DEVELOPMENT OF GENETIC ALGORITHM-BASED METHODOLOGY FOR SCHEDULING OF MOBILE ROBOTS

Abstract

This thesis addresses the issues of scheduling of mobile robot(s) at operational levels ofmanufacturing systems. More specifically, two problems of scheduling of a singlemobile robot with part-feeding tasks and scheduling of multiple mobile robots withpreemptive tasks are taken into account. For the first scheduling problem, a singlemobile robot is considered to collect and transport container of parts and empty theminto machine feeders where needed. A limit on carrying capacity of the single mobilerobot and hard time windows of part-feeding tasks are considered. The objective of thefirst problem is to minimize the total traveling time of the single mobile robot andthereby increase its availability. For the second scheduling problem, a fleet of mobilerobots is considered together with a set of machines to carry out different types of tasks,e.g. pre-assembly or quality inspection. Some of the tasks are non-preemptive while theothers are preemptive. The considered mobile robots have capabilities to not onlytransport non-preemptive tasks between some machines but also process preemptivetasks on other machines. These mobile robots are allowed to interrupt their preemptivetasks to carry out transportation of non-preemptive tasks when needed. The objective ofthe second problem is to minimize the time required to complete all tasks while takingaccount of precedence constraints.To deal with each mentioned scheduling problem, each mathematical model isfirst formulated. This allows describing each problem and finding optimal solutions foreach one. However, the formulated mathematical models could only be applicable tosmall-scale problems in practice due to the significant increase of computation time asthe problem size grows. Note that making schedules of mobile robots is part of real-timeoperations of production managers. Hence to deal with large-scale applications, eachheuristic based on genetic algorithms is then developed to find near-optimal solutionswithin a reasonable computation time for each problem. The quality of these solutions isthen compared and evaluated by using the solutions of the mathematical models asreference points. The results from numerical experiments in this thesis show that theproposed heuristics are capable of solving problems of various sizes and more efficientthan the mathematical models in terms of the objective values when giving the samelimited computation time. The research results are useful for production managers tomake decisions at operational levels and the proposed heuristics could be also applied toa variety of tasks of not only mobile robots but also automatic guided vehicles