we can see that the less the number of first inspectors in
comparision to the other two inspectors, the time taken to
do so is much more greater.
6. CONCLUSION AND FUTURE STUDIES
To decrease occupational accidents, we proposed a
scheduled plan for inspection from workshops. In this
study, from another angle, the issue of routing within the
supply chain framework has been addressed. The
application of routing in this paper is to visit workshops
which were introduced and modeled. The objective
functions not only maximize the number of visited
workshops but also minimize travel times and workshops
visit times. The model is formulated as MILP alongside
with some assumptions that help to reach a real study. To
figure out the best outputs for the proposed model,
several hybrid and meta-heuristic algorithms containing
SEO, SA, RDA, GAKA, and GASA, have been utilized.
To compare the result of solution approaches, we needed
to tune factors of algorithm which were different and
various so the Taguchi method was used. Then, four
metrics alongside with the GAP index were introduced
and applied to compute and measure the best suitable
algorithm. The proposed SEO demonstrated the best
outputs and stability among suggested meta-heuristic and
hybrid algorithms. The outcomes depicted when
inspectors wanted to gain better efficiency of managing
the plan from the daily operations, they could count on
the model stemmed from the supply chain. This model
was a practical appliance for the decision-makers in
making operational decisions. This technical planning
calculates the number of workshops assigned to each
inspector and the occupied time of inspectors as shown
in the case study to maximize the number of visits.
For future studies, extending the mathematical model
by applying uncertainty and stochastic in the parameters
is suggested. Also, new hybrid and evolutionary
algorithms were utilized for evaluating and comparing
the outputs of the model. Some real constraints, such as
social and environmental aspects of Greenhouse Gas
(GHG) emission can be added to the suggested model for
the subsequent expansion of this model.
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