Balancing perfectly periodic service schedules: An application from recycling and waste management

A national recycling and waste management company provides periodic services to its customers from over 160 service centers. The services are performed periodically in units of weeks over a planning horizon. The number of truck‐hours allocated to this effort is determined by the maximum weekly workload during the planning horizon. Therefore, minimizing the maximum weekly workload results in minimum operating expenses. The perfectly periodic service scheduling (PPSS) problem is defined based on the practices of the company. It is shown that the PPSS problem is strongly NP‐hard. Attempts to solve large instances by using an integer programming formulation are unsuccessful. Therefore, greedy BestFit heuristics with three different sorting schemes are designed and tested for six real‐world PPSS instances and 80 randomly generated data files. The heuristics provide effective solutions that are within 2% of optimality on average. When the best found BestFit schedules are compared with the existing schedules, it is shown that operational costs are reduced by 18% on average. © 2012 Wiley Periodicals, Inc. Naval Research Logistics 59: 160–171, 2012     

Algorithm to solve a chance‐constrained network capacity design problem with stochastic demands and finite support

We consider the problem of determining the capacity to assign to each arc in a given network, subject to uncertainty in the supply and/or demand of each node. This design problem underlies many real‐world applications, such as the design of power transmission and telecommunications networks. We first consider the case where a set of supply/demand scenarios are provided, and we must determine the minimum‐cost set of arc capacities such that a feasible flow exists for each scenario. We briefly review existing theoretical approaches to solving this problem and explore implementation strategies to reduce run times. With this as a foundation, our primary focus is on a chance‐constrained version of the problem in which α% of the scenarios must be feasible under the chosen capacity, where α is a user‐defined parameter and the specific scenarios to be satisfied are not predetermined. We describe an algorithm which utilizes a separation routine for identifying violated cut‐sets which can solve the problem to optimality, and we present computational results. We also present a novel greedy algorithm, our primary contribution, which can be used to solve for a high quality heuristic solution. We present computational analysis to evaluate the performance of our proposed approaches. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 236–246, 2016     

Understanding women at war: a mixed-methods exploration of leadership in non-state armed groups

ABSTRACT

Recent efforts aimed at understanding women’s contributions to nonstate armed groups have produced large-scale data sets on female combatants (Wood and Thomas 2017) and more limited data on women’s roles as supporters and leaders in armed groups (Henshaw 2016; 2017, Loken 2018). The present study aims to build on this literature by providing new data on the scope of women’s leadership in insurgent groups. While existing quantitative literature has focused mostly on the experience of female combatants, we argue that the presence of women in leadership roles is crucial to understanding how gender might influence the outcomes of insurgency. We introduce new data on over 200 insurgent groups active since World War II. While our analysis confirms earlier small-sample work demonstrating women’s presence in leadership roles, a qualitative analysis reveals that leadership is often gendered–revealing patterns of tokenization and tracking women to low-prestige leadership roles. At the same time, our findings challenge past research on jihadist organizations, showing limited expansion in the authority of women.