Objective Little is known about the role of parental bereavement regarding alcohol and substance abuse. Our aim was to examine whether the incidence of alcohol and substance abuse is higher in parentally bereaved youth and, if so, what might explain this increased incidence. Method In a longitudinal population-based study conducted between November 2002 and December 2012, the incidence of alcohol and substance abuse or dependence (ASAD) during a period of 5 years was examined (using DSM-IV criteria) in 235 youth whose parents died of suicide, accident, or sudden natural death and 178 demographically similar nonbereaved youth. Results In a period that covered 5 years subsequent to the death, bereaved youth had an increased incidence and earlier time to onset of ASAD relative to nonbereaved controls (incident rate ratio = 2.44; 95% CI, 1.17–5.56). Additionally, youth over the age of 13 years (hazard ratio [HR] = 6.68; 95% CI, 3.22–13.89; P < .001), those who developed a disruptive behavior disorder (HR = 7.55; 95% CI, 1.83–31.22; P = .005), and those who had greater functional impairment (HR = 0.93; 95% CI, 0.90–0.95; P < .001) were at increased risk for ASAD. However, after adjusting for the above-noted variables, the relationship between parental bereavement and pathological youth alcohol and substance use was not statistically significant (HR = 1.73; 95% CI, 0.79–3.81; P = .17). Conclusions Bereaved youth are at greater risk for ASAD than their nonbereaved counterparts, especially adolescent boys with disruptive behavior disorders. The effect of bereavement was explained by its overall impact on greater functional impairment in bereaved offspring. Interventions that help to improve offspring functioning and that prevent or attenuate the development of disruptive behavior disorders have the potential to prevent ASAD in bereaved youth.
As generalizations of the classic set covering problem (SCP), both the set K-covering problem (SKCP) and the set variable (K varies by constraint) K-covering problem (SVKCP) are easily shown to be NP-hard. Solution approaches in the literature for the SKCP typically provide no guarantees on solution quality. In this article, a simple methodology, called the simple sequential increasing tolerance (SSIT) matheuristic, that iteratively uses any general-purpose integer programming software (Gurobi and CPLEX in this case) is discussed. This approach is shown to quickly generate solutions that are guaranteed to be within a tight tolerance of the optimum for 135 SKCPs (average of 67 seconds on a standard PC and at most 0.13% from the optimums) from the literature and 65 newly created SVKCPs. This methodology generates solutions that are guaranteed to be within a specified percentage of the optimum in a short time (actual deviation from the optimums for the 135 SKCPs was 0.03%). Statistical analyses among the five best SKCP algorithms and SSIT demonstrates that SSIT performs as well as the best published algorithms designed specifically to solve SKCPs and SSIT requires no time-consuming effort of coding problem-specific algorithms-a real plus for OR practitioners.Contribution/Originality: This study documents a methodology that iteratively uses integer programming software to efficiently generate solutions that are guaranteed to be very close to the optimums for the set Kcovering problem. A significant benefit of this methodology is that no problem specific algorithm needs to be coded by the user.
A generalization of the 0-1 knapsack problem that is hard-to-solve both theoretically (NP-hard) and in practice is the multi-demand multidimensional knapsack problem (MDMKP). Solving an MDMKP can be difficult because of its conflicting knapsack and demand constraints. Approximate solution approaches provide no guarantees on solution quality. Recently, with the use of classification trees, MDMKPs were partitioned into three general categories based on their expected performance using the integer programming option of the CPLEX® software package on a standard PC: Category A—relatively easy to solve, Category B—somewhat difficult to solve, and Category C—difficult to solve. However, no solution methods were associated with these categories. The primary contribution of this article is that it demonstrates, customized to each category, how general-purpose integer programming software (CPLEX in this case) can be iteratively used to efficiently generate bounded solutions for MDMKPs. Specifically, the simple sequential increasing tolerance (SSIT) methodology will iteratively use CPLEX with loosening tolerances to efficiently generate these bounded solutions. The real strength of this approach is that the SSIT methodology is customized based on the particular category (A, B, or C) of the MDMKP instance being solved. This methodology is easy for practitioners to use because it requires no time-consuming effort of coding problem specific-algorithms. Statistical analyses will compare the SSIT results to a single-pass execution of CPLEX in terms of execution time and solution quality.
The multiple‐choice multi‐dimensional knapsack problem (MMKP) is an NP‐hard generalization of the classic 0–1 knapsack problem. The MMKP has a variety of important industrial and business applications. Approximate solution approaches characteristically give no guarantees on solution quality. Exact solution approaches usually generate solutions that are guaranteed to be close to the optimum but typically take excessive computation times—one to several hours have been recorded in the literature. In this article, we use both simple single‐step and multiple step strategies to demonstrate how a commercial software package (Gurobi in this case) can easily be used to generate guaranteed tightly bounded solutions for 293 MMKP instances commonly tested in the literature. These four simple strategies that require no problem‐specific algorithms are shown to perform competitively with the five best published algorithms for the MMKP. Since no problem‐specific coding is required, these simple strategies are easy for operations research practitioners to use.
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