This paper introduces and solves the static bike rebalancing problem with for bidden temporary operations. In this problem, one aims at finding a minimum cost route in which a vehicle performs a series of pickup and delivery operations while satisfying demand and capacity constraints. In addition, a vehicle can visit stations multiple times but cannot use them to temporarily store or provide bikes. Apart from bike rebalancing, the problem also models courier service transportation and repositioning of inventory between retail stores, where temporary operations are frequently disliked because they require additional manual work and service time. We present some theoretical results concerning problem complexity and worst-case analysis, and then propose three exact algorithms based on different mathematical formulations. Extensive computational results on instances involving up to 80 stations show that an exact algorithm based on a minimal extended network produces the best average results.
The Static Bike Sharing Rebalancing Problem with Forbidden Temporary Operations / Bruck, Bruno; Cruz, Fabio; Iori, Manuel; Subramanian, Anand. - In: TRANSPORTATION SCIENCE. - ISSN 0041-1655. - 53:3(2019), pp. 882-896. [10.1287/trsc.2018.0859]
The Static Bike Sharing Rebalancing Problem with Forbidden Temporary Operations
Manuel Iori;
2019
Abstract
This paper introduces and solves the static bike rebalancing problem with for bidden temporary operations. In this problem, one aims at finding a minimum cost route in which a vehicle performs a series of pickup and delivery operations while satisfying demand and capacity constraints. In addition, a vehicle can visit stations multiple times but cannot use them to temporarily store or provide bikes. Apart from bike rebalancing, the problem also models courier service transportation and repositioning of inventory between retail stores, where temporary operations are frequently disliked because they require additional manual work and service time. We present some theoretical results concerning problem complexity and worst-case analysis, and then propose three exact algorithms based on different mathematical formulations. Extensive computational results on instances involving up to 80 stations show that an exact algorithm based on a minimal extended network produces the best average results.File | Dimensione | Formato | |
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