y. zare mehrjerdi

The orders received from customers in retail companies must be picked in the warehouse before delivery to customers. Order picking is the most costly and time-consuming process in a warehouse and customer orders are usually batched and picked in common tours to reduce travel distance and picking time. On the other hand, the widespread use of the internet and e-commerce has significantly increased the number and decreased the size of orders issued by customers and as a result, the order batching and picking operations in the warehouse has become more complicated. Besides, adopting appropriate policies to distribute orders among customers by taking into account issues such as the time and cost of distributing orders and due dates, is of great importance. Moreover, considering the operations of order picking and distribution in an integrated manner can significantly reduce costs and increase the level of service provided to customers. In this research, the order batching and distribution operations are considered integratedly and a mathematical model is proposed to address the integrated problem by considering minimization of total tardiness as objective function. respectively. For model validation purposes, an iterated local search metaheuristic approach is proposed to solve the proposed model. By using the generated data, it has been shown that the metaheuristic algorithm is able to obtain quality solutions for the problem. Due to the fact that an optimization solver is unable to optimize a small size problem in a realistic time, an upper bound was defined for the purpose of comparing the algorithm results with that. Our comparison of results indicates that for relatively larger sized sample problem the quality of obtained solutions by meta-heurietsic algorithm, in comparion with upper bound, having suitable quality as expected.

Restructuring and deregulation are one of the most significant developments in the world electricity market. In this market, Generation Companies (GenCos) in an oligopolistic game with incomplete information participate in a sealed auction and offer their bids in a certain period according to the market demand. Choosing the best bid to maximize profits and minimize risks in dynamic competition with other players is one of the most important issues for GenCos. The dynamic nature of this problem can help GenCos make the best decision based on learning from the past. Using reinforcement learning and considering risk aversity of the GenCos, this paper provides an agent-based simulation of the bidding behavior of Iran's electricity market. In this simulation, the GenCos optimize their bids using a learning process based on previous bids. Although a few studies have been conducted on the modeling of risk-averse behavior of GenCos under learning conditions, risk-averse analysis based on a history of profits and losses, or sever losses, has not been focused. Thus, in this paper, the learning behavior of the GenCos is modeled by the Q-learning reinforcement learning algorithm and their risk aversion behavior is modeled by the conditional value at risk measure and risk of missed opportunities in terms of the number of auction failures (missed auction opportunities). To validate the functionality of the proposed approach, it was applied to the real data of the electricity market of Yazd province, including five GenCos with the total nominal power of 2550 MW. The results were compared for different learning conditions, risk behaviors of companies, and pay as bid and uniform pricing. The results demonstrate that learning all GenCos leads to increased competition and promoted social welfare. Also, the level of risk aversion of GenCos and the type of clearing mechanism have a direct effect on the GenCos profitability and social welfare. The results can help power plants determine the bidding strategy in competitive conditions by considering their risk level. Likewise, these results assist regulators in designing market rules in line with the actual behavior of GenCos.

Vendor management inventory, as one of the inventory management methods, reduces the cost of inventory in the chain, quick response to customers, increased service level, customer satisfaction, and improves collaboration between the members of supply chain. In this study, a single-product bi-level supply chain model with one manufacturer and several retailers under a vendor management inventory system to select retailers under competitive conditions is investigated. In this study, the retailer selection problem formulated as a Stackelberg game model with consideration of manufacturing as a leader and retailers as followers to help the manufacturer and optimally select his retailers to form a VMI system. The manufacturer delivers the products to the selected retailer at the same time. Also, demand for the product in the retailers market is the decreasing function of price. Due to the prevailing policy and the agreement between the manufacturer and the retailer, the manufacturer is also responsible for managing the retailer's inventory. Therefore, the manufacturer will bear all the costs of maintaining the inventory, whether with himself or the retailer. In return, to accept the responsibility of inventory management by the manufacturer, the retailer pays the manufacturer a fee per unit as inventory costs. The objective of the model is to maximize manufacturing profits at the first level and maximize profits of any retailer at the second level. Model decisions are finding the optimal quantities of wholesale price decisions, product's replenishment cycle time, backorder, retailer selection, and determining whether or not to set up a production line for the manufacturer and retail price quantities for the retailer. In order to validate the proposed model, sensitivity analysis was performed on some parameters. As demonstrated by our numerical studies, the optimal retailer selection can increase the manufacturer's profit by 91% and the selected retailer's profits significantly compared to the non-selection strategy.

Total factor productivity refers to the effectiveness of output generated by the combined effects of different inputs. Compared to the traditional methods of calculating single-factor productivity, total factor productivity has a full attention to inputs and better reflects the overall effectiveness of an economic system. Due to the importance of total factor productivity, it is essential to know about its future and changes. Increasing or decreasing the efficiency of a country is one of the important and effective factors in changing economic conditions. Therefore, measuring productivity can be used to monitor changes in living standards, effectiveness, or competitiveness of the economy.The best indicator that indicates total productivity, is total factor productivity (TFP). Considering the factors affecting the total factor productivity based on Fernands(2014) and the experts' opinions and the multivariate linear regression of the indicators by SPSS software, the model is developed. Because of above reasons, in this research, a system dynamics model has been developed to forecast the results of the actions and policies applied to total factor productivity with an accuracy of 93.2% for Iran. Data of the factors affecting the productivity are extracted from the website of Iran's Statistics Center, the World Bank and Inter development Bank of America for the years 2005 to 2019. Because Vensim provides an integrated framework for conceptualizing, constructing, simulating, analyzing, optimizing, and expanding models for complex dynamic systems, it has been used for its great speed and effectiveness. Independent variables affecting the model include:1) Net rate of increase of population 2) Human capital Index and 3) Physical Capital Stock that affect other parameters. Based on developed model, it is clear that the improvement of the total factor productivity in the country needs change in the three above variables simultaneously or individually or subgroups of them. But we should be careful about their amoun.

Hospitals are one of the most important centers of medical service. At the time of the occurrence of natural and abnormal disasters, the demand for these centers may increase and some of these hospitals will be damaged and lose their functionality. On the other hand, in order to protect the lives of the injured, the distance and time of their transfer to hospitals is of particular importance. The proposed problem addressed simultaneously the crisis and normal conditions and tried to determine the location of hospitals in such a way that with limited resources in hand and the normal capacity and extra that can be used in times of crisis, the minimum distances Have Taking into account the decisions of the hardening of existing hospitals and the location of new hospitals simultaneously, post-abandonment distances are reduced compared to when the location is taken regardless of disability and rehabilitation, and this can be an effective step in crisis management. The capacity of hospitals in normal conditions and in the event of a crisis is limited and considering this in the model gives more realistic results. In this research, the problem of locating new hospitals and hardening a number of existing hospitals with limited available budget has been discussed. First, the potential locations to build a hospital are specified. Then, using the proposed mathematical model, the optimal locations for the construction of new hospitals were determined, as well as decisions regarding the hardening of a number of existing hospitals. In order to solve the model due to its bi-objective function, the Epsilon-constraint Method (II) are used. To evaluate the validity of the model, a case study was carried out based on demographic and geographical information of Yazd city. Regarding the possibility of applying the model to large-scale applications and operating issues as well as NP-hardness of the model, an innovative multi-objective two-phase algorithm is proposed and evaluated based on some numerical examples.