Humanitarian logistics plays a vital role in disaster management. However, it often faces the challenge of unpredictable road conditions when solving relief prepositioning problems to effectively respond to natural disasters. This study examines the location–allocation problem in humanitarian logistics for disaster relief supplies. The research focuses on determining the optimal number and locations of supply distribution points and developing an efficient allocation scheme for relief items. Herein, we propose a two-stage programming model utilizing the distributionally robust optimization (DRO) approach for the location–allocation problem when considering uncertain road conditions. The DRO approach optimizes the expectation value of worst-case across all distributions within a specified ambiguity set. Relief facility locations, inventory levels, and relief supply distribution plans are all determined simultaneously. By utilizing the Wasserstein metric, an ambiguity set is created to characterize the link-wise uncertainties. This metric can capture uncertainties based on finite sample information, ensuring with high probability that the true distribution is contained within the constructed set. To address the problem, We develop an algorithm based on Benders decomposition. To validate the efficacy of the model and methodology, we conduct a real-life case study on the threats posed by hurricanes occurring in the Gulf of Mexico region. We evaluate the performance of our proposed model by comparing it to the scenario-based stochastic programming model and the traditional robust model. Furthermore, we offer managerial insights based on the application of distributionally robust optimization methodology in humanitarian logistics.

Proper prepositioning of emergency supplies can dramatically improve the efficiency of emergency response work. However, the uncertainties of emergency demands and road conditions bring difficulties to the prepositioning of emergency supplies. This paper proposes a two-stage robust model to locate emergency supply points and preposition the corresponding storage amount of emergency supplies, in which we presented two budget sets to describe the uncertainties of demands and road conditions, respectively. The innovative use of variables in the model to limit road capacities addresses the representation of different road interruption scenarios. We proposed an algorithm based on Benders decomposition by transforming the second-stage model into a binary linear programming model. Computational experiments based on the Sioux Falls network demonstrate the validity of the model and algorithm. We conducted sensitivity analyses for some important parameters in the model, such as two uncertainty control parameters, unit transportation cost, budget for the construction of emergency supply points, etc. We find that the uncertainty of road disruptions has a greater impact on the model than the uncertainty of demands. In addition, when the control parameter of the road disruptions exceeds a certain threshold, its influence on the model remains essentially constant.

Automatic design of stochastic local search algorithms has been shown to be very effective in generating algorithms for the permutation flowshop problem for the most studied objectives including makespan, flowtime and total tardiness. The automatic design system uses a configuration tool to combine algorithmic components following a set of rules defined as a context-free grammar. In this paper we use the same system to tackle two of the most studied additional constraints for these objectives: sequence dependent setup times and no-idle constraint. Additional components have been added to adapt the system to the new problems while keeping intact the grammar structure and the experimental setup. The experiments show that the generated algorithms outperform the state of the art in each case.