Journal of Industrial and Systems Engineering
Volume:8 Issue: 1, Winter 2015

For many years, purchasing and supplier performance evaluation have been discussed in both academic and industrial circles to improve buyer-supplier relationship. In this study, a novel model is presented to evaluate supplier performance according to different purchasing classes. In the proposed method, clustering analysis is applied to develop purchasing portfolio model using available data in the organizational Information System. This method helps purchasing managers and analyzers to reduce model development time and to classify numerous purchasing items in a portfolio matrix. In this paper, Neural Networks are used to develop a purchasing classification model capable of classifying purchasing items according to different features. Moreover, a new supplier evaluation model based on different purchasing classes is developed using Neural Networks. The proposed hybrid method to develop purchasing portfolio and supplier evaluation is applicable in large scale manufacturing organizations which need to manage numerous purchasing items. The proposed model is implemented in an automaker purchasing department with a relatively vast supply chain.

Nowadays, industrialization exposes the human and environment resources to serious dangers. The importance of these resources caused the HSE (health, safety and environment) to have a significant contribution in industries’ evaluation, especially in construction industry. While evaluating the project’s success from an HSE point of view, it is not enough to rely solely on the outputs without considering the impact of external factors affecting them. On the other hand, the variety of factors affecting HSE and their different kinds of interactions, forces us to use another aggregation operators rather than linear ones. Choquet integral (CI) is a well-known powerful aggregation operator to be used in such cases. There are different methods to define the coefficients of CI. One of the most recent and prominent methods is “the most representative capacity definition method”. This paper proposes a modification to this method by improving its entropy and consequently the reliability in, as named, non-reference projects evaluation. The modified algorithm is used in evaluating the impact of external factors on HSE performance of power plant construction projects.The results show the prominence of modified algorithm’s entropy compared to the original algorithm. Ultimately the external factors integrated score, which resembles the suitability of project’s environment, is compared with the score defined considering output results. According to results, in some projects there is a deep gap between score of HSE output result and aggregated score of external factors. The gap of two scores potentially figures the internal organizational factors performance.

Membrane technologies for the separation of mixtures have gained an extensive worldwide attraction in the modern industrialized world. They have many industrial and medical applications such as water desalination, wastewater reclamation, gas separation, food and medical applications. However, even though all these applications have their own efficiency and market, the selection of priority applications is very challenging for most developing countries. On the other hand, selecting the optimal priority applications among many alternatives is a multi-criteria decision-making (MCDM) problem. This paper develops an evaluation model based on the AHP and TOPSIS methods for evaluating and ranking membrane applications effectively. The AHP is used to analyze the structure of the selection problem and to determine the weights of the evaluating criteria. It is also used for evaluating the decision-making team members to determine the relative importance of each one of them. A modified technique is proposed to improve the consistency of judgment matrices; then Individual judgments are aggregated by using the weighted geometric mean to obtain the weights of criteria. The modified technique increases the accuracy of decision-making process and saves time to obtain consistent judgment matrices. Finally, the TOPSIS method is employed to calculate the final ranking of the membrane applications. For evaluating the performance and reliability of the proposed model, it is applied in a real case in IRAN.

Game theory is an efficient tool to represent and conceptualize the problems concerning conflict and competition. In recent years and especially for durable products, competition between domestic and foreign brands for gaining market share has received a considerable attention. This paper study electronic commerce concepts by differential game theory and introduce a novel and comprehensive model for analyzing dynamic durable goods supply chains. Manufacturer of domestic brand as leader of the game, announces his wholesale price to his retailer. Then the exclusive retailers of domestic and foreign brands play a Nash differential game in choosing their optimal retail prices and advertising efforts over time. Moreover, online pricing and advertising in a direct sales channel constitute other control variables of the manufacturer. Feedback equilibrium policies for the manufacturer and the retailers are obtained by assuming a linear demand function. Finally a case study and sensitivity analysis are carried out to provide numerical results and managerial insights. We found that there is a reverse relationship between price sensitivity of demand and optimal levels of price and advertising efforts. Increase in advertising effectiveness parameter leads to enhancement of advertising efforts in relative marketing channel, however, does not have a significant effect on pricing decisions.

This paper presents the economic production quantity (EPQ) models for the imperfect quality items produced with/without the presence of shortage condition. The models are presented in a two-level supply chain composed of a single manufacturer and a single buyer to investigate the performance of vendor-managed inventory (VMI) policy. The total costs are minimized to obtain the optimal production lot size and the allowable backorder level before and after applying VMI policy. Numerical examples and sensitivity analysis based on certain parameters are performed to show the capability of the proposed supply chain model enhanced with VMI policy.

Pricing is one of the major aspects of decision making in supply chain. In the previous works mostly a centralized environment is considered indicating the retailers cannot independently apply their decisions on the pricing strategy. Although in a two-echelon decentralized environment it may be possible that supply chain contributors have encountered with different market power situations which provide that some of them try to impose their interests in pricing and/or volume of the products. In such situations the leader-follower Stackelberg game or more specifically bi-level programming seems to be the best approach to overcome the problem. Furthermore, in this study we consider the impacts of disruption risk caused by foreign exchange uncertainty on pricing decisions in a multi-product two-echelon supply chain. Also it is assumed that the market is partitioned to domestic and international retailers with segmented market for each retailer. The purpose of this paper is to introduce decisions policy on the pricing such that the utility of both manufacturer and retailers is met. Since the proposed bi-level model is NP-hard, a simulated annealing method combining with Tabu search is proposed to solve the model. A numerical example is presented to investigate the effect of foreign exchange variation on the decision variables through different scenarios. The results from numerical example indicate that the international retailers are indifferent to the manufacture undergoes changes where the domestic retailers react to changes, dramatically.