جستجوی مقالات مرتبط با کلیدواژه "مدل ریاضی چند هدفه" در نشریات گروه "مدیریت"

One of the most important goals of the crisis management headquarters is the proper preparation and distribution of food to the affected people during a disaster. Due to the limitations related to shelf life and storage of perishable food products, operational costs, and transportation, their distribution is challenging. In this study, we aim to develop a mathematical model using integer linear programming for the optimization of cold food supply chain during disasters. 

This study uses an operational research approach. A two-objective mixed integer linear programming model was designed for a cold, stable and multi-level supply chain in the food industry in the conditions of uncertainty and crisis. In this programming model, the economic order quantity (EOQ) performance optimization method was used to determine the optimal order point. The presented model was solved based on the real data obtained from the crisis management headquarters in Iran and implemented as a pilot for 16 supply centers, 5 distribution channels, and 35 crisis management headquarters with two LP-metric and augmented epsilon constraint (AEC) methods.

The findings of testing research hypotheses using one-way analysis of variance showed that the use of mathematical models had a positive and significant effect on the optimization of the cold food supply chain during disasters. In this regard, there was no significant difference between LP-metric and AEC methods (P>0.05). Also, the use of mathematical models had a positive and significant effect on improving the quality of food industry. Optimizing the supply chain by the mathematical model has a positive and significant effect on the performance of food industries. The optimal order point in the chain model had a EOQ of 3436 units.

By using the presented mathematical model, it is possible to optimize the management process of the cold food supply chain during disasters. It has a higher performance compared to traditional approaches. It is expected that by using the presented mathematical model, relief operations and food distribution during disasters can be facilitated and optimized.

Supply chain optimization involves the integration of supply chain activities and related information flows by improving the state of chain relationships to achieve a secure and sustainable competitive advantage. In this realization, the issue of supply chain optimization has been chosen because to provide goods and services in the economy, we need a mechanism and system to maximize economic efficiency and productivity and provide return goods and services at lower and easier costs and the circular economy to be realized, the main discussion of which is related to the recycling of returned goods and goods that need to be repaired so that they can be reused. This research also emphasizes the use of healthy parts and components in the reproduction of products and this issue is considered for all goods in each part of the supply chain that cannot be used and must be transferred to the reproduction. The results of the implementation of the model presented in the genetic algorithm show that this algorithm was able to reach an almost optimal answer at the right time. This algorithm requires more computational time than LINGO optimization software to solve problems that have very small dimensions but this algorithm needs much less time to solve problems due to the increase in problem dimensions than LINGO optimization software. Therefor in these examples we observed that this algorithm can reach an acceptable answer in a much shorter interval than LINGO optimization software for large scale problems.

The timely production of orders and their delivery to the customer is a competitive advantage that makes production systems and becomes customer satisfaction. Balancing the load of orders on work stations, reducing the time of the production period and minimizing the cost of human skills and access to machines can be components in the cases of balance of production lines. In this article, by addressing the above components of the mathematical model, several objectives for checking the online load balance are presented. Solving the mathematical model is done by introducing the combined meta-heuristic algorithm of multi-objective genetics and ant milk, this solution method selects the fittest parents based on the fitness of the initial population by using the feature of selecting the initial population and having the search memory to find quality answers at the right time. produces. The results obtained compared to the previous online load balancing methods show that the production time and load balance on the workstations have been improved.

With the increasing competition between manufacturers to respond to customers' requirements, the production planning processes in the form of supply chains has become more complex. On the one hand, manufacturers have to produce at the lowest cost in order to create a competitive advantage. On the other hand, environmental requirements and attention to sustainable production have caused more attention to energy consumption in production systems. In this research, for the first time, the green hybrid flow shop scheduling problem is investigated with considering batch delivery system. For this purpose, a bi-objective integer linear programming model is presented. The first goal of the problem is to minimize the total cost related to the total jobs completion times and the costs of dispatching the batches. The second goal is to minimize energy consumption. The mathematical model is converted to a single objective model using Ɛ-constraint method and a number of random instances are evaluated and comprehensively analyzed with its help, and the effect of paying attention to energy consumption on the scheduling of orders is presented. Also, due to the NP-hardness of the problem, an MOTLBO algorithm has been adopted to solve large size instances.

In manufacturing and service processes, facing conditions of uncertainty and changes in the amount of data used in the model causes variable parameters, and therefore it is not uncommon to expect such conditions in decision making. Therefore, not paying attention to such changes causes deviation in planning and deviation from the real situation and causes a lot of damage in the mentioned environments. Due to the importance of the issue, in this paper, the fuzzy optimization approach is used to deal with the uncertainty in the parameters of uncertainty of production stages and product quality in the manufacturer's center and model reconstruction center. The present study intends to design a mathematical model of a multi-objective production program and optimize it to minimize the cost of inventory, production, and manpower, and the maximum quality of the product and maximize the maximum occurrence of uncertainty at each stage of production that causes bottlenecks. It can increase the profitability of Borujen Concrete Parts Company. The results of solving the model by coding in Gamz software and using the comprehensive criterion method include the values of objective functions and decision variables. The results were approved by the officials of Borujen Stream Concrete Parts Company.

Nowadays, business owners and entrepreneurs are constantly looking for ways to increase the flexibility of their employees to meet the goals of their organization to respond the diverse needs of their customers. Optimizing service delivery in production systems and providing appropriate work patterns can ultimately simplify service delivery and increase employee satisfaction. While job rotation is a good way to improve an organization's agility in responding to a variety of demands, addressing this issue without the right attitude will lead to issues such as running costs as well as ergonomics and employee motivation. Hence, in this study, a multi-objective mathematical model of employee scheduling with the goal of minimizing scheduling costs and personnel allocation, enhancing employee satisfaction, and job ergonomics. The Epsilon constraint method is used to solve this multi-objective optimization problem. The results of this study showed that using appropriate multi-objective models can provide optimal results in terms of job rotation tailored to employees' working conditions. Scheduling can also ultimately reduce costs and increase motivation, considering job rotation.

The supply chain management is an important factor in current competitive market. In recent years, the shortage of resources for answering an increasing food demand has increased researchers’ attention to the food supply chain. Given the importance of fish in the Household Food Basket, the development of aquaculture and recycling of returned goods in reverse logistics would significantly help with preserving water resources, as well as sustainable development. Therefore, government agencies and aquaculture industry beneficiaries are interested in reverse logistics. This study is focused on the optimization of a closed-loop supply chain of fish. To this end, a new bi-objective mathematical model is proposed that both minimizes total costs and maximizes fulfilling customers demand in uncertainty situation. Several well-known multi-objective meta-heuristic algorithms and a proposed hybrid meta-heuristic algorithm are applied to identify Pareto solutions. The solutions are then compared in terms of performance metrics. Also, the epsilon-constraint method and sensitivity analysis are used to validate the algorithms and evaluate the performance of the model. Lastly, the VIKOR method is used to select the superior method. To demonstrate the capability of the proposed model, a closed-loop supply chain of trout in northern Iran is investigated as a case study. The results show that the developed model could be effective in reducing the costs and increasing customer satisfaction.

The purpose of this paper is to design a multi-objective mathematical model in order to optimize the four-echelon supply chain of perishable goods using a hybrid algorithm with regard to procurement time, cost and customer satisfaction. Perishable four-echelon food supply chains are considered as different supply chains due to continuous and significant changes in the quality of food products throughout the chain until the end of consumption. In this research, a mathematical model for the location-routing facility in a four-echelon supply chain for perishable products with a simultaneous optimization approach of total supply chain costs, order delivery time, emissions and customer satisfaction is presented. To assess the validity of the research, the mathematical model in Behshahr food industry has been studied and the research problem is presented in the form of a multi-objective nonlinear programming model of mixed integer and to solve it, a hybrid of two refrigeration and red deer algorithms has been used. The results of the proposed algorithm are solved in a case study and the results of the algorithm performance are reviewed based on standard indicators and finally the computational results indicate the efficiency of the algorithm for a wide range of problems of different sizes.

Nowaday, rdue to the growth of large developmental and industrial projects and the importance of on time supply of materials and coordinating with suppliers, the issue of project supply chain has been considered. One of the ways to create this coordination is integrating inventory planning. For this purpose, in this paper, a bi-objective model is developed taking into account the features of the project, which aims to minimize the cost of the project. This model is a combination of resource constraint project scheduling and inventory planning. Therefore in this research, in addition to introducing inventory planning model for the project supply chain, the project's economic quality in accordance with the standards of the engineering system is considered, and also, the effect of discounts (incremental) on project costs to close the model to the real-world conditions is analyzed. To solve the proposed model, the ƹ-constraint method is used and the numerical results obtained from the implementation of the model are reported using this method

The toxic and dangerous waste material from electrical and electronic equipment is a potential threat for environment and the future of human. The users and governments forced manufactures to take a responsibility for recieving and recovering of their products. Recovering of these products has economical benefit and social benefits. Therefore a management system is essential for recovering the electronic products to maximize the social and economical benefits and minimize the environmental impact. In this paper a multi objective mathematical model is proposed based on sustainable development for selecting the proper options to recover the electronic products .Multi objectives genetic algorithm is used to solve the model. The model validation is conducted through a case study with data from industry .

This research aims at developing original components of Decision Support System (DSS) in order to support the evaluation and selection of suppliers in multi products having limited capacity. To achieve this purpose, researchers have proposed three different models based on which suppliers allow selecting their own desired type of discount. Firstly based on the performed studies, some criteria have been suggested for evaluating the suppliers’ capability. These criteria will be selected by the buyer with respect to the purchase condition and type of the industry, and their importance level will be determined. Then based on multi-criteria decision models, evaluation of all the suppliers will be done. In this study fuzzy numbers are applied so as to translate qualitative criteria into quantitative one. Suppliers have been ranked according to the scores they have gained and the selection share of each from the whole quantity has been determined based on the multi-objective mathematical model. The existing model has exerted practical ways such as taking loans with determined interest as well as certain variables in order to compensate the shortage of budget the buyer encounters with. Outputs, which are, the selective suppliers and orders assigned to each of them are also to be considered by using Genetic Algorithm in MATLAB software for three parts in Emersan Company accordingly.