Optimization of Simultaneous Scheduling for Machines and Automated Guided Vehicles Using Fuzzy Genetic Algorithm

Flexible manufacturing system (FMS) has been introduced by the researchers as an integrated manufacturing environment. Automated guided vehicles (AGVs) introduced as the main tool of material handling systems in FMS. While the scheduling of AGVs and machines are highly related; simultaneous scheduling of machines and AGVs has been proposed in the literature. Genetic algorithm (GA) proposed as a robust tool for optimization of scheduling problems. Setting the proper crossover and mutation rates are of vital importance for the performance of the GA. Fuzzy logic controllers (FLCs) have been used in the literature to control key parameters of the GA which is addressed as fuzzy GA (FGA). A new application of FGA method in simultaneous scheduling of AGVs and machines is presented. The general GA is modified for the aforementioned application; more over an FLC is developed to control mutation and crossover rates of the GA. The objective of proposed FGA method is to minimize the makespan, production completion time of all jobs that they are produced simultaneously. An optimal sequence of operations is obtained by GA. There is a heuristic algorithm to assign the AGVs to the operations. As the main findings, the performance of GA in simultaneous scheduling of AGVs and machines is enhanced by using proposed method, furthermore a new mutation operator has been proposed. Several experiments have been done to the proposed test cases. The results showed that tournament selection scheme may outperform roulette wheel in this problem. Various combinations of population size and number of generations are compared to each other in terms of their objective function. In large scale problems FGA method may outperforms GA method, while in small and medium problems they have the same performance. The fluctuation of obtained makespan in FGA method is less than GA method which means that it is more probable to find a better solution by FGA rather than GA

Location and Location-Routing Problems with Disruption Risks

The academic literature on logistics network disruptions has increased sharply recently. Disruptions are random events that cause an element of a logistics network to stop functioning, either completely or partially, for a (typically random) given amount of time. Because of today's globalized threats such as, labor disruptions or failures resulting from harsh weather conditions, there has been a renewed interest in resilient facility location. Design of reliable logistics networks to avoid disruption can be accomplished by fortification of existing facilities and de�fining backup facilities. In this thesis, we will look at two components of a logistics system that can be affected by a disruption: the locations of the facilities, and the routes between a customer and a facility. We study the following three designs of logistics networks under disruption: (i) Reliable Capacitated Facility Location under Disruption, (ii) Shared Capacitated Reliable Facility Location in Presence of Disruption , and (iii) Reliable Facility Location and Routing in Logistics Network in presence of disruption considering backup sharing. A column generation approach is proposed to model and solve all three logistics problems. Results show the effectiveness of the decomposition schemes for solving exactly much larger facility location instances than in the literature. In addition, shared backup is shown to be a very effective scheme for the design of reliable facility locations/roads