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This paper examines the relationship between a prime contractor's financial health and its mergers and acquisitions (M&A) spending in the defense industry. It aims to provide models that give the United States Department of Defense (DoD) indications of future M&A activity, informing decision-makers and contributing to ensuring competitive markets that benefit the consumer.

The study uses panel data regression models on 40 companies between 1985 and 2021. The company's financial health is assessed using industry-standard financial ratios (i.e. measures of profitability, efficiency, solvency and liquidity) while controlling for economic factors such as national productivity, defense budgets and firm size.

The results show a significant relationship between efficiency and M&A spending, indicating that companies with lower efficiency tend to spend more on M&As. However, there was no significant relationship between M&A spending and a company's profitability or solvency. These results were consistent with previous research and the study's hypotheses for profitability and solvency. However, the effect of liquidity was the opposite of the expected result, possibly due to the defense industry's different view on liquidity compared to previous research.

The paper provides insights into the relationship between a prime contractor's financial health and its M&A spending, a topic with limited research. The findings can inform policymakers and regulators on the industrial base's future M&A activity, ensuring competitive markets that benefit the consumer.

The introduction of “should cost” in 2011 required all Major Defense Acquisition Programs (MDAP) to create efficiencies and improvements to reduce a program’s “will-cost” estimate. Realistic “will-cost” estimates are a necessary condition for the “should cost” analysis to be effectively implemented. Owing to the inherent difficulties in establishing a program’s will-cost estimate, this paper aims to propose a new model to infuse realism into this estimate.

Using historical data from 73 Departments of Defense programs as recorded in the selected acquisition reports (SARs), the analysis uses mixed stepwise regression to predict a program’s cost from Milestone B (MS B) to initial operational capability (IOC).

The presented model explains 83 per cent of the variation in the program acquisition cost. Significant predictor variables include: projected duration (months from MS B to IOC); the amount of research development test and evaluation (RDT&E) funding spent at the start of MS B; whether the program is considered a fixed-wing aircraft; whether a program is considered an electronic system program; whether a program is considered ACAT I at MS B; and the program size relative to the total program’s projected acquisition costs at MS B.

The model supports the “will-cost and should-cost” requirement levied in 2011 by providing an objective and defensible cost for what a program should actually cost based on what has been achieved in the past. A quality will-cost estimate provides a starting point for program managers to examine processes and find efficiencies that lead to reduced program costs.

This paper aims to introduce a new mixed integer programming formulation and associated heuristic algorithm to solve the Military Nodal Capacity Problem, which is a type of supply chain network design problem that involves determining the amount of capacity expansion required at theater nodes to ensure the on-time delivery of military cargo.

Supply chain network design, mixed integer programs, heuristics and regression are used in this paper.

This work helps analysts at the United States Transportation Command identify what levels of throughput capacities, such as daily processing rates of trucks and railcars, are needed at theater distribution nodes to meet warfighter cargo delivery requirements.

This research assumes all problem data are deterministic, and so it does not capture the variations in cargo requirements, transit times or asset payloads.

This work gives military analysts and decision makers prescriptive details about nodal capacities needed to meet demands. Prior to this work, insights for this type of problem were generated using multiple time-consuming simulations often involving trial-and-error to explore the trade space.

This work merges research of supply chain network design with military theater distribution problems to prescribe the optimal, or near-optimal, throughput capacities at theater nodes. The capacity levels must meet delivery requirements while adhering to constraints on the proportion of cargo transported by mode and the expected payloads for assets.

This article presents an alternative perspective on the nature and function of the firm.

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.

Recent studies have shown that the contribution of small firms to employment and GDP is increasing. A large amount of work has also established the significance of social and economic variables for entrepreneurial decisions. Very little is known, however, about how government policies and programs influence entrepreneurial activity, and whether these effects are consistent across countries. Using original data from a representative sample of 10,000 individuals and from more than 300 open-ended interviews in 10 countries, this article provides some suggestive evidence that government intervention aimed at enhancing the underlying environment of entrepreneurial decisions may be more effective than intervention designed to provide safety nets.

Supply chains need to balance competing objectives; in addition to efficiency, supply chains need to be resilient to adversarial and environmental interference and robust to uncertainties in long-term demand. Significant research has been conducted designing efficient supply chains and recent research has focused on resilient supply chain design. However, the integration of resilient and robust supply chain design is less well studied. The purpose of the paper is to include resilience and robustness into supply chain design.

The paper develops a method to include resilience and robustness into supply chain design. Using the region of West Africa, which is plagued with persisting logistical issues, the authors develop a regional risk assessment framework and then apply categorical risk to the countries of West Africa using publicly available data. A scenario reduction technique is used to focus on the highest risk scenarios for the model to be tractable. Next, the authors develop a mathematical model leveraging this framework to design a resilient supply network that minimizes cost while ensuring the network functions following a disruption. Finally, the authors examine the network's robustness to demand uncertainty via several plausible emergency scenarios.

The authors provide optimal sets of transshipment hubs with varying counts from 5 through 15 hubs. The authors determine there is no feasible solution that uses only five transshipment hubs. The authors' findings reinforce those seven transshipment hubs – the solution currently employed in West Africa – is the cheapest architecture to achieve resilience and robustness. Additionally, for each set of feasibility transshipment hubs, the authors provide connections between hubs and demand spokes.

While, at the time of this research, three other manuscripts incorporated both resilience and robustness of the authors' research unique solved the problem as a network flow instead of as a set covering problem. Additionally, the authors establish a novel risk framework to guide the required amount of redundancy, and finally the out research proposes a scenario reduction heuristic to allow tractable exploration of 512 possible demand scenarios.

This study aims to compare linear programming and stable marriage approaches to the personnel assignment problem under conditions of uncertainty. Robust solutions should exhibit reduced variability of solutions in the presence of one or more additional constraints or problem perturbations added to some baseline problems.

Several variations of each approach are compared with respect to solution speed, solution quality as measured by officer-to-assignment preferences and solution robustness as measured by the number of assignment changes required after inducing a set of representative perturbations or constraints to an assignment instance. These side constraints represent the realistic assignment categorical priorities and limitations encountered by army assignment managers who solve this problem semiannually, and thus the synthetic instances considered herein emulate typical problem instances.

The results provide insight regarding the trade-offs between traditional optimization and heuristic-based solution approaches.

The results indicate the viability of using the stable marriage algorithm for talent management via the talent marketplace currently used by both the U.S. Army and U.S. Air Force for personnel assignments.

Emissions produced by oceangoing vessels not only negatively affect the environment but also may deteriorate health of living organisms. Several regulations were released by the International Maritime Organization (IMO) to alleviate negative externalities from maritime transportation. Certain polluted areas were designated as “Emission Control Areas” (ECAs). However, IMO did not enforce any restrictions on the actual quantity of emissions that could be produced within ECAs. This paper aims to perform a comprehensive assessment of advantages and disadvantages from introducing restrictions on the emissions produced within ECAs. Two mixed-integer non-linear mathematical programs are presented to model the existing IMO regulations and an alternative policy, which along with the established IMO requirements also enforces restrictions on the quantity of emissions produced within ECAs. A set of linearization techniques are applied to linearize both models, which are further solved using the dynamic secant approximation procedure. Numerical experiments demonstrate that introduction of emission restrictions within ECAs can significantly reduce pollution levels but may incur increasing route service cost for the liner shipping company.

Two mixed-integer non-linear mathematical programs are presented to model the existing IMO regulations and an alternative policy, which along with the established IMO requirements also enforces restrictions on the quantity of emissions produced within ECAs. A set of linearization techniques are applied to linearize both models, which are further solved using the dynamic secant approximation procedure.

Numerical experiments were conducted for the French Asia Line 3 route, served by CMA CGM liner shipping company and passing through ECAs with sulfur oxide control. It was found that introduction of emission restrictions reduced the quantity of sulfur dioxide emissions produced by 40.4 per cent. In the meantime, emission restrictions required the liner shipping company to decrease the vessel sailing speed not only at voyage legs within ECAs but also at the adjacent voyage legs, which increased the total vessel turnaround time and in turn increased the total route service cost by 7.8 per cent.

This study does not capture uncertainty in liner shipping operations.

The developed mathematical model can serve as an efficient practical tool for liner shipping companies in developing green vessel schedules, enhancing energy efficiency and improving environmental sustainability.

Researchers and practitioners seek for new mathematical models and environmental policies that may alleviate pollution from oceangoing vessels and improve energy efficiency. This study proposes two novel mathematical models for the green vessel scheduling problem in a liner shipping route with ECAs. The first model is based on the existing IMO regulations, whereas the second one along with the established IMO requirements enforces emission restrictions within ECAs. Extensive numerical experiments are performed to assess advantages and disadvantages from introducing emission restrictions within ECAs.

The purpose of this paper is to present an integrated model to measure the operational efficiency of the top 40 container ports in the world for a five-year continuous period using a two-stage uncertainty data envelopment analysis (UDEA) combined with fuzzy C-means clustering method (FCM).

UDEA model is adopted for measuring the efficiency of container ports to overcome the limitation of the basic model, which is unable to handle uncertain data that are easy to meet in practice. FCM algorithm is implemented to find similar distribution efficiency scores of two stages and the cluster similar efficiency scores of container ports into various groups.

The combination of the two-stage UDEA model and the FCM algorithm provided a more comprehensive view when evaluating the performance of container ports. The UDEA results show that most of the container ports have reduced their profitability level in the second stage and most of the efficient container ports have turned into inefficient ones because of their small scale.

This paper proposes using the two-stage UDEA model to evaluate port efficiency based on two main aspects of productivity and profitability. Moreover, it combines DEA and FCM algorithms to offer a more comprehensive view when measuring the performance of container ports.