A multiobjective model for maximizing fleet availability under the presence of flight and maintenance requirements

Every aircraft, military or civilian, must be grounded for maintenance after it has completed a certain number of flight hours since its last maintenance check. In this paper, we address the problem of deciding which available aircraft should fly and for how long, and which grounded aircraft should perform maintenance operations, in a group of aircraft that comprise a combat unit. The objective is to achieve maximum availability of the unit over the planning horizon. We develop a multiobjective optimization model for this problem, and we illustrate its application and solution on a real life instance drawn from the Hellenic Air Force. We also propose two heuristic approaches for solving large scale instances of the problem. We conclude with a discussion that gives insight into the behavior of the model and of the heuristics, based on the analysis of the results obtained.     

A hybrid Genetic Algorithm approach to minimize the total joint cost of a single-vendor multi-customer integrated scheduling problem

This paper addresses the scheduling of supply chains with interrelated factories consisting of a single vendor and multiple customers. In this research, one transporter is available to deliver jobs from vendor to customers, and the jobs can be processed by batch. The problem studied in this paper focuses on a real-case scheduling problem of a multi-location hospital supplied with a central pharmacy. The objective of this work is to minimize the total cost, while satisfying the customer’s due dates constraints. A mathematical formulation of the problem is given as a Mixed Integer Programming model. Then, a Branch-and-Bound algorithm is proposed as an exact method for solving this problem, a greedy local search is developed as a heuristic approach, and a hybrid Genetic Algorithm is presented as a meta-heuristic. Computation experiments are conducted to highlight the performance of the proposed methods.     

Optimization models to characterize the broadcast capacity of vehicular ad hoc networks

Broadcast capacity of the entire network is one of the fundamental properties of vehicular ad hoc networks (VANETs). It measures how efficiently the information can be transmitted in the network and usually it is limited by the interference between the concurrent transmissions in the physical layer of the network. This study defines the broadcast capacity of vehicular ad hoc network as the maximum successful concurrent transmissions. In other words, we measure the maximum number of packets which can be transmitted in a VANET simultaneously, which characterizes how fast a new message such as a traffic incident can be transmitted in a VANET. Integer programming (IP) models are first developed to explore the maximum number of successful receiving nodes as well as the maximum number of transmitting nodes in a VANET. The models embed an traffic flow model in the optimization problem. Since IP model cannot be efficiently solved as the network size increases, this study develops a statistical model to predict the network capacity based on the significant parameters in the transportation and communication networks. MITSIMLab is used to generate the necessary traffic flow data. Response surface method and linear regression technologies are applied to build the statistical models. Thus, this paper brings together an array of tools to solve the broadcast capacity problem in VANETs. The proposed methodology provides an efficient approach to estimate the performance of a VANET in real-time, which will impact the efficacy of travel decision making.