Scientia Iranica
Volume:18 Issue: 6, 2011

This paper studies a single product setting in which a firm can be sourced from multiple suppliers. One supplier has an unreliable capacity, while others are reliable but have lower product quality. The addressed context is disruptions due to sanctions, which cause failure in supply from unreliable sources. The important question which emerges here is, “how should companies use different strategies of single/dual/multiple sourcing to handle those potential disruptions?” In this paper, these strategies are addressed and compared, when the demand is sensitive to price and the level of supply risk. Dual sourcing provides the firm with the opportunity of rerouting (from a low quality supplier) after disruption. However, problems of monopoly rise after the disruption, and the buying firm lose bargaining power. Whereas, in triple sourcing, the setup cost could be higher, after the disruption, there would still be competition between two suppliers, and the price would not increase unreasonably. The main focus of the current work is on defining the share of each supplier, and finding suitable sourcing policies (single, dual or triple) to be applied to different probabilities of disruption. The proposed model is applied in the decision making process of a studied supply chain in the automotive industry.

Control charts are the best tools to monitor main process parameters, and the Multivariate Exponentially Weighted Moving Average, MEWMA, type of this tool is used when there are several correlated quality characteristics to be monitored simultaneously where detecting small deviations of the characteristics is desired. In this paper, the models of both the economic and the economic-statistical design problems of MEWMA control charts are solved by a Particle Swarm Optimization (PSO) approach. The comparison study between the economic and the economic-statistical designs shows better statistical performances of the economic-statistical design with negligible increase in cost. Furthermore, in order to demonstrate the application of the proposed methodology and to evaluate its performances, a comparative study is performed between Hooke and Jeeves [Hooke, R. and Jeeves, T.A. “Direct search solution of numerical and statistical problems”, Journal of the Association for Computing Machinery, 8, pp. 212–229 (1961)] method and the proposed method. The results show that the proposed PSO leads to better performances. At the end, some sensitivity analysis on the main parameters of the control chart and the cost parameters are presented.

This paper derives the optimal replenishment lot size and shipment policy for an Economic Production Quantity (EPQ) model with multiple deliveries and rework of random defective items. The classic EPQ model assumes a continuous inventory issuing policy for satisfying demand and perfect quality for all items produced. However, in a real life vendor–buyer integrated system, multi-shipment policy is practically used in lieu of continuous issuing policy and generation of defective items is inevitable. It is assumed that the imperfect quality items fall into two groups: the scrap and the rework-able items. Failure in repair exists, hence additional scrap items generated. The finished items can only be delivered to customers if the whole lot is quality assured at the end of rework. Mathematical modeling is used in this study and the long-run average production–inventory-delivery cost function is derived. Convexity of the cost function is proved by using the Hessian matrix equations. The closed-form optimal replenishment lot size and optimal number of shipments that minimize the long-run average costs for such an EPQ model are derived. Special case is examined, and a numerical example is provided to show its practical usage

Despite the potential benefits the Internet and other related technologies offer, current supply chain management techniques do not take full advantage of these benefits. E-business environments provide a facilitating infrastructure for solving the issues concerning the traditional supply chain, such as the scalability and flexibility for the efficient collaboration between partners. Both internal and external uncertainties to the supply chain can hinder collaboration between partners, and thus also hinder their ability to achieve their best possible performance. This research proposes using agent-based web services to better support collaboration within a supply chain. An advantage of agent-based web services is that they combine the strengths of both web services and multi-agents. Two different collaboration situations are used in order to demonstrate the flexibility of the system. Scalability is demonstrated when the supply chain faces changes in the partnership candidates. Simulation methods are used in order to validate the feasibility of this approach, and statistical tests reveal the robustness of the experimental results across diverse uncertainties.

In this paper, we develop the multi-product, multi-constraint, Single Period Problem (SPP) with uncertain demands, considering an incremental discount situation. Three new models are presented for multi-product, multi-constraint SPP in fuzzy, stochastic and rough environments. We consider constraints, such as service rate, restriction on order quantity and restrictions on warehouse space and budget. We also consider that the order quantity is a multiplier of predefined batch size. Furthermore, three kinds of solution algorithm, (1) harmony search, (2) hybrid intelligent based on harmony search and fuzzy simulation and (3) hybrid intelligent based on harmony search and rough simulation, are presented for the developed models to maximize expected profit. Finally, illustrative examples are presented to show the performance of the developed models and algorithms.

The maximal covering location problem (MCLP) is a challenging problem with numerous applications in practice. Previous publications in the area of MCLP proposed models and presented solution methodologies to solve this problem with up to 900 nodes. Due to the fact that in real-life applications, the number of nodes could be much higher, this paper presents a customized Genetic Algorithm (GA) to solve MCLP instances, with up to 2500 nodes. Results show that the proposed approach is capable of solving problems with a fair amount of exactness. In order to fine-tune the algorithm, Tukey’s Least Significant Difference (LSD) tests are employed on a set of test problems.

The hub location problem is used for many applications, including cargo delivery systems, airline systems, telecommunication network design and so on. Each area has its own characteristics in hub location. In this paper, we study the hub covering problem with different coverage type over complete hub networks. Furthermore, hub set and maximal covering are expressed with single and multiple allocation strategies. First, a quadratic formulation is proposed for single allocation hub set covering problem. Then a linearization idea is considered for it and applied for multiple allocation hub set, single and multiple allocation hub maximal covering problems. The aim of these models is to find the location of hubs and allocate non-hub nodes to the located hub nodes subject to the travel time (cost or distance) between two nodes in origin-destination doesn’t exceed a given bound. The formulations with this coverage constraint have not been remarked in the literature. Two heuristic procedures are proposed to handle these models in an agreeable solution quality and computational time. The computational experience of Turkish dataset was presented for better illustration of proposed model. And a special application on Iranian hub airports location is discussed.

Most organizations still experience a lack of Business Intelligence (BI) in their decision-making processes when implementing enterprise systems, such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Supply Chain Management (SCM). Consequently, a model and techniques to evaluate and assess the intelligence-level of enterprise systems can improve decision support. This paper proposes an expert tool to evaluate the BI competencies of enterprise systems, and combines a comprehensive review of recent literature with statistical methods for factor analysis. A statistical analysis has identified six factors for the evaluation model: “Analytical and Intelligent Decision-support”, “Providing Related Experimentation and Integration with Environmental Information”, “Optimization and Recommended Model”, “Reasoning”, “Enhanced Decision-making Tools”, and finally, “Stakeholder Satisfaction”. Utilizing the extracted loads of each unique criterion, the intelligence of the work systems can be measured and depicted on six dashboards, based on corresponding factors, actualizing an expert tool that can diagnose the intelligence level of enterprise systems. Enterprises can use this approach to evaluate, select, and buy software and systems that provide better decision support for their organizational environment, enabling them to achieve competitive advantage.

This paper presents a computer-aided quality system for visual blemish inspection of epoxy packages of Light-Emitting Diodes (LEDs). The developed system proposes a new global approach for the automated detection of small visual blemishes on epoxy packages of non-diffused LEDs. By means of a Block Discrete Cosine Transform (BDCT) based image restoration scheme, the proposed method is independent of textural features, and thus not confined by the limitations of feature extraction based methods. We apply grey relational analysis to the frequency components in the BDCT domain, and significantly attenuate the large-magnitude frequency components that represent the background texture of the surface, based on their corresponding grey relational grades. Then, by reconstructing the declined frequency components, this study eliminates not only random texture, but also uneven illumination patterns, and retains blemishes in the restored image. Experimental results show that the proposed approach achieves a higher than 97.5% probability of correctly discriminating tiny blemishes from normal regions and a rather lower 0.03% probability of erroneously detecting normal regions as blemishes on the surfaces of LED epoxy packages. Compared with other current methods, this approach has the advantage of higher detection rates lower false alarm rates and shorter average processing time.

An artificial Neural Network (ANN) is an efficient approach applied to solving a variety of problems. The main problem in using ANN is parameter tuning, because there is no definite and explicit method to select optimal parameters for the ANN parameters. In this study, three artificial neural network performance measuring criteria and also three important factors which affect the selected criteria have been studied. Moreover, central composite design has been used to design experiments and also analyze network behavior according to identified parameters, by using the overall desirability function. Then the Genetic Algorithm has been proposed to find optimal parameter status. For this purpose, the proposed method has been illustrated by the numerical example of a well known mathematical function. The results show that the designed ANN, according to the proposed procedure, has a better performance than other networks by random selected parameters and also parameters which are selected by the Taguchi method. In general, the proposed approach can be used for tuning neural network parameters in solving other problems.