Disasters such as hurricanes, earthquakes and floods continue to have devastating socioeconomic impacts and endanger millions of lives. Shelters are safe zones that protect victims from possible damage, and evacuation routes are the paths from disaster zones toward shelter areas. To enable the timely evacuation of disaster zones, decisions regarding shelter location and routing assignment (i.e., traffic assignment) should be considered simultaneously. In this work, we propose a risk-averse stochastic programming model with a chance constraint that takes into account the uncertainty in the demand of disaster sites while minimizing the total evacuation time. The total evacuation time reflects the efficacy of emergency management from a system optimal (SO) perspective. A conditional value-at-risk (CVaR) is incorporated into the objective function to account for risk measures in the presence of uncertain post-disaster demand. We resolve the non-linear travel time function of traffic flow by employing a second-order cone programming (SOCP) approach and linearizing the non-linear chance constraints into a new mixed-integer linear programming (MILP) reformulation so that the problem can be directly solved by state-of-the-art optimization solvers. We illustrate the application of our model using two case studies. The first case study is used to demonstrate the difference between a risk-neutral model and our proposed model. An extensive computational study provides practical insight into the proposed modeling approach using another case study concerning the Black Saturday bushfire in Australia.

With the development of society, public resources for healthcare are increasingly inadequate to meet the demands for the services. Therefore, it is extremely important for policymakers to provide citizens with the most effective healthcare services within the limited available resources. In order to achieve positive effect rescue operations in the Emergency Medical Services (EMS) system, the problems including where to locate the ambulance facilities and how many ambulance vehicles should be allocated to the stations have become the focus of attention. In this paper, we study the problem based on the demand for EMS in Songjiang District, Shanghai, China, followed by the joint planning of Emergency Medical Services management, which typically consists of ambulance facility locations planning and patient’s assignment to hospitals. We proposed a modified Double Standard Model (DSM) to maximize the demand points covered at least two times within the minimum coverage criteria. The problem is solved by integer linear programming technique with the CPLEX software and we make a comparison between the solutions and the locations which exist in the emergency system used by the Songjiang emergency center. Our results show that the demand coverage rate and response time can be efficiently improved through relocating the current facilities without additional vehicle resources.

Financial flow is an important part of supply chain management (SCM) and increasingly playing a crucial role as the amount of global trade increases. Reasonable and scientific financial operation is necessary in closed-loop supply chain management, especially when customer demand is uncertain. However, financial flow, which may lead to an increase in effectiveness, has rarely been considered in the literature. In this paper, we present a closed-loop supply chain design with financial management problem, which is tackled as a stochastic programming model with ambiguity demand set. The main contributions of this work include: (i) A joint chance constrained programming model is proposed to maximize the total profit, and (ii) financial flow and uncertain demand are both taken into consideration. According to the characteristic of the problem, we chose four approaches, namely sample average approximation (SAA), enhanced sample average approximation (ESAA), Markov approximation (MA), and mixed integer second-order conic program (MI-SOCP). Computational experiments were conducted to compare the adopted methods, and 10,000 scenarios were generated to examine the reliability of the methods. Numerical results revealed that the Markov approximation approach can achieve more reliable solutions.

Cooperative relationships and interactive decision behaviors between online stores and third-party logistics (TPL) have attracted increasing attention from academics as well as practitioners with the development of the online retailing industry. However, the decisions and performance of each member in a decentralized supply chain are not in line with the actual performance and decision behaviors of them, especially when taking individual altruism preference into account. Hence, without considering the truism of channel members will lead to unrealistic decisions and obtain anamorphic performance of firms and the entire system based on conventional self-interested models. This paper develops an evolutionary bilateral decision-making model to characterize the effects of all members' altruism in an online shopping service supply chain (OSSSC). The model considers different altruism of each member in the supply chain and their changes at various intervals. The decision equilibrium and interactive rules of all members are deeply analyzed based on the evolutionary bilateral decision-model as the change of altruism of each party. Our results identify that: altruism preference impacts the decisions of all participants in the OSSSC system. When the TPL and the online store have high altruism and low altruism, respectively, both retail price and logistics service price tend toward low levels and all participants have high utilities. However, this situation is more favorable for the online store. In this case, changes in the altruism level of either party will adversely impact the utilities of all parties and the performance of the whole system; specially, when the online stores are self-interested while the TPL has altruism that fluctuates within a specific range, improving logistics service level will help to lower the retail price and increase market shares. Numerical experiments are conducted to examine and verify the results and some optimal managerial insights for online retailers and TPLs are provided.