A two-stage stochastic programming model for collaborative asset protection routing problem enhanced with machine learning: a learning-based matheuristic algorithm

Mine planning involves the systematic design and coordination of mineral extraction from the earth’s crust, integrating exploration, production, and various engineering considerations. With increasing emphasis on environmental responsibility, the mining industry is under pressure to incorporate environmental considerations into mine planning. This paper addresses the precedence-constrained production scheduling problem (PCPSP) within the context of green long-term mining planning, aiming to optimize extraction processes while restricting carbon emission. Given the NP-hard nature of the PCPSP, this study introduces an adaptive large neighborhood search (ALNS) algorithm tailored specifically for long-term mine planning. A range of computational experiments have been carried out, including parameter tuning for the ALNS algorithm, comparisons against an exact solver, and analysis of our destroy-repair operators to determine their key elements. The efficacy of developed ALNS, evaluated using various benchmarks, reveals optimality gaps around 0.08. These results highlight the effectiveness of the proposed approach in addressing the PCPSP within mine planning and production scheduling, providing insights into improving mining operations while considering environmental concerns.

Freight bus is a new public transportation means for city logistics, and each freight bus can deliver and pick up goods at each customer/supplier location it passes. In this paper, we study the route planning problem of freight buses in an urban distribution system. Since each freight bus makes a tour visiting a set of pickup/delivery locations once at every given time interval in each day following a fixed route, the route planning problem can be considered a new variant of periodic vehicle routing problem with pickup and delivery. In order to solve the problem, a Mixed-Integer Linear Programming (MILP) model is formulated and an Adaptive Large Neighborhood Search (ALNS) algorithm is developed. The development of our algorithm takes into consideration specific characteristics of this problem, such as fixed route for each freight bus, possibly serving a demand in a later period but with a late service penalty, etc. The relevance of the mathematical model and the effectiveness of the proposed ALNS algorithm are proved by numerical experiments.

Additive manufacturing (AM) is progressively transforming supply chains with its advantages of design flexibility, high material utilization, and short production cycles. To fully realize the benefits of AM in supply chains, it is essential to comprehensively consider production and delivery strategies. This paper addresses the synchronization of production and delivery in a localized supply chain using AM. This new operational model integrates production and logistics activities to facilitate immediate delivery upon product completion. We first develop a mixed-integer programming model to describe the problem of minimizing the total time required to serve all customers with the constraints of production schedules and delivery routes. To solve this problem, we then design an enhanced adaptive large neighbourhood search (ALNS) algorithm that incorporates reinforcement learning (RL). The RL component optimizes the operator selection strategy during the search, with the proximal policy optimization algorithm enhancing the stability and speed of the optimization process. Finally, we conduct extensive numerical experiments to validate the effectiveness of the proposed algorithm by comparing it with commercial solvers and other heuristics. In addition, we explore various strategies for operating AM-enabled supply chains and provide insights to help companies better coordinate their operations in this new model.

<p style='text-indent:20px;'>The Vehicle Routing Problem with Multiple Time Windows (VRPMTW) is a generalization of problems in real life logistics distribution, which has a wide range of applications and research values. Several neighborhood search based methods have been used to solve this kind of problem, but it still has drawbacks of generating numbers of infeasible solutions and falling into local optimum easily. In order to solve the problem of arbitrary selection for neighborhoods, a series of neighborhoods are designed and an adaptive strategy is used to select the neighborhood, which constitute the Adaptive Large Neighborhood Search(ALNS) algorithm framework. For escaping from the local optimum effectively in the search process, a local search based on destroy and repair operators is applied to shake the solution by adjusting the number of customers. The proposed method allows infeasible solutions to participate in the iterative process to expand the search space. At the same time, an archive is set to save the high-quality feasible solutions during the search process, and the infeasible solutions are periodically replaced. Computational experimental results on VRPMTW benchmark instances show that the proposed algorithm is effective and has obtained better solutions.</p>

In recent years, with the increasing adoption of electric vehicles (EVs) in waste collection systems, research on related vehicle routing optimization has garnered significant attention. However, existing studies predominantly focus on traditional fuel-powered vehicles, while constraints unique to EVs—such as limited driving range and charging requirements—further complicate the problem. This paper addresses the route optimization problem for electric vehicles in bulky waste collection. A route optimization model incorporating three-dimensional packing constraints is established. Feasible solutions are initially constructed using a Genetic Algorithm (GA), followed by refinement through an improved Adaptive Large Neighborhood Search (ALNS) algorithm. Enhanced destruction and repair operators are designed to simultaneously optimize charging station selection and the efficient spatial arrangement of bulky waste. Simulation experiments across multiple datasets validate the algorithm's feasibility. Results demonstrate that the proposed method effectively resolves charging demands during bulky waste collection and significantly reduces operational costs.

The mixed fleet heterogeneous dial-a-ride problem (MF-HDARP) consists of designing vehicle routes for a set of users by using a mixed fleet including both heterogeneous conventional and alternative fuel vehicles. In addition, a vehicle is allowed to refuel from a fuel station to eliminate the risk of running out of fuel during its service. We propose an efficient hybrid adaptive large neighborhood search (hybrid ALNS) algorithm for the MF-HDARP. The computational experiments show that the algorithm produces high quality solutions on our generated instances and on HDARP benchmarks instances. Computational experiments also highlight that the newest components added to the standard ALNS algorithm enhance intensification and diversification during the search process.

The use of electric vehicles in urban transportation is increasing daily due to their energy efficiency and environmental friendliness. In last-mile logistics, route optimization must consider charging station locations while balancing operational costs and customer satisfaction. In this context, solutions for cost-oriented route optimization have been presented in the literature. On the other hand, customer satisfaction is also important for third-party logistics companies. This study discusses the Capacitated Electric Vehicle Routing Problem with Time Windows (CEVRPTW) encountered in last-mile logistics. This article defines the objective function of minimizing total tardiness and compares the routes between the service provider logistics company and the customer receiving the service. In this study, the CEVRPTW was solved for the minimum total tardiness objective function with the hybrid adaptive large neighborhood search (ALNS) algorithm. The success of ALNS was proven by comparing the differences between the optimal solutions obtained with the CPLEX Solver and the ALNS solutions. Tardiness objective function-specific operators for ALNS are proposed and supported by local search and VNS algorithms. The findings of this study contribute to the literature by analyzing the balance trade-offs between customer-oriented and cost-oriented and the effect of time windows on the number of vehicles.

In this paper, we study the dynamic meal delivery routing problem (MDRP) with time-sensitive customers. The multi-objective MDRP optimization model is developed to maximize customer satisfaction and minimize delay penalty cost and riding cost. To solve the dynamic MDRP, a novel waiting strategy is proposed to divide the dynamic problem into a series of static subproblems. This waiting strategy utilizes the decision threshold to determine rerouting points based on the number of dynamic meal orders. Meanwhile, time-sensitive priority is introduced to accelerate assignment and routing decisions for orders from customers with high time sensitivity. For each static subproblem, a hybrid AGA–ALNS algorithm that incorporates the adaptive genetic algorithm and adaptive large neighborhood search is developed to improve both the global and local search capabilities of the genetic algorithm. We validate the performance of the proposed waiting strategy and the AGA–ALNS algorithm through numerical instances. In addition, managerial insights are obtained from sensitivity analysis experiments.

Managing yarn dyeing processes is one of the most challenging problems in the textile industry due to its computational complexity. This process combines characteristics of multidimensional knapsack, bin packing, and unrelated parallel machine scheduling problems. Multiple customer orders need to be combined as batches and assigned to different shifts of a limited number of machines. However, several practical factors such as physical attributes of customer orders, dyeing machine eligibility conditions like flotte, color type, chemical recipe, and volume capacity of dye make this problem significantly unique. Furthermore, alongside its economic aspects, minimizing the waste of natural resources during the machine changeover and energy are sustainability concerns of the problem. The contradictory nature of these two makes the planning problem multi-objective, which adds another complexity for planners. Hence, in this paper, we first propose a novel mathematical model for this scientifically highly challenging yet very practical problem from the textile industry. Then we propose Adaptive Large Neighbourhood Search (ALNS) algorithms to solve industrial-size instances of the problem. Our computational results show that the proposed algorithm provides near-optimal solutions in very short computational times. This paper provides significant contributions to flexible manufacturing research, including a mixed-integer programming model for a novel industrial problem, providing an effective and efficient adaptive large neighborhood search algorithm for delivering high-quality solutions quickly, and addressing the inefficiencies of manual scheduling in textile companies; reducing a time-consuming planning task from hours to minutes.

This paper studies a joint multi-depot home health care and dialysis problem of routing and scheduling decisions of health specialists. The fleet consists of electric vehicles, which use both public and private charging stations. We formulate the problem as a mixed integer linear programming model. We describe a hybrid adaptive large neighborhood search (ALNS) algorithm, which integrates construction heuristic to generate initial solution and local search procedure based on variable neighborhood descent. The hybrid ALNS successfully combines existing heuristic mechanisms and introduces several new problem-specific procedures to effectively handle the complex structure of the problem. We conduct experiments on realistic benchmark instances to investigate various problem specifications, such as constructed teams, usage rate of fast and super-fast charging technologies, and public and private charging options. We analyze the performance of the hybrid ALNS and its mechanisms. The algorithm obtained good quality results on the complex optimization problem.

We study a class of vehicle‐routing problems with simultaneous pickup and delivery (VRPSPD). In VRPSPDs, each customer may require a certain quantity of goods delivered from the depot and a quantity of goods to be picked up and returned to the depot. Besides the standard VRPSPD, we address (1) the VRPSPD with time limit (VRPSPDTL), which imposes a time limit on the routes of the transportation vehicles, (2) the VRPSPD with time windows (VRPSPDTW), which takes customer time windows into account, (3) the VRP with divisible deliveries and pickups (VRPDDP), which allows for fulfilling the delivery and pickup requests of each customer in two separate visits, (4) the previously unstudied VRP with restricted mixing of divisible deliveries and pickups (VRPRMDDP), which accounts for the difficulty of rearranging the vehicle load by additionally requiring that a certain percentage of the vehicle capacity must remain unoccupied when both types of demand are simultaneously loaded, and (5) the previously unstudied VRPDDP with time windows (VRPDDPTW). We develop a hybrid heuristic solution method which combines an adaptive large neighborhood search algorithm with a path relinking approach, called ALNS‐PR, and we demonstrate the competitiveness of our algorithm on benchmark instances proposed in the literature. Especially on VRPSPDTL, VRPSPDTW, and VRPDDP instances, our ALNS‐PR proves to be superior to the majority of comparison algorithms and is able to obtain numerous new best solutions.

This paper introduces the Fleet Size and Mix Vehicle Routing Problem with Synchronized Visits (FSM-VRPS), an extension of the Vehicle Routing Problem with Synchronization (VRPS), where a mixed fleet composed of electric and conventional bikes, and passenger cars having different acquisition costs are considered. The problem consists of planning a set of different vehicle routes to serve a set of clients who may require more than one visit by different healthcare specialists, and some of these visits should be synchronized. Moreover, each client must be visited within a specified time window. In addition, the problem uses bikes to reduce Carbon Dioxide (CO2) emission for environmentally cleaner routing operations. This problem has many real-life applications, such as the scheduling of visits for homecare givers in the healthcare sector. We present a mixed integer linear-programming formulation and develop a Multi-Start Adaptive Large Neighborhood Search with Threshold Accepting algorithm. The results showed that our algorithm is highly effective on the FSM-VRPS, as well as on the heterogeneous VRPS. We also demonstrate the advantage of adopting different types of vehicles in terms of reducing the number of vehicles and costs. The analysis of the results also indicated that the new components added to the standard Adaptive Large Neighborhood Search algorithm enhanced intensification and diversification mechanisms during the search process.

Motivated by a situation faced by infrastructure service providers operating in urban areas with accessibility restrictions, we study the truck and trailer routing problem with time windows (TTRPTW). In this problem the vehicle fleet consists of trucks and trailers which may be decoupled. A set of customers has to be served and some of the customers can only be accessed by the truck without the trailer. This gives rise to the planning of truck-and-trailer routes containing truck-only subroutes, in addition to truck-only routes and truck-and-trailer routes without subroutes. We propose a branch-and-price algorithm for the TTRPTW, using problem specific enhancements in the pricing scheme and alternative lower bound computations. We also tailor an adaptive large neighborhood search algorithm to the TTRPTW in order to obtain good initial columns. When compared to existing metaheuristic algorithms we obtain highly competitive results. Some instances with up to 100 customers are solved to optimality with the proposed branch-and-price algorithm.

Our work addresses the scheduling of mobile robots for transportation and processing of operations on machines in a flexible manufacturing system. Both mobile robots and automated guided vehicles (AGVs) can transport components among machines in the working space. Nevertheless, the difference is that mobile robots considered in this work can process specific value-added operations, which is not possible for AGVs. This new feature increases complexity as well as computational demands. To summarize, we need to compute a sequence of operations on machines, the robot assignments for transportation, and the robot assignments for processing. The main contribution is the proposal of an adaptive large neighborhood search algorithm with the sets of exploration and exploitation heuristics to solve the problem considering makespan minimization. Experimental evaluation is presented on the existing benchmarks. The quality of our solutions is compared to a heuristic based on genetic algorithm and mixed-integer programming proposed recently. The comparison shows that our approach can achieve comparable results in real time which is in order of magnitude faster than the earlier heuristic.1

Different urban transportation flows (e.g., passenger journeys, freight distribution, and waste management) are conventionally separately handled by corresponding single‐purpose vehicles (SVs). The multi‐purpose vehicle (MV) is a novel vehicle concept that can enable the sequential sharing of different transportation flows by changing the so‐called modules, thus theoretically improving the efficiency of urban transportation through the utilization of higher vehicles. In this study, a variant of the pick‐up and delivery problem with time windows is established to describe the sequential sharing problem considering both MVs and SVs with features of multiple depots, partial recharging strategies, and fleet sizing. MVs can change their load modules to carry all item types that can also be carried by SVs. To solve the routing problem, an adaptive large neighborhood search (ALNS) algorithm is developed with new problem‐specific heuristics. The proposed ALNS is tested on 15 small‐size cases and evaluated using a commercial MIP solver. Results show that the proposed algorithm is time‐efficient and able to generate robust and high‐quality solutions. We investigate the performance of the ALNS algorithm by analyzing convergence and selection probabilities of the heuristic solution that destroy and repair operators. On 15 large‐size instances, we compare results for pure SV, pure MV, and mixed fleets, showing that the introduction of MVs can allow smaller fleet sizes while approximately keeping the same total travel distance as for pure SVs.