Wencheng Wang’s research while affiliated with Shanghai University and other places

Diesel-driven rubber-tired gantry cranes play an important role in the carbon emission and operating efficiency of the port. Retrofitting diesel-driven rubber-tired gantry cranes to electric-driven rail-mounted gantry cranes is one of the effective ways to reduce carbon emissions. However, the retrofit of diesel-driven rubber-tired gantry cranes will seriously affect the port’s operating cost and operating efficiency. Therefore, this study investigates the retrofit problem of diesel-driven rubber-tired gantry cranes considering carbon emissions tax. The problem is described as a mixed-integer programming model that aims to minimize the sum of delay penalty cost, carbon emissions cost, and port investment cost. Two heuristic algorithms are designed to solve the model. Some acceleration strategies are proposed to improve the efficiency of the designed algorithms. Taking Jakarta International Container Terminal as a case study, a large number of numerical experiments are carried out to verify the efficiency of the designed algorithms. The results of the sensitivity analysis experiment show that the implementation of a carbon tax policy can promote the retrofit of rubber-tired gantry cranes, thereby reducing the carbon emissions of the port.

Exhaust fumes from the shipping industry pose a serious threat to the environment and residents’ health in coastal areas. The use of shore power during ship berthing is an effective measure to reduce air pollution near the port. Due to the high investment cost, the deployment of the ship-borne power receiving system is seriously hindered. This study analyzes the impact of government-subsidy-based incentive policy and berthing-priority-based incentive policy on ship-borne power receiving system deployment. The two incentive policies are compared in a quantitative way. A nonlinear programming model is constructed to optimize the ship-borne power receiving system deployment, berth allocation, and the order of ships being served in each berth. Due to the complexity of the proposed model, a sequential heuristic algorithm is designed. A critical-shaking neighborhood search method is applied to improve the solution quality of the sequential heuristic algorithm. In extensive numerical experiments, the effectiveness and efficiency of the improved algorithm are verified by comparing it with particle swarm optimization algorithm, tabu search algorithm, and a lower bound of the proposed model. Experimental results show that the berthing-priority-based incentive policy is recommended to be implemented when the government subsidy rate is low. Based on the experimental results, some management implications are outlined.

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.

Purpose Environmental issues have become an important concern in modern supply chain management. The structure of closed-loop supply chain (CLSC) networks, which considers both forward and reverse logistics, can greatly improve the utilization of materials and enhance the performance of the supply chain in coping with environmental impacts and cost control. Design/methodology/approach A biobjective mixed-integer programming model is developed to achieve the balance between environmental impact control and operational cost reduction. Various factors regarding the capacity level and the environmental level of facilities are incorporated in this study. The scenario-based method and the Epsilon method are employed to solve the stochastic programming model under uncertain demand. Findings The proposed stochastic mixed-integer programming (MIP) model is an effective way of formulating and solving the CLSC network design problem. The reliability and precision of the Epsilon method are verified based on the numerical experiments. Conversion efficiency calculation can achieve the trade-off between cost control and CO 2 emissions. Managers should pay more attention to activities about facility operation. These nodes might be the main factors of costs and environmental impacts in the CLSC network. Both costs and CO 2 emissions are influenced by return rate especially costs. Managers should be discreet in coping with cost control for CO 2 emissions barely affected by return rate. It is advisable to convert the double target into a single target by the idea of “Efficiency of CO 2 Emissions Control Reduction.” It can provide managers with a way to double-target conversion. Originality/value We proposed a biobjective optimization problem in the CLSC network considering environmental impact control and operational cost reduction. The scenario-based method and the Epsilon method are employed to solve the mixed-integer programming model under uncertain demand.

Unmanned aerial vehicles (UAVs) are widely used to perform monitoring tasks both in the military and civilian areas, and the planning of their routes is critical. This study investigates a routing problem in which UAVs monitor a set of areas with different accuracy requirements. This problem is a variant of the classical vehicle routing problem (VRP), where one must determine not only the order in which to visit a set of nodes located in the plane, but also the height at which to visit them, which impacts the accuracy level and the service time. An integer programming model is formulated to optimize flight routes and minimize the total time needed to complete the monitoring tasks. A tabu search metaheuristic is developed for the problem. Extensive numerical experiments are conducted to assess the efficiency of the heuristic.

The continuous influence of global warming has profoundly changed the operation mode of the traditional supply chain network. The balance between costs reduction and environmental protection has become an effective attempt to improve sustainable competitiveness for enterprises. This study presents an integration perspective for developing a green and sustainable closed-loop supply chain (CLSC) network under uncertain demand. A bi-objective optimization model is proposed with two objectives for CO 2 emissions and total operating cost. Decisions regarding the environmental level and the factors affecting the facilities capacity level have also been considered. The scenario-based method is adopted to represent the uncertain demand in the stochastic programming model. A Lagrangean relaxation method is developed to solve the model. The experimental results demonstrate the validity and efficiency of the proposed model and solution method. Several potentially useful managerial implications are also obtained for practitioners.

Coal mine safety is the foundation of coal mine enterprise development, and inspections performed by safety personnel are an indispensable part of ensuring the production of coal mines. The complex network of tunnels in underground mines restricts the efficiency of safety inspection routes; therefore, planning these routes is critical. This study investigates a routing problem in which safety personnel start from multiple sites to inspect a set of sites. A mixed integer linear programming (MILP) model is formulated to optimize the inspection routes. Three methods are used to address the problem: a local branching (LB)-based solution method, a tabu search-based solution method and a back propagation (BP) neural network method. Numerical experiments are conducted to assess the efficiency of the methods on different scales.

This paper investigates a stowage planning problem, in which a liner ship will visit a sequence of ports, the number of available quay cranes in ports and the numbers of loading/unloading containers in ports are uncertain. This stowage planning problem is about how to assign the loaded containers to the bays of the ship considering uncertain information in the future, so as to minimise the sum of the expected quay crane handling time at the ports. Based on stochastic programming, a two-stage decision model is proposed for this problem. A particle swarm optimisation based solution method is developed to solve the model for large-scale problem instances. Numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution method.

This paper investigates crane scheduling problems for a new type of automated container terminal system, which is based on multi-storey frame bridges. For the new design concept, this paper studies how to schedule two types of cranes, i.e., quay cranes and bridge cranes that transfer containers between different storeys. The schedules of these two types of cranes impact the operation efficiency of the terminal system. Mathematical models are proposed for the two scheduling problems. Meta-heuristics are developed to solve them. Numerical experiments are conducted to validate the effectiveness of the proposed models and the efficiency of the proposed solution method.