  Product reuse and recovery is an efficient tool that helps companies to
simultaneously address economic and environmental dimensions of sustainability.
This paper presents a novel problem for stock management of reusable products
in a single-vendor, multi-product, multi-retailer network. Several constraints,
such as the maximum budget, storage capacity, number of orders, etc., are
considered in their stochastic form to provide a more realistic framework. The
presented problem is formulated as a constrained nonlinear mathematical model.
The chance-constrained programming method is suggested to deal with the
constraints' uncertainty. Regarding the nonlinearity of the model, grey wolf
optimizer (GWO) and whale optimization algorithm (WOA) as two novel
metaheuristics are presented as solution approaches, and the sequential
quadratic programming (SQP) exact algorithm validates their performance. The
parameters of algorithms are calibrated using the Taguchi method for the design
of experiments. Extensive analysis is established by solving several numerical
results in different sizes and utilizing several comparison measures. Also, the
results are compared statistically using proper parametric and non-parametric
tests. The analysis of the results shows a significant difference between the
algorithms, and GWO has a better performance for solving the presented problem.
In addition, both algorithms perform well in searching the solution space,
where the GWO and WOA differences with the optimal solution of the SQP
algorithm are negligible.
