Abstract: The main objective of the current work is to introduce sustainability factors in optimizing the supply chain model for process industries. The supply chain models are normally based on purely economic considerations related to costs and profits. To account for sustainability, two additional factors have been introduced; environment and risk. A supply chain for an entire petroleum organization has been considered for implementing and testing the proposed optimization models. The environmental and risk factors were introduced as indicators reflecting the anticipated impact of the optimal production scenarios on sustainability. The aggregation method used in extending the single objective function to multi-objective function is proven to be quite effective in balancing the contribution of each objective term. The results indicate that introducing sustainability factor would slightly reduce the economic benefit while improving the environmental and risk reduction performances of the process industries.
Abstract: We consider a typical problem in the assembly of
printed circuit boards (PCBs) in a two-machine flow shop system to
simultaneously minimize the weighted sum of weighted tardiness and
weighted flow time. The investigated problem is a group scheduling
problem in which PCBs are assembled in groups and the interest is to
find the best sequence of groups as well as the boards within each
group to minimize the objective function value. The type of setup
operation between any two board groups is characterized as carryover
sequence-dependent setup time, which exactly matches with the real
application of this problem. As a technical constraint, all of the
boards must be kitted before the assembly operation starts (kitting
operation) and by kitting staff. The main idea developed in this paper
is to completely eliminate the role of kitting staff by assigning the
task of kitting to the machine operator during the time he is idle
which is referred to as integration of internal (machine) and external
(kitting) setup times. Performing the kitting operation, which is a
preparation process of the next set of boards while the other boards
are currently being assembled, results in the boards to continuously
enter the system or have dynamic arrival times. Consequently, a
dynamic PCB assembly system is introduced for the first time in the
assembly of PCBs, which also has characteristics similar to that of
just-in-time manufacturing. The problem investigated is
computationally very complex, meaning that finding the optimal
solutions especially when the problem size gets larger is impossible.
Thus, a heuristic based on Genetic Algorithm (GA) is employed. An
example problem on the application of the GA developed is
demonstrated and also numerical results of applying the GA on
solving several instances are provided.
Abstract: Because of importance of energy, optimization of
power generation systems is necessary. Gas turbine cycles are
suitable manner for fast power generation, but their efficiency is
partly low. In order to achieving higher efficiencies, some
propositions are preferred such as recovery of heat from exhaust
gases in a regenerator, utilization of intercooler in a multistage
compressor, steam injection to combustion chamber and etc.
However thermodynamic optimization of gas turbine cycle, even
with above components, is necessary. In this article multi-objective
genetic algorithms are employed for Pareto approach optimization of
Regenerative-Intercooling-Gas Turbine (RIGT) cycle. In the multiobjective
optimization a number of conflicting objective functions
are to be optimized simultaneously. The important objective
functions that have been considered for optimization are entropy
generation of RIGT cycle (Ns) derives using Exergy Analysis and
Gouy-Stodola theorem, thermal efficiency and the net output power
of RIGT Cycle. These objectives are usually conflicting with each
other. The design variables consist of thermodynamic parameters
such as compressor pressure ratio (Rp), excess air in combustion
(EA), turbine inlet temperature (TIT) and inlet air temperature (T0).
At the first stage single objective optimization has been investigated
and the method of Non-dominated Sorting Genetic Algorithm
(NSGA-II) has been used for multi-objective optimization.
Optimization procedures are performed for two and three objective
functions and the results are compared for RIGT Cycle. In order to
investigate the optimal thermodynamic behavior of two objectives,
different set, each including two objectives of output parameters, are
considered individually. For each set Pareto front are depicted. The
sets of selected decision variables based on this Pareto front, will
cause the best possible combination of corresponding objective
functions. There is no superiority for the points on the Pareto front
figure, but they are superior to any other point. In the case of three
objective optimization the results are given in tables.