Journal of Operational Research in its Applications
Year:2022 | Volume:19 | Issue:3 ( 74)|Papers Count:8

The plenty of data on the Internet has created problems for users and has caused confusion in finding the proper information. Also, users' tastes and preferences change over time. Recommender systems can help users find useful information. Due to changing interests, systems must be able to evolve. In order to solve this problem, users are clustered that determine the most desirable users, it pays attention to the user's rating of the items. The time parameter has been considered in the proposed method of Genetic Algorithm-Simulated Algorithm (SAGA) of this paper which can improve user prioritization based on time. In the proposed method, using the Memetic evolutionary algorithm, the clusters are improved over time, which it provides appropriate suggestions to the user. The system also performs optimal evolutionary clustering using item properties for the cold start item problem, and user demographic information for the cold start user problem. The proposed method has been evaluated using the Movielens dataset and experimental results show that the proposed SAGA method with an accuracy of 0. 89 has a better performance in the accuracy of predictions and suggestions to users than existing methods.

In this paper, the dual fuzzy equations modeling is considered to determine the fuzzy equilibrium point of the market. By defining three sets of solutions for the dual fuzzy equations, the minimum symmetric fuzzy price, the maximum symmetric fuzzy price and the asymmetric fuzzy price are obtained, and accordingly, the fuzzy equilibrium point of the market and fuzzy supply and demand functions are calculated, and by proposing a theorem, the conditions under which the dual fuzzy equations considered to determine the fuzzy market price always have fuzzy solutions are presented. By discussing the drawbacks of the previous method of calculating the fuzzy market price, it is demonstrated that the method proposed in this paper has resolved the disadvantages. Finally, numerical examples show the efficiency and superiority of the proposed method.

Transportation costs today are considered as one of the most important costs affecting the cost of goods and the final price of consumption for the customer, especially in supply chain management. Therefore, it is necessary to focus on transportation costs due to lower prices, and consequently increase customer satisfaction and increase the position of the industry among competitors. Given that the available resources (capacity of supply centers) are generally considered as the minimum capacity available in the model, generally to solve such models, first the fuzzy supply limit is definitively converted and then using methods. The existing solution is a crisp problem that unfortunately does not fit well with the nature of the uncertainty in the decision. Therefore, in this paper, in order to adapt to the real conditions, the supply constraint in the flexible fuzzy transportation model is studied. For this purpose, a fuzzy mathematical programming approach to the transportation problem with flexible fuzzy constraints is proposed to obtain the most satisfactory solution. Finally, a numerical example for the proposed model and the proposed solution method is considered.

The main purpose of this research is to determine the optimal number of equipment for the production system of a two-stage assembly flow shop scheduling problem. In a two-stage assembly flow system, product items are first produced in the first station, and in the second station, one or more machines assemble them together. In some studies, it has been considered that the equipment failure probability function follows an exponential distribution. In the real world, however, this assumption does not true. If the equipment failure probability function does not follow the exponential distribution, the best approach in estimating the reliability of production systems is to use simulation methods. In this research, first, the factors affecting the reliability of the production system are identified and then through the design methods of experiments and simulation approach, the reliability function of the system is estimated. Numerous factors such as the reliability of the production system, the total price of equipment, and the required area of the establishment plan can be considered as evaluation criteria. Then, taking into account the above criteria and using the combined method of fuzzy multi-criteria decision making consisting of the best-worst fuzzy methods and TOPSIS, the best option has been selected from among the possible options.

In this study, a model was designed to develop and optimize the blood demand of hospitals, reduce the shortage and loss of blood and decrease the remaining inventory in hospitals as well as minimizing cycle time of blood transfusion to medical centers in the blood supply chain. This model includes donation groups, tablets,Fixed / mobile centers, laboratory centers and hospitals. The proposed model is bi-objective, three-level, multi-period for considering the uncertainty of the amount of demand in hospitals in fuzzy. The model addressed in this study includes parameters such as blood supply, demand, capacity of each center and related costs in the blood supply chain, include the possibility of lateral delivery of blood products. Therefore, the designed model was first examined in terms of important parameters with the optimization approach, ε,-constraint method in small size and numerical example in GAMS software and Then, to find the optimal Pareto solution, NSGAII meta-heuristic algorithm (Non-dominated Sorting Genetic Algorithm 2(was used to search the optimal Pareto solution, results of NSGAII algorithm show that it works better than ε,-constraint method.

The subject of this research is to identify and investigate the factors affecting supply chain management. This research has been done in Saipa Automotive Group. The experts of this research consist of 50 experts of this company in the field of supply chain. The experts of this research were people who either have an academic education in the supply chain or have more than 10 years of experience in this field and in this regard are completely knowledgeable staff in this field and in fact the management of the supply chain of Saipa Automotive is responsible. These are people. Due to the limited number of experts in this company, the counting method was used. The questionnaire used in this study was standardized. The researcher presented his hypotheses according to the identified influencing factors on the supply chain. These factors include: reliability, accountability, agility, costs and asset management. The researcher tested these hypotheses using structural equation method and smart pls software. The results of the study showed the effectiveness of the identified dimensions on supply chain management. Among the identified dimensions, asset management had a negative and inverse effect on supply chain management.

The main objective of reliability redundancy allocation problems (RRAP), is to maximize the overall system reliability through adding additional parallel components or by improving the reliability of the system components. In this paper, second objective of minimizing the overall system failure risk is also considered in the RRAP problem, and this bi-objective problem is developed considering fuzzy constraints including cost, weight and reliability. In order to obtain the failure risk function of the system, is used the Failure Mode and Effect Analysis (FMEA) method and the Risk Priority Numbers (RPN) of components are determined by multiplying coefficients occurrence probability, intensity and detectability of each risk that the functions of risks occurrence probability define with considering reliability and number of parallel components. Regarding the fact that the RRAP problem is NP-hard, the modified Cuckoo search algorithm is applied to produce efficient solutions. The proposed model and its solving method is applied in the unrepairable connections of a flying object as case study and the validity and efficiency of proposed model is verified.

In today’, s world with new improvements and managerial approaches toward lean production and inventory decrease, empowering supply chains in disruption management have become more important. The main goal of current research is to present a mathematical model for supply, production, inventory and distribution planning in supply chain after disruption occurrence. This kind of planning is known as recovery planning in scope of disruption management studies which tries to recover the disrupted system by minimizing the total cost of the supply chain. A disruption may occur in any supplier and cause interruption in replenishment and accordingly customer’, s service. The main goal of this research is helping decision makers to choose best supply, production and ordering quantity after supply disruption occurrence so that SC recovers and returns to pre-disruption plan with minimum recovering cost. The studied supply chain contains several suppliers, a manufacturer and several retailers which try to serve customers’,demands. The suggested recovery plan is a non-linear programming model, so a heuristic method was designed and coded in MATLAB to solve it. To evaluate the applicability of suggested heuristic, the model was solved by GAMS for several random experiments. The deviation of the heuristic solutions from GAMS values is very small which proves that the use of heuristic is acceptable and reliable.