Abstract
This work introduces the combination of two multi-objective optimization techniques, which are full consistency method (FUCOM) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, which can be applied to any kind of process integration involving multiple objective optimization problems. Multi-objective optimization is a branch of operational research dealing with finding optimal results in complex scenarios including various indicators, conflicting objectives, and criteria. The developed approach will be divided into two phases: (1) to determine the intensity of importance of criteria (objectives) and (2) to optimize and develop ranking of alternatives available in multi-objective problems. FUCOM is a model for determining weight coefficient of respective criteria objectives in multi-objective problems. FUCOM involves simple mathematical calculation and yields more consistent results. On the other hand, VIKOR is a well-established decision-making technique for process integration. In this work, we developed FUCOM-VIKOR approach to integrate the utility measure (positive attribute) and regret measure (negative attribute) of alternatives to respective criteria. The optimization model is formulated to obtain the most optimal solution that achieves the evaluation criteria that defined by industrial stakeholders by a given set of constraints. The evaluation criteria considered in this work include economic performance, environmental impact, and social impact. A case study on recycling of cleaning solution in rubber glove manufacturing process is used to illustrate the proposed methodology.
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The authors confirm that the data supporting the findings of this study are available within the article.
Abbreviations
- c ∈ C :
-
washing tanks
- d ϵ D :
-
SLS equipment
- AF :
-
annualized factor
- C ccs :
-
heat capacity of concentrated cleaning solution
- C dcs :
-
heat capacity of diluted cleaning solution
- C fw :
-
heat capacity of freshwater
- \( {C}_c^{wt} \) :
-
heat capacity of the cleaning solution in washing tank c
- F max, PT :
-
maximum mass flow rate of fresh cleaning solution charged from the preparation tank in the current practice
- H y :
-
yearly operating time
- L :
-
distance of pipeline
- \( {F}_c^{asp} \) :
-
the mass flow rate of the removal of particle solids from the surface of glove hand mold to the cleaning solution in washing tank c
- q :
-
fixed cost parameter for building one pipeline
- r :
-
the variable cost parameter based on the cross-sectional area of pipe
- T in, HU :
-
temperature of cleaning solution entering the heating unit in system
- T out, HU :
-
temperature of cleaning solution leaving the heating unit in system
- T out, PT :
-
outlet temperature diluted cleaning solution from preparation tank
- T rt :
-
ambient room temperature
- U ccs :
-
unit cost of concentrated cleaning solution
- U d, m :
-
incremental cost of SLS equipment d based on the equipment inlet flow rate
- U d :
-
the initial investment cost of a SLS equipment d
- U e :
-
unit cost of electricity
- U fw :
-
cost of fresh water
- U ww :
-
unit cost for sludge treatment
- v :
-
stream velocity
- \( {Y}_c^{fasp} \) :
-
the maximum allowable concentration of suspended solid in washing tank c
- Y ccs :
-
concentration of cleaning agent in concentrated cleaning solution
- \( {Y}_d^{rsp} \) :
-
is the concentration of suspended solid in cleaning solution after treated from SLS equipment d
- Y dcs :
-
concentration of cleaning agent in preparation tank
- Y sp :
-
average targeted concentration of suspended solid of the all treated cleaning solution
- ε d :
-
suspended solid removal efficiency of SLS equipment d
- \( {\sigma}_{F_d^{in}}^{max} \) :
-
upper limit for the inlet flow rate of SLS equipment d
- \( {\sigma}_{F_d^{in}}^{min} \) :
-
lower limit for the inlet flow rate of SLS equipment d
- \( \Delta {T}_c^{wt} \) :
-
temperature loss to the surroundings in washing tank c
- ρ :
-
density of cleaning solution
- C f :
-
equivalence carbon footprint due to electricity
- C ww :
-
equivalence carbon footprint due to wastewater treatment process
- UB d :
-
upper flow rate constraint of SLS equipment d
- LB d :
-
lower flow rate constraint of SLS equipment d
- n d :
-
number of node of SLS equipment d
- \( {R}_d^I \) :
-
intrinsic reliability of SLS equipment d
- \( {F}_c^{in, HU} \) :
-
mass flow rate of cleaning solution from heating unit in the system to the washing tank c (kg/h)
- F in, HU :
-
total mass flow rate entering heating unit (kg/h)
- \( {F}_c^{in, PT} \) :
-
mass flow rate of cleaning solution from preparation tank to washing tank c (kg/h)
- F in, PT :
-
total mass flow rate entering the preparation tank (kg/h)
- F out, PT :
-
mass flow rate of fresh cleaning solution charged from the preparation tank (kg/h)
- F out, PT :
-
total mass flow rate leaving the preparation tank (kg/h)
- \( {F}_{c,d}^{out} \) :
-
mass flow rate of contaminated solution from washing tank c to SLS equipment in the centralized hub (kg/h)
- F in, ccs :
-
mass flow rate of concentrated cleaning solution entering preparation tank (kg/h)
- \( {F}_d^{in, HU} \) :
-
mass flow rated of cleaning solution being treated in SLS equipment d to the heating unit in centralized hub (kg/h)
- \( {F}_{d,s}^{out} \) :
-
outlet mass flow rate of SLS equipment d that discharged as sludge (kg/h)
- F d, ss :
-
mass flow rate of suspended solid being removed by SLS equipment d as sludge (kg/h)
- \( {F}_d^{in} \) :
-
total inlet mass flow rate of contaminated cleaning solution received by SLS equipment d (kg/h)
- \( {F}_{fw}^{in} \) :
-
mass flow rate of fresh water entering preparation tank (kg/h)
- \( {Q}_{HU}^{in} \) :
-
heat gained by the heating unit in system (kJ/h)
- \( {Q}_{pt}^{in} \) :
-
heat supplied in preparation tank (kJ/h)
- \( {Q}_{wt,c}^{in} \) :
-
heat supplied to the washing tank c (kJ/h)
- SC d :
-
investment cost of a SLS equipment d ($/year)
- Z d :
-
binary variable used to determine the existence of a SLS equipment d
- FC :
-
cost of raw material consumption ($/year)
- HUC :
-
heating utility cost ($/year)
- PCR :
-
piping cost for delivering treated cleaning solution from system back to washing tanks ($/year)
- PCT :
-
piping cost for delivering contaminated solution to system ($/year)
- TAC :
-
total annual cost ($/year)
- TPC :
-
total piping cost ($/year)
- TSC :
-
SLS equipment investment cost ($/year)
- WC :
-
sludge treatment cost ($/year)
- C i :
-
criterion score
- ACF :
-
annualized carbon footprint (kg/year)
- R d :
-
reliability
- S i :
-
utility index
- R i :
-
regret index
- Q i :
-
VIKOR index
- υ :
-
VIKOR coefficient
- CR :
-
consistency ratio
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Funding
This study was funded by Taylor’s University, TRGS/ERFS/2/2018/SOE/002, and Monash University Malaysia Postgraduate Scholarship
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MC Ong and YT Leong conceived the idea. MC Ong designed the overall methodology, developed the mathematical model, and performed the computation. YT Leong verified the overall methodology and supervised the findings of this work. MC Ong wrote the manuscript with the support from IML Chew and YK Wan in the aspect of technical writing.
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Ong, M.C., Leong, Y.T., Wan, Y.K. et al. Multi-objective Optimization of Integrated Water System by FUCOM-VIKOR Approach. Process Integr Optim Sustain 5, 43–62 (2021). https://doi.org/10.1007/s41660-020-00146-3
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DOI: https://doi.org/10.1007/s41660-020-00146-3