multi objective model

Wastewater, especially industrial wastewater, serves as a reservoir for pathogenic microorganisms capable of causing contamination and infection. If wastewater disposal management is not conducted with proper health and hygiene considerations, these microorganisms can spread through air, water, or via carriers to others. High competition among manufacturing industries, coupled with environmental pressures and internal requirements to reduce costs and delivery times, improve quality, and enhance suppliers' ability to produce diverse and new products in shorter timeframes, as well as the potential entry of foreign competitors into the industry, alongside environmental and health issues, necessitate better performance compared to other competitors in this industry, which provides a suitable context for this research. In this research, an intelligent model for planning and investing in urban surface wastewater collection infrastructure and its impact on pollutant dispersion was developed. After collecting data and modeling, and considering the problem's assumptions, it can determine the reduction of surface wastewater collection costs and the reduction of pollutant emissions to the environment based on the optimal routing of wastewater collection vehicles. Moreover, due to the NP-Hard nature of the problem, the multi-objective grey wolf optimization algorithm was used to minimize both objective functions under different scenarios and conditions. The results showed that the proposed model is capable of determining the problem in various dimensions from the perspective of increasing the number of wastewater collection vehicles, the number of candidate wastewater collection locations, the increase in wastewater accumulation at locations, the increase in the number of vehicles in the network, and the increase in the distance of candidate wastewater locations in a desirable manner to ensure that all accumulated wastewater at the candidate locations is collected by the network vehicles

Integration of supply chain decisions reduces costs and delivery time to customers, but uncertainty in supply chain offending parameters, including demand, disrupts this integration. This demand uncertainty has led to an increase in interest in the use of probabilistic planning and simulation models in supply chain modeling. Therefore, the overall purpose of this study was to model and develop a multi-level, multi-product, multi-period supply chain network model with conflicting objectives such as cost minimization, delivery time minimization, and maximizing system-wide reliability. The supply chain network under study had 4 levels or subsystems of suppliers, manufacturers, distributors and retailers. In this study, it was assumed that demand is random and follows a probabilistic distribution function. Therefore, the simulation technique was used to estimate costs including shipping costs and lost sales costs and other costs. After developing a multi-objective model, for the studied problem, scenario building was done based on different perspectives on inventory levels (minimum inventory, maximum inventory and average inventory level) and based on each scenario, values related to the objectives were estimated. In the end, based on the Parthian answers obtained for each case of the model, based on Vickor decision-making method, the final ranking of the answers and selection of the best case from the proposed model was performed. The results showed that the second scenario, ie considering the average inventory level, was selected as the best solution for the described model

Four - level supply chain is a chain of different products because of continuous and significant changes in the quality of food products throughout the supply chain. The aim of this paper is designing a fuzzy multi - objective model for optimization of facility location in a perishable products of supply chain using the combination of two heuristic Banders and Lagrange algorithms. The present study is applicable in terms of purpose and data collection method. in this study, the mathematical model for the problem of locating facility in a four - level supply chain for perishable products is presented at the same time the supply chain costs, order delivery time, emissions and customer satisfaction level. to evaluate the validity of the research, the mathematical model was studied in Kaleh food industries and the research problem was presented in the form of a nonlinear model of complex integer programming. the results of the proposed algorithm in the case study of the solution and the results of the algorithm performance based on standard indices and finally computational results show the performance of the algorithm for a wide range of different.

Today, green supply chain managers in leading companies strive to offer green logistics and improve their environmental performance throughout the supply chain as a strategic weapon to gain a sustainable competitive advantage by creating profitability and satisfaction across the supply chain. Therefore, considering the purpose of the research, which is to design a multi-objective model of closed-loop supply chain networks in the automotive industry according to energy efficiency and time efficiency plans, we try to model the closed-loop supply chain in the automotive industry.

In this study, we use the MOPSO method to facilitate its implementation and its ability to provide good convergence, as well as to maintain a proper balance between exploitation and exploration, as well as the NSGA II genetic algorithm.

In the study of the findings of the proposed algorithms, it found that the average error resulting from these algorithms is less than 0.04. The results also show that the proposed algorithms have the necessary efficiency in solving these problems.

We note the significant findings of our model as follows: (1) An efficient closed-loop network that shows the economic benefits of considering the value of time due to the recycling of worn-out products. (2) Has the ability to demonstrate the capacity to achieve maximum benefits in terms of cost value as well as environmental prospects.

Pharmaceutical Industry in Iran has difficulties such as inappropriate distribution, expired drugs and late drug delivery to patients. To overcome these issues and reach a sustainable development, the environmental and social aspects must be considered beside the economic aspects. In this paper, we address the Pharmaceutical distribution network and design a novel model that helps the distributing companies to decide strategic (determining the main and local distribution centers) and tactical decision (determining the flow) according to the three aspects of sustainability. The solution through the evolutionary algorithms presents the best selection of local distribution centers and the optimum amount of flow of medicine through the network with respect to three aspects of sustainability.
  In order to compete in the universal market, pharmaceutical companies must amend their competency. This coemption market needs to select the certain level of commitment for sustainability aspects to reach the sustainable supply chains. For this purpose, this study considers three aspects of sustainability (i.e. economical, environmental, and social pillars) to design the pharmaceutical distribution network. So, the model has three objective functions. The first function is to minimize the cost of transportation through the network, the second is to minimize the CO2 emissions through the network and the third is to maximize the job creation through the network.
Carter and Rogers (2008) used the concept of ‘true sustainability’. They suggested that the consideration of environmental and social issues should be “coupled with economic objectives” and assist the company’s strategic long-term planning. Mota et al. (2014) considered this research question: “How can sustainability be integrated in to supply chains' design and planning decisions?” Several methods and frameworks have been proposed to evaluate environmental effects.
This model is a multi-objective mixed integer linear programming (MILP) and is NP-hard. We have used the evolutionary algorithms (NSGA-II, MOPSO) to solve the problem and achieve the Pareto frontier. The Taguchi design experimental design has been used in order to tune the parameters of the algorithm. For comparing two evolutionary algorithms’ performance indicators of spacing (S), mean ideal distance (MID), Time and number of solutions (NOS) have been calculated.
As a real case study, the proposed model is used in Darupakhsh. This company is one of the biggest pharmaceutical companies in Iran. The model has been customized with Darupakhsh’s features for selected medicine. This company has a main distribution center and twenty local distribution centers. Twenty important customers have been assumed in this research. Solving the model with the evolutionary algorithms of NSGA-II and MOPSO and comparing to traditional method of weighted sum, highlighted the superior of MOPSO in economical and environmental aspects. The weighted sum method had a better answer in the social aspect. Overall, the power of evolutionary algorithms have been proved because of the Pareto frontier present to the researcher.
This research focused on designing the sustainable pharmaceutical distribution network. The proposed model is a multi-objective mixed integer linear programming (MILP). This model was tested in Darupakhsh Company. In this case, MOPSO had better performance than NSGA-II. The solution presents the best selection of local distribution centers and the optimum amount of flow of medicine through the network with respect to three aspects of sustainability.For further research, studying the uncertain parameters in this model and a suitable approach to deal with uncertainty could be proposed.
References
Ahmadi, A., Mousazadeh, M., Torabi, S. A., & Pishvaee, M. S. (2018). “OR Applications in Pharmaceutical Supply Chain Management”. International Series in Operations Research & Management Science. 262,461–491.
Mota,B., Gomes, Ke., Carvalho, A., & Barbosa-Povoa, A. (2014). “Towards supply chain sustainability: economic, environmental and social design and planning”. Journal of Cleaner Production, 107, 14–27.
Mousazadeh, M., Torabi, S.A., & Zahiri, B. (2015). “A robust Possibilistic Programming approach for pharmaceutical supply chain network design”, Computers and Chemical Engineering, 82, 115- 128.

The aim of this paper is to provide a multi-objective fuzzy model to optimize shareholders portfolio by genetic approaches. To achieve this goal , a portfolio optimization model is proposed with eleven objective function to maximize ROA, ROE, net profit margin, operating profit margin, earnings per share (EPS), the price-to-earnings ratio per share (P/E), rate of income growth, net profit growth rate, earnings per share growth rate and minimize business risk, financial risk and six restriction. In this study, fuzzy AHP approach was used to determine the fuzzy weight of goals. Jimenez method was used to consider the fuzzy values in the research model according to the fuzzy weights obtained by AHP methods output. Finally, a genetic algorithm was used to solve the proposed model. Developed toolbox of Mat lab 2016 was used for this purpose. The proposed model in this research was implemented in 30 companies listed in the Tehran Stock Exchange and optimal portfolio was determined for different amounts of alpha in Jimenez method.

Designing an appropriate industrial clusters model as a general method in the regional economy is an important problem for economic and industrial development planners. Paying attention to the objectives of sustainable development and the green economy, in addition to the traditional objectives of economic growth, is the need of countries today. In order to meet the goals of sustainable development, the multi-objective mathematical programming and dynamic model of industrial clusters was designed and tested to optimize four profit, employment, cost of transportation of materials and environmental appraisal score of the cluster. In designing parts of the model in the assignment of firms and the establishment of relations between and within cooperation networks, the concepts and research literature of cellular manufacturing were used because of proximity of the concepts and extent of studies in this field. The problem solving results by decomposition methods and weighting functions objectives with experts' opinion and analysis of experiments confirmed that the optimal results of experiments have been presented 99% improvement in minimizing the metric function of the model.

Designing an appropriate industrial clusters model as a general method in the regional economy is an important problem for economic and industrial development planners. Paying attention to the objectives of sustainable development and the green economy, in addition to the traditional objectives of economic growth, is the need of countries today. In order to meet the goals of sustainable development, the multi-objective mathematical programming and dynamic model of industrial clusters was designed and tested to optimize four profit, employment, cost of transportation of materials and environmental appraisal score of the cluster. In designing parts of the model in the assignment of firms and the establishment of relations between and within cooperation networks, the concepts and research literature of cellular manufacturing were used because of proximity of the concepts and extent of studies in this field. The problem solving results by decomposition methods and weighting functions objectives with experts' opinion and analysis of experiments confirmed that the optimal results of experiments have been presented 99% improvement in minimizing the metric function of the model.