Abstract
This paper considers the product service scheduling problem with service matching. This problem is a complex scheduling problem that integrates multiple traveling salesman problem (MTSP) and multiple service types. To solve the problem, a tabu search method was presented. With the objective of minimizing the total of engineers’ travel distances, total of the customer penalty values, and makespan, an optimization model of this problem is established. The historical search solutions are taken as tabu object, and limiting the quantity of search neighborhood solution is taken as the aspiration level. At last, the adaptability, validity, and stability of the model are verified by an example.
Highlights
With the continuous development of industrial technology, the scale of production is constantly expanding, which leads to homogenizing of products
Yongzhen et al [9] considered the multiple traveling salesman problem (MTSP) with the Min-Max objective, which includes more than one salesman to serve a set of cities while minimizing the maximum distance traveled by any salesman and proposed a novel memetic algorithm, which integrates with a sequential variable neighborhood descent that is a powerful local search procedure to Journal of Advanced Transportation exhaustively search the areas near the high-quality solutions
Yousefikhoshbakht et al [12] presented a new modified version of the ant colony optimization mixed with insert, swap, and 2-opt algorithm called NMACO for solving MTSP, which utilizes an effective criterion for escaping from the local optimum points
Summary
With the continuous development of industrial technology, the scale of production is constantly expanding, which leads to homogenizing of products. Samerkae et al [3] introduced a new algorithm in competition-based network to solve the Min-Max MTSP, which needs the maximum distance among all salesmen to be minimum. Yongzhen et al [9] considered the MTSP with the Min-Max objective, which includes more than one salesman to serve a set of cities while minimizing the maximum distance traveled by any salesman and proposed a novel memetic algorithm, which integrates with a sequential variable neighborhood descent that is a powerful local search procedure to Journal of Advanced Transportation exhaustively search the areas near the high-quality solutions. Yuan et al [14] proposed a new crossover operator called two-part chromosome crossover for solving MTSP using a genetic algorithm for near-optimal solutions. We proposed a tabu search method for product service scheduling problem with service matching.
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