This part of the review aims to reduce the start-up burden of data collection and descriptive analytics for statistical modeling and route optimization of risk associated with motor vehicles. From a data-driven bibliometric analysis, we show that the literature is divided into two disparate research streams: (a) predictive or explanatory models that attempt to understand and quantify crash risk based on different driving conditions, and (b) optimization techniques that focus on minimizing crash risk through route/path-selection and rest-break scheduling. Translation of research outcomes between these two streams is limited. To overcome this issue, we present publicly available high-quality data sources (different study designs, outcome variables, and predictor variables) and descriptive analytic techniques (data summarization, visualization, and dimension reduction) that can be used to achieve safer-routing and provide code to facilitate data collection/exploration by practitioners/researchers. Then, we review the statistical and machine learning models used for crash risk modeling. We show that (near) real-time crash risk is rarely considered, which might explain why the optimization models (reviewed in Part 2) have not capitalized on the research outcomes from the first stream.

Job rotation is an organizational strategy that can be used, in part, to reduce occupational exposure to physical risk factors associated with work-related musculoskeletal disorders (MSDs). Recent studies, however, suggest that job rotation schedules may increase the overall risk of injury to workers included in the rotation scheme. We describe a novel optimization framework evaluating the effectiveness of a job rotation scheme using the fatigue failure model of MSD development and a case study with real injury data. Results suggest that the effect of job rotation is highly-dependent on the composition of the job pool, and inclusion of jobs with higher risk results in a drastic decrease in the effectiveness of rotation for reducing overall worker risk. The study highlights that in cases when high-risk jobs are present, job redesign of those high risk tasks should be the primary focus of intervention efforts rather than job rotation. Practitioner Summary: Job rotation is often used in industry as a method to “balance” physical demands experienced by workers to reduce musculoskeletal disorder (MSD) risk. This article examines the efficacy of reducing MSDs through job rotation using numerical simulation of job rotation strategies and utilizing the fatigue failure model of MSD development.

This paper discusses the use of polyhedral approximations in solving p-order cone programming (pOCP) problems, or linear problems with p-order cone constraints, and their mixed-integer extensions. In particular, it is shown that the cutting-plane technique proposed in Krokhmal and Soberanis [Risk optimization with p-order conic constraints: A linear programming approach, Eur. J. Oper. Res. 201 (2010), pp. 653–671, http://dx.doi.org/10.1016/j.ejor.2009.03.053] for a special type of polyhedral approximations of pOCP problems, which allows for generation of cuts in constant time not dependent on the accuracy of approximation, is applicable to a larger family of polyhedral approximations. We also show that it can further be extended to form an exact solution method for pOCP problems with O(ϵ⁻¹) iteration complexity. Moreover, it is demonstrated that an analogous constant-time cut-generating algorithm exists for recursively constructed lifted polyhedral approximations of second-order cones due to Ben-Tal and Nemirovski [On polyhedral approximations of the second-order cone, Math. Oper. Res. 26 (2001), pp. 193–205. Available at http://dx.doi.org/10.1287/moor.26.2.193.10561]. It is also shown that the developed polyhedral approximations and the corresponding cutting-plane solution methods can be efficiently used for obtaining exact solutions of mixed-integer pOCP problems.