This paper addresses the challenge of rising operational costs in last-mile delivery caused by end-customer no-shows. The study proposes a collaborative operational framework for last-mile delivery that accommodates roaming customers, enabling them to be serviced by multiple depots as they transition between different locations. A mixed-integer programming (MIP) model is formulated to minimize the operational costs of last-mile delivery under the proposed framework. To improve the model’s practicality and computational efficiency, an adaptive large neighborhood search (ALNS) algorithm is developed, incorporating tailored neighborhood structures. Furthermore, a late acceptance strategy is embedded within the algorithm to mitigate the risk of premature convergence to local optima. The experimental results demonstrate that, in the absence of depot collaboration, the multi-depot model achieves a 16.9% reduction in operational costs compared to the single-depot model. Moreover, when depot collaboration is enabled, the average cost reduction percentage significantly increases to 40.37%. Notably, under the multi-depot collaborative framework, considering customers' roaming behavior—as opposed to fixed single-location assumptions—leads to a substantial 54.6% reduction in operational costs.

This paper proposes a new hybrid algorithm to solve the multi-compartment vehicle routing problem (MCVRP) with a heterogeneous fleet of vehicles for the fuel delivery problem of a previous study of twenty petrol stations in northeastern Thailand. The proposed heuristic is called the Fisher and Jaikumar Algorithm with Adaptive Large Neighborhood Search (FJA-ALNS algorithm). The objective of this case is to minimize the total distance, while using a minimum number of multi-compartment vehicles. In the first phase, we used the FJA to solve the MCVRP for the fuel delivery problem. The results from solving the FJA were utilized to be the initial solutions in the second phase. In the second phase, a hybrid algorithm, namely the FJA-ALNS algorithm, has been developed to improve the initial solutions of the individual FJA. The results from the FJA-ALNS algorithm are compared with the exact method (LINGO software), individual FJA and individual ALNS. For small-sized problems (N=5), the results of the proposed FJA-ALNS and all methods provided no different results from the global optimal solution, but the proposed FJA-ALNS algorithm required less computational time. For larger-sized problems, LINGO software could not find the optimal solution within the limited period of computational time, while the FJA-ALNS algorithm provided better results with much less computational time. In solving the four numerical examples using the FJA-ALNS algorithm, the result shows that the proposed FJA-ALNS algorithm is effective for solving the MCVRP in this case. Undoubtedly, future work can apply the proposed FJA-ALNS algorithm to other practical cases and other variants of the VRP in real-world situations.

The hub location problem (HLP) is one of the strategic planning problems encountered in different contexts such as supply chain management, passenger and cargo transportation industries, and telecommunications. In this paper, we consider a reliable uncapacitated multiple allocation hub location problem under hub disruptions. It is assumed that every open hub facility can fail during its use and in such a case, the customers originally assigned to that hub, are either reassigned to other operational hubs or they do not receive service in which case a penalty must be paid. The problem is modeled as two-stage stochastic program and a metaheuristic algorithm based on the adaptive large neighborhood search (ALNS) is proposed. Extensive computational experiments based on the CAB and TR data sets are conducted. Results show the high efficiency of the proposed solution method.