Catálogo de publicaciones - libros

In many data grid applications, data can be decomposed into multiple independent sub datasets and distributed for parallel execution and analysis. This property has been successfully exploited for scheduling divisible load on large scale data grids by Genetic Algorithm (GA). However, the main disadvantages of this approach are its large choromosome length and execution time required. In this paper, we concentrated on developing an Adaptive GA (AGA) approach by improving the choromosome representation and the initial population. A new chromosome representation scheme that reduces the chromosome length is proposed. The main idea of AGA approach is to integrate an Adaptive Divisible Load Theory (ADLT) model in GA to generate a good initial population in a minimal time. Experimental results show that the proposed AGA approach obtains better performance than Standard GA (SGA) approach in both total completion time and execution time required.

This paper presents formulas for approximating the distribution of the cycle time of a job in a two-stage fork-join network in equilibrium. The key step is characterizing the departure process from the first node. Statistical tests justify that the approximate distribution is a good fit to the actual one. We discuss related approximations for -stage networks, and present a formula for approximating the mean cycle time in a -stage fork-join network.

In this paper, we address and solve the scheduling problem for thin film transistor array (TFT-array) factories. The TAFSP is a variation of parallel machine scheduling problem, which involves the characteristics of process window constraint, machine dedication constraint, mask availability constraint, and mask setup and transportation activities. Hence, we propose an integer programming formulation to solve the TAFSP. To increase the applicability of the integer programming model in real environment, depth-search strategy incorporates with the strong branching rule is adopted to increase the solving efficiency. Computational results show that a good-quality feasible solution can be obtained in an acceptable computational time for a real-world case.

This paper presents a common weight multi-criteria decision making (MCDM) approach for determining the best decision making unit (DMU) taking into consideration multiple inputs and outputs. Its robustness and discriminating power are illustrated through comparing the results with those obtained by data envelopment analysis (DEA) and its extensions such as cross efficiency analysis and minimax efficiency DEA model, which yield a ranking with an improved discriminating power. Several examples reported in earlier research addressing DEA’s discriminating power are used to illustrate the application of the proposed approach. The results indicate that the proposed framework enables further ranking of DEA-efficient DMUs with a notable saving in the number of mathematical programming models solved.

This work evaluates the performance of constraint programming (CP) and integer programming (IP) formulations to solve the Simple Assembly Line Balancing Problem (SALBP) exactly. Traditionally, its exact solution by CP or IP and standard software has been considered to be inefficient to real-world instances. However, nowadays this is becoming more realistic thanks to recent improvements both in hardware and software power. In this context, analyzing the best way to model and to solve SALBP is acquiring relevance. The aim of this paper is to identify the best way to model SALBP-1 (minimizing the number of stations, for a given cycle time) and SALBP-2 (minimizing the cycle time, for a given number of stations). In order to do so, a wide computational experiment is carried out to analyze the performance of one CP and three IP formulations to solve each problem. The results reveal which of the alternative models and solution techniques is the most efficient to solve SALBP-1 and SALBP-2, respectively.

The problem of aggregating multiple numerical criteria to form overall objective functions is of considerable importance in many disciplines. The ordered weighted averaging (OWA) aggregation, introduced by Yager, uses the weights assigned to the ordered values rather than to the specific criteria. This allows one to model various aggregation preferences, preserving simultaneously the impartiality (neutrality) with respect to the individual criteria. However, importance weighted averaging is a central task in multicriteria decision problems of many kinds. It can be achieved with the Weighted OWA (WOWA) aggregation though the importance weights make the WOWA concept much more complicated than the original OWA. We show that the WOWA aggregation with monotonic preferential weights can be reformulated in a way allowing to introduce linear programming optimization models, similar to the optimization models we developed earlier for the OWA aggregation. Computational efficiency of the proposed models is demonstrated.

In real life, the capacity of any distributor’s warehouse is limited. Excess stock must be held in a rented warehouse whenever the capacity of the distributor’s own warehouse is insufficient. Furthermore, we also choose to store in the rented warehouse if it has better facilities and/or a lower cost. In this paper, we consider a single-producer–single-distributor inventory model with deteriorating items in a two-warehouse environment. The rented warehouse normally has better facilities for preservation as compared with one’s own warehouse. Besides, there is an incentive offered by a rented warehouse that allows the rental fee to decrease over time. The incentive mechanism can be proved to perform better than the one without incentive. The object of this study is to develop an optimal joint economic lot size (JELS) policy from the perspectives of the producer and the distributor. Moreover, a criterion to consider the length of time of rented warehouse usage is proposed. Simulated Annealing (SA) method has been developed to find the global optimum for a complex cost surface through stochastic search process. A computer program in C-language has been developed for this purpose and is implemented to derive the optimum decision for the decision maker. Numerical examples and sensitivity analyses are given to illustrate the results.

Quality function deployment (QFD) is a product/service design and improvement tool which is basically a transformation of vague and imprecise customer needs into measurable product/service attributes. This article integrates compromise programming based goal programming into the QFD process to determine to what extent the product/service attributes should be improved. The fuzzy set theory is applied to the model to deal with the imprecise nature of data. Differing from existing QFD applications, our proposed methodology applies analytic network process to evaluate the inner dependencies among customer needs, among product attributes and also the relationships between them. Furthermore, it determines the best product/service in the market as the goal employing compromise programming. Finally, the methodology ends with the goal programming method which consists of this predefined goal and the product/service provider’s budget limitation. A real-world application on e-learning products provided by the higher education institutions in Turkey illustrates the applicability of our proposed methodology.

In this paper, the () which optimally assigns new adding and splitting cells in (Personal Communication Service) network to switches in an (Asynchronous Transfer Mode) network is studied. In NEP, the locations of all cells (or , ) in PCS network are fixed and known, but new switches should be installed to ATM network and the topology of the backbone network may be changed. Given some potential sites of new switches, the problem is to determine how many switches should be added to the backbone network, the locations of new switches, the topology of the new backbone network, and the assignments of new adding and splitting cells in the PCS to switches on the new ATM backbone network in an optimum manner. The goal is to do the expansion in as attempt to minimize the total communication cost under budget and capacity constraints. The NEP is modeled as a complex integer programming problem and finding an optimal solution to this problem is . A heuristic algorithm is proposed to solve this problem. The proposed heuristic algorithm consists of four phases: (RCPP), (CCP), (SSP), and (BDP). Experimental results indicate that the proposed algorithm can find good solution.

Semiconductor production turnkey service (SPTS) coordinates wafer fabrication with the outsourcing of the remaining processes including circuit probing testing (C/P testing), integrated circuit (IC) assembly and final testing for buyers. To reduce the production cost and lead time in the supply chain, wafer fabricators must be responsible for the production and distribution planning for the SPTS. Therefore, this research develops an integer-programming (IP) -based model of collaborative production-distribution planning. Under this model, multi-products, multi-stages, and multi-outsourcing factories with different processing capabilities are considered. However, the IP model cannot solve the problem within a polynomial time when the problem becomes as complicated as those in real practice. To confront this problem, we adopt and modify the generalized saving algorithm (GSA) so that the proposed algorithm can solve complicated real-world problems efficiently. The numerical results show that the proposed algorithm can significantly increase the solving efficiency.