Most decision support systems involve rnulti-objective 
optimization problems (MOOP). Previous research has 
usually treated these MOOPs by heuristically combining 
the multiple objectives into a single one. However, it 
is extremely difficult to incorporate a decision maker's 
(DM) preference structure and have an appropriate way 
for this combination. This has been a hurdle for the DM 
to obtain an optimal tradeoff solution. The fundamental 
theme of this research work is to develop the foundation 
of an efficient rnulti-objective optimization strategy 
from a theoretical perspective and demonstrate its 
applications as the core search engine of decision support 
systems for real-world problems. In this thesis, a new 
evolutionary rnulti-objective optimization strategy--Multi-
Objective Differential Evolution (MODE) is proposed. The 
MODE is developed for both continuous (C-MODE) and discrete 
(D-MODE) domains. A set of performance metrics called Pareto 
Front Approximation Error (PFAE) is introduced to evaluate 
the non-dominated solutions obtained by optimization approaches. 
A theoretical foundation to explain the behaviors of MODE is 
developed through proofs of convergence of the MODE algorithms. 
The analysis of the D-MODE is treated based on a Markov model 
while the C-MODE is modeled in the global random search 
framework. The convergence of the population to the Pareto 
front with probability one is developed. A set of guidelines 
on the parameter setting of the C-MODE is derived based on 
both mathematical modeling and simulation analysis of its 
operators. A class of decision problems arising in integrated 
design, supplier, and manufacturing (DSM) planning involved in 
modular product development is formally modeled as a multi-objective 
optimization assignment problem. The MODE is applied to solving 
such MOOPs to generate the Pareto optimal solutions for further 
decision making. An object oriented framework of a multi-objective 
decision support system is proposed for such DSM planning with MODE 
as the core search engine. This can serve as a prototype for other 
applications. The routing problems in wireless networks is formally 
modeled as a MOOP that optimizes both power consumption and 
communication latency. The MODE concept is implemented with its 
internal data structure tailored to the network specifics.