A full investigation of the directional congestion in data envelopment analysis

Somayeh Khezri, Akram Dehnokhalaji, Farhad Hosseinzadeh Lotfi

Research output: Contribution to journalArticle


One of interesting subjects in Data Envelopment Analysis (DEA)
is estimation of congestion of Decision Making Units (DMUs). Con-
gestion is evidenced when decreases (increases) in some inputs result in
increases (decreases) in some outputs without worsening (improving)
any other input/output. Most of the existing methods for measuring
the congestion of DMUs utilize the traditional denition of congestion
and assume that inputs and outputs change with the same proportion.
Therefore, the important question that arises is whether congestion
will occur or not if the decision maker (DM) increases or decreases
the inputs dis-proportionally. This means that, the traditional deni-
tion of congestion in DEA may be unable to measure the congestion
of units with multiple inputs and outputs. This paper focuses on the
directional congestion and proposes methods for recognizing the di-
rectional congestion using DEA models. To do this, we consider two
dierent scenarios: (i) just the input direction is available. (ii) none
of the input and output directions are available. For each scenario,
we propose a method consists in systems of inequalities or linear pro-
gramming problems for estimation of the directional congestion. The validity of the proposed methods are demonstrated utilizing two nu-
merical examples.
Original languageEnglish
JournalRAIRO - operations research
Publication statusAccepted/In press - 18 Sep 2019

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