سال:1393 | دوره: | شماره: |تعداد مقالات:8

A three-stage production system is considered in this paper. There are two stages to fabricate and provide the parts and an assembly stage to assemble the parts and complete the products in this system. Suppose that a number of products of different kinds are ordered. Each product is assembled with a set of several parts. At first the parts are produced in the first stage with parallel machines and then they are controlled and provided in the second stage and finally the parts are assembled in an assembly stage to complete the products. Two objective functions are considered: (1) minimizing the completion time of all products (makespan), and (2) minimizing the sum of earliness and tardiness of all products (Si(Ei/Ti). Since this type of problem is NP-hard, a new multi-objective algorithm is designed for searching local Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, various test problems are designed and the reliability of the proposed algorithm, based on some comparison metrics, is compared with two prominent multi-objective genetic algorithms, i.e. NSGA-II and SPEA-II. The computational results show that the performance of the proposed algorithms is good in both efficiency and effectiveness criteria.

This study presents an integrated intelligent algorithm to valuate the level of job satisfaction (JS) in services. Job Satisfaction plays an important role as a competitive advantage in organizations especially in helth industry. Recruitment and retention of human resources are persistent problems associated with this field. Most of the researchs have focused on job satisfaction factors and few of researches have noticed about its effects on productivity. However, little researchs have focused on the factors and effects of job satisfaction simultanosly by system dynamics approaches.In this paper, firstly, analyses the literature relating to system dynamics and job satisfaction in services specially at a hospital clinic and reports the related factors of employee job satisfaction and its effects on productivity. The conflicts and similarities of the researches are discussed and argued. Then a novel procedure for job satisfaction evaluation using (Artificial Neural Networks) ANNs and system dynamics is presented. The proposed procedure is implemented for a large hospital in Iran. The most influencial factors on job satisfaction are chosen by using ANN and three differents dynamics scenarios are built based on ANN's result. The modelling effort has focused on evaluating the job satisfaction level in terms of key factors which obtain from ANN result such as Pay, Work and Co-Workers at all three scenarios. The study concludes with the analysis of the obtained results. The results show that this model is significantly usfule for job satisfaction evaluation and provide policy makers with an appropriate tool to make more accurate decision on kind of reward. This is because the proposed approach is capable of handling non-linearity, complexity as well as uncertainty that may exist in actual data and situation.

In order to perform Preventive Maintenance (PM), two approaches have evolved in the literature. The traditional approach is based on the use of statistical and reliability analysis of equipment failure. Under statistical-reliability (S-R)-based PM, the objective of achieving the minimum total cost is pursued by establishing fixed PM intervals, which are statistically optimal, at which a decision to replace or overhaul equipments or components is made. The second approach involves the use of sensor-based monitoring of equipment condition in order to predict occurrence of machine failure. Under condition-based (C-B) PM, intervals between PM works are no longer fixed, but are performed only “when needed”. It is obvious that Condition Based Maintenance (CBM) needs an on-line inspection and monitoring system that causes CBM to be expensive. Whenever this cost is infeasible, we can develop other methods to improve the performance of traditional (S-R)-based PM method. In this research, the concept of Bayesian inference was used. The time between machine failures was observed, and Bayesian inference is employed in (S-R)-based PM, it is tried to determine the optimal checkpoints.

Bus systems are unstable without considering any control. Thus, we are able to consider some control strategies to alleviate this problem. A holding control strategy is one commonly used real-time control strategy that can improve service quality. This paper develops a mathematical model for a holding control strategy. The objective of this model is to minimize the total cost related to passengers at any stop. To solve the model, particle swarm optimization (PSO) is proposed. The results of the numerical examples show that the additional total cost caused by service irregularity is reduced by 25% by applying the presented holding model to the given problem.

Generally, human resources play an important role in manufacturing systems as they can affect the work environment. One of the most important factors impressing on worker performance is there being an interactional interest between workers in workshops. In this paper, we deal with this new concept in cellular manufacturing systems (CMS). Besides the existence of interactional interest, workers could be able to work with machines in their cells. In an ideal situation, all workers could be able to work with all machines in their cells, while there would be an interactional interest between each pair of them in the cells. Two matrices named “Task matrix” and “Interest matrix” are used to model the proposed problem. By minimizing the voids of these two matrices in a diagonal form simultaneously, we seek the ideal situation above. Because of nonhemogeneus matrices, a bi-objective mathematical model is developed. The e-constraint method is applied as an optimization tool to solve the bi-objective model. Finally some numerical examples are solved to exhibit the capability of the presented problem.

In this paper the problem of serial batch scheduling in a two-stage hybrid flow shop environment with minimizing Makesapn is studied. In serial batching it is assumed that jobs in a batch are processed serially, and their completion time is defined to be equal to the finishing time of the last job in the batch. The analysis and implementation of the prohibited transference of jobs among the machines of stage one in serial batch is the main contribution of this study. Machine set-up and ready time for all jobs are assumed to be zero and no Preemption is allowed. Machines may not breakdown but at times they may be idle. As the problem is NP-hard, a genetic algorithm is developed to give near optimal solutions. Since this problem has not been studied previously, therefore, a lower bound is developed for evaluating the performance of the proposed GA. Many test problems have been solved using GA and results compared with lower bound. Results showed GA can obtain a near optimal solution for small, median and large size problems in reasonable time.

Flexible flow shop scheduling problem (FFS) with unrelated parallel machines contains sequencing in flow shop where, at any stage, there exists one or more processors. The objective consists of minimizing the maximum completion time. Because of NP-completeness of FFS problem, it is necessary to use heuristics method to address problems of moderate to large scale problem. Therefore, for assessment the quality of this heuristic, this paper develop a global lower bound on FFS makespan problems with unrelated parallel machines.

The main goal in this paper is to propose an optimization model for determining the structure of a series-parallel system. Regarding the previous studies in series-parallel systems, the main contribution of this study is to expand the redundancy allocation parallel problems to systems that have repairable components. Therefore, the considered systems in this paper are the systems that have repairable components in their configurations and subsystems. The suggested optimization model has two objectives: maximizing the system mean time to first failure and minimizing the total cost of the system. The main constraints of the model are: maximum number of the components in the system, maximum and minimum number of components in each subsystem and total weight of the system. After establishing the optimization model, a multi-objective approach of Imperialist Competitive Algorithm is proposed to solve the model.