genetic algorithm (ga)

A multi-product system is one of the different types of manufacturing systems, in which a large number of products are produced that complement each other and have interdependence. These types of systems have recently been widely used in various industries. In some types of multi-product manufacturing industries that offer their products as a package, the scheduling of the production of components of each package affects the time it takes to complete the package. Therefore, a new problem has been defined that the primary purpose of its production scheduling, in addition to reducing the completion time of the products, is to make various items forming a package, get ready over a short interval of time and be supplied to the sales unit so that the package can be delivered to the final consumer. The purpose of this paper is to express the problem of production scheduling of multi-product production systems in the form of linear programming. For this purpose, two mathematical models are presented, and their functions are compared. Besides, an efficient genetic algorithm is proposed to solve the problem, which is able to solve the problem in a reasonable time, with acceptable accuracy.

Optimized asset tracking with Radio Frequency Identification (RFID) as a complicated innovation that requires much money to be implemented has become more popular in the healthcare industry. Considering the use of more antennas in each reader, we present a modern heuristic methods, hybrid of Genetic Algorithms (GA) and Simulated Annealing (SA) for the purpose of placing readers in an emergency department of a hospital with an RFID network. In this study, a multi-objective function is developed for the network coverage maximization and the minimization of total cost, tag reader collision, interference, energy consumption, and path loss in a simultaneous way. The proposed algorithm provides savings (on average) in the total cost of the RFID network through the efficient use of three types of readers with one, two and four antenna ports. Additionally, by testing three scenarios, the effect of algorithms in achieving the optimal solution is indicated by the simulated results.  Besides, the results of GA-SA is compared to the results of GA and other existing models in the relevant literature. It is shown that its main advantage is the use of multi-antenna RFID readers, which reduces the total cost of the RFID network and also increase network coverage with fewer readers and antennas. In other words, contributions for the research are proposing a hybrid GA-SA algorithm, developing a multi-objective function, testing the algorithm in a hospital setting, and comparing the results of GA-SA with GA.

In this study, a robust optimization model is introduced, we propose a location-routing problem with simultaneous pickup and delivery under a hard time window that has a heterogeneous and limited depot and vehicle capacities and multi-variety of products and uncertain traveling time that considering all of these constraints together make the problem closer to real practical world’s problems, that not been studied in previous papers. For this purpose, a mixed-integer linear programming (MILP) model is proposed for locating depots and scheduling vehicle routing with multiple depots. Then, the robust counterpart of the proposed MILP model is proposed. The results show that the GA performs much better than the exact algorithm concerning time. GAMS software fails to solve the large-size problem, and the time to find a solution grows exponentially with increasing the size of the problem. However, the GA quite efficient for problems of large sizes, and can nearly find the optimal solution in a much shorter amount of time. Also, results in the Robust model show that increasing the confidence level has led to an increase in the value of the objective function of the robust counterpart model, this increase does not exhibit linear behavior. At 80% confidence level, the minimum changes in the objective function are observed, if we want to obtain a 90% confidence level, it requires more cost, but increasing the confidence level from 70% to 80% does not need more cost, so an 80% confidence level can be considered as an ideal solution for decision-makers.

Flexible manufacturing system (FMS) readily addresses the dynamic needs of the customers in terms of variety and quality. At present, there is a need to produce a wide range of quality products in limited time span. On-time delivery of customers’ orders is critical in make-to-order (MTO) manufacturing systems. The completion time of the orders depends on several factors including arrival rate, variability, and batch size, to name a few. Among those, batch size is a significant construct for effective scheduling of an FMS, as it directly affects completion time. On the other hand, constant batch size makes MTO less responsive to customers’ demands. In this paper, an FMS scheduling problem with n jobs and m machines is studied to minimize lateness in meeting due dates, with focus on the impact of batch size. The effect of batch size on completion time of the orders is investigated under following strategies: (1) constant batch size, (2) minimum part set, and (3) optimal batch size. A mathematical model is developed to optimize batch size considering completion time, lateness penalties and setup times. Scheduling of an FMS is not only a combinatorial optimization problem but also NP-hard problem. Suitable solutions of such problems through exact methods are difficult. Hence, a meta-heuristic Genetic algorithm is used to optimize scheduling of the FMS.

As ranking is one of the most important issues in data envelopment analysis (DEA), many researchers have comprehensive studies on the subject and presented different approaches. In some papers, DEA and Analytic hierarchy process (AHP) are integrated to rank the alternatives. AHP utilizes pairwise comparisons between criteria and units, assessed subjectively by the decision maker, to rank the units. In this paper, a nonlinear programming (NLP) model is introduced to derive the true weights for pairwise comparison matrices in AHP. Genetic algorithm (GA) is used in order to solve this model. We use MATLAB software to solve proposed model for ranking the alternatives in AHP. A numerical example is applied to illustrate the proposed model.

This paper presents a novel Electric Vehicle (EV) coordinated control scheme for Fuel Cell Plug-in Hybrid Electric Vehicle (FC-PHEV) based on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) self adjusting algorithms. The proposed EV drive system comprised a FC, Ni-MH battery as secondary DC energy source, and the induction motor. The use of PSO/GA soft computing search and optimization algorithms ensures optimized online control settings. The AC drive utilized the simple hysteresis current controller with PSO/GA optimal slip driven by the reference speed and current signals. The main features of the control scheme are to track speed reference trajectory with minimum over-current, enhanced energy utilization, EV-drive loss reduction and dynamic drive response for parameters variation and stabilization of the common DC-link bus voltage. The integrated DC-AC drive-scheme is fully stabilized using a novel FACTS Green Plug Filter Compensator (GPFC) scheme that ensures stabilized dc bus voltage, minimal inrush current and DC-bus transient conditions. Simulation and experimental results indicate that the strategy can ensure the stabilization of the DC-common bus and the efficient utilization of the FC-Battery DC sources.

The growth in environmental concerns, and the rapid increase on the electric power demand, increased the interest in renewable energy. Wind energy source has the ability to provide electric power in rural or isolated areas. The variation of output power with the variation of wind speed can cause significant power quality issues, especially in the case of a standalone generation system. The paper presents a number of novel self adjusting wind energy utilization schemes using the modified single phase operation of the three phase induction generator supplemented by novel voltage stabilization switched filter compensation scheme. The family of series-parallel switched capacitor filter schemes is controlled by a dynamic Particle Swarm Optimization PSO and Genetic Algorithm GA search techniques error driven self adjusting controller to ensure voltage stabilization, minimum impact of the electric load excursions and wind variations on terminal voltage. A prototype of the GP-EM-EE device has been built and tested in the laboratory. Some selected experimental results are provided for the validation of the system. Based on extensive simulation results using MATLAB/SIMULINK and the prototype laboratory scheme, it has been established that the performance of the controllers both in transient as well as in steady state is quite satisfactory and it can also stabilize the network voltage. The proposed switched smart filter using Green Plug/Energy Management/Energy Economizer GPEM- EE devices for small single phase induction motors can be used in residential motor drives for water pumping, ventilation, air conditioning, compressors, refrigeration applications.