International Journal of Industrial Engineering & Production Research

 Abstract: Local flux may be distorted in many regions of core, although total flux is usually sinusoidal. When attempting to predict the loss distribution in materials operating under localized distorted flux conditions, which occur in machines and transformer cores, it is essential that proper account of the waveform be taken. Moreover for development of new magnetic materials and generation of better magnetic sheets, it is necessary to implement detailed measurement for their property specifications. One of these property specifications is loss under distorted flux conditions. A high precision Single Sheet Tester (SST) was implemented in which the specification of the sample sheet will be measured by software processing of B and H. The finite element method was used for the magnetic field study. The field distribution was calculated inside and outside the sample, in which way the error was obtained. By different section of the winding in exciting coil the field uniformity was improved and finally the implemented system shows error less than 0.6% in measurement of hysterics loss of magnetic sheets. Loss due to distorted flux was measured for different harmonics and in distinct amplitudes and phases. A range of non-oriented and grain oriented materials were tested under distorted flux waveform condition. For non-oriented sheets loss measured about 10% by applying 15% third harmonic to exciting waveform, while this value was about 25% for many of grain oriented sheets. Moreover, based on implemented measurements, harmonic phase affects on loss and makes about 22% error in loss prediction for non-oriented sheets.

  The problem of lot sizing, sequencing and scheduling multiple products in flow line production systems has been studied by several authors. Almost all of the researches in this area assumed that setup times and costs are sequence –independent even though sequence dependent setups are common in practice. In this paper we present a new mixed integer non linear program (MINLP) and a heuristic method to solve the problem in sequence dependent case. Furthermore, a genetic algorithm has been developed which applies this constructive heuristic to generate initial population. These two proposed solution methods are compared on randomly generated problems. Computational results show a clear superiority of our proposed GA for majority of the test problems.

  In this paper, we consider a flow shop scheduling problem with bypass consideration for minimizing the sum of earliness and tardiness costs. We propose a new mathematical modeling to formulate this problem. There are several constraints which are involved in our modeling such as the due date of jobs, the job ready times, the earliness and the tardiness cost of jobs, and so on. We apply adapted genetic algorithm based on bypass consideration to solve the problem. The basic parameters of this meta-heuristic are briefly discussed in this paper. Also a computational experiment is conducted to evaluate the performance of the implemented methods. The implemented algorithm can be used to solve large scale flow shop scheduling problem with bypass effectively .

  In this paper flexible job-shop scheduling problem (FJSP) is studied in the case of optimizing different contradictory objectives consisting of: (1) minimizing makespan, (2) minimizing total workload, and (3) minimizing workload of the most loaded machine. As the problem belongs to the class of NP-Hard problems, a new hybrid genetic algorithm is proposed to obtain a large set of Pareto-optimal solutions in a reasonable run time. The algorithm utilizes from a local search heuristic for improving the chance of obtaining more number of global Pareto-optimal solutions. The solution method uses from a perturbed global criterion function for guiding the search direction of the hybrid algorithm. Computational experiences show that the hybrid algorithm has superior performance in contrast to previous studies .

  This paper aimed at estimation of the per capita consumption of electricity in residential sector based on economic indicators in Iran. The Genetic Algorithm Electricity Demand Model (GAEDM) was developed based on the past data using the genetic algorithm approach (GAA). The economic indicators used during the model development include: gross domestic product (GDP) in terms of per capita and real price of electricity and natural gas in residential sector. Three forms of GAEDM were developed to estimate the electricity demand. The developed models were validated with actual data, and the best estimated model was selected on base of evaluation criteria. The results showed that the exponential form had more precision to estimate the electricity demand than two other models. Finally, the future estimation of electricity demand was projected between 2009 and 2025 by three forms of the equations linear, quadratic and exponential under different scenarios .

This paper considers a bi-objective mathematical model for locations of landfills, transfer stations and material recovery facilities (MRFs) in order to serve the entire regions and simultaneously identify the capacities of landfills. This is a mixed-integer programming (MIP) model, whose objectives are to minimize the total cost and pollution simultaneously. To validate the model, a numerical example is solved an augmented ε-constraint method and the associated computational results are presented to show the number of solid waste facilities and location of sites for solid waste facilities.