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Documents authored by Zhao, Jingyang

Document
DOI: 10.4230/LIPIcs.MFCS.2025.93
Improved Approximation Algorithms for Capacitated Vehicle Routing with Fixed Capacity

Authors: Jingyang Zhao and Mingyu Xiao

Published in: LIPIcs, Volume 345, 50th International Symposium on Mathematical Foundations of Computer Science (MFCS 2025)

Abstract
The Capacitated Vehicle Routing Problem (CVRP) is one of the most extensively studied problems in combinatorial optimization. Based on customer demand, we distinguish three variants of CVRP: unit-demand, splittable, and unsplittable. In this paper, we consider k-CVRP in general metrics and on general graphs, where k is the vehicle capacity. All three versions are APX-hard for any fixed k ≥ 3. Assume that the approximation ratio of metric TSP is 3/2. We present a (5/2 - Θ(√{1/k}))-approximation algorithm for the splittable and unit-demand cases, and a (5/2 + ln 2 - Θ(√{1/k}))-approximation algorithm for the unsplittable case. Our approximation ratio is better than the previous results when k is less than a sufficiently large value, approximately 1.7 x 10⁷. For small values of k, we design independent and elegant algorithms with further improvements. For the splittable and unit-demand cases, we improve the approximation ratio from 1.792 to 1.500 for k = 3, and from 1.750 to 1.500 for k = 4. For the unsplittable case, we improve the approximation ratio from 1.792 to 1.500 for k = 3, from 2.051 to 1.750 for k = 4, and from 2.249 to 2.157 for k = 5. The approximation ratio for k = 3 surprisingly achieves the same value as in the splittable case. Our techniques, such as EX-ITP - an extension of the classic ITP method, have the potential to improve algorithms for other routing problems as well.
Cite as

Jingyang Zhao and Mingyu Xiao. Improved Approximation Algorithms for Capacitated Vehicle Routing with Fixed Capacity. In 50th International Symposium on Mathematical Foundations of Computer Science (MFCS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 345, pp. 93:1-93:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{zhao_et_al:LIPIcs.MFCS.2025.93,
  author =	{Zhao, Jingyang and Xiao, Mingyu},
  title =	{{Improved Approximation Algorithms for Capacitated Vehicle Routing with Fixed Capacity}},
  booktitle =	{50th International Symposium on Mathematical Foundations of Computer Science (MFCS 2025)},
  pages =	{93:1--93:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-388-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{345},
  editor =	{Gawrychowski, Pawe{\l} and Mazowiecki, Filip and Skrzypczak, Micha{\l}},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2025.93},
  URN =		{urn:nbn:de:0030-drops-242008},
  doi =		{10.4230/LIPIcs.MFCS.2025.93},
  annote =	{Keywords: Combinatorial Optimization, Capacitated Vehicle Routing, Approximation Algorithms, Graph Algorithms}
}
Document
DOI: 10.4230/LIPIcs.ISAAC.2024.59
Approximation Algorithms for Cumulative Vehicle Routing with Stochastic Demands

Authors: Jingyang Zhao and Mingyu Xiao

Published in: LIPIcs, Volume 322, 35th International Symposium on Algorithms and Computation (ISAAC 2024)

Abstract
In the Cumulative Vehicle Routing Problem (Cu-VRP), we need to find a feasible itinerary for a capacitated vehicle located at the depot to satisfy customers' demand, as in the well-known Vehicle Routing Problem (VRP), but the goal is to minimize the cumulative cost of the vehicle, which is based on the vehicle’s load throughout the itinerary. If the demand of each customer is unknown until the vehicle visits it, the problem is called Cu-VRP with Stochastic Demands (Cu-VRPSD). In this paper, we propose a randomized 3.456-approximation algorithm for Cu-VRPSD, improving the best-known approximation ratio of 6 (Discret. Appl. Math. 2020). Since VRP with Stochastic Demands (VRPSD) is a special case of Cu-VRPSD, as a corollary, we also obtain a randomized 3.25-approximation algorithm for VRPSD, improving the best-known approximation ratio of 3.5 (Oper. Res. 2012). At last, we give a randomized 3.194-approximation algorithm for Cu-VRP, improving the best-known approximation ratio of 4 (Oper. Res. Lett. 2013).
Cite as

Jingyang Zhao and Mingyu Xiao. Approximation Algorithms for Cumulative Vehicle Routing with Stochastic Demands. In 35th International Symposium on Algorithms and Computation (ISAAC 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 322, pp. 59:1-59:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{zhao_et_al:LIPIcs.ISAAC.2024.59,
  author =	{Zhao, Jingyang and Xiao, Mingyu},
  title =	{{Approximation Algorithms for Cumulative Vehicle Routing with Stochastic Demands}},
  booktitle =	{35th International Symposium on Algorithms and Computation (ISAAC 2024)},
  pages =	{59:1--59:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-354-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{322},
  editor =	{Mestre, Juli\'{a}n and Wirth, Anthony},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2024.59},
  URN =		{urn:nbn:de:0030-drops-221878},
  doi =		{10.4230/LIPIcs.ISAAC.2024.59},
  annote =	{Keywords: Cumulative Vehicle Routing, Stochastic Demands, Approximation Algorithms}
}
Document
DOI: 10.4230/LIPIcs.MFCS.2022.83
Improved Approximation Algorithms for the Traveling Tournament Problem

Authors: Jingyang Zhao, Mingyu Xiao, and Chao Xu

Published in: LIPIcs, Volume 241, 47th International Symposium on Mathematical Foundations of Computer Science (MFCS 2022)

Abstract
The Traveling Tournament Problem (TTP) is a well-known benchmark problem in the field of tournament timetabling, which asks us to design a double round-robin schedule such that each pair of teams plays one game in each other’s home venue, minimizing the total distance traveled by all n teams (n is even). TTP-k is the problem with one more constraint that each team can have at most k consecutive home games or away games. The case where k = 3, TTP-3, is one of the most investigated cases. In this paper, we improve the approximation ratio of TTP-3 from (1.667+ε) to (1.598+ε), for any ε > 0. Previous schedules were constructed based on a Hamiltonian cycle of the graph. We propose a novel construction based on triangle packing. Then, by combining our triangle packing schedule with the Hamiltonian cycle schedule, we obtain the improved approximation ratio. The idea of our construction can also be extended to k ≥ 4. We demonstrate that the approximation ratio of TTP-4 can be improved from (1.750+ε) to (1.700+ε) by the same method. As an additional product, we also improve the approximation ratio of LDTTP-3 (TTP-3 where all teams are allocated on a straight line) from 4/3 to (6/5+ε).
Cite as

Jingyang Zhao, Mingyu Xiao, and Chao Xu. Improved Approximation Algorithms for the Traveling Tournament Problem. In 47th International Symposium on Mathematical Foundations of Computer Science (MFCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 241, pp. 83:1-83:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{zhao_et_al:LIPIcs.MFCS.2022.83,
  author =	{Zhao, Jingyang and Xiao, Mingyu and Xu, Chao},
  title =	{{Improved Approximation Algorithms for the Traveling Tournament Problem}},
  booktitle =	{47th International Symposium on Mathematical Foundations of Computer Science (MFCS 2022)},
  pages =	{83:1--83:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-256-3},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{241},
  editor =	{Szeider, Stefan and Ganian, Robert and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2022.83},
  URN =		{urn:nbn:de:0030-drops-168813},
  doi =		{10.4230/LIPIcs.MFCS.2022.83},
  annote =	{Keywords: Sports scheduling, Traveling Tournament Problem, Approximation algorithm}
}
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