نشریه پژوهشهای مهندسی صنایع در سیستمهای تولید
پیاپی 1 (بهار و تابستان 1392)

This paper studies the multi-factory production (MFP) network scheduling problem where a number of different individual factories join together to form a MFP network, in which these factories can operate more economically than operating individually. However, in such network which known as virtual production network with selfish factories, each individual factory usually focuses on self-benefits and does not care much about the others within the network. In this study, we proposed a heuristic algorithm for sum of completion time minimization. Here, although each factory has an objective function, Pareto concept are introduced in single-objective environments. Therefore, this paper proposed a weighted Lp-metric based framework. We investigated the performance of our algorithm in comparison with imperialist competitive algorithm and obtained results are reported.

Advanced planning and scheduling is a production management process in which the resources and production capacities are optimally assigned to the customers’ demands. This approach can particularly be applicable in the complex environments. A common assumption in the advanced planning and scheduling problems is that the processing time of a given product is constant and independent of its position in the production sequence. However, in the real-world situations, an operator’s skill may continuously be improved when the production time is passing which is known as the learning effect phenomenon. In this article, with regard to the learning effect, an extended multi-product optimization framework for the advanced planning and scheduling problem of a typical flexible production environment is developed to provide a more ability to address the actual situations. Due to the high computational complexity of the proposed model, a multi-stage genetic solution algorithm is also presented. Numerical results confirm that the proposed algorithm can obtain the optimum/near optimum solutions in much less computational times compared to the exact solutions.

Most of the fuzzy supply chain network design problems are single-product models; in addition, the related quality characteristics are ignored in such models. This study tries to increase the efficiency of designed supply chain by considering a multi-product supply chain and giving more attention to effective criteria. In this research, two important problems in supply chain management i.e. supplier selection and supply chain network design are considered simultaneously. Using the fuzzy principal component analysis, a criterion named PCA-score is introduced for considering of the all criteria in an integrated measure. It is used for selecting the supply chain elements. In this study, the parameters have been assumed as trapezoidal fuzzy numbers in order to be closer to the real world situations. A two objectives fuzzy mathematical programming problem is presented and after transforming the fuzzy problem to the equivalent crisp one, LP metric method is used to optimize the crisp problem. Finally, the applicability and efficiency of the model are analyzed using a simulated numerical example.

In this paper, we investigate network location problem for congestible facilities, considering balking, the reluctance of customers to join the queue upon arrival. Balking often happens when the estimated waiting times by customers are more than their patience threshold. Based on the number of current customers, each customer decides to join or leave the system upon the arrival. Since balking is a usual issue in the real world situations, taking it into consideration increases the reality of mathematical models. In the proposed model, the selection of candidate sites and the assignment of customers to facilities are performed in such a manner that the total lost demands is minimized. We use a monotonically decreasing function to calculate arrival rates for facilities. In order to evaluate the proposed mathematical model, some numerical examples are presented and solved using GAMS.

In the big industries, the volume of commodities that used in a production cycle is capital-intensive. Therefore, the inventory management would be important especially in determining the capacity of warehouses, managing costs and responding to transportation concerns. It will be more considerable in the area of replenishment planning for distribution centers and even selling shelves. The main objective of this paper is to provide a multi-objective replenishment model by considering lead-time for the orders as well as obtaining the optimal numbers of replenishments and the quantities of orders. This model designed for the make-to-order inventory replenishment system by using a distributor. Confining the total numbers of replenishments as well as minimizing inventory costs is the two defined objectives. The inputs of the model are delivery limitations of suppliers and capacity constraint of distributor's warehouse. The model solved by standard multi-objective methods in discrete and continuous space. Then the two sensitivity analyses performed on the "numbers of replenishments" and "ratio of holding and back-order cost". The results show that the total cost of system would be raise upon increasing numbers of replenishments. Also After a specific value for the number of replenishments, the total cost would have significant jump because of enforcing to do unnecessary replenishments.

A new meta-heuristic algorithm according to harmony search procedure is developed. It provides a new structure of hybrid algorithms, which organically merges the particle swarm optimization and simulated annealing into HS algorithm. In order to evaluate the proposed algorithm, instance problems in 9 benchmark is generated. The proposed algorithm and basic harmony search, improved harmony search and global best harmony search are solved and the results of each algorithm are compared. The results show that the proposed algorithm has the better performance to the other algorithm. In order to make use of this proposed algorithm in production problems, it has also applied this new method for solving parallel machine scheduling problem

Standardization of information is one of the most important policies in integration of its components, and as one of the three main procedures in supply chains. Standardization system is one of the most practical systems and provides a sufficient area of activity for integrated management of information in chains. Identifying the real needs of beneficiaries and meeting them with tools of this system results in gaining value and their satisfaction. Due to restriction of sources, ranking and proper choosing of tools for implementation are vitally important. In this paper, a new approach is provided that with use of fuzzy hierarchical analysis techniques and analytic model of Kano, at first the needs of beneficiaries of the national standardization system are identified and organized, and then its tools would be clustered and ranked. By prioritizing those tools that have more effect on increasing the beneficiaries’ satisfaction, utilizing sources will have more efficiency. For examining and validating the used approach, a case study on standardization system of Iran’s commodities and services (Iran Code) has been provided and its positive results consist of clustering and ranking the available tools in this system according to the highest satisfaction caused, with more accurate results compared to traditional methods.