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Purpose The purpose of this paper is to formulate and solve a new emergency evacuation planning problem. This problem addresses the needs of both able and disabled persons who are evacuated from multiple pick-up locations and transported using a heterogeneous fleet of vehicles. Design/methodology/approach The problem is formulated using a mixed integer linear programming model and solved using a heuristic algorithm. The authors analyze the selected heuristic with respect to key parameters and use it to address theoretical and practical case studies. Findings Evacuating people with disabilities has a significant impact on total evacuation time, due to increased loading/unloading times. Additionally, increasing the number of large capacity vehicles adapted to transport individuals with disabilities benefits total evacuation time. Research limitations/implications The mathematical model is of high complexity and it is not possible to obtain exact solutions in reasonable computational times. The efficiency of the heuristic has not been analyzed with respect to optimality. Practical implications Solving the problem by a heuristic provides a fast solution, a requirement in emergency evacuation cases, especially when the state of the theater of the emergency changes dynamically. The parametric analysis of the heuristic provides valuable insights in improving an emergency evacuation system. Social implications Efficient population evacuation studied in this work may save lives. This is especially critical for disabled evacuees, the evacuation of whom requires longer operational times. Originality/value The authors consider a population that comprises able and disabled individuals, the latter with varying degrees of disability. The authors also consider a heterogeneous fleet of vehicles, which perform multiple trips during the evacuation process.

This paper addresses the modeling and optimization of resource availability in car parks, serving different priority classes of customers. The authors examine various formulations of the problem concerning two general objectives: a) increasing the availability for high priority customers and b) maximizing the aggregate service level. In the current context, priority classes are specified according to different space reservation options provided by the parking management company (monthly parking, hourly parking, parking on demand, etc.). Based on actual historical traffic data and under certain methodological assumptions, they calculate the arrival and service rates for each class of customers. These are subsequently used as inputs in a Markov model that describes the evolution of the number of free parking spaces in time, given that some spaces are reserved for higher priority classes. Optimization techniques and OR heuristics are applied to deal with numerical aspects of the associated reservation planning issues.

High resource availability in computer networks has become a critical issue as it can be affected by the increasing number of network users. A methodology based on classifying website visitors into priority groups is proposed, assuring high availability for priority classes, based on reserved website resources that can be accessed only by these groups and simultaneously providing as many resources as possible to lower priority visitors. A birth-death process is proposed to model website visitors' arrival and service. An optimization problem is solved to determine the optimal trade off between resource availability for high priority visitors and free resource access to lower priority visitors. The major contribution of this paper consists in deriving formulas for the probability that a website visitor has no further access to resources and in determining the optimal reserved resources assuring the above trade off.

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