  Production logistics has an important role as a chain that connects the
components of the production system. The most important goal of production
logistics plans is to keep the flow of the production system well. However,
compared to the production system, the level of planning, management, and
digitalization of the production logistics system is not high enough, so it is
difficult to respond flexibly when unexpected situations occur in the
production logistics system. Optimization and heuristic algorithms have been
proposed to solve this problem, but due to their inflexible nature, they can
only achieve the desired solution in a limited environment. In this paper, the
relationship between the production and production logistics system is analyzed
and stochastic variables are introduced by modifying the pickup and delivery
problem with time windows (PDPTW) optimization model to establish a flexible
production logistics plan. This model, taking into account stochastic
variables, gives the scheduler a new perspective, allowing them to have new
insights based on the mathematical model. However, since the optimization model
is still insufficient to respond to the dynamic environment, future research
will cover how to derive meaningful results even in a dynamic environment such
as a machine learning model.
