TSP with Multiple Time-Windows and Selective Cities

Mesquita, Marta; Murta, Alberto G.; Paias, Ana; Wise, Laura

doi:10.1007/978-3-642-41019-2_12

Cited by 2 publications

(8 citation statements)

References 9 publications

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“…SROP can be modeled as a TSPSTW, with additional constraints, on a single commodity network as proposed in Mesquita et al. (). Alternatively, SROP may be viewed as a variant of the m ‐TSPTW (

m = 2

) and modeled on a two‐commodity network, each commodity being associated with a circuit, starting and ending at the city of Lisbon.…”

Section: Problem Formulationmentioning

confidence: 99%

“…In Section 3.1.2, we reference a sequential approach already presented in Mesquita et al. (), which will be compared with the new proposals from a computational point of view.…”

Section: Solution Approachmentioning

confidence: 99%

“…Set L = L 1 ∪ L 2 , with L 1 = {0, n + p + 1} and L 2 = {n + p + 2, n + p + 3}, includes replicas of the port of Lisbon to represent the starting and ending location of the route in the first circuit, L 1 , and the starting and ending location of the route in the second circuit, L 2 . SROP can be modeled as a TSPSTW, with additional constraints, on a single commodity network as proposed in Mesquita et al (2013). Alternatively, SROP may be viewed as a variant of the m-TSPTW (m = 2) and modeled on a two-commodity network, each commodity being associated with a circuit, starting and ending at the city of Lisbon.…”

Section: Problem Formulationmentioning

confidence: 99%

“…In Section 3.1.1, we propose two new alternative sequential approaches, Seq and SeqPlus, that use the same hierarchic ordering of the decisions but allow different subproblem integration between two levels of decision. In Section 3.1.2, we reference a sequential approach already presented in Mesquita et al (2013), which will be compared with the new proposals from a computational point of view.…”

Section: Sequential Approachesmentioning

confidence: 99%

“…Furthermore, the SROP may be viewed as a real‐world application of the TSP presented in Mesquita et al. (), namely the TSP with selective cities and multiple time windows (TSPSTW). In the TSPSTW, the set of cities is divided into two subsets: mandatory cities and selective cities.…”

Section: Introductionmentioning

confidence: 99%

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A metaheuristic approach to fisheries survey route planning

Mesquita

Murta²,

Paias

et al. 2016

Int Trans Operational Res

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Every autumn, a research vessel carries out a sampling survey tour to estimate the abundance of groundfish species of the Portuguese continental waters. The sampling operations are carried out at predefined geographical locations, the fishing stations, within predefined multiple time windows. The vessel route starts and ends at the port of Lisbon, and must visit all fishing stations. According to a predefined periodicity, the vessel must enter a port to supply food, refuel, and/or change crew. Given the geographical locations of the fishing stations/ports and current weather conditions, the objective is to minimize the total traveled distance and completion time. We present a mixed integer linear program to describe the problem and propose two sequential heuristic approaches that combine genetic algorithms and adaptive large neighborhood search to solve it. Computational experience with real data shows that the proposed heuristics are suitable tools to solve the problem.

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“…SROP can be modeled as a TSPSTW, with additional constraints, on a single commodity network as proposed in Mesquita et al. (). Alternatively, SROP may be viewed as a variant of the m ‐TSPTW (

m = 2

) and modeled on a two‐commodity network, each commodity being associated with a circuit, starting and ending at the city of Lisbon.…”

Section: Problem Formulationmentioning

confidence: 99%

“…In Section 3.1.2, we reference a sequential approach already presented in Mesquita et al. (), which will be compared with the new proposals from a computational point of view.…”

Section: Solution Approachmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Sequential Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations