@article{Ha2018,

title = {On the min-cost Traveling Salesman Problem with Drone},

author = {Quang Minh Ha and Yves Deville and Quang Dung Pham and Minh Hoàng Hà},

doi = {10.1016/j.trc.2017.11.015},

issn = {0968-090X},

journal = {Transportation Research Part C: Emerging Technologies},

volume = {86},

pages = {597--621},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nguyen2022,

title = {The min-cost parallel drone scheduling vehicle routing problem},

author = {Minh Anh Nguyen and Giang Thi-Huong Dang and Minh Hoàng Hà and Minh-Trien Pham},

doi = {10.1016/j.ejor.2021.07.008},

issn = {0377-2217},

journal = {European Journal of Operational Research},

volume = {299},

number = {3},

pages = {910--930},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Vu2022,

title = {Solving the time dependent minimum tour duration and delivery man problems with dynamic discretization discovery},

author = {Duc Minh Vu and Mike Hewitt and Duc D. Vu},

doi = {10.1016/j.ejor.2022.01.029},

issn = {0377-2217},

journal = {European Journal of Operational Research},

volume = {302},

number = {3},

pages = {831--846},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Vidal2021,

title = {Arc Routing with Time-Dependent Travel Times and Paths},

author = {Thibaut Vidal and Rafael Martinelli and Tuan Anh Pham and Minh Hoàng Hà},

doi = {10.1287/trsc.2020.1035},

issn = {1526-5447},

journal = {Transportation Science},

volume = {55},

number = {3},

pages = {706--724},

publisher = {Institute for Operations Research and the Management Sciences (INFORMS)},

abstract = { Vehicle routing algorithms usually reformulate the road network into a complete graph in which each arc represents the shortest path between two locations. Studies on time-dependent routing followed this model and therefore defined the speed functions on the complete graph. We argue that this model is often inadequate, in particular for arc routing problems involving services on edges of a road network. To fill this gap, we formally define the time-dependent capacitated arc routing problem (TDCARP), with travel and service speed functions given directly at the network level. Under these assumptions, the quickest path between locations can change over time, leading to a complex problem that challenges the capabilities of current solution methods. We introduce effective algorithms for preprocessing quickest paths in a closed form, efficient data structures for travel time queries during routing optimization, and heuristic and exact solution approaches for the TDCARP. Our heuristic uses the hybrid genetic search principle with tailored solution-decoding algorithms and lower bounds for filtering moves. Our branch-and-price algorithm exploits dedicated pricing routines, heuristic dominance rules, and completion bounds to find optimal solutions for problems counting up to 75 services. From these algorithms, we measure the benefits of time-dependent routing optimization for different levels of travel-speed data accuracy. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nguyen2023,

title = {The Parallel Drone Scheduling Traveling Salesman Problem with Collective Drones},

author = {Minh Anh Nguyen and Minh Hoàng Hà},

doi = {10.1287/trsc.2022.1192},

issn = {1526-5447},

journal = {Transportation Science},

volume = {57},

number = {4},

pages = {866--888},

publisher = {Institute for Operations Research and the Management Sciences (INFORMS)},

abstract = { In this paper, we study a new variant of the parallel drone scheduling traveling salesman problem that aims to increase the utilization of drones, particularly for heavy item deliveries. The system under consideration adopts a technology that combines multiple drones to form a collective drone (c-drone) capable of transporting heavier items. The innovative concept is expected to add further flexibility in vehicle assignment decisions. An especially difficult challenge to address is the collaboration among drones because it requires temporal synchronization between their delivery tours. To better model the reality, we also consider that drone power consumption is a nonlinear function of both speed and parcel weight. We first develop a two-index mixed integer linear programming (MILP) formulation from which a simple branch and cut is developed to solve small-size instances to optimality. To efficiently handle larger problem instances, we propose a ruin-and-recreate metaheuristic with problem-tailored removal and insertion operators, in which an efficient move evaluation procedure based on the topological sort is designed to deal with the complexity of the synchronization constraints. Computational experiments demonstrate the validity of the developed MILP model and the performance of the proposed metaheuristic. Sensitivity analyses based on the classification and regression tree are performed to investigate the benefits of using c-drones and the important factors affecting the efficiency of the new transportation system.  History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Vu2020,

title = {Dynamic Discretization Discovery for Solving the Time-Dependent Traveling Salesman Problem with Time Windows},

author = {Duc Minh Vu and Mike Hewitt and Natashia Boland and Martin Savelsbergh},

doi = {10.1287/trsc.2019.0911},

issn = {1526-5447},

journal = {Transportation Science},

volume = {54},

number = {3},

pages = {703--720},

publisher = {Institute for Operations Research and the Management Sciences (INFORMS)},

abstract = { We present a new solution approach for the time-dependent traveling salesman problem with time windows. This problem considers a salesman who departs from his home, has to visit a number of cities within a predetermined period of time, and then, returns home. The problem allows for travel times that can depend on the time of departure. We consider two objectives for the problem: (1) a makespan objective that seeks to return the salesman to his home as early as possible and (2) a duration objective that seeks to minimize the amount of time that he is away from his home. The solution approach is based on an integer programming formulation of the problem on a time-expanded network, because doing so enables time dependencies to be embedded in the definition of the network. However, because such a time-expanded network (and thus, the integer programming formulation) can rapidly become prohibitively large, the solution approach uses a dynamic discretization discovery framework, which has been effective in other contexts. Our computational results indicate that the solution approach outperforms the best-known methods on benchmark instances and is robust with respect to instance parameters. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hà2014,

title = {An exact algorithm and a metaheuristic for the generalized vehicle routing problem with flexible fleet size},

author = {Minh Hoàng Hà and Nathalie Bostel and André Langevin and Louis-Martin Rousseau},

doi = {10.1016/j.cor.2013.08.017},

issn = {0305-0548},

journal = {Computers & Operations Research},

volume = {43},

pages = {9--19},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Crainic2016,

title = {Service Network Design with Resource Constraints},

author = {Teodor Gabriel Crainic and Mike Hewitt and Michel Toulouse and Duc Minh Vu},

doi = {10.1287/trsc.2014.0525},

issn = {1526-5447},

journal = {Transportation Science},

volume = {50},

number = {4},

pages = {1380--1393},

publisher = {Institute for Operations Research and the Management Sciences (INFORMS)},

abstract = { We first present a new service network design model for freight consolidation carriers, one that selects services and routes both commodities and resources needed to support the services that transport them, while explicitly recognizing that there are limits on how many resources are available at each terminal. We next present a solution approach that combines column generation, meta-heuristic, and exact optimization techniques to produce high-quality solutions. We demonstrate the efficacy of the approach with an extensive computational study and benchmark its performance against both a leading commercial solver and a column generation-based heuristic. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pham2017,

title = {Solving the multi-vehicle multi-covering tour problem},

author = {Tuan Anh Pham and Minh Hoàng Hà and Xuan Hoai Nguyen},

doi = {10.1016/j.cor.2017.07.009},

issn = {0305-0548},

journal = {Computers & Operations Research},

volume = {88},

pages = {258--278},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Vu2022b,

title = {Branch-and-check approaches for the tourist trip design problem with rich constraints},

author = {Duc Minh Vu and Yannick Kergosien and Jorge E. Mendoza and Pierre Desport},

doi = {10.1016/j.cor.2021.105566},

issn = {0305-0548},

journal = {Computers & Operations Research},

volume = {138},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}