The environmental changes caused by industrial activities have spurred a significant interest in designing supply chain networks by considering environmental issues such as CO2 emission. The pivotal role of taking uncertainty and risk into account in closed-loop supply chain networks has induced numerous researchers and practitioners to develop appropriate decision making tools to cope with these issues in such networks. To design a supply chain regarding environmental impacts under uncertainty of the input data and to cope with the operational risks, this paper proposes a multi objective possibilistic optimization model. The proposed model minimizes traditional costs such as cost of products shipment, purchasing machines and so on, as well as minimizing the environmental impact, and as a results strikes a balance between the two objective functions. Furthermore, in order to solve the proposed multi objective fuzzy mathematical programming model, an interactive fuzzy solution approach is applied. Numerical experiments are used to prove the applicability and feasibility of the developed possibilistic programming model and the usefulness of the applied hybrid solution approach.
Dynamic pricing is a kind of pricing strategy in which the price of products varies based on present demand value. So far, several research works have been reported for using neural network for pricing, such as predicting demand and modeling the customer's choices. However, less work has been performed on using them for optimizing pricing policies. In this project, we try to explain the way of combining neural network and evolutionary algorithms to optimize pricing policies. We create a neural network on the basis of demand model and benefit from evolutionary algorithms for optimizing the resulted model. This has got two privileges: First, necessary flexibilities are created by using neural network to model different demand scenarios that is occurred with different products and services, and second, using evolutionary algorithms provides us with the ability of solving complicated models. Wavelet neural network has been used and the resulted pricing policy has been compared with other demand models that are widely used. The results show that the suggested model match up well under different scenarios and presents a better pricing policy than other suggested models.
In this study, an integrated approach is presented for analyzing the impact of resilience engineering and ergonomics factors in aerospace supply chain using data envelopment analysis (DEA). The proposed approach selects the preferred supplier by considering traditional supply chain factors as well as resilience engineering and ergonomics factors. Also, the relevant performance efficiency of each decision making unit is calculated. The case study of this paper is the supply chain of real commercial airlines. Thus, the aerospace standards as well as resilience and ergonomics factors are considered to be modeled by the mathematical programming approach. 22 suppliers are evaluated by analyzing inputs and outputs through data envelopment analysis, and each supplier is considered as a decision making unit (DMU). In this study, the most effective factors are identified as “reliability”, “Human resource management”, “supplier’s delay” and “availability”. Also, “lead time” shows the highest potential for improvement. This study helps decision makers identify the weaknesses of their supply chain management to establish a performance improvement plan in aerospace industry.