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The present study aims at designing a cold multi-cycle supply chain based on a multi cross-dock system taking into account uncertainty. In the first step, we identified the factors and variables of the model. In the second, by selecting the study period through designing data collection forms and using the documents reviewing methodologies, the raw data required to measure the final indicators were collected and processed in the project model. Then, they were analyzed considering the research topic and using the techniques of genetic algorithm and particle swarm optimization. The primary objective function is minimizing the cost of transportation and warehousing throughout the supply chain, the second minimizing the total operation time and the number of vehicles within the supply chain, and the third maximizing the product freshness time. Also meta-heuristic optimization methods (strongly adjustable) were adopted to deal with the travel time of suburban vehicles. We also provide an example of the performance of optimization models for a small-sized sample. The computational results showed that longer travel time and further distance do not necessarily increase costs. In fact, it is possible to distribute the products with the right number of trucks at an optimal cost at the right time.

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In this article, the modeling and solution of a cryptocurrency capital portfolio optimization problem has been discussed. The presented model, which is based on Markowitz's mean-variance method, aims to maximize the non-deterministic internal return and minimize the cryptocurrency investment risk. A combined PSO and SCA algorithm was used to optimize this two-objective model. The results of the investigation of 40 investment portfolios in a probable state showed that with the increase in the internal rate of return, the investment risk increases. So in the optimistic state, there is the highest internal rate of return and in the pessimistic state, there is the lowest investment risk. Investigations of the investment portfolio in the probable state also showed that more than 80% of the investment was made to optimize the objective functions in 5 cryptocurrencies BTC, ETH, USTD, ADA, and XRP. So in the secondary analysis, it was observed that in the case of investing in the top 5 cryptocurrencies, the average internal rate of return increased by 9.92%, and the average investment risk decreased by 0.1%.
 

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The current research provides a mixed integer nonlinear mathematical programming model for a company that operates with several stores and multiple products, in which demand for each customer is characterized using fuzzy logic by triangular numbers, while the replenishment policy of each store for any product is the popular economic order quantity (EOQ) model under backorder. The throughput, dispatch, and budget constraints are considered in the proposed EOQ model. The objective is to integrate a vendor selection problem and EOQ policy, in which a multi-sourcing strategy is considered. In the proposed strategy, the ordered value of each store for any product can be split between one or more vendors. As such, a set of selected vendors can replenish each store for each product. This research aims to answer the following question as follows: (i) which vendors are chosen; (ii) which store is allocated to the selected vendors for each product; (iii) what is the optimal value for the inventory decisions.
The aim is to reduce the total cost of the company, including costs related to the vendor selection decisions along with the inventory decisions. To solve the mathematical model, a novel and practical genetic algorithm (GA) is developed then the response surface methodology (RSM) is utilized to tune its parameters. At the end, some numerical instances under different categories are evaluated to explain the applicability of the proposed approach.