A heuristic for multi-objective Chinese postman problem

Prakash, Satya; Sharma, Mahesh Kumar; Singh, Amarinder

doi:10.1109/iccie.2009.5223529

Cited by 2 publications

(2 citation statements)

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“…Besides, the multiobjective PMP has previously studied aggregating multiple criteria by means of ordered weighted average (or ordered median) operators (Fernández et al, 2014). Regarding to the biobjective CPP, Prakash et al (2009) present a heuristic algorithm to solve multiobjective CPP problem and implement the same on a biobjective CPP. Majumder et al (2019) present a multiobjective CPP under the framework of uncertainty theory.…”

Section: Introductionmentioning

confidence: 99%

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The biobjective minimum‐cost perfect matching problem and Chinese postman problem

Pozo,

Puerto,

Roldán

2023

Int Trans Operational Res

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In this paper, we address the biobjective versions of the perfect matching problem (PMP) and the Chinese postman problem (CPP). Both problems are solved by means of integer formulations or separating blossom inequalities, exploiting the PMP relationship with the CPP. In both cases, we first find the set of supported nondominated solutions and then we use them to obtain the nonsupported ones. The set of supported nondominated solutions are obtained solving scalarized integer formulations. To obtain the sets of nonsupported solutions, we resort to solving lexicographic problems based on adding additional linear constraints to the original problems. For this reason, we also characterize the combinatorial structure of the PMP vertices with one or two additional constraints. We also investigate when it is possible to use the PMP to solve CPP in the biobjective case. We report computational experiments comparing the different approaches and formulations based on different types of graphs with up to 700 nodes.

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Section: Introductionmentioning

confidence: 99%

“…Regarding to the biobjective CPP, Prakash et al. (2009) present a heuristic algorithm to solve multiobjective CPP problem and implement the same on a biobjective CPP. Majumder et al.…”

Section: Introductionmentioning

confidence: 99%