Study of fleet assignment problem using a hybrid technique based on Monte Carlo simulation and genetic algorithm

Fleet assignment problem (FAP) is the assignment of an aircraft model to each scheduled flight based on key operational variables such as cost, revenue, passenger travel demand and aircraft specifications. FAP is an important aspect of aircraft planning within an airline. While many developed economy have automated this planning task, developing economy such as Nigeria mainly depend on manpower to carry out this task. The aim of this paper is to solve a FAP using a hybrid technique based on the combination of Monte-Carlo (MC) simulation and Genetic Algorithm (GA). The objective function is total cost and variation in aircraft models and passenger traffic associated with different scheduled flight were considered. MC simulation which was carried out based on the numerical approximation of normal distribution cumulative distribution function (cdf) was used to estimate the expected passenger spill rate, while genetic algorithm was used for the optimization. The result was found to be satisfactory, as optimal fleet plan was achieved in approximately fifteen seconds of program run time, as against not less than an hour usually spend using human effort to solve FAP. Also the optimized plan resulted to a thirty percent saving in comparison to the actual plan implemented by the airline. It is therefore recommended that MC-GA optimization technique should be considered as an alternative technique applicable for FAP optimization.Keywords: Fleet assignment, genetic algorithm Monte-Carlo simulation, optimizatio

A Taguchi based iterative wing structural design for a low speed, hybrid UAV

An iterative structural design process for the conventional, composite wing of a low speed, hybrid Unmanned Aerial Vehicle (UAV) is presented in this paper. The relevant design goals are light weight, strength and stiffness derived from the customer specifications, conceptual design and STANAG-4671 airworthiness standards. To achieve the design goals, a modified Taguchi model was applied in conducting iterative Finite Element Analysis of the loads and stresses on the wing model, using ABAQUS CAE. In this analysis, the pitch of the rib and thicknesses of the spar and skin were applied as control factors in three levels, leading to an L9 orthogonal array. Mass, maximum deflection and Tsai-Hill failure index of the wing structure were measured as responses. The result shows that varying the skin thickness had the most impact on the wing mass, failure index and maximum deflection. The design goal of wing mass- less than 2.5kg, deflection of 10% and Tsai-Hill failure index value- less than 1 were achieved after 9 iterations

Landing gear disassembly sequence planning using multi-level constraint matrix ant colony algorithm

The life cycle of most complex engineering systems is greatly a function of maintenance. Generally, most maintenance operation usually requires the removal of failed part. Disassembly sequence planning is an optimization program that seeks to identify the optimal sequence for the removal of the failed part. Most studies in this area usually, use single constraint matrix while implementing varied complex algorithm to identify the optimal sequence that saves time associated with carrying out maintenance operation. The used of single constraint matrix typically has the drawback of computer higher storage requirement as well as time consumption. To address this problem, this study proposes Multi-Level Constraint Matrix Ant Colony Algorithm (MLCMACA). MLCMACA efficiency was validated using complex aircraft landing gear systems in comparison with genetic algorithms. The result shows MLCMACA superior performance from the perspective of reduced search time and faster tracking of optimal disassembly sequence. Hence is recommended for handling of disassembly sequence planning problems

Optimal sensor suite selection for helicopter enhanced vision in all-weather-all-environment operation using multi criteria decision making techniques

Optimum sensor selection for helicopter enhanced vision in all-weather operations is a strategic issue and has a significant impact on safety, efficiency and utility of military and Emergency Service Helicopters. On the other hand, selecting the optimal sensor among many alternatives is a multi-criteria decision-making (MCDM) problem. The sensor selection task in this paper is modelled as a stepwise Analytic Hierarchy Process (AHP) to guide the selection process, based on criteria relating to environmental conditions (fog, rain, dust) and sensor characteristics (detection range, update rate, resolution). Result of this study reveals that a combination of millimeter wave radar, passive millimeter wave camera and infrared camera is the optimal suite having the highest value among all the alternatives considered. This result will guide decision makers at the Headquarters of the Nigerian Air force and indeed other helicopter operators in their quest to equip helicopters for operation in adverse weather conditions