To mitigate or reduce various losses and improve efficiency of disaster response, the focus of this paper is to design optimized strategies of emergency organization allocation regarding sustainability. Firstly, an integrated framework including several elements such as emergency organization, task, decision-agents, environment and their relations is developed from a systematic perspective. Then, this problem is formulated as a novel multi-objective 0–1 integer programming model to minimize total weighted completion times, total carbon emissions and total emergency costs. Next, branch and bound approach and handling strategies for multiple objectives are designed to solve this model. Finally, a case study from the Wenchuan earthquake is presented to illustrate the proposed model and solution strategies. Computational results demonstrate their significant potential advantages on allocating emergency organization from the perspectives of best practice, objective functions, preferences of decision-agents, and problem size.

With regard to task distribution in a ridesharing company, both the suitability of the tasks assigned to the drivers and the acceptability of the riders receiving the service should be simultaneously considered to improve the sustainability regarding Hitch services. Firstly, the process of the bi-directional choice between the drivers and the riders is described as a one-to-one two-sided matching problem. Next, prospect theory is used to characterize the psychological perceived behavior of both sides towards the matching scheme under the multiple criteria. Thus, the suitability function concerning the drivers and the acceptability function regarding the riders are naturally constructed. Following this, a two-sided matching decision model with two objectives is proposed. Finally, numerical experiments are presented to verify the feasibility and effectiveness of the proposed model. Besides, managerial insights associated with how to set the optimization objectives under unbalanced supply-demand in ridesharing companies are given. Increasingly, this paper aims to not only validate the proposed methodology, but also to highlight the importance and urge of incorporating sustainability into the task distribution problem in ridesharing.

In order to reduce the energy consumption of furnaces and save costs in the product delivery time, the focus of this paper is to discuss the uncertainty of demand in the rolling horizon and to globally optimize the sustainability of the production in the aluminum furnace hot rolling section in environmental and economic dimensions. First, the triples $\alpha /\beta /\gamma$ are used to describe the production scheduling in the aluminum furnace hot rolling section as the scheduling of flexible flow shop, satisfied to constraints of demand uncertainty, operation logic, operation time, capacity and demand, objectives of minimizing the residence time of the ingot in the furnace and minimizing the makespan. Second, on the basis of describing the uncertainty of demand in rolling horizon with the scenario tree, a multi-objective mixed integer linear programming (MILP) optimization model for sustainable production in the aluminum furnace hot rolling section is formulated. Finally, an aluminum alloy manufacturer is taken as an example to illustrate the proposed model. The computational results show that when the objective weight combination takes the value of $\alpha =0.7, \beta =0.3$, the sustainability indicators of the environmental and economic dimensions can be optimized to the maximum extent possible at the same time. Increasingly, managerial suggestions associated with the trade-off between environmental and economic dimensions are presented. Scheduling in the rolling horizon can optimize the production process of the aluminum furnace hot rolling section globally, indicating that it is more conducive to the sustainable development of the environment and economic dimensions than scheduling in a single decision time period.