Akademik Veri Yönetim Sistemi
Tezin Türü: Doktora
Tezin Yürütüldüğü Kurum: Marmara Üniversitesi, Fen Bilimleri Enstitüsü, Türkiye
Tezin Onay Tarihi: 2021
Tezin Dili: İngilizce
Öğrenci: GÖZDE ALP
Danışman: Ali Fuat Alkaya
Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
Özet:
Shift scheduling and vehicle routing processes are indispensable while
planning most of the business operations. In this study, we firstly
handled these processes separately and then we combined them. In the
first part of the study; the mathematical formulation of a fairness
oriented shift scheduling problem (FOSSP) is offered. A novel hybrid
algorithm that has hyper heuristic (HH) neighborhood search behaviors
embedded in migrating birds optimization (MBO) is introduced (HHMBO).
HHMBO is applied on FOSSP and is compared with the reputed algorithms
through extended computational experiments. Results prove that new
hybrid computational intelligence technique is promising especially for
large sized FOSSP instances. In the second part of the study; the
workforce scheduling and vehicle routing problems are brought together
and vehicle routing problem in the presence of shift assignment (VRPSA)
is introduced to the literature. The multi-objective mathematical model
of the problem is verified on a solver and also solved using a set of
evolutionary algorithms. Dynamic neighbour generation framework for
multi-objective optimization problems is introduced to the literature
for solving VRPSA. Experimental results show that the proposed framework
is definitely promising and robust in large sized problem instances in
terms of hypervolume (HV) and inverted generational distance (IGD)
metrics. Additional experiments are conducted on multi-objective
benchmark problems to analyze the achievement of DNG framework. DNG is
integrated to a set of multi-objective optimization algorithms.
Experiments demonstrate that DNG based versions of algorithms are
significantly better than their original variations on the average in
terms of both IGD and HV metrics.