To obtain the optimal routes, researchers implemented particle swarm optimization [17; 18], genetic algorithms [19; 20], and heuristic algorithms [21], incorporating some modification strategies to solve the route optimization problem. Emerging metaheuristics combining greedy randomized search with adaptive neighborhood selection have shown particular promise in dynamic delivery environments [22]. Considering the dynamic routing environment and traffic congestion, [PERSON] et al. [23] proposed a fissile ripple-spreading algorithm based on co-evolutionary path optimization. The designed algorithm performs well in simultaneously solving the delivery sequence and shortest path problems. [PERSON] et al. [24] developed an adaptive extensive neighborhood search (ALNS) algorithm for the route planning of freight buses with pickup and delivery. [PERSON] et al. [25] designed a two-stage model using mixed-integer programming to optimize the rider scheduling for food takeaway delivery, which helps reduce the delivery cost. [PERSON] et al. [26] improved the ant colony algorithm by integrating a density peak clustering algorithm and a local optimization strategy. The improved algorithm performed well in solving the traveling salesman problem. Novel pheromone updating mechanisms with backtracking capabilities have recently enhanced algorithm convergence in complex routing scenarios [27]. For example, [PERSON] et al. [28] designed a delivery path optimization based on a clone adaptive ant colony optimization for the last-mile assignment problem. By applying a new distribution cost fitness function, the improved algorithm shows a faster convergence speed, and its results regarding the economic expenditure and delivery time are lower than other algorithms. Taking real-time traffic conditions into consideration, [PERSON] et al. [29] proposed a hybrid ant colony system and virtual transformation method (ACS-VTM) for delivery routing optimization, where this algorithm significantly improves vehicle utilization and delivery efficiency. With the increasing demand for takeaway delivery, merchants are inclined to hire a certain number of riders to operate the delivery process by themselves. The merchant self-operated takeaway delivery model, which has significant advantages in controlling delivery time and goods quality, is gaining increasing attention. For riders, they are more concerned about the fairness of order assignment. This paper studied the merchant self-operated takeaway delivery by dividing it into order assignment and delivery path planning. To guarantee fairness in the order assignment process, we allocated customer orders to riders based on traveling distance, order quantity, and consumer location distribution. For delivery path planning, the optimal route with the least travel distance or time should be presented to the rider in a limited timeframe, which requires the algorithm to find the optimal route with a fast convergence speed. The merchant self-operated takeaway delivery model was established, and it was solved using a framework that integrates K-means clustering with an improved ant colony optimization algorithm. The order quantity fairness and traveling distance fairness were considered during the solving process to balance the workload distribution between riders.