theory for simulated annealing is reviewed, as well as recent advances in the analysis of finite time performance. Other local search algorithms are discussed in terms of their relationship to simulated annealing. The chapter also presents practical guidelines for the implementation of simulated annealing in terms of cooling schedules, neighborhood functions, and appropriate applications.

On the convergence of generalized hill climbing algorithms

Generalized hill climbing (GHC) algorithms provide a general local search strategy to address intractable discrete optimization problems. GHC algorithms include as special cases stochastic local search algorithms such as simulated annealing and the noising method, among others. In this paper, a proof of convergence of GHC algorithms is presented, that relaxes the sufficient conditions for the most general convergence proof for stochastic local search algorithms in the literature. Note that classical convergence proofs for stochastic local search algorithms require either that an exponential distribution be used to model the acceptance of candidate solutions along a search trajectory, or that the Markov chain model of the algorithm must be reversible. The proof in this paper removes these limitations, by introducing a new path concept between global and local optima. Convergence is based on the asymptotic behavior of path probabilities between local and global optima. Examples are given to illustrate the convergence conditions. Implications of this result are also discussed.

A class of convergent generalized hill climbing algorithms

Generalized hill climbing (GHC) algorithms have been presented as a modeling framework for local search strategies applied to address intractable discrete optimization (minimization) problems. GHC algorithms include simulated annealing (SA), pure local search (LS), and threshold accepting (TA), among others, as special cases. A particular class of GHC algorithms is designed for discrete optimization problems where the objective function value of a globally optimal solution is known (in this case, the task is to identify an associated optimal solution). This class of GHC algorithms is shown to converge, and six examples are provided that illustrate the diversity of GHC algorithms within this class of convergent algorithms. Implications of these results are discussed.

Discrete manufacturing process design optimization using computer simulation and generalized hill climbing algorithms

Discrete manufacturing process designs can be modelled using computer simulation. Determining optimal designs using such models is very difficult, due to the large number of manufacturing process sequences and associated parameter settings that exist. This has forced researchers to develop heuristic strategies to address such design problems. This paper introduces a new general heuristic strategy for discrete manufacturing process design optimization, called generalised hill climbing (GHC) algorithms. GHC algorithms provide a unifying approach for addressing such problems in particular, and intractable discrete optimization problems in general. Heuristic strategies such as simulated annealing, threshold accepting, Monte Carlo search, local search, and tabu search (among others) can all he formulated as GHC algorithms. Computational results are reported with various GHC algorithms applied to computer simulation models of discrete manufacturing process designs under study at the Materials Process Design Bra...

A measure of cross-training benefit versus job skill specialization

Given ever-higher labor costs, organizations should periodically assess the match of personnel skills and quantities with required duties. Consolidating similar functional specialties can improve efficiency by increasing staffing for high-demand jobs, or by identifying areas where staff may be reduced. However, such consolidation activities are often done anecdotally, and can potentially overlook successful skill pairings. We propose a model that enables an objective, repeatable skills consolidation assessment process. Our model-a cost/benefit ratio-identifies skill pairings which are likely to merge successfully, by comparing the costs of training to the benefits of increased staffing level efficiencies for these jobs.

A note on supply chain coordination for joint determination of order quantity and reorder point using a credit option

Credit options and side payments are two methods suggested for achieving coordination in a two-echelon supply chain. We examine the credit option coordination mechanism introduced by Chaharsooghi and Heydari [Chaharsooghi, S., & Heydari, J. (2010). Supply chain coordination for the joint determination of order quantity and reorder point using credit option. European Journal of Operational Research, 204(1), 86–95]. This method assumes that the supplier’s opportunity costs are equal to the reduction in the buyer’s financial holding costs during the credit period. In this note, we show that Chaharsooghi and Heydari’s method is not applicable when buyer and supplier opportunity costs are not equal. We introduce an alternate per order rebate method that reduces supply chain costs to centralized management levels.

Using Information-theoretic Principles to Analyze and Evaluate Complex Adaptive Supply Network Architectures

Abstract Information-theoretic principles can be applied to the study of complex adaptive supply networks (CASN). Previous modeling efforts of CASN were impeded by the complex, dynamic nature of the systems. However, information theory provides a model-free approach to the problem removing many of those barriers. Understanding how principles such as transfer entropy, excess entropy/predictive information, information storage, and separable information apply in the context of supply networks opens up new ways of studying these complex systems. Additionally, these principles provide the potential for new business analytics which give managers of CASN new insights into the systems health, behavior, and eventual control strategies.

Quality assurance staffing impacts in military aircraft maintenance units

Purpose – This paper summarizes our research into the impact that current Air Force quality assurance staffing practices have on key unit performance metrics.Design/methodology/approach – Interviews and Delphi surveys culminated in the development of a quality assurance staffing effectiveness matrix. The matrix was used to calculate historical quality assurance staffing effectiveness at 16 Air Force combat aircraft units. Effectiveness scores were then regressed with unit historical data for 25 metrics.Findings – Nine metrics were deemed statistically significant, including break rates, cannibalization rates, flying schedule effectiveness rates, key task list pass rates, maintenance scheduling effectiveness rates, quality verification inspection pass rates, repeat rates, dropped objects counts and safety/technical violations counts. An example benefit‐cost analysis for changes in quality assurance staffing effectiveness presents compelling evidence for maintenance managers to carefully weigh decisions to ...

A maintenance resources capacity estimator

Combat aircraft operations are usually constrained by limits on the logistics resources available. However, models that can compute the logistics resources necessary to support planned combat aircraft activity typically are custom applications that are data-intensive and difficult to use. This paper introduces a method for the Argentine Air Force (AAF) to estimate the mean number of aircraft that can be restored in a given time between consecutive sorties, given specified maintenance resources and base physical geometry. This maintenance resources evaluation technique (MRET) uses an analytical approach to estimate the mean and variance of aircraft unscheduled downtime. These parameters are then used in a Monte Carlo simulation of scheduled and unscheduled maintenance tasks necessary to prepare aircraft for the next sortie. When programmed in a spreadsheet, the MRET combines a high response speed with a moderately detailed scenario description, making the model suitable for the AAF.

Case Study: Energy Industry Economic Impacts from Ohio River Transportation Disruption

Recent history is full of water transport disruption events that have had significant economic effects on the waterside industries. Such disruptions may be either natural or man-made disasters or planned outages on the rivers lock and dam structures. To assess coal-based economic impacts for the Ohio River Basin, we developed a network flow model to represent waterside coal-fired power plants situated along the Ohio River, their respective coal supplying mines, and the various transportation modes that connect them. We show that significant transportation-centric insights can be derived by using only commonly available spreadsheet-based analysis tools, open-source information systems, and web-based geographic tools.