(723a) Supply Chain Management Optimization Using a Hybrid Simulation Based Optimization Approach

Supply Chain
Management Optimization using a Hybrid Simulation based Optimization Approach

Nihar, Marianthi Ierapetritou

Department of Chemical and
Biochemical Engineering, Rutgers University, Piscataway, NJ

A
supply chain is a network of suppliers, production facilities, warehouses and
markets designed to acquire raw materials, manufacture and store and distribute
products among the markets. The entire process is driven by the demand
generated at the markets. Since there are different entities involved and they
work towards their individual interests, the optimum performance of the whole
network is often not achieved. It has been shown that greater efficiency and
reduced costs can be achieved through proper coordination among the entities in
terms of material, financial and information flow [1, 2].

In
order to gain competitive edge in a global economy, companies aim at optimizing
their overall supply chain. In a typical supply chain, there are a large number
of entities. The interacting behavior of these entities among themselves is
complex. This makes the problem of optimization large and complex. Mathematical
modeling has been very widely used to formulate these problems. Several
mathematical models use mixed integer linear programming (MIP) and mixed
integer non-linear programming to solve the SC optimization problems [3, 4]. Stochastic models have also
been developed for supply chain networks in uncertain environments [5]. However, these approaches lack realism since they
are not able to capture the complex relations among the different entities
comprising the network. Simulation models are another category of models that
have been used to solve such problems. They help mimic complex relations among the
different entities of the supply chain. Agent based models, a class of
simulation models, treat the different entities as autonomous agents, each having
its own set of behavioral rules. It has been shown that using this approach, it
is possible to govern the interaction among the different entities of the whole
network and thus reach at improved solutions [6]. Compared to the optimization based methods,
limited work has been done using this approach.

Analytic
and simulation models can be regarded as the two extremes of a range of
mathematical models available for modeling [7]. Hybrid simulation/analytic models which are
combinations of these two types of models enable us to take advantages of both
of them and can be very useful [8, 9]. Such models are useful for
stochastic optimization as well since they can mimic real systems including
their stochastic and nonlinear behaviors. In the recent years, such models have
been used to explore the supply chain domain. It is an active area of research
and rapid progress is being made.

In
this work, we demonstrate the use of a hybrid modeling approach to solve a
small scale supply chain management problem. The problem has been formulated as
a mathematical model minimizing the overall cost incurred over a number of
planning periods. The objective of using the hybrid approach is to overcome the
computational complexity involved in solving the large scale mixed integer
nonlinear problem (MINLP) and to obtain a solution which represents supply
chain reality more closely. The proposed framework involves the integration of
the solution strategies of separate optimization and simulation models. The
optimization model solves the planning and scheduling problem while the
simulation model is used to verify the quality of the solution obtained by
comparing it with real world scenarios. An iterative solution procedure has
been used such that there is communication between the two models in terms of
some decision variables in every cycle. The optimization model of the problem
has been developed in GAMS while the simulation model has been developed using
the JAVA Repast tool. Various scenarios involving different number of planning
periods and different sets of deterministic demand have been used to
investigate the proposed framework. The effectiveness of the approach has been
shown in terms of the computational effort required and the quality of the solution
achieved.

Considering
the increasing environmental concerns and associated legislations these days, the
proposed framework has been extended to account for environmental impact through
a multi-objective optimization approach.  In addition to the economic objective
which minimizes the overall operation cost of the supply chain, an
environmental objective which minimizes the environmental cost associated with
the manufacturing and transportation processes of the network is also
considered. The consideration of an environmental objective at the stage of
supply chain management is critical in order to make a holistic assessment. Incorporating
the environmental aspects at a later stage might lead to decreasing the
environmental impact locally while causing an overall increase in the
environmental costs. The simulation model is able to assess the various
environmental aspects of the supply chain and decide whether the solution
provided by the optimization model meets the environmental requirements or not.
The presence of the two objective functions helps maintain the environmental
performance of the supply chain while minimizing the overall cost.

References:

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2.            Varma,
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