Blood supply chain network design considering responsiveness and reliability in conditions of uncertainty using the lagrangian relaxation algorithm

The growing need for adequate and safe blood and the high costs of health systems have prompted governments to improve the functioning of health systems. One of the most critical parts of a health system is the blood supply chain, which accounts for a significant share of the health system's costs. In the present study, with an operational approach, the total network costs are minimized along with the minimization of transportation time and lead time of delivery of blood products. Also, determining the optimal routing decisions is improved the level of responsiveness and reliability of the network. In this research, a multi-objective stochastic nonlinear mixed-integer model has been developed for Tehran's blood supply chain network. Robust scenario-based programming is capable of effectively controlling parametric uncertainty and the level of risk aversion of network decisions. Also, the proposed reliability approach controls the adverse effects of disturbances and creates an adequate confidence level in the capacity of the network blood bank. Lastly, the model is solved through the Lagrangian relaxation algorithm. Comparison of the results shows the high convergence rate of the solutions in the Lagrangian relaxation algorithm

A bi-objective blood supply chain model under uncertain donation, demand, capacity and cost: a robust possibilistic-necessity approach

This paper addresses a multi-objective blood supply chain network design, considering economic and environmental aspects. The objective of this model is to simultaneously minimize a blood supply chain operational cost and its logistical carbon footprint. In order to embed the uncertainty of transportation costs, blood demand, capacity of facilities and carbon emission, a novel robust possibilistic-necessity optimization used regarding a hybrid optimistic-pessimistic form. For solving our bi-objective model, three multi-objective decision making approaches including LP-metric, Goal-Programming and Torabi-Hassini methods are examined. These approaches are assessed and ranked with respect to several attributes using a statistical test and TOPSIS method. Our proposed model can accommodate a wide range of decision-makers viewpoints with the normalized objective weights, both at the operational or strategic level. The trade-offs between the cost and carbon emission for each method has been depicted in our analyses and a Pareto frontier is determined, using a real case study data of 21 cities in the NorthWest of Iran considering a 12-month implementation time window

Designing and Solving Location-Routing-Allocation Problems in a Sustainable Blood Supply Chain Network of Blood Transport in Uncertainty Conditions

Purpose: In this paper, a location-routing-allocation problem in a multi-objective blood supply chain network was designed to reduce the total cost of the supply chain network, the maximum unmet demand from distribution of goods, and decline greenhouse gas emissions due to the transport of goods among different levels of the network. The network levels considered for modeling include blood donation clusters, permanent and temporary blood transfusion centers, major laboratory centers and blood supply points. Other objectives included determining the optimal number and location of potential facilities, optimal allocation of the flow of goods between the selected facilities and determining the most suitable transport route to distribute the goods to customer areas in uncertainty conditions. Methodology: Given that the model was NP-hard, the NSGA II and MOPSO algorithms were used to solve the model with a priority-based solution. Findings: The results of the design of the experiments showed the high efficiency of the NSGA II algorithm in comparison with the MOPSO algorithm in finding efficient solutions. Originality/Value: This study addresses the issue of blood perishability from blood sampling to distribution to customer demand areas

Developing an integrated blood supply chain network in crisis conditions considering the concentration of sites in facilities and blood types substitution

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

Dynamic temporary blood facility location-allocation during and post-disaster periods

The key objective of this study is to develop a tool (hybridization or integration of different techniques) for locating the temporary blood banks during and post-disaster conditions that could serve the hospitals with minimum response time. We have used temporary blood centers, which must be located in such a way that it is able to serve the demand of hospitals in nearby region within a shorter duration. We are locating the temporary blood centres for which we are minimizing the maximum distance with hospitals. We have used Tabu search heuristic method to calculate the optimal number of temporary blood centres considering cost components. In addition, we employ Bayesian belief network to prioritize the factors for locating the temporary blood facilities. Workability of our model and methodology is illustrated using a case study including blood centres and hospitals surrounding Jamshedpur city. Our results shows that at-least 6 temporary blood facilities are required to satisfy the demand of blood during and post-disaster periods in Jamshedpur. The results also show that that past disaster conditions, response time and convenience for access are the most important factors for locating the temporary blood facilities during and post-disaster periods

Green supply chain based on artificial intelligence of things (AIoT)

Purpose: The most important driving force for the IoT is artificial intelligence. The dramatic growth of the Internet of Things in various fields necessitates the use of artificial intelligence capabilities in the optimal use of data. By combining these technologies, it reduces cost, automation and productivity more dynamically. This hybrid technology is called artificial intelligence of things (AIoT). Methodology: Intelligent solutions in the supply chain, i.e. the use of the Internet of Things with the capability of artificial intelligence, has been able to make various industries great. Findings: Due to the colorful role of IoT technology in the sustainability of industrial systems, this paper provides a framework for the implementation of an AIoT-based green supply chain. This framework shows a clear path to understanding the impact of this hybrid supply chain technology. Originality/Value: In his paper, a framework for the implementation of an AIoT-based green supply chain is provided

Concentration and its Effect on Advertising: Case Study: Iranian Food and Beverage Industries

Purpose: Todays, advertising intensity is different among various types of market structure. In other words, concentration as an important indicator of market structure plays significant role in the firms’ decision about the amount of advertising expenditures. This study aims at analyzing the relationship between competition index (concentration) and advertising in the Iranian food and beverage industries. Methodology: Using a panel of 22 four-digit Iranian food and beverage industries, this study analyses the relationship between advertising intensity and concentration over the period 2007– 2019. Findings: The results show that an inverted U-shaped relationship exists between the advertising intensity and concentration. Also, the profitability has negative and export intensity has positive and significant effects on the advertising intensity. Originality/Value: The structure-conduct-performance (SCP) paradigm suggests that performance of the industry is affected by the conduct of the participants in the market, which is influenced by the companies’ market structure