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In disaster rescue, multiple resource allocation strategies before and after disasters are a key research issue. Response planning for sudden-onset disasters must take into account the inherent uncertainties associated with the catastrophe and potential ensuing disasters. This article investigates the problem of resource allocation optimization in the context of natural disasters accompanied by the occurrence of multiple secondary hazards. A two-stage stochastic optimization model is proposed to simulate the scenario of random occurrence and severity of multiple natural hazards to optimize the resource allocation of rescue teams, warehousing items and medical resources when multiple natural hazards occur in combination. A particle swarm optimization (PSO) algorithm is designed and compared with a commercial solver (CPLEX) and lower bound at various scales. The results show that PSO performs optimally for large-scale cases. A case study, undertaken in Beijing, demonstrates that this method can improve the estimated casualty rescue success rate to 90.6%. [ABSTRACT FROM AUTHOR]

To effectively decrease emissions and pollution emitted by equipment in container ports, many ports began to update equipment using diesel fuels to achieve the aim of green ports. This paper studies an internal truck renewal problem in container ports and proposes a two-stage stochastic programming model to optimally determine internal truck composition optimization adjustment including three renewal modes, i.e. purchasing, retrofitting, and chartering. An exact solution procedure based on Benders decomposition accelerating by Pareto-optimal cuts is developed for the proposed model. Given the problem background of Shanghai Yangshan Deep Water Port, comprehensive computational experiments are carried out to verify the effectiveness of the proposed mathematical model and the performance of the solution approach. According to the numerical experiments, some suggestions for the management of renewing internal trucks in green ports are summarized at the end of the paper. [ABSTRACT FROM AUTHOR]

The green and sustainable development of ports is an issue of great concern to governments, industries, and academia. Carbon emissions near ports mainly come from ships and trucks. The effective allocation of resources and the implementation of a carbon tax policy may reduce carbon emissions near ports. Therefore, this paper investigates an integrated optimization problem of berths, quay cranes, and yard spaces allocation for tidal ports with channel capacity constraints under carbon tax policy. A mathematical model that aims to minimize the sum of the total carbon emission costs and total penalty costs is proposed. To solve the model efficiently, a heuristic algorithm based on the sequential method is developed. Numerical experiments are performed to verify the efficiency of the proposed algorithm. Based on the results of numerical experiments, some potentially useful managerial implications are outlined. [ABSTRACT FROM AUTHOR]

The truck and drone-based cooperative model of delivery can improve the efficiency of last mile delivery, and has thus increasingly attracted attention in academia and from practitioners. In this study, we examine a vehicle routing problem and apply a cooperative form of delivery involving trucks and drones. We propose a mixed-integer programming model and a branch-price-and-cut-based exact algorithm to address this problem. To reduce the computation time, we design several acceleration strategies, including a combination of dynamic programming and calculus-based approximation for the pricing problem, and various effective inequalities for the restricted master problem. Numerical experiments are conducted to validate the effectiveness and efficiency of the proposed solution. [ABSTRACT FROM AUTHOR]

This study proposes a strategic level decision method for yard cranes (YCs) transformation and deployment for the purpose of building efficient and energy‐saving green ports with low carbon emission. An integer programming model is embedded in the method for the above decision considering some green technology for YC transformation. The aim of the model is to minimize the investment cost for purchasing and transforming YCs as well as the operation costs during the planning horizon. A neighborhood descent particle swarm optimization algorithm is developed for solving the proposed model. Real data based on numerical experiments are conducted to verify the effectiveness of the proposed decision method. Take the case of Shanghai Yangshan Deep Water Port, the carbon emission is reduced by 415,756.8 kg and the energy consumption costs are saved 1,226,016 RMB. The above method may be potentially useful for the practitioners on the strategic level decisions of YCs transformation and deployment. [ABSTRACT FROM AUTHOR]