Reverse Logistics Network Design for Plastic Waste Management in Jakarta: Robust Optimization Method
Corresponding email: romadhani.ardi@ui.ac.id
Published at : 07 Dec 2023
Volume : IJtech
Vol 14, No 7 (2023)
DOI : https://doi.org/10.14716/ijtech.v14i7.6681
Ardi, R., Nurkamila, S., Citraningrum, D.L., Zahari, T.N., 2023. Reverse Logistics Network Design for Plastic Waste Management in Jakarta: Robust Optimization Method. International Journal of Technology. Volume 14(7), pp. 1560-1569
| Romadhani Ardi | Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, Kampus Baru UI, Depok 16424, Indonesia |
| Syifa Nurkamila | Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, Kampus Baru UI, Depok 16424, Indonesia |
| Dyas Latiefah Citraningrum | Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, Kampus Baru UI, Depok 16424, Indonesia |
| Teuku Naraski Zahari | Energy Economics Research Group, Graduate School of Energy Science, Kyoto University, Yoshida-Honmachi, Sakyo-Ku, Kyoto, 606-8501, Japan |
Indonesia
experiences
a consistent annual escalation in plastic production, primarily
attributed to the
high demands from numerous industries. As a result of this escalated production rate, more significant quantities of plastic waste
continue to be produced each year. In
this regard, it is essential to comprehend that uncontrolled plastic waste generates harmful
substances for humans and the environment. A reverse logistics network was introduced and
developed to decrease the damaging effects of this waste on the environment. The
Indonesian plastic waste reverse logistics system encountered some
uncertainties due to limited data availability, showing significant
fluctuations. To address these uncertainties, this study proposed a
robust optimization model for the
management of plastic waste within the reverse logistics system in Jakarta. The
results showed that the model could accurately identify optimal facility
locations and determine the exact quantity to transport between facilities
while considering social, economic, and environmental factors. The results also
showed that the proposed model minimized cost by 332 million USD, reduced gas emissions to
626 million m3 (ca. 1.2 billion kgCO2), and maximized
labor by 611 thousand people.
Network design; Plastic waste management; Reverse Logistics; Robust optimization
The absence of comprehensive and accurate
data on reverse logistics requires the consideration of uncertain variables or
parameters when making decisions regarding the networks (Rahimi
and Ghezavati, 2018). To address these uncertainties,
it was recommended that the reverse logistics network incorporate a robust
design (Pishvaee,
Rabbani and Torabi, 2011). Uncertainty conditions have
prevailed in different regions, particularly in Jakarta, a city generating
approximately eight thousand tons of plastic waste daily (Kristanto,
Jansen and Koven, 2020). Following this, the Ministry of
Environment and Forestry in Indonesia has stated that the availability and
accuracy of data for plastic waste in Jakarta are currently sporadic and vary (Ministry
of Environment and Forestry, 2020).
Previous studies on reverse logistics
network design have utilized mathematical models for multi-product design
primarily. These studies were observed to utilize specific parameters and
single objective functions (Sadrnia,
Langarudi, and Sani, 2020; Paydar and Olfati, 2018; Kilic, Cebeci, and Ayhan,
2015). Therefore, this study aims to
develop a reverse logistics network design model for plastic waste management
in Jakarta using a robust optimization method. The model has several
objectives, including minimizing costs, reducing carbon emissions, and creating
employment opportunities while optimizing the network. It is essential to
acknowledge that this study expands a previous work comprising a mixed-integer
programming model for designing a reverse logistics network dedicated to
plastic waste management in urban settings (Nurkamila
and Ardi, 2022). Our current study utilizes a
robust optimization approach, and its results could serve as the foundation to
provide essential insights to aid the government in decision-making.
Literature Review
2.1. Plastic Waste Management in Indonesia
Plastic waste management refers to the systematic handling, collection, disposal, recycling, and overall regulation of plastic materials to minimize environmental impact, promote sustainability, and mitigate the adverse effects of plastic pollution. This definition aligns with what was posited in the Government Regulation of Indonesia Number 81 of 2012 on the Management of Household and Household-like Wastes. Here, it was established that the handling of all waste, including plastic, comprises a series of processes, namely sorting, collection, transportation, processing, and ultimate waste disposal. Accordingly, these processes were executed at designated facilities such as temporary dumping places (TPS), final landfills (TPA), Waste Banks, Hoarders, and Recyclers.
Figure 1 Reverse
logistics network of plastic waste management in Indonesia
Initiating the
reverse logistics network for plastic waste in Indonesia comprised the transfer
of plastic waste to designated facilities, including Waste Banks or temporary
dumping places, informally known as "TPS" in Indonesia. This process
extends from the point of origin to the recycling phase at the Recycler. Figure
1 shows a schematic representation of the reverse logistics network for plastic
waste in Indonesia
(Ministry
of Environment and Forestry, 2020).
2.2. Reverse Logistics Network Design of
Plastic Waste
The importance of the circular economy and
sustainability underscore the significance of reverse logistics for plastic
waste (Valenzuela
et al., 2021). A reverse logistics network
design comprises strategic decisions concerning waste management facilities,
allocation, and product movement, including opening or closing facilities.
Many previous works developed plastic waste
reverse logistics analysis using mathematical models, particularly emphasizing
the use of mixed-integer linear programming (Demirel, Demirel and Gökçen, 2016).
Accordingly, some studies have utilized robust optimization models (Xu et al., 2021). Some of
the objective functions of these models have considered the environmental (Trochu, Chaabane and
Ouhimmou, 2020; Bing et al., 2015), economic (Galvez et al., 2015), and social
(Pedram et al., 2017) impact. In
this regard, the objective function, incorporating environmental considerations,
was oriented towards minimizing emissions and mitigating production processes
that harm the environment within the logistics network (Safdar et al., 2020). A specific
environmental consideration in a previous study was the reduction of the costs
of carbon dioxide emission associated with the processing and transportation of
plastic waste products within the networks (Bing, Bloemhof-Ruwaard and van der Vorst, 2014).
Moreover,
the objective function, incorporating economic considerations, is concerned
with assessing the impact of various cost components on the profitability or
lack thereof of the network (Safdar et al., 2020). An
economic consideration aimed to minimize the fixed costs associated with
facility construction, processing costs at hoarder centers and all reprocessing
facilities, manufacturing and material costs, shortage costs, and
transportation costs. The study aimed to decrease the entire cost of a
closed-loop supply chain (Pourjavad and Mayorga, 2018). Finally,
the objective function incorporating social aspects focused on how to enhance
social responsibility (Pedram et al., 2017)
It is essential
to acknowledge that only a few studies have considered the collective impact of
the triple bottom line, namely economic, environmental, and social (Safdar et al., 2020). Most
studies considered either only the economic aspect (Roudbari, Ghomi, and Sajadieh, 2021;
Xu et al., 2017; Kilic, Cebeci, and Ayhan, 2015) or economic
and environmental aspects (Yu and Solvang, 2016; Xiao et al.,
2019). In
addition, several models have also considered uncertainty (Roudbari, Ghomi, and Sajadieh, 2021;
Sadrnia, Langarudi and Sani, 2020; Xu
et al., 2017).
This study is an extension of an initial work (Nurkamila and Ardi, 2022) where the objective was only to minimize cost. The current study introduces three objective functions: minimizing costs, reducing gas emissions, and maximizing job creation. Under this, the study commenced with data collection through a literature review on reverse logistics network design and reverse logistics network applied for managing plastic waste in Jakarta. The methodology is shown in Figure 2.
Figure 2 Methodology
3.1. Mixed Integer Linear
Programming
Linear programming is an
optimization method that addresses problems with objective functions,
constraints, and decision variables in linear functions (Safdar
et al., 2020). It is the most widely used method
for improving supply chain management in the context of the agri-food
supply chain (Deepradit,
Ongkunaruk and Pisuchpen, 2020). Furthermore, it could solve routing and planning ocean transportation
(Soegiharto
et al., 2022). Previous works have
adopted linear
programming to optimize the plastic waste management network with
specific consideration
of costs (Castro-Amoedo
et al., 2021).
3.2. Robust Optimization
Robust Optimization is a method for handling data uncertainty, which is presumed to be encapsulated within a designated uncertainty set (Gorissen, Yanikoglu and den Hertog, 2015). This method could function reliably even under unfavorable circumstances, offering the best alternative in the worst-case situation (Ben-Tal, Ghaoui, and Nemirovski, 2009). The general linear programming model is shown by equation (1) as follows:
where 
Assume that the box uncertainty set 
where 
contains each uncertain parameter. By removing
the uncertainty from problems, robust optimization creates a single
deterministic problem known as a robust counterpart. As a result, the following
definition, as shown in equation (3) applies to a robust counterpart for an
uncertain linear programming problem:
3.3. Nominal Model for Jakarta
Plastic Waste Management Reverse Logistics Network
The design of the plastic waste reverse logistics network in Jakarta was prototyped using a mathematical model. Before creating the mathematical model, this study proposed a conceptual model that could represent real systems. Following this, the conceptual model for reverse logistics network design for managing plastic waste in Jakarta is shown in Figure 3. This model consists of several assumptions including c Clients, w Waste Banks, s TPSs, h Hoarders, r Recyclers, and a TPAs.
Figure 3 The conceptual model of Jakarta
plastic waste management reverse logistics
The model of the reverse logistics network introduced
in previous
studies (Roudbari, Ghomi, and Sajadieh, 2021; Safdar et al., 2020; Xu et
al., 2017) served as the basis for the nominal model proposed
in this study. Sets,
parameters, and decision variables are shown from Table 1 to Table 3.
Table 1 Definition of the model sets
Table 2 Definition of the model parameters
Table 3 Decision variables of the model
The nominal model aims to minimize facility costs, reduce carbon emissions from transportation, and maximize job creation. Its economic, environmental, and social objectives are to reduce costs (equation 4), minimize emissions (equation 5), and maximize work generation (equation 6) respectively.
Transshipment constraints are formulated in equations (7) through (12), while the constraints in (equation 13) through (19) comprised limitations on capacity. Finally, the constraints in equation (18) and equation (19) are for defining the decision variables as binary & continuous.
4.1. Application of the Model
This
study adopted a robust optimization model in a case study on plastic waste
management in Jakarta, Indonesia, focusing on a specific set of plastic waste.
The Jakarta reverse logistics network for plastic waste management comprises
260 Clients, 1262 Waste Banks, 1262 TPS, one TPA, 784 Hoarders, and 19
Recyclers.
This study proposed a robust optimization model designed specifically
for Jakarta's reverse logistics network, addressing the effective management of
plastic waste. The model incorporated the process of clients transferring waste
to Waste Banks and TPS, which subsequently experienced recycling at Hoarder or
disposal at TPA. Regarding tackling data uncertainty, the model considers
economic, environmental, and social aspects. It also aided the determination of
optimal locations and transportation routes for plastic waste in Jakarta. This
study showed that the availability of several new facilities, including 125
Waste Banks, 154 TPS, and 275 Hoarders, along with two Recyclers, was essential
to optimize plastic waste management. Following this, the considered
uncertainty in this model was confined to the parameter of the quantity of
returned plastic from consumers, which was assumed to reside within a box of
uncertainties. Hence, future studies might explore additional uncertainty issues,
such as facility capacity and plastic waste quality.
This
study was supported by Universitas Indonesia through Hibah Publikasi Terindeks
Internasional (PUTI) Q2 Grant 2022, grant number
NKB-711/UN2.RST/HKP.05.00/2022.
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