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One of the main challenges in global procurement problems is the uncertainty in the demand and supply sides of supply chains. Besides, decision making in the stochastic supply chains is a complex problem. A powerful technique for decision analysis in complex stochastic problems is simulation. In this paper we propose a simulation-based optimization approach to solve a bi-objective (profit and service level) supply chain with uncertain customer demands and disruption events in the suppliers. The basic assumptions used in this paper are adopted from the multi-period newsvendor problem. In addition, based on the risk attitude of the buyers (retailers), to cope with the uncertainties, they can sign an option contract, reserving additional capacity in the secondary suppliers. Hence, a simulation approach is used to model the behavior (risk attitude) of the buyers. Indeed, because of the demand uncertainty, at the beginning of each contract period, buyers should decide on the amount of ordering from the primary suppliers. The risk attitude of the retailer (as a spectrum) is defined based on the amount of ordering from the primary supplier. Also, we use the Non-dominated Sorting Genetic Algorithm to optimize the bi-objective model. Finally, a numerical example has been solved with the proposed algorithm and the results are reported. The results showed that if the profit is more important than service level, the risk sensitive retailer prefers to show more risk averse behavior.

Background Irrational use of antibiotics is proving to be a major concern to the health systems globally. This results in antibiotics resistance and increases health care costs. In Iran, despite many years of research, appreciable efforts, and policymaking to avoid irrational use of antibiotics, yet indicators show suboptimal use of antibiotics, pointing to an urgent need for adopting alternative approaches to further understand the problem and to offer new solutions. Applying the Complex Adaptive Systems (CAS) theory, to explore and research health systems and their challenges has become popular. Therefore, this study aimed to better understand the complexity of the irrational use of antibiotics in Iran and to propose potential solutions. Method This research utilized a CAS observatory tool to qualitatively collect and analyse data. Twenty interviews and two Focus Group discussions were conducted. The data was enriched with policy document reviews to fully understand the system. MAXQDA software was used to organize and analyze the data. Result We could identify several diverse and heterogeneous, yet highly interdependent agents operating at different levels in the antibiotics use system in Iran. The network structure and its adaptive emergent behavior, information flow, governing rules, feedback and values of the system, and the way they interact were identified. The findings described antibiotics use as emergent behavior that is formed by an interplay of many factors and agents over time. According to this study, insufficient and ineffective interaction and information flow regarding antibiotics between agents are among key causes of irrational antibiotics use in Iran. Results showed that effective rules to minimize irrational use of antibiotics are missing or can be easily disobeyed. The gaps and weaknesses of the system which need redesigning or modification were recognized as well. Conclusion The study suggests re-engineering the system by implementing several system-level changes including establishing strong, timely, and effective interactions between identified stakeholders, which facilitate information flow and provision of on-time feedback, and create win-win rules in a participatory manner with stakeholders and the distributed control system.