two-objective optimization

The meat supply chain is crucial in meeting household nutritional needs, public health, and food security. The meat supply chain's inherent dynamic and uncertain nature, along with existing risks and disruptions, has made optimizing the supply chain for resilience unavoidable. For various reasons, suppliers may experience partial disruptions and be unable to service their customers promptly. In this paper, we propose an integrated design of a fresh meat supply chain network that considers resilience strategies such as capacity expansion through contracts with reliable suppliers under demand uncertainty using a robust approach. The objectives of this research are to minimize total transportation costs and fixed costs and maximize the service level of the supply chain. Key decisions in the proposed supply chain network include selecting farms from available farms, allocating locations to slaughterhouses, selecting retailers for selling meat products and processed meat products, determining the flow of selected materials between facilities at different levels of the proposed supply chain network, identifying meat supply chain network risks, and determining the level of supply chain service. The problem is modeled based on a bi-objective mixed-integer programming approach. Finally, the augmented ε-constraint method is used to solve the problem, and the performance and efficiency of the model are evaluated and analyzed using numerical examples and compared to the base model in the research literature.

In CNC machines, changes in machining conditions such as speed and feed rate will also change the operating time. Changes in these conditions also result in changes in the production cycle time and production costs. Tool life is also sensitive to these changes. Appropriate machining time is generally determined by assuming a certain lifetime for CNC machine tools to minimize production costs. However, minimizing costs usually results in increased machining time and lower output rates.

In this research, the optimal machining time is determined using a bi-objective model including minimizing the cost and total production time of a robotic cell with a CNC machine and a material handling robot. It has assumed that identical productions are produced in this robotic cell. Using the Epsilon constraint method, the proposed model is coded in GAMS software and its results are reported.

In this research, the lifespan of the CNC machine tools can be considered as a determined or probable value. The results showed that decreasing the operation time at different speeds does not necessarily impose the same cost on the system. Therefore, it is necessary to be more careful in choosing the appropriate machining time for different tools and parts. Paying attention to the rate of suddenly tool breakdowns is also important in choosing the appropriate time for machining. Using a set of non-dominated solutions, it is possible to determine the appropriate machining time in different parts to achieve a suitable level of problem objectives.

 In this research, for the first time, the failure rate of the tool as one of the cost factors in the robotic cell has been added to the cost function of a production cycle and its effect on determining the appropriate machining time has been investigated.

There are different factors, including economic development and population explosion, causing the rapid increase in municipal solid waste generation rates. In today's municipal solid waste management, a considerable amount of financial and human resources is spent on collection and transportation, and little attention has been paid to production, storage in place, recycling, and disposal. As proper collection and transportation are important issues in municipal solid waste system, creation of energy from waste as well as recycling and disposal are also significant, attention must be paid to the optimal usage of dirty gold (i.e. municipal solid wastes) for pollution reduction. Moreover, determining suitable locations for transfer stations and waste disposal facilities is a very complex problem, needing a comprehensive review and evaluation process accounting for the requirements of municipal, environmental, and governmental regulations. In this context, it is vital to develop an effective and efficient approach for designing and planning a waste management system. In order to realize this goal, a three-level network consisting of customers, transfer stations, and disposal facilities (recycling, composting, wasteincinerator, and landfill centers) was considered in the present paper. A bi-objective mixed-integer linear mathematical programming model was presented for location and allocation of municipal solid waste. Model goals were minimizing waste disposal cost and greenhouse gas emission. Other points taken into consideration were a variety of products derived from recycling waste, transferring them to sales areas, and the limitations of number and capacity for transfer stations and waste disposal facilities. Districts 1 to 8 and District 22 of Tehran were selected as a case study for measuring model performance. The results of the case study and sensitivity analysis demonstrated that the optimal solution requires the location of disposal facilities in different districts of Tehran, although these facilities incur considerable costs.

Over product oriented course to industry one, firms' competitiveness is being complicated to gain more portion of market that lead to dynamic and more variation environment. In this situation, customers find more authority to select their favorite products and services. Response to market fluctuation to supply customers' needs is considered as an important tool to firms' promotion. Needs to reduce costs and improve organization process cause to pay more attention to supply chain concept. The main goal of each supply chain satisfies customers' needs with the lowest cost and highest efficiency.Structurally, supply chain includes retailor, wholesaler, distributor, manufacturer, and supplier. An efficient logistic network should be designed so that response to uncertainty. Since time and cost are the most important factors in reverse logistic design, a fuzzy two objectives optimization model is proposed in this study. First, a fuzzy mathematical programming model presented. The aim of this model is determining delivery goods amount among centers so that total cost and total delay time are minimized. In this research is used fuzzy approach to cover uncertainty in reverse logistic network. A numerical example has been produced and solved by GAMS. In order to solve the problem in large scale a cuckoo optimization meta heuristic algorithm is developed. The results indicate that the total amount sent to the manufacturer of the values obtained from the exact solution and the objective function value decreases with increasing number of iterations which this indicates proper / valid operation is the proposed algorithm.