A heuristic algorithm that uses iteratively LPT and MF approaches on different job and machine sets constructed by using the current solution is developed to solve a classical multiprocessor scheduling problem with the objective of minimizing the makespan. Computational results indicate that the proposed algorithm is very competitive with respect to well-known constructive algorithms for a large number of benchmark instances.

This case study offers an information technology perspective of a school's transition from a traditional learning environment to a blended learning environment in a non-traditional setting. The blended learning environment changes how a school utilizes technology and aligns the school goals to meet all students' individual needs. Specifically, the paper focuses on the technology integration pathways taken by this non-traditional school. It discusses the process of strategic planning, network infrastructure evaluation, implementation, and results.

The high utilization level of emergency departments in hospitals across the United States has resulted in the serious and persistent problem of ambulance diversion. This problem is magnified by the cascading effect it has on neighboring hospitals, delays in emergency care, and the potential for patients’ clinical deterioration. We provide a predictive tool that would give advance warning to hospitals of the impending likelihood of diversion. We hope that with a predictive instrument, such as the one described in this paper, hospitals can take preventive or mitigating actions. The proposed model, which uses logistic and multinomial regression, is evaluated using real data from the Emergency Management System (EM Systems) and 911 call data from Firstwatch® for the Metropolitan Ambulance Services Trust (MAST) of Kansas City, Missouri. The information in these systems that was significant in predicting diversion includes recent 911 calls, season, day of the week, and time of day. The model illustrates the feasibility of predicting the probability of impending diversion using available information. We strongly recommend that other locations, nationwide and abroad, develop and use similar models for predicting diversion.

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This research effort is undertaken to determine the impact that one hospital’s diversion status has on other hospitals in a region and the strength of these interactions. The conditional probability of one hospital going on diversion given that another is already on diversion is evaluated. Based on this analysis, the strength of interactions among the hospitals is established. Through statistical analyses of historical data, the strength of the mutual effects of diversion among a collection of hospitals is determined. These effects are mutual if one hospital’s diversion status affected another’s, then the reverse was also true. The intensity of these interactions between hospitals is varied, some being stronger than others. The model illustrates an approach to studying the cascading effects of diversion among hospitals in a region. This is important, because the status of any hospital in a region can signal the likelihood of impending diversion in every other hospital in the region. This allows actions that might prevent the occurrence of diversion or mitigate the cascading effects of Emergency Medical Systems diversion.