A fuzzy decision support system for evaluating Lean-Green and company-specific production system.

Lean production is regarded as systematic solution for identifying and eliminating waste through continuous improvement of manufacturing processes to meet customers' requirements. Due to rising environmental concerns and the significant environmental impact of companies operations, issues such as environmental management and green production are on board agendas. In essence, green production seeks to reduce the environmental impacts of business processes. In order to examine green production, an original equipment manufacturer in the supply chain of tractor manufacturing industry is selected for this work to enable a focus on lean production aspects and its specific requirements to evaluate a company-specific production system (XPS). The results suggest that the company utilizes six lean-green production criteria including suitable and green packaging, quality improvement, waste reduction, environmental pollution reduction, transport management and additional transportation reduction, and cost reduction. Furthermore, continuous improvement and integrated auditing are selected as two critical and fundamental elements with the highest ranks, regarding the relationships between the XPS elements and lean-green production criteria.

Evaluation of the effective factors of unloading and loading goods in ports and the impact on the environment using the MCDM method.

Today, the impact of transportation on sustainable development is undeniable. Maritime transport, as one of the important pillars of this industry and due to its advantages such as low cost and large volume of freight, plays a key role in the development of foreign trade. This study uses TOPSIS and DEMATLE methods to evaluate the factors affecting the unloading and loading of goods on the port's environment. The results of TOPSIS method on the main criteria showed that the atmospheric factors' criterion had the highest weight and the port criterion (includes activities that occur in the port) had the lowest weight. In the sub-criteria section, the suitable visibility of the crane operator had the highest rank and the sub-criterion of storage capacity and its (storage) area obtained the lowest rank. Also, the results of DEMATEL method on the cause-and-effect relationship of sub-criteria of unloading and loading of goods showed that coastal transport equipment, unloading and loading equipment, and safety required for unloading goods are influenced higher than other sub-criteria and also the impact of safety required for unloading goods, proper visibility of the crane operator, and the ability of unloading equipment is greater than other sub-criteria. The results of cause-and-effect relationship for sub-criteria of the port environment showed that the impact of sewage, sanitary effluents from the port and ships, and hazardous waste is higher than other sub-criteria and also the impact of petroleum hydrocarbons, sediments, and toxic substances is higher than other sub-criteria.

An artificial neural network based mathematical model for a stochastic health care facility location problem.

This research is conducted to investigate the problem of locating the trauma centers and helicopters' station in order to optimize the trauma care system. The stochastic characteristics of the system, such as stochastic transferring time of the patients, stochastic demand and stochastic servicing time of the patients in trauma centers are taken into account. The problem is first modeled as a stochastic mixed-integer linear mathematical model. In the proposed model, minimizing the total cost, minimizing the transferring time, and minimizing the waiting time inside the trauma center are considered as the three separate objectives. The third objective cannot be expressed by an analytical expression because of the complexity inside a trauma center. Therefore, an artificial neural network (ANN) is first trained by a simulation model and then is utilized to estimate the third objective function. A hybrid multi-objective algorithm is developed based on a non-dominated sorting water flow algorithm in order to search the solution space. Different numerical examples are applied to study the performance of the proposed method. The computational results show that the combination of simulation, ANN, and optimization technique provides an effective means for the highly complex optimization problems.