Unlike its inherent beneficial attributes, the blood supply chain is very complex due to its uncertain nature and irregular donation besides a high vulnerability to disruptions. In this regard, this study provides a multi‐objective integrated resilient‐efficient model to design a blood supply chain network under uncertainty. To evaluate the efficiency, an augmented form of the data envelopment analysis model is incorporated into the extended model. On the other hand, to improve supply chain resilience, different resiliency measures as optimization tools are introduced and inserted into the considered network. As blood donors have a main role in the blood supply chain, this paper undertakes motivational social aspects to stimulate blood donation for reducing possible shortages. Furthermore, to handle the multi‐objective problem, an interactive fuzzy approach is utilized. Finally, to indicate the applicability of the extended model and its solution approaches, a case study is provided considering realistic data. The results revealed that simultaneously considering all three measures of efficiency, resilience, and cost can assist in improvement of blood supply chain network design and a trade‐off among these three measures can be set by decision maker's preferences.
Considering and analyzing various kinds of cooperation among supply chain members is an option for better managing each channel. It is noteworthy that in many real‐world cases, each of vertical and horizontal cooperation has an important role in the success of supply chains. Nevertheless, only vertical cooperation in most previous research is considered. This paper addresses both vertical and horizontal cooperation in two competitive reverse supply chains, each of which includes one collector, one remanufacturer, and one retailer. Our primary concern is to analyze quality improvement competition between the remanufacturers. Moreover, retail price competition between the retailers and the quality competition are simultaneously considered in the extended model. In this research, the investigated system has been analyzed under different structures including decentralized, centralized, horizontal cooperation, and coordinated decision‐making models. The results show that when the remanufacturers cooperate horizontally, the profit of each collector and that of the retailer will decrease compared with those in the decentralized structure. To overcome this problem, a new coordination contract named multiple‐link two‐part tariff is proposed to simultaneously coordinate the members of each chain. The proposed contract effectively convinces the remanufacturers to participate in the coordination model instead of the horizontal cooperation. Moreover, it provides a win–win–win condition for all chain members and improves the quality level of the remanufactured products. The results indicate the proper performance of the proposed contract in improving the benefits of the competing chains, especially when there exists no intense competition between the remanufacturers (i.e., when the market sensitivity to the quality of the remanufactured products is low and consequently less effort is needed to increase the quality of the remanufactured products). Moreover, the proposed contract not only is able to simultaneously increase both remanufactured products demand and of end‐of‐life products supply but also involves both economic and environmental benefits.
A traveling salesman problem (TSP) is an NP-hard optimization problem. So it is necessary to use intelligent and heuristic methods to solve such a hard problem in a less computational time. This paper proposes a novel hybrid approach, which is a data mining (DM) based on multi-objective particle swarm optimization (MOPSO), called intelligent MOPSO (IMOPSO). The first step of the proposed IMOPSO is to find efficient solutions by applying the MOPSO approach. Then, the GRI (Generalized Rule Induction) algorithm, which is a powerful association rule mining, is used for extracting rules from efficient solutions of the MOPSO approach. Afterwards, the extracted rules are applied to improve solutions of the MOPSO for large-sized problems. Our proposed approach (IMOPSP) conforms to a standard data mining framework is called CRISP-DM and is performed on five standard problems with bi-objectives. The associated results of this approach are compared with the results obtained by the MOPSO approach. The results show the superiority of the proposed IMOPSO to obtain more and better solutions in comparison to the MOPSO approach.
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