SUMMARY In this report a case study of reverse logistics of waste wood and wood products is presented as the coordination and control; physical pickup and delivery of the material, parts
INTRODUCTION
Climate change is a global issue that needs to be addressed worldwide
The scarcity of resources is a driving force behind the political and economic decisions made around the world In this context, a balance must be struck among the various aspects of sustainability, including economic, ecological, and social welfare Several documents, directives, and strategic initiatives have been introduced with the aim of developing a low-carbon economy in the European Union (EU)
The European Commission has put forward a strategy for sustainable growth,
"Bioeconomy for Europe (2012)," stating that Europe needs to completely change its approach to the production, consumption, processing, storage, recycling, and disposal of bioresources to address issues like population growth and climate change
In the European bioeconomy, the forest sector plays a crucial role Using wood products can significantly reduce greenhouse gas emissions In pursuit of sustainable development, with the expectation of bringing forth new products and processes that optimize the reuse and utilization of forest resources step by step, the European Commission announced the new EU forestry strategy in 2013 This strategy is dedicated to innovation, research, and rural development, emphasizing the importance of building with wood
The new cycle of programming policies for 2014-2020 encourages national and regional authorities to develop research and innovation strategies towards "smart specialization." These documents have, are, and will continue to influence the activities of reverse logistics and wood recycling
Countries in Europe like Germany, Finland, Norway, and others have also introduced policies aimed at building strong economies, sustainable energy, and sustainable development At the international level, wood recycling is related to
The Role of Wood and Wood Products in the Bioeconomy
Wood is a natural, renewable, reusable, and recyclable raw material that can play a major role in minimising the negative effects on the climate and environment when it is sourced from sustainably managed forests Forest biomass is currently the most important source of renewable energy and now accounts for around half of the European Union’s total renewable energy consumption Wood products contribute to climate change mitigation throughout their entire life cycle While in the forest, trees naturally draw CO2 from the atmosphere During their service life wood products store CO2 in products and components of the built environment; with extended service lives, the amount of carbon stored in these wood products exceeds the energy required to harvest and produce them When substituted for fossil fuels at the end of their service life, wood products release their stored carbon back to the atmosphere and provide significant energy returns The amount of carbon released during conversion to energy, however, was already incorporated in the woody biomass in forests during the extended service life of the timber products used for energy recovery
Letureq studied effective ways of using wood production from the perspective of climate change mitigation Two major opposing concepts were compared, carbon sequestration and biomass carbon neutrality The former advocates that woody resources are regarded as ‘renewable’ and their use as an energy source is ‘neutral’ with respect to the greenhouse effect The carbon sequestration concept calls for increasing the carbon stock in forests, wood products, and other long-term wood storage Comparison of the heat generation carbon footprint between wood and other fuels has revealed that the intrinsic carbon emission factor for wood is the highest among all fuels in common use However, this concept of wood carbon neutrality neglects the possibility of carbon storage in long-life wood products Furthermore, Kim and Song
(2014) compared life-cycle assessments of two wood waste recycling systems, particleboard production and energy recovery, and concluded the particleboard production scenario has a greater environmental benefit in reducing greenhouse gas emissions However, upcycling offers even greater carbon saving potential through sequestration than downgrading recovered materials to particles for particleboard.
METHODOLOGY
Problem descriptions
1.1 Flows of Waste Wood a Case Study of Slovenia —
In Slovenia, wood waste primarily originates from the remnants of construction activities, the demolition of structures, household furnishings such as furniture, and wooden packaging The utilization of wood-based materials for residential construction, which function as load-bearing components, comprises a mere 3% of residential structures (Table 5; Statistical Office of the Republic of Slovenia 2004) Nonetheless, mixed-material constructions also make a significant contribution to the recoverable wood waste supply Non-wooden edifices often incorporate wooden elements such as windows and roof trusses Since 2002, the development of wooden housing construction has been on the rise, propelled by advancements in technology and the emergence of environmentally-friendly building trends (Kitek Kuzman 2010; Kušar
2010) These structures are anticipated to be significant sources of recoverable wood waste in the future After the demolition, the wood can be transformed into fresh goods Demolition wood, typically characterized by its large size, dryness, and lack of treatment, can be processed more easily into high-value goods
A considerable quantity of high-quality reclaimed timber is anticipated in Slovenia, particularly due to the refurbishment of pre-1992 prefabricated houses, which are required to comply with new thermal standards by the year 2020 (Kitek Kuzman and Kutnar 2014) The specific volume of timber that can be reclaimed is presently unknown, but the renovations will result in the production of materials that will require processing Ideally, this timber should be reused in valuable, long-lasting products However, Slovenia lacks the necessary infrastructure to fully exploit the potential of this reclaimed timber
Fig 2.1 Dwellings in Slovenia by building material type in 2002 (Statistical Office of the Republic of Slovenia 2004)
Slovenia has made significant strides in managing wood waste, particularly from municipal sources and wooden packaging Over a six-year period from 2008 to 2013, these types of wood waste saw an impressive 260% increase in collection, totaling around 19,000 metric tons (as detailed in Table 6) This growth paralleled a 430% rise in the recovery of wood materials from municipal waste, with a similar increase in tonnage (Table 6)
Significantly, nearly half of the waste from wood-related industries, including pulp, paper, sawmilling, and panel manufacturing, is categorized under item 03 in the List of Wastes (refer to Table 2) About half of this waste is repurposed by the producers themselves for energy or further manufacturing The Statistical Office of the Republic of Slovenia's 2014a and 2014c reports indicate that the remainder is either used by other manufacturers or energy producers, exported, or stored for later use Notably, there was a decrease in wood waste from these industries, from 741,047 tonnes in 2006 to 314,947 tonnes in 2013 This trend reflects the production dip due to the economic recession (Statistical Office of the Republic of Slovenia 2014a)
The supply of discarded roundwood from both industrial and public sources significantly influences businesses reliant on this resource This necessitates a robust system for collection and processing, considering the variability in availability and quality Efficient management is crucial for maximizing the economic and environmental benefits of recycled wood A possible solution to improve this process could be a transnational logistics network This approach would particularly benefit wood recycling in Slovenia, considering the size of its national market, and could serve as a model for handling used wood-based materials through reverse logistics
Fig 2.2 Municipal waste wood and wooden packaging collected and recovered in
Slovenia from 2008 through 2013 in tonnes (Statistical Office of the Republic of
The initial advancement of what is known as reverse logistics was greatly influenced by the 1990 paper authored by Vandermerwe and Oliff This publication provided a methodical examination of product recovery, with a particular emphasis on its business implications The authors challenged the prevailing approach of "buy-use- dump" and instead advocated for the adoption of reconsumption cycles and a reverse logistics supply chain infrastructure that operates in both directions In 1992, Stock's article underscored the role of reverse logistics in supply chain management, specifically highlighting waste reduction Subsequently, in 1993, Kopicki et al dug deeper into the market aspects of reuse and the extension of product lifespan Stock, in
1998, presented a comprehensive perspective on the implementation of reverse logistics processes in business practices
The difficulty of selecting the optimal placement for a warehouse, also known as the facility location problem, aims to minimize transportation costs by establishing an efficient reverse logistics network Initially applied to forward supply chains, quantitative models for the warehouse location problem have been modified to address reverse logistics, where the direct retrieval of used goods from customers is not feasible Reverse logistics networks often require additional facilities to accumulate and transport returned goods in bulk
Barros et al (1998) suggested a network for the recycling of construction waste sand, which involved the use of two intermediate facilities: one for receiving and storing clean sand, and another for cleaning contaminated sand This particular model, known as the multilevel capacitated warehouse location problem, expands upon the fundamental warehouse location problem by taking into account warehouse capacities and optimizing the placement and distribution of facilities in the reverse logistics network accordingly
Spengler et al (1997) developed a mixed-integer linear programming (MILP) model for recycling industrial by-products, applied to the German steel industry This model included the allocation of recycling plants and capacity calculations, incorporating economic factors Andel (1995) suggested a cost-efficient design for return logistics transportation routes, centralizing returns through third-party providers who also handle sorting and regional transportation Young (1996) described a closed- loop reverse logistic system focusing on product disposal or reuse, emphasizing the role of distributors in waste removal, handling methods, and warehousing costs
By the late 1990s, most reverse logistics management solutions were problem- specific and lacked generality Fleischman (2001) redefined reverse logistics as the process of planning, implementing, and controlling the efficient, effective inbound flow and storage of secondary goods for recovering value and proper disposal
Fleischman et al (2001) proposed a generic recovery network model (RNM), considering both forward and reverse flows and optimizing distribution and recovery This mixed-integer linear program formulation integrated two models: one for the forward chain from production facilities to customers via warehouses, and another connecting customers to production facilities through disassembly centers Isabel
(2007) later noted that while this model offered generality, it lacked real-world aspects like production/storage capacity limits, multiproduct production, and uncertainty in demand/return flows in the reverse logistics network They proposed an improved formulation with a case study for an Iberian company
Simons (1998) described a reverse logistics system used by a wood panel and building material company, supplying construction sites with containers for wood products and hiring third-party logistics for pick-up, transport, and delivery The company also recovered pallets used for product delivery, outsourcing pallet collection Materials not recyclable were burned While effective for this case, this reverse logistic model is not universally applicable to all wood-based building materials and products.
Methodology - Implementation of the Model
This section describes technical implementation of the reverse logistics model for wood recovery The user interface and some processing steps are implemented in Web-based frameworks to provide familiar and highly customisable interactions for users
The architecture of the systems follows the fundamental client server type – architecture (see Fig 2.3)
Furthermore, both the front-end and back-end applications contain several different technologies to support scalability and stability On the sever side, an Apache2 http server (version 2.2.22) hosts the CodeIgniter PHP framework (version 2.2.1), implementing a 'model-view-controller' architectural pattern for enhanced security and acting as an interface between the database and the model The PHP framework ensures security and serves as an interface between the database and the model A MySQL database is used to store all inputs and outputs of each computed instance of a model The model itself was implemented in MATLAB with an optimisation toolbox containing an integer and mixed-integer linear programme solver using the branch-and- bound method The connection between the server and client sides uses a REST protocol The client side was implemented using Google’s AngularJS library (version
1.3) with modern HTML/JavaScript bindings Location-based data are rendered using the Google Maps API The graphical interface was built with the Bootstrap front-end library (version 3) Once the model data are prepared on the client side, we utilise the REST interface to queue computation jobs on the server side They decided to implement the job queue to support multiple simultaneous users committing different instances of a model for computation Furthermore, this also solves possible race conditions and minimizes the impact on limited hardware resources The jobs from the queue are processed using MATLAB in a first in, first out fashion After the computation is complete the result is parsed using a PHP script, saved into a local MySQL database, and finally the client is notified about the results to be viewed and inspected
The application was designed as a ‘wizard’ guiding a user through the necessary steps of building the model In this section, they take a more detailed view of the client side of the application and steps that need to be taken to build a custom model successfully
In the first step, the user sets global parameters such as costs associated with transportation costs including building sort and decontamination centres This step is followed by adding accumulation centres, their locations, and capacities (Fig 2.4)
Fig 2.4 Step two—adding accumulation centres (circles with an “A”)
In the third step, the user defines the possible locations for decontamination centres (Fig 2.5) Note that these are potential locations as the evaluation of the model will either place them or not based on the value of decontamination facilities The sorting facilities are currently considered to be at the same locations as decontamination centres (sorting and combined decontamination and sorting) although in the future they may be treated separately from them Note that this sets the distance matrix Y to 0 Moreover, the user also has to set the expected processing capacity of individual decontamination centres In this case, capacity is the centre’s annual throughput capability The total sum of the capacities of all possible decontamination facilities must be equal to or greater than the sum of all waste wood collected
After all accumulation and decontamination locations are entered, the user also has to enter their contact details so that the system will notify them upon successful completion of the computation Before sending all the data be computed, the application calculates the distance matrix X (note, the matrix Y = 0) It is calculated using distances from the Google Maps API Moreover, to speed up the computation process the distances are cached and possibly reused
Fig 2.5 Step three—adding decontamination centres (squares with a “D”)
Finally, the application stores all previously entered models, which can be reused by simply selecting them from a list
Fig 2.6 Computation results Note that to improve legibility the of accumulation centres is cut after the first centre
When the computation on the server is completed, the the user receives a notification indicating that the results are prepared for examination The detailed outcomes are then presented on a map, with additional information about each accumulation center conveniently presented in tabular form below the map
The system renders the decontamination centres that should be constructed in green and the unselected locations are rendered in gray The model parameters are located on the left sidebar with some additional outputs of the model including the total value of the minimisation function cost in CO2 emissions on yearly basis The CO2 emissions do not include operating emissions, as these data are not readily available and must still be collected By clicking on a placed decontamination centre a table containing more details is displayed below the map This table contains a list of accumulation centres and the amounts of waste wood that will feed the selected decontamination centre For improved visualisation the routes taken for transporting waste wood from an accumulation centre to a decontamination centre are emphasised.
PROGRAMMING MODEL
Mathematical model
In its simplest form, a reverse logistics network for wood product recovery and processing is a generalised CWLP problem augmented with additional model entities to reflect some specific requirements of wood recovery The purpose of the model is to determine the optimal quantity and location of processing facilities (including accumulation, sorting and decontamination facilities) based on costs (including constructing, operational, transport costs)
Our model for reverse logistics reflects the steps in the wood recovery process Unfortunately, the path recovered wood products must take involves activities that are either not present in conventional primary manufacturing or that must be modified to accommodate recovered wood Waste wood comes in different sizes, shapes, qualities, and amounts, and therefore requires assessment before its future use is determined First, it is collected and stored in a suitable environment (i.e., protected from rain) that the article modelled as accumulation facilities (A) In Slovenia’s recovery system, accumulation centres perform basic sorting of waste wood into “junk” wood
(unrecoverable wood destined for landfilling, called J), into low-quality wood for energy production (burning, called E), and in some cases into wood for further processing
Next, in an implemented wood recovery network, the wood selected for further processing is sorted either manually or by machine into different categories at sorting facilities (called S) In many cases, before further processing, the wood must be decontaminated (e.g., removing fasteners, such as nails and screws, or removing painted or other finished surfaces) at decontamination facilities (called D) Depending on economic concerns and business objectives, decontamination and sorting facilities may be combined (called D/S)
Fig 3.1 Model entities and their relationships
Sorting facilities sort waste wood into the following general categories: wood that meets the size and quality requirements for further processing, wood best suited for energy production (E), and wood that must be landfilled (J) Finally, the highest quality waste wood is ready for further processing (called P) or, in some cases, may be sent directly to the market as a raw material (called M)
To set up model equations, we associate a number of attributes with each entity These are: PA, PS, PD denoting Boolean values indicating if a accumulation, sorting, decontamination facility exists or not; Acap Scap, Dcap denoting the facilitíe’ capacity;
AO, SO, DO for the operation cost of the facilities in a whole period of time; Aprodcap, Sprodcap, Dprodcap as the production capability of the facilities; and Aconstruct, Sconstruct, Dconstruct denoting the construction cost of the facilities The annual need for processing wood is called need Transport cost per km is defined transport in a whole period of time
Furthermore, each facility has geographic coordinates that are used to calculate the distance between them and the transportation cost To simplify the modelling we define distance matrices separately for facilities A and S as a matrix DistanceAS— — and for facilities S and D as a matrix DistanceSD —
Fig 3.2 Original model objective function
As the original mathematical model of the article works well with authors’ datasets and these datasets are not included in the paper itself, it is impossible for us to reenact the original computational progress One of the biggest problem is that the facilities have not existed yet, which means the specific wood throughput at each node could not be defined Therefore, it is an utmost mission to modify the paper mathematical model in a more approachable direction In our team’s proposed model, the wood throughput at each node is assumed to be equal
The criterion equation (1) is a function that minimises the building cost, transportation cost, and operation cost The building cost is composed by the cost of building all the facilities (A, S, and D) that we will need to process the waste wood The transportation costs consist of the cost of moving all the wood from A to S, and the cost of moving the wood to be decontaminated from S to D The products in denominators of transportation cost calculation part ensure the throughput is equally distributed at all nodes The last part of the criterion equation is the operation cost of each facility 1.3.2 Constraints
The first constraint (2) makes sure that assigned accumulation centers could 𝑘 hanlde all the demand in processing waste wood The second constraint (3) guarantees that the amount of wood that we are going to process in the sorting centres is the same as the amount of wood we receive from our accumulation centres, of course, we have to get rid of J and E rate in waste wood before The third constraint (4) restricts the amount of wood being processed in a sorting centre to the centre’s maximum capacity This is followed by the fourth constraint (5), which mandates that all the wood that must be decontaminated is processed in the decontamination centres Finally, the fifth constraint (6) caps the amount of wood processed in the decontamination centres at their capacity
The evaluation of the constraints programming model gives us the optimal facility placement based on cost considerations and processing rates.
Model Data
The original dataset of the article was not included and it based on the Slovenia conditions like geographical locations, currency, scale of the reverse logistics Therefore, our team decided to generate a new dataset that may fit more with Vietnam conditions that allow us get better insights into this wood reverse logistics model In order to work easily with CPLEX, we prepared the data in Excel files
The mathematical model contains a lot of parameters but we can classify them into 5 main categories: accumulation, sorting, decontamination facilities, distance matrices from A to S and from S to D
The first column of each facilities’ dataset show labels or address of the relative facilities, which are the districts of Ho Chi Minh City We chose these districts to locate the facilities in order to simplify and fasten the data collection process Based on their differences in economical developments The following column “Capacity” denoting the facilitíes’ capacity Acap Scap, Dcap We also defined the facilities locations in geographical coordinates in “x” and “y” Next, AO, SO, DO is determined in the
“Operation” columns (calculated from daily operation cost “Daily Operation”) Aconstruct, Sconstruct, Dconstruct is determined in the “Construct” columns Anumber, Snumber and Dnumber count the total amount of choices given for every facility existence Beware that the cost to operate facilities and transportation cost per unit of waste wood (“Transportation” cell) is calculated in a whole period of time(“Time” cell) Sprodcap, Dprodcap, the production capability of the facilities, is included in “Production” columns We tried to assume these parameters to be practical and meaningful but limited experience in this field and data sources restricted us to fulfill the research
To be more particular on how we gained the coordinates of the facilities locations, we applied a helpful addon on Google Sheet, which is named “Geocode by Awesome Table” From simple input like the text addresses, this tool can automatically display their latitudes and longitudes from Google Map searching
Fig 3.7 A short description of “Geocode by Awesome Table” addon
Each facility has geographic coordinates that are used to calculate the distance between them for the transportation cost Here, we created distance matrices separately for facilities A andS (DistanceAS) and for facilities S and D (DistanceSD) by Python, with the help of wonderful libraries “distance_matrix” tool from “scipy.spatial” helped us convert the coordinates arrays in Excel files into distance matrices quickly
Fig 3.8 Jupyter Notebook code to prepare arrays for converting
Fig 3.9 Jupyter Notebook code and output of converting arrays into distance matrix
Fig 3.10 Jupyter Notebook code and output of converting arrays into distance matrix
Fig 3 Jupyter Notebook code to save the dataframes as Excel files11
Fig 3.12 Matrix DistanceAS in Excel
Fig 3.13 Matrix DistanceSD in Excel
Code models and solution
The model was solved by Cplex Programming because it provides user with quick and accurate results Furthermore, Cplex Programming uses decision optimization technology to optimize your business decisions, develop and deploy optimization models quickly, and create real-world applications that can significantly improve business outcomes
The code model, which displays the Cplex process and result, has been attached to this report
* Creation Date: Oct 19, 2023 at 8:47:54 PM
*********************************************/ using CP;// "using CP" stands for Constraint Programming Constraint Programming is a paradigm in computer science and operations research that deals with solving problems by modeling them as a set of constraints over variables It's particularly useful for solving problems where you must find values for a set of variables that satisfy certain constraints In the provided code, "using CP" means that the model has been implemented using Constraint Programming techniques This approach is well-suited for problems that involve managing and optimizing resources, like the allocation of resources (facilities) in this case Constraint Programming works by defining variables and specifying constraints on those variables The solver then explores possible combinations of variable assignments to find a solution that satisfies all constraints while optimizing an objective function
//A: set of accumulation facilities int Anumber = ; range A = 1 Anumber;
//S: set of sorting facilities int Snumber = ; range S = 1 Snumber;
//D: set of decontamination facilities (k) int Dnumber = ; range D = 1 Dnumber;
//J: The sorting of waste wood into “junk” wood rate float J = ;
//E: The sorting of waste wood into energy production rate float E = ;
// (assumption)The number of recovered wood products comes in different sizes, shapes, qualities, and it is collected and stored in accumulation facilities int need = ;
// Capacity of accumulation facilities A , sorting facilities S, decontamination facilities
D int Acap[A] = ; int Scap S[ ] = ; int Dcap[D] = ;
// Annual operational cost of accumulation facilities A , sorting facilities S, decontamination facilities D int AO A[ ] = ; int SO[S] = ; int DO[D] = ;
// Production capability of sorting facilities S, decontamination facilities D int Sprodcap S[ ] = ; int Dprodcap[D] = ;
// Construction cost of accumulation facilities A , sorting facilities S, decontamination facilities D float Aconstruct[A] = ; float Sconstruct S[ ] = ; float Dconstruct[D] = ;
// transport cost/km = ? vnd float transport = ;
// Matrix distance from accumulation facilities A to sorting facilities S and from sorting facilities S to decontamination facilities D float DistanceAS[A][ ] = ; S float DistanceSD S[ ][D] = ;
// Decision Variables dvar boolean PA[A];//array of boolean decision variables representing if accumulation facilities A[i] open PA[i] (i in A) = 1 else PA[i]=0 dvar booleanPS[S];//array of boolean decision variables representing if sorting facilities S[i] open PS[j] ( j in S)= 1 else PS[j]=0 dvar boolean PD[D];// array of boolean decision variables representing if decontamination facilities D[i] open PD[k] ( k in D)= 1 else PD[k]=0
//Building_cost: Construction cost of each facilities will be summed up
//If the facilities is open for example PA[1]=1, the Construction cost of accumulation facilities A[1] will be add in Building_cost dexpr float Building_cost= sum(i in A) Aconstruct i[ ] * PA[i] + sum(jin S) Sconstruct j[ ]
* PS[j] + sum(k in D Dconstruct[k) ] * PD[k];
//Transportation_cost: Transportation_cost from accumulation facilities A to sorting facilities S, and from sorting facilities S to decontamination facilities D
//If the accumulation facilities A and sorting facilities S or sorting facilities S and decontamination facilities D open simultaneously
//(for example PA[1]=1 , PS[1]=1) the transportation cost from PA[1] to PS[2] will be add in Transportation_cost automatively else it will be 0 dexpr float Transportation_cost transport=( * sum(i in A, j in S) DistanceAS i j[ ][ ] * PA[i]
* PS[j] * Sprodcap j[ ])/(sum (i in A)PA[i]*sum (j inS)PS[j])
+ (transport *sum(jin , S k in D) DistanceSD j k[ ][ ] * PS[j] * PD[k] * Sprodcap j[ ] * ( 1
- E - J))/(sum (j in S)PS[j]*sum (k in D)PD[k]);
//Operational_cost This cost is smilar with Building_cost dexpr float Operational_cost=sum(i in A) AO[i] * PA[i] + sum(j inS SO[j) ] * PS[j] + sum(k in D) DO[ ] * PD[k]; k
// Objective: Minimise Building cost + Transportation cost + Operational cost minimize Building_cost+ Transportation_cost+ Operational_cost; subject to {
//Ensures that sum of the opened accumulation facilities A capacity must be greater than or equal to the expected amount of wood needed (need) sum(i in A) Acap i[ ] * PA[ ] >= need; i
//Ensures that sum of the opened sorting facilities S production capacity must be greater than or equal to the amount of wood that is coming from the opend accumulation facilities (A) after landfilling and burning sum(i in A) Acap i[ ] * PA[ ] * (1 - E - ) = sum(kin D) Dprodcap k[ ] * PD[k] * ( - - 1 E J); }
SheetConnection my_sheet ("WoodData.xlsx");
Anumber from SheetRead(my_sheet, "Accumulation!H2");
Snumber from SheetRead(my_sheet, "Sorting!H2");
Dnumber from SheetRead(my_sheet, "Decontamination!H2");
Acap from SheetRead(my_sheet, "Accumulation!B2:B13");
Scap from SheetRead(my_sheet, "Sorting!B2:B7");
Dcap from SheetRead(my_sheet, "Decontamination!B2:B6");
AO from SheetRead(my_sheet, "Accumulation!E2:E13");
SO from SheetRead(my_sheet, "Sorting!E2:E7");
DO from SheetRead(my_sheet, "Decontamination!E2:E6");
Sprodcap from SheetRead(my_sheet, "Sorting!I2:I7");
Dprodcap from SheetRead(my_sheet, "Decontamination!I2:I6");
Aconstruct from SheetRead(my_sheet, "Accumulation!F2:F13");
Sconstruct from SheetRead(my_sheet, "Sorting!F2:F7");
Dconstruct from SheetRead(my_sheet, "Decontamination!F2:F6"); transport from SheetRead(my_sheet, "Accumulation!H4");
DistanceAS from SheetRead(my_sheet, "DistanceAS!B2:G13");
DistanceSD from SheetRead(my_sheet, "DistanceAS!B2:F7");
Fig 3.14 Solution with objective 23.247.510,982 (thousands VND)
Fig 3.15 Results of Opend Accumulation facilities
The results show that the Accumulation facilities at Quan 1, Quan 3, Quan 10 should be opened
Fig 3.16 Results of Opend Sorting facilities
The results show that the Accumulation facilities at Quan Phu Nhuan should be opened
Fig 3.17 Results of Opend Decontamination facilities
Verify and testing
As the solution, which was presented above, let make a verification for this result:
First, the total capacity of chosen Accumulation facilities is 37000 which satisfied with the total need (30000) in 5 years Furthermore, the number of wood products which transported to Sorting facilities after sorting of waste wood into “junk” wood and low-quality wood for energy production is 7500 Therefore, the chosen Sorting facilitieshave enough capacity to handle the wood products
Second, re-calculate the building cost:
The calculation for Operational cost and Transportation cost are smilar to the buiding cost
Finnally, let’s look inside the cost Because the given data includes the total process for 5 years so the transportation cost will be significantly higher than other costs Therefore, if we construct more facilities with the mission that trade off Building cost for reducing the Transportation cost, the total cost will not be improve significantly
In conclusion, the result solving by Cplex was highly confident.
CONCLUSION
Conclusions
The forest-based sector can become a leader in achieving the European Commission’s ambitious CO2 emissions reduction goal (Roadmap 2050) with innovative production technologies, reduced energy consumption, and increased wood products recycling The use of forest products in products with long service lives, such as in the built environment, allows for the possibility of extended storage of atmospheric carbon dioxide
Forest-based industries are continually developing advanced processes, materi- als, and wood-based solutions to meet evolving demands and to increase competitiveness Furthermore, new advanced wood-based materials with improved intrinsic properties that promote efficient product reuse, recycling, and end-of-life use can pave the way to a low-carbon economy Interactive assessment of process parameters, product properties, and environmental impacts should be used to aid development of innovative wood processes and manufacturing technologies, existing and planned, which embrace the cradle- -cradle paradigm Recycling, upcycling, and to end-of-life disposal options need to be integrated in a fully developed industrial ecology Intelligent material reuse and upcycling concepts could reduce the amount of waste destined for landfills or downcycling However, in order to develop and/or optimise waste-wood processing to minimise environmental impacts, much more information must be gathered about relevant process factors This includes the development of chain of custody procedures throughout the entire life cycle, including a robust reverse logistics system, which present new or expanded business opportunities to logistics operators and wood processors.
Suggestions and Implications 27 REFERENCES
The current trajectory of the wood recycling market in Vietnam exhibits a marked growth, driven by the objective of achieving sustainable and environmentally responsible development The act of recycling and reutilizing wooden materials not only aids in waste reduction, but also effectively preserves our finite natural resources
In order to stimulate investment in the realm of wood recycling, the Vietnamese government has implemented various policies and incentives, specifically designed to promote recycling and ecologically mindful activities However, this industry encounters pertaining to waste management and the development of recycling infrastructure The COVID-19 pandemic also had a huge negative impact, forcing some wood recycling facilities to temporarily close, but then gradually recovered
Research from Slovenia has opened a new perspective on the importance of reverse logistics in improving the sustainability of wood recovery and recycling processes This also emphasizes the role of infrastructure in synthesizing, classifying and processing waste wood Optimizing economic and environmental benefits from recycled materials becomes even more important when considering fluctuations in wood scrap supply and quality Therefore, building a comprehensive system that can adapt to these changes is essential
For Vietnam, a country with a thriving construction industry and a growing awareness of sustainable practices, the results from the Slovenian study are of great value Applying the reverse logistics model in Vietnam will increase the ability to recover and reuse wood, reduce waste and promote a circular economy The following recommendations are made based on the results of the above study:
1 Establish collection centers: We should devote resources to developing waste collection facilities capable of handling the classification and processing of scrap wood These centers will act as initial collection points, where the wood will be sorted based on quality and reusability
2 Develop a reverse logistics network: It is extremely important to establish a network that allows the reverse movement of materials from the point of processing back to the point of reuse or recycling The network will include transport, storage and treatment facilities specifically designed to efficiently process waste wood
3 Encourage wood recovery: Encouraging businesses and consumers to actively participate in wood recovery activities through the provision of benefits, significantly increases the volume of wood moved from landfills bury Benefits may include tax incentives, subsidies for the use of recycled wood or programs that recognize sustainable practices
4 Invest in processing infrastructure: To maintain the value of recovered wood, we need to invest heavily in processing infrastructure capable of cleaning, refurbishing and reusing wood into high value products effectively
5 Support for policies and regulations: The government plays a crucial role in implementing policies to fortify the recycling sector, establishing benchmarks, and enforcing regulations to promote the use of repurposed materials
6 Focus on raising community awareness: The spread of information regarding the benefits of wood recycling, along with the distribution of knowledge on methods through which individuals can actively involve, takes on a position of utmost importance in ensuring the efficiency of any reverse logistics initiative
Applying lessons from Slovenia to the Vietnamese context, we see that combining traditional cultural customs with modern environmental protection efforts is essential Vietnam's long history of carpentry and wood processing can become a solid foundation for creating a market for recycled wood products The blend of local craftsmanship and recycled materials will open up opportunities to develop a unique and sustainable wood industry
Besides, the rapid urbanization process in Vietnam also brings both challenges and opportunities As old buildings give way to new developments, wood recovery and recycling policies need to be reviewed and applied This is not only a solution to protect the environment but also opens up job opportunities and boosts the local economy
In summary, research from Slovenia has opened up an exemplary direction for Vietnam in developing a reverse logistics system for wood recovery By learning from Slovenia's experience and adapting to local conditions, Vietnam can make important strides in the field of environmental protection and sustainable development.