Search

In this study, the design of a two-echelon distribution supply chain network for the seasonal products with multiple vendors (manufacturers) and buyers (retailers), and a set of warehouses for each vendor are considered. The locations of the buyers are known and the capacity of the warehouses is restricted while the buyers purchase different products from the vendors under all unit discount policy. The main objective of this research is to find out the optimal locations of the potential vendors in addition to the quantity ordered (allocation) by the buyers so that the total inventory cost including ordering (transportation), holding and the purchasing costs is minimized. Besides, the distance from the buyers to the vendors is considered as the Euclidean distance. The total budget to buy the products is limited and the production capacity of each vendor is also restricted. To solve the problem, a modified particle swarm optimization (MPSO) algorithm is applied where the results are validated using a genetic algorithm (GA). Finally, some computational examples are generated to assess the algorithms' performance where MPSO shows a better efficiency in comparison with the GA. [ABSTRACT FROM AUTHOR]

In this paper, the water cycle algorithm (WCA), a recently developed metaheuristic method is proposed for solving multi-objective optimization problems (MOPs). The fundamental concept of the WCA is inspired by the observation of water cycle process, and movement of rivers and streams to the sea in the real world. Several benchmark functions have been used to evaluate the performance of the WCA optimizer for the MOPs. The obtained optimization results based on the considered test functions and comparisons with other well-known methods illustrate and clarify the robustness and efficiency of the WCA and its exploratory capability for solving the MOPs. [ABSTRACT FROM AUTHOR]

Amulti-itemmultiperiod inventory control model is developed for known-deterministic variable demands under limited available budget. Assuming the order quantity is more than the shortage quantity in each period, the shortage in combination of backorder and lost sale is considered. The orders are placed in batch sizes and the decision variables are assumed integer. Moreover, all unit discounts for a number of products and incremental quantity discount for some other items are considered.While the objectives are to minimize both the total inventory cost and the required storage space, the model is formulated into a fuzzymulticriteria decision making (FMCDM) framework and is shown to be a mixed integer nonlinear programming type. In order to solve the model, a multiobjective particle swarm optimization (MOPSO) approach is applied. A set of compromise solution including optimum and near optimum ones via MOPSO has been derived for some numerical illustration, where the results are compared with those obtained using a weighting approach. To assess the efficiency of the proposed MOPSO, the model is solved using multi-objective genetic algorithm (MOGA) as well. A large number of numerical examples are generated at the end, where graphical and statistical approaches show more efficiency of MOPSO compared with MOGA. [ABSTRACT FROM AUTHOR]

Injectable calcium phosphate cements have been introduced as adjuncts to internal fixation for treating selected fractures. These cements harden without producing much heat, develop compressive strength, and are remodeled slowly in vivo. The main purpose of the cement is to fill voids in metaphyseal bone, thereby reducing the need for bone graft. However, such cements may also improve the holding strength around metal devices in osteoporotic bone. This paper presents the optimum mechanical behavior of calcium phosphate cement/hydroxyl group functionalized multi-walled carbon nanotubes/bovine serum albumin (CPC/MWCNT-OH/BSA) composites in terms of compressive strength using well-known metaheuristic optimizers. The process parameters studied were wt% of MWCNT-OH (0.2-0.5 wt%) and wt% of BSA (5-15 wt%). The obtained results from metaheuristic algorithms were compared with the results from the response surface methodology (RSM) in the literature. The results obtained from metaheuristic algorithms outperformed the results given by the RSM in terms of less error percentage and high compressive strength. [ABSTRACT FROM AUTHOR]

To achieve greater automation of their business processes, organizations face the challenge of integrating disparate systems. In attempting to overcome this problem, organizations are turning to different kinds of enterprise integration. Implementing enterprise integration is a complex task involving both technological and business challenges and requires appropriate middleware technologies. Different enterprise integration solutions provide various functions and features which lead to the complexity of their evaluation process. To overcome this complexity, appropriate tools for evaluating the core integration features of enterprise integration solutions is required. This paper proposes a new comprehensive framework for evaluating the core integration features of both intra-enterprise and inter-enterprise Integration's enabling technologies, which simplify the process of evaluating the requirements met by enterprise integration middleware technologies. The proposed framework for evaluating the core integration features of enterprise integration middleware technologies was enhanced using the structural and conceptual aspects of previous frameworks. It offers a new schema for which various enterprise integration middleware technologies are categorized in different classifications and are evaluated based on their supporting level for the core integration features' criteria. These criteria include the functional and supporting features. The proposed framework, which is a revised version of our previous framework in this area, has developed the scope, structure and content of the mentioned framework. [ABSTRACT FROM AUTHOR]

Selection of processing and geometrical parameters is a crucial step in the extrusion process design. Optimized parameters may result in desirable microstructure at minimum load. The purpose of this paper is determination of the optimal cold forward extrusion parameters with the minimization of tool load as the objective function. This paper deals with different optimization approaches in order to determine the optimal values of logarithmic strain, die angle, and friction with the purpose of finding the minimal tool loading obtained by cold forward extrusion process. The obtained extrusion force model as a fitness function was used to carry out the optimization. Based upon the objective function, metaheuristic algorithms such as genetic algorithm and simulated annealing were adopted as optimization methods for finding the optimum values of cold forward extrusion parameters and the obtained results were compared with those in literature. The better results lead to the smallest energy consumption, longer tool life, better formability of the work material, and the quality of the finished product. [ABSTRACT FROM AUTHOR]

In today's world, customer purchasing behavior prediction is one of the most important aspects of customer attraction. Good prediction can help to develop marketing strategies more accurately and to spend resources more effectively. When designing a customer prediction system (CPS) two issues are key, namely, feature selection and the prediction method to be used. Furthermore, it seems necessary to design CPSs with both high computational speed and good prediction abilities. The purpose of this paper is to develop such a system by using a hybrid approach. The resulting system is a hybrid CPS (HCPS) and is based on Multiple Forward Stepwise Logistic Regression (MFSLR) model. The MFSLR model combines a forward stepwise regression (FSR) technique that rapidly selects an optimal subset of features with multiple logistic regression (MLR) technique. In practice, the new MFSLR model provides very good prediction results. Since customer identification is one of the principal concerns in the insurance industry, an insurance company dataset has been used. The obtained results show that the FSR selects around 55% of the initially available features, in this way considerably reducing computational costs. In addition, the results show that the MLR method leads to more accurate prediction than some other methods we tried, namely, feedforward neural networks, radial basis networks and regression trees. [ABSTRACT FROM AUTHOR]

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously. [ABSTRACT FROM AUTHOR]

The increasing advances of new Internet technologies in all application domains have changed life styles and interactions. E-learning and collaborative learning environment systems are originated through such changes and aim at providing facilities for people in different times and geographical locations to cooperate, collaborate, learn and work together by using various educational services. One of the most important requirements of learners in online and virtual environments is the ability to ask questions and receive appropriate answers. The nature of such environments and the lack of physical existence of teachers make such issues critical and challenging problems. This paper presents a multi-agent system for building a question-answering system in learning management systems and collaborative learning environments. In the proposed system. after validating the content of questions, all available resources including course materials, frequently asked questions and responses from other learner~ will be gathered and finally using a recommender system, the most appropriate answer(s) with respect to several criteria such as learner's knowledge, research background, history of previous questions, and the candidate answers relevant to the question will be suggested. A simplified version of the system has been implemented and integrated to a well known open source collaborative learning environment system in order to simulate and evaluate the applicability and appropriateness of the proposed system. The result shows that the proposed question-answering system may be used efficiently and expanded to accommodate further advanced capabilities. [ABSTRACT FROM AUTHOR]

There are many usages for the Web services in the World Wide Web. For creating new services we can compose other developed services in the way we want to use them. The large amount of Web services make composing of services a time consuming and impossible job. So to compose services some automated and semi-automated ways were developed. One of these ways is semantic Web services (SWS). In this paper we propose a method based on data mining techniques on Web services to find the best composition among possible compositions to improve quality and performance of Web service composition. Association rules, Classification and clustering are used as data mining approach to improve performance of the Web Service Composition [ABSTRACT FROM AUTHOR]