Mohsen Jami; Hamidreza Izadbakhsh; Alireza Arshadi Khamseh
Abstract
In the management of the blood supply chain network, the existence of a coherent and accurate program can help increase the efficiency and effectiveness of the network. This research presents an integrated mathematical model to minimize network costs and blood delivery time, especially in crisis conditions. ...
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In the management of the blood supply chain network, the existence of a coherent and accurate program can help increase the efficiency and effectiveness of the network. This research presents an integrated mathematical model to minimize network costs and blood delivery time, especially in crisis conditions. The model incorporates various factors such as the concentration of blood collection, processing, and distribution sites in facilities, emergency transportation, pollution, route traffic (which can cause delivery delays), blood type substitution, and supporter facilities to ensure timely and sufficient blood supply. Additionally, the model considers decisions related to the location of permanent and temporary facilities at three blood collection, processing, and distribution sites, as well as addressing blood shortages. The proposed model was solved for several problems using the Augmented epsilon-constraint method. The results demonstrate that deploying advanced processing equipment in field hospitals, concentrating sites in facilities, and implementing blood type substitution significantly improve network efficiency. Therefore, managers and decision-makers can utilize these proposed approaches to optimize the blood supply chain network, resulting in minimized network costs and blood delivery time.IntroductionOne of the most important aspects of human life is health, which has a significant impact on other aspects of life. In this study, a two-objective mathematical programming model is proposed to integrate the blood supply chain network for both normal and crisis conditions at three levels: blood collection, processing and storage, and blood distribution. The proposed two-objective mathematical model simultaneously minimizes network costs and response time. The model is solved using the augmented epsilon-constraint method. To enhance the responsiveness to patient demand in healthcare facilities and address shortages, the model considers the concentration of levels (collection, processing and storage, and distribution of blood to patients) in facilities, blood type substitution, and supporter facilities. In blood type substitution, not every blood type can be used for every patient. Among several compatible blood groups, there is a prioritization for blood type substitution, allowing for an optimal allocation of blood groups based on the specific needs.Materials and MethodsIn this research, a two-objective mathematical programming model is proposed to design an integrated blood supply chain network at three levels: collection, processing, and distribution of blood in crisis conditions. The proposed model determines decisions related to the number and location of all permanent and temporary facilities at the three levels of blood collection, processing, and distribution, the quantity of blood collection, processing, and distribution, inventory levels and allocation, amount of blood substitution, and transportation method considering traffic conditions. Achieving an optimal solution for the developed two-objective model, which minimizes both objective functions simultaneously while considering the trade-off between the objective functions, is not feasible. Therefore, multi-objective solution methods can be used to solve problems considering the trade-off between objectives. In this research, the augmented epsilon-constraint method is employed to solve the proposed two-objective mathematical model. In this method, all objective functions, except one, are transformed into constraints and assigned weights. By defining an upper bound for the transformed objective functions, they are transformed into constraints and solved.Discussion and ResultsAlthough the two-objective mathematical model is transformed into a single-objective model using the augmented epsilon-constraint method, this approach can still yield Pareto optimal points. Therefore, managers and decision-makers can create a balanced blood supply chain network considering the importance of costs and blood delivery time. Sensitivity analysis was conducted to examine the effect of changes in the weights of the objective functions and the blood referral rate (RD parameter) on the values of the objective functions for three numerical examples. With changes in the weights of the objective functions relative to each other, the trend of changes in the values of the first and second objective functions for all three solved problems is similar. Specifically, when reducing the weight of the first objective function from 0.9 to 0.1, the values of the first objective function increase, while the values of the second objective function decrease when the weight of the second objective function increases from 0.1 to 0.9. The total amount of processed blood in field hospitals and main blood centers was compared for equal weights and time periods for the three problems. Additionally, the amount of processed blood in field hospitals is significantly higher than in main blood centers. This indicates that eliminating the cost and time of blood transfer in field hospitals (due to the concentration of blood collection, processing, and distribution levels) results in an increased amount of processed blood compared to main blood centers (single-level facilities), ultimately leading to a reduction in network costs.ConclusionThis study presents a two-objective mathematical model for the blood supply chain network, integrating pre- and post-crisis conditions. Decisions are proposed for the deployment of four types of facilities, including temporary blood collection centers, field hospitals, main blood centers, and treatment centers, at three levels of blood collection, processing, and distribution. Additionally, inventory, allocation, blood group substitution, blood shortage, transportation mode, and route traffic (delivery delays) are considered for four 24-hour periods in the model. For the first time in this field, knowledge of concentration levels in facilities is utilized, with simultaneous existence of the three levels of blood collection, processing, and distribution in field hospitals. This problem is formulated in a mixed-integer linear programming model with two objective functions aiming to minimize system costs and blood delivery time. The proposed model is solved using the augmented epsilon-constraint evolution method. Sensitivity analysis is conducted for the weights of the objective functions, and additional experiments (RD parameter) are performed. The sensitivity analysis on the weights of the objective functions reveals that reducing the weight of the first objective function leads to a decrease in blood delivery time, while increasing the weight of the second objective function results in an increase in network costs. The investigation of the impact of reducing the amount of additional testing (RD parameter) on the values of the objective functions confirms that advanced equipment at the processing sites of field hospitals reduces network costs and blood delivery time.
Abstract
One of the most important problems of logistic networks is designing and analyzing of the distribution network. The design of distribution systems raises hard combinatorial optimization problems. In recent years, two main problems in the design of distribution networks that are location of distribution ...
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One of the most important problems of logistic networks is designing and analyzing of the distribution network. The design of distribution systems raises hard combinatorial optimization problems. In recent years, two main problems in the design of distribution networks that are location of distribution centres and routing of distributors are considered together and created the location-routing problem. The location-routing problem (LRP), integrates the two kinds of decisions. The classical LRP, consists in opening a subset of depots, assigning customers to them and determining vehicle routes, to minimize total cost of the problem. In this paper, a dynamic capacitated location-routing problem is considered that there are a number of potential depot locations and customers with specific demand and locations, and some vehicles with a certain capacity. Decisions concerning facility locations are permitted to be made only in the first time period of the planning horizon but, the routing decisions may be changed in each time period. In this study, customer demands depend on the products offering prices. The corresponding model and presented results related to the implementation of the model by different solution methods have been analysed by different methods. A hybrid heuristic algorithm based on particle swarm optimization is proposed to solve the problem. To evaluate the performance of the proposed algorithm, the proposed algorithm results are compared with exact algorithm optimal value and lower bounds. The comparison between hybrid proposed algorithm and exact solutions are performed and computational experiments show the effectiveness of the proposed algorithm.
Ali Mohaghar; Sara Aryaee; Jalil Heidary; Ara Toomanian
Abstract
Nowadays banks, credit and financial institutions are trying to increase profits, reduce costs, compete with rivals, attract customers and increase productivity. One of the factors that contributes to the implementation of these strategies is the optimum locations of branches. Locating the new branches ...
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Nowadays banks, credit and financial institutions are trying to increase profits, reduce costs, compete with rivals, attract customers and increase productivity. One of the factors that contributes to the implementation of these strategies is the optimum locations of branches. Locating the new branches of Mehr Eghtesad bank in the region 1 in Tehran city is the aim of this research. Since the focus of service centers such as banks is on maximal or full service to customers, among the all the covering models, Maximal Covering Location model is chosen as the best option to locate the new bank branches. To this end, related literature about locating bank branches, Geographical Information system (GIS) and maximal covering location problem (MCLP) examined. Then, through library Studies and interviewing with managers and experts, the researcher chose effective criteria and sub criteria for locating bank branches. The weights of criteria and sub criteria were determined through filling the questionnaires by managers. GIS used to extract some input data for the model and weighted maximal covering model (MCLM) with partial covering used to choose the best locations. Mathematical programming model formulated with 363 binary variables, 122 constraints, 121 demand areas, 121 potential points, the 1000 m buffer, α = 0.75, b = 50%, θ = 2 and s= 8 & 30 branches (with two different scenarios) and solved with GAMS optimization software. It is clear that by solving the first scenario, eight suitable locations and second scenario thirty suitable locations to open new branches will be generated.
Nafiseh Aghabozorgi; Seyed Mojtaba Sajadi; Mahdi Alinaghian
Volume 13, Issue 38 , October 2015, , Pages 99-132
Abstract
Today, field of production and service is faced with competition among the supply chains by changing the competition pattern of the independent companies. Most of the small and medium businesses (SMEs) still use traditional viewpoint for supply, production and distribution planning. It means, each of ...
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Today, field of production and service is faced with competition among the supply chains by changing the competition pattern of the independent companies. Most of the small and medium businesses (SMEs) still use traditional viewpoint for supply, production and distribution planning. It means, each of these SMEs plan their tasks independently, this will increase the total cost of the supply chain in many cases. In this study, a robust model of inventory-locating supply chain is proposed in three-level with uncertain demand. The model has been considered in single-period, multi-product state along with some transportation models with three levels of producers – distributors- retailers in certain and robust mode. Considering some transportation models along with robusting the model and the possibility of sending goods directly from the factory to the retailer is one of the innovations of this study. The objectives of the proposed model are to minimize the total cost of the three-leveled supply chain and to find the amount of safety stock. The certain model is solved by GAMS and the robust model is also solved by GAMS in single-objective mode and then transferred to the augmented ε-constraint method. The results have been discussed after solving the model.
Mehdi Seifbarghy; Razieh Forghani; Zarifeh Rathi
Volume 8, Issue 18 , September 2010, , Pages 1-13
Abstract
Maximal Covering Location Problem (MCLP) aims at maximizing a population of customers which are located within a specified range of time or distance from some new servers which should be located. A number of extensions have been proposed for this problem, one of which is considering queuing constraints ...
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Maximal Covering Location Problem (MCLP) aims at maximizing a population of customers which are located within a specified range of time or distance from some new servers which should be located. A number of extensions have been proposed for this problem, one of which is considering queuing constraints in the mode; for example, location of a limited number of servers in such a way as to maximize the covering considering the constraint regarding to the queue length. In this paper, we extend the proposed model by Correa and Lorena [3] which maximizes the covering. We consider a more objective function in such a way as to minimize the total distance between the servers and demand points. A genetic algorithm based heuristic is proposed to solve the model and results are compared with that of given by CPLEX as a standard solver to estimate the performance of the given algorithm.
