The vehicle routing optimization model under the customer loss mechanism is established with the objective of minimizing the sum of vehicle fixed costs, variable routing costs, and time window penalty costs and an improved genetic algorithm is employed to solve this model.
Abstract
In the field of instant delivery, the mismatch between delivery resources and customer demands has led to increasingly significant customer losses. To address this issue, this study introduces the customer loss mechanism and constructs an evaluation function to screen out resource-intensive customers, thereby clarifying the scope of delivery services. Based on this, this study establishes the vehicle routing optimization model under the customer loss mechanism with the objective of minimizing the sum of vehicle fixed costs, variable routing costs, and time window penalty costs. An improved genetic algorithm is employed to solve this model. Case study results demonstrate that the improved genetic algorithm outperforms traditional genetic algorithms and tabu search algorithms in convergence speed, optimization capabilities, and stability, reducing total delivery cost by 36.25% and 4.18%, respectively, with zero delivery violations. Regarding model performance, when proactively excluding 8.33% of customers, the total delivery cost is reduced by 17.18%, primarily driven by the reduction in fleet size. Furthermore, large-scale experiments reveal a pronounced leverage effect: excluding a mere 5% of marginal customers counter-intuitively reduces both fleet size and travel distance, while a 10% loss yields an 18.39% total delivery cost reduction with zero violations, proving that the mechanism precisely screens out inefficient nodes rather than arbitrarily rejecting them. Sensitivity analysis further confirms the model’s robustness across varying resource tightness, demonstrating that proactive customer loss is a feasible and effective strategy for improving resource utilization through precise resource focusing.
This study investigates the dynamic-demand green vehicle routing problem with soft time windows (DDGVRPSTW). A two-stage optimization model is developed to minimize total distribution cost, including vehicle operating cost, fixed dispatch cost, fuel consumption cost, carbon emission cost, and time window penalty cost. To solve the model, a hybrid artificial bee colony state-transition algorithm (HABC-STA) is proposed. In the pre-optimization stage, multiple initial routes are generated and refined to obtain an initial distribution plan. In the dynamic optimization stage, customer information is updated at a specified event time, and four state-transition operators are used to search the neighborhood of the current solution and generate a revised routing plan with lower cost. Computational results on Solomon benchmark instances and a real-world case study show that the proposed method effectively reduces both total cost and environmental cost. The results also indicate that selecting an appropriate distribution scheme can significantly reduce fuel consumption and carbon emissions while improving overall routing efficiency.
Ming He, Kaijun Zhou, Qian Wang et al.· Electronics· 0 citations
Distribution companies now face challenges in allocating resources efficiently to maintain competitiveness in an increasingly competitive market. The problem of distributing goods from a depot to customers, where customer demand may exceed vehicle capacity and delivery and pickup are performed simultaneously, can be solved using a variant of VRP called Split Load Vehicle Routing Problem with Simultaneous Delivery and Pickup (SLVRPSDP). State of the art in this article, the SLVRPSDP problem is solved using the Iterated Local Search Random Variable Neighborhood Descent (ILS-RVND) algorithm. There are three stages in the ILS RVND algorithm, namely initialization of the initial solution with Sequential Insertion, improvement of the solution with the RVND algorithm, and acceptance of optimal conditions with a perturbation procedure. The Borland Delphi 7.0 program was designed by creating a flowchart, coding, and implementation with inputs in the form of points as customers, distances between customers, demands and returns, vehicle capacity, ILS iterations, and ILS-RVND iterations. The program’s output consists of the routes formed, total distance traveled, number of vehicles used, and graph visualization. Testing the real-world case study data of goods distribution at China Express compared to the ILS-RVND algorithm on SLVRPSDP produced a solution with a small gap.
Afril Istihawa, S. Wahyuningsih· Journal of Physics, Conferen...· 0 citations
To address contactless delivery and fully account for customers’ expected delivery time preferences, this paper presents a mixed-integer programming model for minimizing truck–drone cooperative delivery costs. The model takes truck stopping points, truck routes, drone routes, and customer service time windows as decision variables and minimizes the sum of truck operating costs, drone operating costs, and time-window penalty costs as the objective. Based on the characteristics of the model, a two-stage algorithm is designed. In Stage 1, an improved K-Means clustering method partitions customers into sub-regions, with each cluster centroid serving simultaneously as a temporary truck stop and a drone launch point. In Stage 2, a variable neighborhood simulated annealing (SAVN) algorithm jointly optimizes truck and drone cooperative delivery routes to achieve minimum delivery cost. The correctness and effectiveness of the model and algorithm are verified by benchmarking against an exact solver and two traditional heuristic algorithms. A real-world case study in Chongqing, China, further shows that the two-stage algorithm achieves moderate cost savings and substantial solution-time reduction compared with simultaneous truck–drone route generation.
The rapid development of shared delivery, parcel lockers, and community pickup services has enabled customers to receive orders at multiple alternative service locations. In such scenarios, the fixed-location assumption adopted in traditional electric vehicle routing problems is no longer appropriate. This paper investigates the Electric Vehicle Routing Problem with Time Windows and Flexible Service Locations (EVRPTW-FSL), in which customers can be served at one selected location from a candidate set, while each service location may accommodate multiple customers subject to capacity limits. A mixed-integer optimization model is developed to jointly determine service location assignments, vehicle routing, and charging decisions under vehicle capacity, battery range, partial recharging, and customer time-window constraints. To balance operational efficiency and customer convenience, the objective minimizes the total travel cost and the customer deviation cost incurred when a customer is assigned to an alternative service location rather than the original service location. To solve this NP-hard problem, a Modified Adaptive Large Neighborhood Search with Fix-and-Optimize mechanism (MALNS-FO) is proposed, incorporating specialized operators such as location association destroy, location similarity destroy, and route reconstruction repair, as well as a fix-and-optimize mechanism. Computational experiments demonstrate that the proposed method consistently outperforms benchmark approaches in solution quality and computational efficiency. Results further show that introducing flexible service locations can significantly reduce fleet usage and routing cost by consolidating spatially dispersed demand. Moreover, moderate customer flexibility provides substantial operational benefits while maintaining acceptable service deviation levels.
The findings demonstrate that metaheuristic techniques consistently outperform traditional algorithms in complicated, constraint-rich situations and emphasize the need of cost-effective, data-driven metaheuristic optimization in current logistics planning.
K. Khaw, C. Tan· International Journal on Rob...· 0 citations
