Abstract Many variants of the vehicle routing problem (VRP) pose significant computational challenges in logistics optimization, and improvement heuristics have emerged as effective tools for refining solutions found by local search methods and meta-heuristics. This paper introduces exact route-modifying improvement models (RMIMs). They are improvement models that aim to assemble high-quality solutions by selecting routes from a pool while allowing modifications to be made to the selected routes. These models can be embedded in a complete heuristic or used to post-optimize solutions produced by other methods. We evaluate our proposed models on vehicle routing problems with intra-route constraints, including the multi-trip VRP (MTVRP), the pickup and delivery problem with time windows (PDPTW), and the VRP with time windows (VRPTW). For the MTVRP, we propose a full matheuristic that uses a RMIM to achieve best known solutions for most benchmark instances for the MTVRP. By warm-starting with the current best known solutions from the literature the RMIMs improve many existing solutions for both the PDPTW and the VRPTW. These findings showcase the value of using RMIMs to enhance solutions to different types of VRPs.

Abstract Efficient and robust charging infrastructure plays a key role in accelerating the adoption of electric buses (eBuses) in urban transit systems. Unlike diesel buses, eBuses depend on strategically placed fast-charging stations to maintain continuous operation while ensuring high service quality. Planners must balance infrastructure investment, network reliability, and operational feasibility when determining optimal charging locations. Providing redundant charging access prevents disruptions from station failures, energy consumption fluctuations, and scheduling uncertainties. This work introduces an Iterated Local Search (ILS) algorithm that optimises the number and placement of charging stations. The approach improves robustness by incorporating backup charging stations and flexible energy redistribution techniques. We evaluate performance using real-world public transportation data from Cork and Dublin, comparing results against the state-of-the-art Large Neighborhood Search (LNS) method. Our experiments show that ILS outperforms LNS in 81.2% of cases by requiring fewer charging stations, whereas LNS performs better in 7.8% of cases. ILS is particularly effective in high-energy-demand scenarios with strict timetable constraints and realistic discharging rates, demonstrating its effectiveness in ensuring viable and scalable charging station placement.

Abstract The Distributor’s Pallet Loading Problem aims to optimize the loading of different 3D boxes on the minimum number of pallets. We consider an Integer Linear Programming (ILP) model for the problem that includes constraints deriving from real applications, such as stability and compression limits. In order to solve the ILP problem efficiently, we propose a method that exploits Machine Learning algorithms to classify predetermined layers of boxes, based on their “importance” of being used for an ILP solution. This classification is used to heuristically limit the number of layers taken into account by the ILP solver. We demonstrate the effectiveness of our approach by comparing the ILP solution with and without the Machine Learning component. The numerical results show that the proposed Machine Learning matheuristic approach achieves optimized pallet loading solutions in significantly reduced computational time.

Abstract This work introduces the Single Source Capacitated Partial Set Covering Location Problem (SSCPSCLP). Given a set of potential capacitated facilities and a set of customers with their demands, this problem consists of determining the facilities to be opened and the customers to be served to minimize the total cost of opening facilities, ensuring that at least a minimum amount of the total demand is served, only covered customers can be served, the capacity of the facilities is not exceeded, and each customer can be served by at most one facility. This work introduces a mathematical model for the problem and, since it is NP-hard, develops an algorithm based on the Tabu Search (TS) metaheuristic to handle large-scale instances. The TS algorithm explores the problem’s solution space through two neighborhoods, one of which is incorporated into a local search procedure that is periodically applied. The initial solution is generated either by a constructive greedy heuristic or by solving the linear relaxation of the SSCPSCLP. Four TS variants were proposed, differing in the method for generating the initial solution and in the use of local search. The results of computational experiments on instances from the literature show that the TS algorithm finds high-quality solutions in a shorter runtime than a mathematical programming-based solver and that the version using initial solutions from the linear relaxation of the problem and local search performs best.

The p-median problem has a central importance in the context of location planning problems. An extended version of this problem is the capacitated p-median problem (CPMP) which is used for diverse applications in urban planning and medical care units location. Given its relevance and its practicality, in this paper we present a large neighborhood search (LNS) and a study of hyper-heuristics for the CPMP. We propose and analyze various destruction operators within the framework of LNS to efficiently explore diverse neighborhoods. An exact solver is used in the repair phase. Additionally, these operators are also used for the hyper-heuristics, which are high-level problem-independent solution approaches, to propose new low-level heuristics. We provide a comparison of the LNS and of the best performing hyper-heuristics with state-of-art approaches for this problem. The proposed solution methods provide a lower average GAP value compared to the state of the art and find better solutions for several instances. We also give a detailed analysis of the performance of the low-level heuristics and their impact on the instances with different structure. These results confirm the robust performance of hyper-heuristics for the CPMP, which have the advantage to allow the employment on similar scenarios with minor adjustments.