نشریه بین المللی مهندسی صنایع و مدیریت تولید
سال بیست و دوم شماره 3 (پاییز 1390)

This paper proposes a new intelligent approach for solving multi-response statistical optimization problems. In most real world optimization problems, we are encountered adjusting process variables to achieve optimal levels of output variables (response variables). Usual optimization methods often begin with estimating the relation function between the response variable and the control variables. Among these techniques, the response surface methodology (RSM) due to its simplicity has attracted the most attention in recent years. However, in some cases the relationship between the response variable and the control factors is so complex; hence a good estimate of the response variable cannot be achieved using polynomial regression models. An alternative approach presented in this study employs artificial neural networks to estimate response functions and genetic algorithm to optimize the process. Furthermore, the proposed approach uses taguchi robust parameter design to overcome the common limitation of the existing multi-response approaches, which typically ignore the dispersion effect of the responses. In order to evaluate the effectiveness of the proposed method, the method has been applied to a numerical example from litterateur and the results were satisfactory.

Cell formation is the first and most important problem in designing cellular manufacturing systems. We have modeled the dynamic cell formation problem with respect to the minimization of the intercellular movement and cellular reconfiguration costs. Due to the nonpolynomiality of the cell formation models, a newly introduced metaheuristic namely the grenade explosion method (GEM) is applied to solve the proposed model. We have introduced some modifications to improve the performance of the standard GEM which are modifying the location of the grenade explosion in each step, modifying the correction process of the infeasible solutions and introducing free grenade as a new component of the GEM. The proposed modified GEM is compared to the standard GEM and a simulated annealing algorithm through some numerical examples. The computational results illustrate the preference of the GEM algorithms over the simulated annealing algorithm. Furthermore, the results obtained using the modified GEM are superior to those ones achieved using the standard GEM.

Greedy randomize adaptive search procedure is one of the repetitive meta-heuristic to solve combinatorial problem. In this procedure, each repetition includes two, construction and local search phase. A high quality feasible primitive answer is made in construction phase and is improved in the second phase with local search. The best answer result of iterations, declare as output. In this study, GRASP is used to solve the QAP problem. The resulting on QAP library standard problem is used to demonstrate the high performance of suggested algorithm.

There are miscellaneous quality characteristics in healthcare which are interested to be monitored. However, monitoring each characteristic needs a special statistical method. There are some characteristics with very small incidence rates that it’s usually considered not to be necessary to monitoring them, since their incidence rates are so small that p and np charts are not able to monitor them. Such characteristics are called “rare health-related events”. In this paper, while introducing the most common methods for monitoring the rare events, we are going to compare their performances based on the average run length (ARL).

In this paper, a discrete event simulation model of Iranian railway system is used for analyzing performance of the system in freight sector. The model analysis shows more sensitivity of the system on loading and unloading facilities than on number of wagons and locomotives. System optimization; through simulation optimization technique, shows that more than 4% of all wagons available in the network are not needed. Data Envelopment Analysis (DEA) method is used for analyzing overall performance of the system. Based on the DEA results, the system currently perform more than 19% under nominal performance, and that will be improved more than 5% after removing the excess wagons.

Nowadays, scheduling problems have a considerable application in production and service systems. In this paper, we consider the scheduling of n jobs on a single machine assuming no machine idleness, non-preemptive jobs and equal process times. In many of previous researches, because of the delivery dalays and holding costs, earliness and tardiness penalties emerge in the form of linear combinations in objective functions. However, minimizing the weighted quadratic earliness and tardiness penalties is considered in this paper. A branch and bound algorithm including some dominance rules, lower bounds and upper bounds is proposed to solve the problem. Finally, the efficiency of proposed method is proved via some test problems.

Electric power industry have always try to provide reliable electricity to customers and at the same time decrease system costs. High Voltage circuit-breakers are an essential part of the power network. This study has developed a maintenance and replacement scheduling model for high voltage circuit- breakers that minimize maintenance costs while maintaining the acceptable reliability. This model, quantify the effect of maintenance on reliability. A mixture weibull distribution is used to system’s failure rate function and a hybrid hazard rate recursion rule based on the concept of age reduction factor and hazard rate increase factor is built up to model system reliability. The model is based on sequential imperfect maintenance policy. Whenever the equipment reliability reaches the threshold R, an imperfect preventive maintenance (PM) is performed on the equipment. The optimal reliability threshold R is determined by minimizing the maintenance cost per unit time in the residual life of the system. Finally, the model is applied on Esfahan Regional Electric Company circuit-breakers and a maintenance and replacement scheduling is presented.

A linear programming model has been presented for finding an appropriate planning in order to maximize hotel revenue. In this model, a special planning horizon has been considered that includes several busy days of a year. There are several reservation periods that may start a few months earlier. Each period of reservation may have different prices and so result different incomes. Hotel customers demand for different rooms and lengths of stay. Two situations of demand, deterministic and stochastic have been considered. We use different scenarios for stochastic demand. Finally, this model is solved by Lingo for cases discussed above.

Majority of models in location literature are based on assumptions such as point demand, absence of competitors, as well as monopoly in location, products, and services. However in real-world applications, these assumptions are not well-matched with reality. In this study, a new mixed integer nonlinear programming model based on weighted goal programming approach is proposed to maximize the captured demand while minimizing the fixed costs of locating new facilities. To make the proposed model optimization simple, the proposed model is linearized using proper modeling techniques. Although goal programming method does not ensure obtaining Pareto-optimal solutions, solving a numerical example demonstrates the possibility of obtaining Pareto-optimal solutions for the research problem besides higher speed of the classical branch & bound method in obtaining the optimal solution in comparison to the conventional single objective model.