Carsharing systems have attracted considerable market interest owing to their positive impact on resolving social and environmental pollution problems such as traffic congestion, lack of parking spaces, and greenhouse gas emissions. However, one-way carsharing systems often struggle with vehicle imbalance issues, which can affect customer satisfaction. To address this, a one-way electric carsharing model was designed to maximize operator revenue by introducing customer space flexibility. A mixed integer nonlinear program was formulated to optimize consumer flexibility, relocation, and employee-hiring decisions. The variation in the average vehicle charge at each station was captured using the state of change (SOC) model. Real data from the Evo carsharing company in Vancouver, Canada, were used to conduct numerical tests, verifying the effectiveness of the rolling horizon method in producing high-quality solutions. The results indicate that customer space flexibility can enhance the operational efficiency of carsharing systems without increasing the number of vehicles or parking spaces. This approach can also boost total system revenue and customer satisfaction, with departure space flexibility showing a significantly greater impact than arrival space flexibility.

The rapid increase in the ownership of private vehicles has exacerbated social and environmental problems, such as traffic congestion and exhaust emissions, and carsharing services are regarded as an effective method for solving these problems. In a one-way carsharing system, customer demand is highly variable, and the volume of demand in different locations changes continuously. As a result, the facility planning for a one-way carsharing system is a difficult issue for the managers. In this study, a dynamic optimization method based on a mixed integer programming model is proposed to solve this issue. In the model, various costs, such as station construction, parking space operation, vehicle maintenance, and vehicle rescheduling, are considered, and the objective of optimization is to maximize the profits of the operators. To solve the proposed model, an adaptive Jaya algorithm is proposed. Finally, the case of Vancouver, Canada, is used to verify the effectiveness of the proposed model, and the experimental results show that compared with those of the traditional single-period static planning method, the dynamic planning method proposed in this study can increase the average revenue of the operators by 10.8% and the average customer service rate by 2.95%.

To guarantee that emergency calls can be responded to in time, the government is obliged to implement an effective ambulance location plan. In practice, emergency medical service (EMS) systems work in an uncertain environment with stochastic demand, response time, and travel time. The uncertainty of these factors significantly affects ambulance location planning. However, most recent studies in this field fail to adequately consider the effect of the spatial uncertainty of demand since it is difficult to describe quantitatively. As a result, most allocation plans are not efficient. In this study, Gaussian mixture model clustering is innovatively utilized to quantitatively describe spatially uncertain demand. Accordingly, the chance constraint programming model for ambulance allocation planning is developed. The objective is to minimize the sum of the cost of patient lives lost and the operational cost of the emergency facilities. Two years of data from the Shanghai Songjiang District are used to validate the proposed method. The data from 2013 are utilized to fit the spatial distribution of demand. The data from 2014 are used to test and verify the obtained models. The experimental results demonstrate that the delay time can be significantly decreased with the proposed methods. Furthermore, compared with other classic assumptions for the spatial randomness of demand in this field, better service performance and lower cost are obtained with the proposed methods.

To ensure a timely response to emergencies, governments are obliged to implement effective ambulance allocation plans. In practice, an emergency medical service (EMS) system works in an uncertain environment, with stochastic demand, response-times, and travel-times. This uncertainty significantly affects ambulance allocation planning. However, few studies in this field adequately consider the effect of spatiotemporal uncertainty in demand, because it is difficult to measure it quantitatively. As a result, few analytic models capture the dynamic nature of an EMS system and, thus, the allocation plans they generate are not efficient in practice. Therefore, this study proposes a simulation-based optimization method for ambulance allocation. A simulation model is constructed to mimic the operational processes of an EMS system, and to evaluate the performance of an ambulance allocation plan in an uncertain environment. Gaussian mixture model clustering is used to derive the uncertain spatial demand. Then, the simulation generates emergency demand based on the obtained spatial distribution. A Gaussian-process-based search algorithm is used together with the simulation model to identify optimal solutions. To validate the proposed method, a case study is conducted using data on emergency patients in the Shanghai Songjiang District. Compared with the current plan adopted in Songjiang, the experimental results demonstrate that the delay time and frequency of the EMS system can be reduced significantly by employing the proposed methods. Furthermore, nearly 41% of the allocation cost can be saved.

Multiproduct supply chain network design has been a hot and important issue in the field of operational and management science. In literature, the interactive influence on the production capacity of different types of products is ignored. As a result, the plan obtained by the optimization model is not effective in practice. In this study, the different productivities for different types of products are considered, and some new production capacity constraints are introduced. Based on these constraints, a multi-objective optimization model is developed to solve the supplier selection and production planning problems. Both economic and environmental performances are considered in this model. The objectives are to minimize the total cost and carbon emissions. Finally, a case study about a multiproduct supply chain network is performed to validate the model proposed. In the future research, more experiments will be designed to compare the difference between optimization with and without consideration of the environmental objective and the interactive influence of multiproduct.

Greenhouse gas (GHG) emissions cause climate changes, and their impact on the environment continues to increase. As a result, there is an urgent need to accelerate efforts to reduce GHG emissions. In industry, the majority of the current methods of reducing GHG emissions depend on technical enhancements of the facility and equipment. These methods focus on local optimization for carbon emission reduction in enterprises and may require additional time, money and effort for satisfactory implementation. Unlike technical approaches that focus on the equipment, this paper proposes an approach that combines carbon footprint analysis and production planning. The carbon footprint of the entire company’s operational process can be analyzed systematically using this approach. Subsequently, carbon emissions can be reduced significantly through production planning and optimization. To test the effectiveness of the proposed approach, a pharmaceutical enterprise is employed as an example. A production planning model is constructed based on the energy consumption analysis of different units and equipment. Using this model, the carbon emissions of the enterprise can be analyzed, and the corresponding production plan can be developed. To determine the optimal solution, a hybrid discrete particle swarm algorithm is developed and tested based on real data collected from the pharmaceutical enterprise. The experimental results demonstrate that the proposed novel approach is effective and carbon emissions of the enterprise can be reduced by an average of 6.77%, or a reduction in CO2 emissions of 610.2 tons per year.

Clinical pathway (CP) is a tool to improve service quality and efficiency of the medical institutions. However, most of the clinical pathways designed with the traditional methods are static and non-adaptive. Recently, the process mining techniques are receiving increasing attentions. It can not only help the clinical pathways designers to discover the sequence of activities, but also provide execution information for analyzing variances and correcting design errors. In this article, first, a literature review is presented. In the analysis of 37 studies from the period 2004-2013, three research aspects (process discovery for clinical pathways design, variants analysis and control, continuous evaluation and improvement) are explored, and the weaknesses of the methods are analyzed. It is found that the mining algorithms developed are not efficient enough to deal with the unstructured processes, the models obtained cannot give a good explanation of the variants, and the lack of systemic thinking makes the improvement process of CP very tedious. Based on the analysis, finally, four key trends are identified: (1) further analysis of the variants, (2) integrated process management, (3) customization and (4) self-learning improvement of the clinical pathway.

To now, agent scheduling for a multi-skill call center is still a unsolved problem. The deployment of multi-skill agents and the evaluation of service level are the most critical and difficult issues. Some related studies try to solve the problem utilizing heuristic algorithm supported by simulation analysis. However, since the simulation analysis is required for each round of heuristic calculation, the computing efficiency is too low to be useful in real practices. In this paper, a novel agent scheduling framework is proposed which consists of two interactive modules: (1) simulation module is used to derive the evaluation function that describes the relationship between the service performance and the key parameters, e.g. arrival rate and service rate. (2) A scheduling algorithm module is used to search out the optimal solution accordingly. This approach does not need the interaction between simulation and heuristic searching. As a result, the computing efficiency can be improved significantly.