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This paper aims to define the class of fragment constructive heuristics used to compute feasible solutions for the traveling salesman problem (TSP) into edge-greedy and vertex-greedy subclasses. As these subclasses of heuristics can create subtours, two known methodologies for subtour elimination on symmetric instances are reviewed and are expanded to cover asymmetric problem instances. This paper introduces a third novel subtour elimination methodology, the greedy tracker (GT), and compares it to both known methodologies.

Computational results for all three subtour elimination methodologies are generated across 17 symmetric instances ranging in size from 29 vertices to 5,934 vertices, as well as 9 asymmetric instances ranging in size from 17 to 443 vertices.

The results demonstrate the GT is the fastest method for preventing subtours for instances below 400 vertices. Additionally, a distinction between fragment constructive heuristics and the subtour elimination methodology used to ensure the feasibility of resulting solutions enables the introduction of a new vertex-greedy fragment heuristic called ordered greedy.

This research has two main contributions: first, it introduces a novel subtour elimination methodology. Second, the research introduces the concept of ordered lists which remaps the TSP into a new space with promising initial computational results.

This paper is motivated by the need to assess the risk profiles associated with the substantial number of items within military supply chains. The scale of supply chain management processes creates difficulties in both the complexity of the analysis and in performing risk assessments that are based on the manual (human analyst) assessment methods. Thus, analysts require methods that can be automated and that can incorporate on-going operational data on a regular basis.

The approach taken to address the identification of supply chain risk within an operational setting is based on aspects of multiobjective decision analysis (MODA). The approach constructs a risk and importance index for supply chain elements based on operational data. These indices are commensurate in value, leading to interpretable measures for decision-making.

Risk and importance indices were developed for the analysis of items within an example supply chain. Using the data on items, individual MODA models were formed and demonstrated using a prototype tool.

To better prepare risk mitigation strategies, analysts require the ability to identify potential sources of risk, especially in times of disruption such as natural disasters.

The purpose of the present study was the development of a methodology for translating predicted rates of decompression sickness (DCS), following tower escape from a sunken submarine, into predicted probability of survival, a more useful statistic for making operational decisions.

Predictions were made, using existing models, for the probabilities of a range of DCS symptoms following submarine tower escape. Subject matter expert estimates of the effect of these symptoms on a submariner’s ability to survive in benign weather conditions on the sea surface until rescued were combined with the likelihoods of the different symptoms occurring using standard probability theory. Plots were generated showing the dependence of predicted probability of survival following escape on the escape depth and the pressure within the stricken submarine.

Current advice on whether to attempt tower escape is based on avoiding rates of DCS above approximately 5%–10%. Consideration of predicted survival rates, based on subject matter expert opinion, suggests that the current advice might be considered as conservative in the distressed submarine scenario, as DCS rates of 10% are not anticipated to markedly affect survival rates.

According to the authors’ knowledge, this study represents the first attempt to quantify the effect of different DCS symptoms on the probability of survival in submarine tower escape.

This study aims to explore the viability of using C-17 reduced-engine taxi procedures from a cost savings and capability perspective.

This study model expected engine fuel flow based on the number of operational engines, aircraft gross weight (GW) and average aircraft groundspeed. Using this model, the research executes a cost savings simulation estimating the expected annual savings produced by the proposed taxi methodology. Operational and safety risks are also considered.

The results indicate that significant fuel and costs savings are available via the employment of reduced-engine taxi procedures. On an annual basis, the mobility air force has the capacity to save approximately 1.18 million gallons of jet fuel per year ($2.66m in annual fuel costs at current rates) without significant risk to operations. The two-engine taxi methodology has the ability to generate capable taxi thrust for a maximum GW C-17 with nearly zero risks.

This research was limited to C-17 procedures and efficiency improvements specifically, although it suggests that other military aircraft could benefit from these findings as is evident in the commercial airline industry.

This research recommends coordination with the original equipment manufacturer to rework checklists and flight manuals, development of a fleet-wide training program and evaluation of future aircraft recapitalization requirements intended to exploit and maximize aircraft surface operation savings.

If implemented, the proposed changes would benefit the society as government resources could be spent elsewhere and the impact on the environment would be reduced. This research conducted a rigorous analysis of the suitability of implementing a civilian airline’s best practice into US Air Force operations.

Present a method to impute missing data from a chaotic time series, in this case lightning prediction data, and then use that completed dataset to create lightning prediction forecasts.

Using the technique of spatiotemporal kriging to estimate data that is autocorrelated but in space and time. Using the estimated data in an imputation methodology completes a dataset used in lightning prediction.

The techniques provided prove robust to the chaotic nature of the data, and the resulting time series displays evidence of smoothing while also preserving the signal of interest for lightning prediction.

The research is limited to the data collected in support of weather prediction work through the 45th Weather Squadron of the United States Air Force.

These methods are important due to the increasing reliance on sensor systems. These systems often provide incomplete and chaotic data, which must be used despite collection limitations. This work establishes a viable data imputation methodology.

Improved lightning prediction, as with any improved prediction methods for natural weather events, can save lives and resources due to timely, cautious behaviors as a result of the predictions.

Based on the authors’ knowledge, this is a novel application of these imputation methods and the forecasting methods.

The role of clusters in the development and growth of local and national economies has been extensively studied and discussed in global literature. Different methodologies are used for analysing the impact these have in national and regional economies, such as the input–output (IO) and gravity models. This paper aims to detail the methodologies present in the literature and propose a new robust theoretical framework, which facilitates the evaluation and comparison among maritime clusters in terms of attractiveness assisting stakeholders to devise strategies, which will attract companies.

An index is created composed of five key categories, namely, infrastructure, financing, governance, manpower and institution/legislation. For the analysis of the index, multi-attribute utility theory (MAUT) is used as a tool to evaluate the importance and performance of the different attributes using both quantitative and qualitative criteria. The methodology has been tested via the use the Piraeus maritime cluster.

The framework has been tested on its robustness and friendliness to the user providing useful insights to the stakeholders. Among the results has been the importance of the finance, manpower and infrastructure attributes, which appear to promote the cluster’s attractiveness. In addition, legislation and institutional partnerships, along with Government support, need to take place improve the performance of the cluster.

A key limitation is the fact that the methodology has been tested in a single case. Applying the methodological framework in a wider sample of clusters will significantly improve the present work.

The proposed model takes further existing research in the field via adopting the philosophy of the World Bank’s Logistics Performance Index. Among the benefits of the proposed index is that it offers the flexibility and robustness to compare among different maritime clusters globally and can be readily used as a benchmarking policy tool at national, regional and global levels at any given point in time and attribute dimension.

Regional policy instruments are typically driven by economic rationales, from either a firm or industrial perspective. Yet too often, these rationales are taken as ex ante to the contexts within which firms and industries compete. Recent regional development research has urged a better link be developed between the individual, the firm, and their context, so as to understand the role of regions in supporting effective competitiveness of organizations. In this article, recent research themes are explored that may shed light on the nature of this relationship and that can be developed into an investigative methodology that could aid policy practitioners in generating policy instruments that reflect differing societal constructions of SME reality.

This paper aims to address the reasons behind the varying levels of volatile conflict and peace as seen during the Arab Spring of 2011 to 2015. During this time, higher rates of conflict transition occurred than normally observed in previous studies for certain Middle Eastern and North African countries.

Previous prediction models decrease in accuracy during times of volatile conflict transition. Also, proper strategies for handling the Arab Spring have been highly debated. This paper identifies which countries were affected by the Arab Spring and then applies data analysis techniques to predict a country’s tendency to suffer from high-intensity, violent conflict. A large number of open-source variables are incorporated by implementing an imputation methodology useful to conflict prediction studies in the future. The imputed variables are implemented in four model building techniques: purposeful selection of covariates, logical selection of covariates, principal component regression and representative principal component regression resulting in modeling accuracies exceeding 90 per cent.

Analysis of the models produced by the four techniques supports hypotheses which propose political opportunity and quality of life factors as causations for increased instability following the Arab Spring.

Of particular note is that the paper addresses the reasons behind the varying levels of volatile conflict and peace as seen during the Arab Spring of 2011 to 2015 through data analytics. This paper considers various open-source, readily available data for inclusion in multiple models of identified Arab Spring nations in addition to implementing a novel imputation methodology useful to conflict prediction studies in the future.

The purpose of this study is to review the reasoning of the judgment of the United Kingdom Supreme Court in Versloot Dredging BV and another (Appellants) v. HDI Gerling Industrie Versichering AG and Others (Respondents) [2016] UKSC 45 in finding that there is no remedy or sanction for the use of fraudulent devices (so-called “collateral lies”) in insurance claims and to consider potential implications for underwriters.

The methodology is a typical case law analysis starting from case facts and the reasoning with short comments on legal implications.

Despite no sanction provided by law for the use of fraudulent devices, the room still opens for the underwriters to stipulate the consequence of using the fraudulent devices by the express term in the insurance contract.

The main implication from the judgment is that underwriters are likely to incur more investigating costs for insurance claims.

This work raises awareness of the marine insurance industry (especially underwriters) as to the approach of the English law towards the use of fraudulent devices.

Rapid sensitivity analysis and near-optimal decision-making in contested environments are valuable requirements when providing military logistics support. Port of debarkation denial motivates maneuver from strategic operational locations, further complicating logistics support. Simulations enable rapid concept design, experiment and testing that meet these complicated logistic support demands. However, simulation model analyses are time consuming as output data complexity grows with simulation input. This paper proposes a methodology that leverages the benefits of simulation-based insight and the computational speed of approximate dynamic programming (ADP).

This paper describes a simulated contested logistics environment and demonstrates how output data informs the parameters required for the ADP dialect of reinforcement learning (aka Q-learning). Q-learning output includes a near-optimal policy that prescribes decisions for each state modeled in the simulation. This paper's methods conform to DoD simulation modeling practices complemented with AI-enabled decision-making.

This study demonstrates simulation output data as a means of state–space reduction to mitigate the curse of dimensionality. Furthermore, massive amounts of simulation output data become unwieldy. This work demonstrates how Q-learning parameters reflect simulation inputs so that simulation model behavior can compare to near-optimal policies.

Fast computation is attractive for sensitivity analysis while divorcing evaluation from scenario-based limitations. The United States military is eager to embrace emerging AI analytic techniques to inform decision-making but is hesitant to abandon simulation modeling. This paper proposes Q-learning as an aid to overcome cognitive limitations in a way that satisfies the desire to wield AI-enabled decision-making combined with modeling and simulation.