The bi-objective covering tour problem

International audienceIn this article, we develop a heuristic two-phase solution procedure for the dial-a-ride problem with two objectives. Besides the minimum cost objective a client centered objective has been defined. Phase one consists of an iterated variable neighborhood search-based heuristic, generating approximate weighted sum solutions; phase two is a path relinking module, computing additional efficient solutions. Results for two sets of benchmark instances are reported. For the smaller instances, exact efficient sets are generated by means of the e-constraint method. Comparison shows that the proposed two-phase method is able to generate high-quality approximations of the true Pareto frontier

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“…[25,26,31,35,38,39]. Very recently, a survey on multi-objective routing problems has been published by Jozefowiez et al [27].…”

Section: Related Workmentioning

confidence: 98%

A heuristic two‐phase solution approach for the multi‐objective dial‐a‐ride problem

et al. 2009

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“…This was introduced by Current & Schilling [8] and further studied by Gendreau et al [9] and Jozefowieza et al [10]. This problem has two node sets as the BCSP does.…”

Section: Literature Reviewmentioning

confidence: 99%

A Bi-Modal Routing Problem with Cyclical and One-Way Trips: Formulation and Heuristic Solution

Grinde

2017

Information Technology and Management Science

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-A bi-modal routing problem is solved using a heuristic approach. Motivated by a recreational hiking application, the problem is similar to routing problems in business with two transport modes. The problem decomposes into a set covering problem (SCP) and an asymmetric traveling salesperson problem (ATSP), corresponding to a hiking time objective and a driving distance objective. The solution algorithm considers hiking time first, but finds all alternate optimal solutions, as inputs to the driving distance problem. Results show the trade-offs between the two objectives.

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“…With respect to the second objective, MTP aims to minimize the average distance from any node not on the tour to a nearest node on the tour, and MCTP aims to maximize the number of vertices covered at tour stops within a given covering distance. Recently, [18] proposed a bi-objective version of CTP which shared similar objectives as MTP, but with no specified number of tour stops p. Although formulating similar bi-objectives as MCTP, UDMP does not need to specify an a priori p value.…”

Section: Multiple Objectives Tour Planningmentioning

confidence: 99%

Energy-constrained bi-objective data muling in underwater wireless sensor networks

Shen

Chen

2010

The 7th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (IEEE MASS 2010)

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Abstract-For underwater wireless sensor networks (UWSNs), data muling is an effective approach to data gathering, where sensor data are collected when a mobile data mule travels within the wireless communication range of the sensors. However, given the constrained energy available on a data mule and the energy consumption of its motions and communications a data mule may be prevented from visiting every deployed sensor in a tour. We formulate the tour planning of a data mule collecting sensor data in UWSNs as an energy-constrained bi-objective optimization problem termed the Underwater Data Muling Problem (UDMP). UDMP has the two conflicting objectives of maximizing the number of sensors contacted and minimizing the length of a tour, while satisfying the energy constraint on the data mule at all times. We design two heuristic algorithms to solve one special case and one generalized case of this NP-hard problem, respectively. Each algorithm computes a set of Pareto-efficient solutions addressing the tradeoff between the two optimization objectives to facilitate tour planning. Simulation results validate the effectiveness of both algorithms.

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The bi-objective covering tour problem

Cited by 76 publications

References 10 publications

A heuristic two‐phase solution approach for the multi‐objective dial‐a‐ride problem

A heuristic two‐phase solution approach for the multi‐objective dial‐a‐ride problem

A Bi-Modal Routing Problem with Cyclical and One-Way Trips: Formulation and Heuristic Solution

Energy-constrained bi-objective data muling in underwater wireless sensor networks

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