1.Introduction

In logistic field, most of researchers aim to meet the challenge of integrating new scientific data in terms of methods, software, managerial solution, to support supply decision making, furthermore environmental dimension is among the most attractive area of study to deal with. Nowadays, a set of supply chain (SC) models have been proposed in the literature, integrating the environmental dimension, using various modeling tools and methods [1]-[4]. Mainly; this paper proposes a modeling framework of the fish cannery supply chain (FCSC), by integrating the environmental constraints and it contributes in helping and assisting researchers, as well as practitioners to establish a global model of the green fish cannery supply chain (FCSC), where few articles seek to tackle modeling studies in this area of study, particularly as an industrial supply chain [5]-[7], followed by a numerical resolution using the LINGO software. The remainder of the paper starts with a relevant literature review, which typically tackle the cannery fish supply chain (FCSC), and the main keywords in section 2.

And subsequent to a detailed description of the case study in Section 3. The model formalization and discussion are addressed in Section 4. Section 5 provides the numerical resolution, while Section 6 addresses the conclusion, limitations and future research direction.

2. Literature Review

2.1. Fish cannery supply chain

The Agri-food industry, in particular the fish cannery industry, is chosen as a case study. Generally, there are many kinds of fish, such as sardines and mackerel, which are among the most consumed fish in the Mediterranean region [8]; While in tropical and subtropical oceans, western and central pacific ocean (in particular Asian country) tuna is the most common cannery industry, Southeast Alaska and Puget Sound, Washington State, USA are known by salmon fish etc. One must bear in mind the time constraint since this industry is almost seasonal, operation is limited to about 3 months in a year for salmon [7], and up to 9 months/year for the other types. In this case study objectives are established as to demonstrate the applicability of the model whatever the purposes, and to visualize its added value on the one hand, by treating a case of an area rarely addressed in research as an industrial SC.

The environmental constraints or impact resulting from this type of industry is treated separately from its modeling. Among the most significant and widespread environmental impact of this industry: water pollution, or wastewater [9]-[11], waste fish [7], most of research works that deal with this impact propose solutions to minimize it. On the other hand, fish processing waste has an interesting energy value. The increase of the aforementioned wastes as well as the increasing of the renewable energy market confirms that this waste could have a place as a future source of biofuels [12].

Furthermore; and for our knowledge, there is no standard model for this type of industry as a whole SC case study. In [6] the authors have dealt with the fish cannery industry, notably the case of tuna, under a model that encompasses multiple fishing fleets including canneries, in the form of scenarios by exploiting the future results of the world tuna fishery through a simple presentation: climate change effects, changes in global tuna demand, and changes in access to fishing areas. Also an optimization, mathematical model is reported by authors of [7], which aims to optimize food portion in packaging, actually the model is presented in a case study of a cannery portion of fish. We notice well that these two examples consist in treating this type of SC in the manner that the raw material is of exhaustible nature (in the biological way also), which strongly supports the scarcity of research works dealing with a global model of the green industrial SC (including all environmental impact), particularly the proposed case study.

2.2. MCDM method: AHP and TOPSIS

MCDM (Multi-Criteria Decision Making) gathers a set of sophisticated methodological tools to assist in decision making to cope with complex decisions. It is a powerful method for evaluating and ranking one or more customized solution from a set of options that consider multiple indicators, which are typically contradictory. In particular, it allows us to highlight conflicts within the metric indicators, to identify an effective and structured framework and, finally, to make the holistic trade-offs necessary to reach a decision”[13].

AHP (Analytic hierarchy process) method is among the MCDM tools, to hierarchize criteria in order to achieve a specific goal, where scores of all criteria are grouped into a unique aggregate score. In literature, there is a considerable amount of research work that has used the AHP method either to evaluate performance of the green SC [14], [15] and for ranking the key performance indicators [16], also for assessment of the sustainability of the SC [17], or for the risk assessment [18], [19], commonly AHP is well-known for supplier selection [20]-[22], also it’s applied in reverse logistics.

TOPSIS (technique for order preference by similarity ideal solution) has been proposed for the first time by [23]. TOPSIS is an effective method to solve existing problems of multi-attribute decision making with finite alternatives. The concept of this method is to classify the alternatives by calculating the distance of each alternative in relation to the ideal solution and the ideal negative solution of the problems in order to determine the optimal alternative. These ideal and negative-ideal solutions are calculated in considering other alternatives [24].

Most of papers utilized both AHP followed by TOPSIS which is preferred in comparison, [25]. In [26], authors applied the robust Analytical Hierarchy Process (AHP)-Fuzzy and TOPSIS for the evaluation and selection of contractors [27] the Fuzzy Analytical Hierarchy Process (FAHP) and (TOPSIS) is used to evaluate performance, and particularly in the selection of reverse logistics service [28]. In notably, this paper exclusively applies these two methods for the classification of processes and their environmental impacts.

In the following paragraph, a model of the (FCSC) is presented. Furthermore, the two methods: AHP and TOPSIS would be used, in order to classify the environmental impacts and the SC’s processes.

3. Case study

This section presents a case study of the fish cannery industry, this option of industry is exclusively treated as a SC model in this paper in addition most of the research works focus on the biological aspect in studying this case study. Data is collected from 4 anonymous companies of the fish cannery industry in the same city in Morocco, preserving all the needed information, with the aim to establish a model of green (FCSC), which may help industrialists in this field, in the one hand, and researchers in the future, in the other hand, by providing a global framework of green SC modeling. Therefore, this type of industry is characterized by a set of activities from fish procurement to distribution.

After a five-month visit to the four companies, we have collected data related to each process of this SC, these processes may change in terms of appointment from one company to another, but the activities remain the same. Reception of raw materials from suppliers (different) and distribution of final product to a limited number of destinations (clients, retailers…). These companies use only Small fish species such as sardines. The set of processes is presented in fig.1 below, we opted for this organizational chart in order to highlight the objective of this paper, namely to meet the optimization of environmental constraints in SC modeling. Particularly, the fig.1 below shows the impact of waste water at all levels (till the process of cooling):

Figure 1: Fish canning industry supply chain (flow chart) [10]

3.1. Description

Hereafter a detailed description of the SC’s characteristics, and the common elements between the four companies, all these data could be used in the resolution of the models, and to establish an action plan (improvement). In the next sections collected data are also used in AHP and TOPSIS (Water consumption, energy consumption, noise …).

Table 1: Case study data collection

3.2. Material and Method

This type of SC promotes mathematical modeling, given the nature of its well-defined and flexible activities to model them mathematically. As evoked at the beginning of this paper, the analytical modeling has opted for, by proposing a mathematical model to an objective function which minimizes the environmental impacts, and helps in decision making, regarding the actions to be undertaken. For the mathematical model, only impacts whose parameters can be used in mathematical modeling are retained. In addition, for the identification of the significant impact, the TOPSIS method is used in order to hierarchize processes according to their criticality regarding environmental impacts as to classify them from the worst alternative, also by further using the AHP method to classify the different environmental impacts and then using this ranking in TOPSIS method.

3.3. Identification of environmental Constraints

The environmental analysis presents a set of steps to be followed, to achieve a set of aspects, and significant impacts, of the different processes or activities of the SC. In addition the environmental analysis is required by ISO14001 standard, but it is indeed an optional direction. In this case study, we are interested in tackling, the most relevant significant impact regarding the critical activities or processes, and as we have previously mentioned, we opted for the AHP and TOPSIS methods which allows us to inherit the desired criteria, in a fuzzy environment to assist in the selection of processes and environmental impact.

Our main goal from AHP method is to classify criteria according to their criteria weight, the criteria adopted, according to the companies’ experts of our case study, are the most relevant to prioritize in order to conduct a study of the most significant environmental impacts, namely: water consumption, energy consumption, noise, effluent discharge, air pollution, and then the ranking result are further used in  TOPSIS, which is chosen to rate and compare processes alternatives in a fuzzy environment, to be taken into account in our modeling.

Figure 2: The structure of AHP method

3.4. AHP Method

A hierarchical structure is constructed using five criteria and four alternatives through the literature review and taking opinions from the four experts from each company. In order to achieve the main goal (namely: weight of the selected environmental impacts).

The pairwise comparison matrix determines the relative importance (table 2) of different attributes or criteria with respect to the goal, in table 3 below:

Table 2: Scale of relative importance

The normalized pair-wise matrix is elaborated in table 4. The new pair-wise matrix as shown below in table 5 is established by calculating the weighted sum value and each criterion weight with its rate:

Table 3: Pair-wise comparison matrix

Table 4: Normalized pair-wise matrix

Table 5: New pair-wise matrix (non-normalized)

Table 6: Standard of RI

The relative weights are given by the eigenvector (w) corresponding to the maximum eigenvalue ( λmax), such as :

λmax=(5.36+5.29+4.2+5.65+5.27)/5=  5.154

A consistency index (CI) is calculated. Equation (1) describes the formula for the coherence index. The consistency ratio (CR) is calculated. Indeed, the CR allows checking if the evaluations are consistent or not. The CR can be determined by taking the ratio of the CI and the random index (RI).

Consistency index (C.I.)

( λmax –n)/(n-1)

C.I=(5.154-5)/(5-1)=0.04

Consistency Ratio

C.I/ RI=0.04/1.12=0.034 <0.10 => so our matrix is reasonably consistent

Table 7 presents the result of criteria ranking:

3.5. TOPSIS Method

Define abbreviations and acronyms the first time they are used in the text, even after they have been defined in the abstract. Do not use abbreviations in the title or heads unless they are unavoidable. The TOPSIS method is used to classify (rate) processes according to the selected environmental impact, then to use them in the model formalization, for this case study only some environmental impacts are used in mathematical modeling, regarding priorities given by experts.

Table 7: Criteria ranking

Table 8: Selection of the best

The transformation of units among various criteria into common measurable units to allow comparisons between criteria. Then, the normalized values of the alternatives are determined X_ij is the numerical score of alternative j on criterion i. The corresponding normalized value  is defined as follows :

Table 9: Non measurable criteria scale

The weighted normalized decision matrix vij can be calculated by multiplying the normalized evaluation matrix  by its associated weight wi to obtain the result:

 Table 10: Normalized decision matrix

Table 11: Weighted Normalized decision matrix

Ideal best value: correspond to the minimum value (because we are interested in minimizing impact)

Ideal worst value: correspond to the maximum value (because we are not interested in maximizing impact)

Euclidean distance from ideal best and worst:

For the P_j must be ranked in ascending order, as long as our goal is to classify the most activities that generate more environmental impacts. A set of alternatives can then be ranked in order of preference in descending order of .

Table 12: Criteria (processes) ranking

Based on the results of the TOPSIS method, “baking” is identified as the most critical process to be considered in the modeling. The table 13 below contains the set of significant aspect and related significant impacts, according to each selected process, this study is carried out, with the help of expert panel:

Table 13: Significant aspects and impacts

4.       The model Formalization

In the model formalization phase, and after selecting the method/tool to be applied, as well as the identification of the significant impacts, all the model elements are presented below:

Hypothesis and constraints

-Non-regularity of procurement

-Persistence of the raw material

Sets

J: Set of processes j of the SC.

I: Total energy consumption (type)

Parameters

–   : Water consumption in process j.

– : Processing time of the quantity Qj of raw material (product) used in process j.

–   : Electrical energy consumption in process j.

–   : Energy (fuel) consumed in process j.

Decision Variables

 –   : Quantity of raw material (product) used in process j.

Data

– : Total energy (fuel oil) consumed (l/tone).

– : Total electrical energy consumed (kw/tone).

–  : Total quantity of water consumed (m3/tone).

– : Total amount of raw material per day.

–n₀ : The number of baking ovens available at the plant.

–Q₀ : Maximum amount of raw material to be baked in n0 oven.

–P_j: Percentage of water consumption of process water j.

– D: Total processing time of Qj from raw material to final product

Objective Function

For environmental impact of noise and effluent discharge, a set of actions is proposed to improve and to optimize theses impacts in Fig.3 below. Hereafter the objective function “(1)” that minimizes the environmental impact of the selected processes, by taking into account various variables as the quantity of raw material, etc.

NB: The environmental impact of water consumption, in transport and reception process, is excluded due to the negligible volume of water consumed during this process.

Constraints

• Constraint of electrical energy consumption

• Constraint of maximum quantity to be processed in the baking process:

• Water consumption percentage constraint for all processes j:

• Water consumption constraint for all processes j :

• Constraint of energy consumption of transport and baking processes (fuel oil):

• Constraint on the total quantity of raw material to be processed Qj:

• Constraint of the total processing time from raw material Qj to finished product:

The objective function (1) maximizes the amount Qj of raw material to be transformed during each of the six processes in order to minimize the energy consumed and the amount of water consumed. Constraints (2) and (6) ensure that the energy consumed by each process for the transformation of the quantity Qj of raw material versus the total energy consumed is satisfied. Constraint (3) limits the available backing capacity. Constraints (4), (5) determine respectively the percentage and quantity of water consumed for each process j. Constraints (7) and (8) determine respectively the total daily quantity and the processing time.

The proposed model is a mathematical linear programming model whose objective function is to minimize the following environmental impact: Energy and water consumption. All parameters, data and decision variables may be adjusted according to the studied SC; it is also conceivable to include other constraints. Indeed, the proposed model is quite streamlined and has not addressed all potential conditions or assumptions. The flow chart in fig.3 below is designed as an improvement action to monitor all environmental impacts, in particular noise, and effluents discharge. In the next section, a numerical resolution of this model is proposed.

5. Numerical resolution

The numerical resolution is performed using the LINGO software (from LINDO SYSTEM INC).Data: (for the transformation of the quantity QJ of the raw material.

Table 14: Numerical data of the studied supply chain

Figure 3:flow chart for the monitoring of the environmental impacts

The objective function :

The objective of this section is to present the application of our mathematical model, by proposing numerical examples. The problem is solved by LINGO 18, on a computer with 1.83 GHz and 2 GB RAM:

Global optimal solution found.

  Objective value:                              810.8794

  Infeasibilities:                              0.000000

  Total solver iterations:                             4

  Elapsed runtime seconds:                          1.16

For the values of Qj:

Variable           Value        Reduced Cost

 Q1              2.473881          0.000000

 Q5              5.375000          0.000000

We notice that the optimal quantity (to reduce water and energy consumption) of the raw material or semi-finished product in the baking process is: Q1=2.473881tone, for the duration of 67min, and for the cooling process Q5=5.375000 tone for the duration of 120 min (2h).

6. Conclusion

The research on green SC modeling has been flourishing, in recent years, but continues to require more and more in-depth research for future studies. Nevertheless, when it comes to academicians, and industrialists, the aim of achieving an optimal model of the green supply chain is very challenging to decide which methods or tools are suitable. On the one hand, this paper provides a theoretical contribution to the body of literature, to our knowledge, there is virtually no model of the (FCSC) that takes into account all environmental impacts. In the other hand, it has some managerial implications, the proposed model can be deployed by supply chain managers, as it demonstrated in the case of fish cannery, and also the result of implementation of this model on this case study can be exploited in other fields of application.

The paper starts with literature review of the main terminology such as the (FCSC). Next, it invests in a case study which is less often dealt with as a whole supply chain, namely the case of a fish cannery, where the paper further uses relevant methods in doing so, like AHP and TOPSIS.

Moreover, the proposed mathematical model aims to minimize the identified significant impacts, and as for some directions of future research, it would be interesting to deal with other software for the model resolution, even further to include the social and economic hypotheses in order to address the whole sustainable (FCSC) modeling. The proposed framework may also serve as a preliminary approach for modeling the green supply chains regardless of their nature.

1. A.J.C. Trappey, C. V. Trappey, C.T. Hsiao, J.J.R. Ou, C.T. Chang, “System dynamics modelling of product carbon footprint life cycles for collaborative green supply chains,” International Journal of Computer Integrated Manufacturing, 25(10), 934–945, 2012, doi:10.1080/0951192X.2011.593304.
2. F. Gautier, P. Lacomme, P. Pariente, S.K. Tchomte, N. Tchernev, “Linear model for supply chain operational planning and carbon footprint optimization,” Supply Chain Forum, 14(2), 40–53, 2013, doi:10.1080/16258312.2013.11517314.
3. Ole Ottemöller, “Modelling Change in Supply Chain Structures and its Effect on Freight Transport Demand,” Schriftenreihe Des Instituts Für Verkehr, 38(April 2017), 2017.
4. W. Guo, Q. Tian, Z. Jiang, H. Wang, “A graph-based cost model for supply chain reconfiguration,” Journal of Manufacturing Systems, 48(March), 55–63, 2018, doi:10.1016/j.jmsy.2018.04.015.
5. S. Tabrizi, S.H. Ghodsypour, A. Ahmadi, “Modelling three-echelon warm-water fish supply chain: A bi-level optimization approach under Nash–Cournot equilibrium,” Applied Soft Computing Journal, 71, 1035–1053, 2018, doi:10.1016/j.asoc.2017.10.009.
6. C. Mullon, P. Guillotreau, E.D. Galbraith, J. Fortilus, C. Chaboud, L. Bopp, O. Aumont, D. Kaplan, “Exploring future scenarios for the global supply chain of tuna,” Deep-Sea Research Part II: Topical Studies in Oceanography, 140, 251–267, 2017, doi:10.1016/j.dsr2.2016.08.004.
7. F.K. Omar, C.W. De Silva, “Optimal portion control of natural objects with application in automated cannery processing of fish,” Journal of Food Engineering, 46(1), 31–41, 2000, doi:10.1016/S0260-8774(00)00068-6.
8. V. Ferraro, A.P. Carvalho, C. Piccirillo, M.M. Santos, P.M. Paula, M. E. Pintado, “Extraction of high added value biological compounds from sardine, sardine-type fish and mackerel canning residues – A review,” Materials Science and Engineering C, 33(6), 3111–3120, 2013, doi:10.1016/j.msec.2013.04.003.
9. P. Chowdhury, T. Viraraghavan, A. Srinivasan, “Biological treatment processes for fish processing wastewater – A review,” Bioresource Technology, 101(2), 439–449, 2010, doi:10.1016/j.biortech.2009.08.065.
10. R.O. Cristóvão, V.M.S. Pinto, A. Gonçalves, R.J.E. Martins, J.M. Loureiro, R.A.R. Boaventura, “Fish canning industry wastewater variability assessment using multivariate statistical methods,” Process Safety and Environmental Protection, 102, 263–276, 2016, doi:10.1016/j.psep.2016.03.016.
11. A. Val del Rio, A. Pichel, N. Fernandez-Gonzalez, A. Pedrouso, A. Fra-Vázquez, N. Morales, R. Mendez, J.L. Campos, A. Mosquera-Corral, “Performance and microbial features of the partial nitritation-anammox process treating fish canning wastewater with variable salt concentrations,” Journal of Environmental Management, 208, 112–121, 2018, doi:10.1016/j.jenvman.2017.12.007.
12. G.K. Kafle, S.H. Kim, K.I. Sung, “Ensiling of fish industry waste for biogas production: A lab scale evaluation of biochemical methane potential (BMP) and kinetics,” Bioresource Technology, 127, 326–336, 2013, doi:10.1016/j.biortech.2012.09.032.
13. R. Gao, H.O. Nam, W. Il Ko, H. Jang, “Integrated system evaluation of nuclear fuel cycle options in China combined with an analytical MCDM framework,” Energy Policy, 114(December 2017), 221–233, 2018, doi:10.1016/j.enpol.2017.12.009.
14. K. Govindan, S. Rajendran, J. Sarkis, P. Murugesan, “Multi criteria decision making approaches for green supplier evaluation and selection: A literature review,” Journal of Cleaner Production, 98, 66–83, 2015, doi:10.1016/j.jclepro.2013.06.046.
15. C.H. Kuei, C.N. Madu, C. Lin, “Developing global supply chain quality management systems,” International Journal of Production Research, 49(15), 4457–4481, 2011, doi:10.1080/00207543.2010.501038.
16. VK Sharma,P Chandana,A Bhardwaj, “Critical factors analysis and its ranking for implementation of GSCM in Indian dairy industry,” J. Manuf. Technol. Manag., 5(6), 911–922, 2014, doi:10.1108/JMTM-03-2014-0023.
17. F. Dehghanian, S. Mansoor, M. Nazari, “A framework for integrated assessment of sustainable supply chain management,” IEEE International Conference on Industrial Engineering and Engineering Management, 279–283, 2011, doi:10.1109/IEEM.2011.6117922.
18. K. Zimmer, M. Fröhling, P. Breun, F. Schultmann, “Assessing social risks of global supply chains: A quantitative analytical approach and its application to supplier selection in the German automotive industry,” Journal of Cleaner Production, 149, 96–109, 2017, doi:10.1016/j.jclepro.2017.02.041.
19. F.T.S. Chan, N. Kumar, “Global supplier development considering risk factors using fuzzy extended AHP-based approach,” Omega, 35(4), 417–431, 2007, doi:10.1016/j.omega.2005.08.004.
20. K. Shaw, R. Shankar, S.S. Yadav, L.S. Thakur, “Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain,” Expert Systems with Applications, 39(9), 8182–8192, 2012, doi:10.1016/j.eswa.2012.01.149.
21. V. Mani, R. Agrawal, V. Sharma, “Supplier selection using social sustainability: AHP based approach in India,” International Strategic Management Review, 2(2), 98–112, 2014, doi:10.1016/j.ism.2014.10.003.
22. S. Gold, A. Awasthi, “Sustainable global supplier selection extended towards sustainability risks from (1+n)th tier suppliers using fuzzy AHP based approach,” IFAC-PapersOnLine, 28(3), 966–971, 2015, doi:10.1016/j.ifacol.2015.06.208.
23. K. Huwang, C.L., & Yoon, K.P “Multiple attribute decision making:methods and applications,” Springer-Verlag, 1981.
24. T.C. Wang, T.H. Chang, “Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment,” Expert Systems with Applications, 33(4), 870–880, 2007, doi:10.1016/j.eswa.2006.07.003.
25. S. Senthil, B. Srirangacharyulu, A. Ramesh, “A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics,” Expert Systems with Applications, 41(1), 50–58, 2014, doi:10.1016/j.eswa.2013.07.010.
26. Sonia M. Lo1, James C. Chen, and T.-S.L. “RESPONSE TO DEMAND UNCERTAINTY OF SUPPLY CHAINS: A VALUE-FOCUSED APPROACH WITH AHP AND TOPSIS,” International Journal of Industrial Engineering, 25(6), 739–756, 2018.
27. P. Kumar, R.K. Singh, “A fuzzy AHP and TOPSIS methodology to evaluate 3PL in a supply chain,” Journal of Modelling in Management, 7(3), 287–303, 2012, doi:10.1108/17465661211283287.
28. A. Jayant, P. Gupta, S.K. Garg, M. Khan, “TOPSIS-AHP based approach for selection of reverse logistics service provider: A case study of mobile phone industry,” Procedia Engineering, 97, 2147–2156, 2014, doi:10.1016/j.proeng.2014.12.458.

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16. Daniela Halidini Qendraj, Evgjeni Xhafaj, Alban Xhafaj, Etleva Halidini, "Ranking the Most Important Attributes of using Google Classroom in online teaching for Albanian Universities: A Fuzzy AHP Method with Triangular Fuzzy Numbers and Trapezoidal Fuzzy Numbers", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 297–308, 2021. doi: 10.25046/aj060134
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18. Aicha Ousrhire, Hassane Oulidi Jarar, Abdessamad Ghafiri, "Multi-Criteria Decision Analysis Coupled with GIS and Remote Sensing Techniques for Delineating Suitable Artificial Aquifer Recharge Sites in Tafilalet Plain (Morocco)", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1109–1124, 2020. doi: 10.25046/aj0506135
19. Aaron Don M. Africa, Emmanuel T. Trinidad, Lawrence Materum, "Projection of Wireless Multipath Clusters Using Multi-Dimensional Visualization Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1064–1070, 2020. doi: 10.25046/aj0506129
20. Gehad Ali Alsayed, Zahraa Ismail, Sameh O. Abdellatif, "Investigating the Optical Behavior of Single/Multi-Dimensional Photonic Crystal Structures for Photovoltaic Applications", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 951–958, 2020. doi: 10.25046/aj0506113
21. Yohei Yamauchi, Mitsuyuki Saito, "Adaptive Identification Method of Vehicle Model for Autonomous Driving Robust to Environmental Disturbances", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 710–717, 2020. doi: 10.25046/aj050685
22. Alexander Raikov, "Accelerating Decision-Making in Transport Emergency with Artificial Intelligence", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 520–530, 2020. doi: 10.25046/aj050662
23. Ravi Sekhar, Tejinder Paul Singh, Pritesh Shah, "Complex Order PI\(^{\alpha + j\beta} \)D\(^{\gamma+j\theta}\) Design for Surface Roughness Control in Machining CNT Al-Mg Hybrid Composites", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 299–306, 2020. doi: 10.25046/aj050636
24. Sergiy Kostrikov, Rostyslav Pudlo, Dmytro Bubnov, Vladimir Vasiliev, Yury Fedyay, "Automated Extraction of Heavyweight and Lightweight Models of Urban Features from LiDAR Point Clouds by Specialized Web-Software", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 72–95, 2020. doi: 10.25046/aj050609
25. Yonatan López Santos, Diana Sánchez-Partida, Patricia Cano-Olivos, "Strategic Model to Assess the Sustainability and Competitiveness of Focal Agri-Food Smes and their Supply Chains: A Vision Beyond COVID 19", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1214–1224, 2020. doi: 10.25046/aj0505147
26. Mohammed Chaouki Abounaima, Loubna Lamrini, Noureddine EL Makhfi, Mohamed Ouzarf, "Comparison by Correlation Metric the TOPSIS and ELECTRE II Multi-Criteria Decision Aid Methods: Application to the Environmental Preservation in the European Union Countries", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1064–1074, 2020. doi: 10.25046/aj0505131
27. Gene Patrick Rible, Nicolette Ann Arriola, Manuel Ramos Jr., "Modeling and Implementation of Quadcopter Autonomous Flight Based on Alternative Methods to Determine Propeller Parameters", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 727–741, 2020. doi: 10.25046/aj050589
28. Abdelghani Lakhdar, Aziz Moumen, Laidi Zahiri, Mustapha Jammoukh, Khalifa Mansouri, "Experimental and Numerical Study of the Mechanical Behavior of Bio-Loaded PVC Subjected to Aging", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 607–612, 2020. doi: 10.25046/aj050574
29. Marco Bindi, Igor Aizenberg, Riccardo Belardi, Francesco Grasso, Antonio Luchetta, Stefano Manetti, Maria Cristina Piccirilli, "Neural Network-Based Fault Diagnosis of Joints in High Voltage Electrical Lines", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 488–498, 2020. doi: 10.25046/aj050458
30. Nittaya Kerdprasop, Kittisak Kerdprasop, Paradee Chuaybamroong, "Computational Intelligence and Statistical Learning Performances on Predicting Dengue Incidence using Remote Sensing Data", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 344–350, 2020. doi: 10.25046/aj050440
31. Mainar Swari Mahardika, Achmad Nizar Hidayanto, Putu Agya Paramartha, Louis Dwysevrey Ompusunggu, Rahmatul Mahdalina, Farid Affan, "Measurement of Employee Awareness Levels for Information Security at the Center of Analysis and Information Services Judicial Commission Republic of Indonesia", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 501–509, 2020. doi: 10.25046/aj050362
32. Med Hedi Moulahi, Faycal Ben Hmida, "Degradation Process in Pipeline and Remaining Useful Lifetime Estimation Based on Extended Kalman Filtering", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 457–468, 2020. doi: 10.25046/aj050357
33. Suchitra Abel, Yenchih Tang, Jake Singh, Ethan Paek, "Applications of Causal Modeling in Cybersecurity: An Exploratory Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 380–387, 2020. doi: 10.25046/aj050349
34. Leila Amdah, Adil Anwar, "BPMN4 Collaboration: An Extension for collaborative Business Process", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 297–305, 2019. doi: 10.25046/aj040638
35. Noor Syahirah Nordin, Mohd Arfian Ismail, Vitaliy Mezhuyev, Shahreen Kasim, Mohd Saberi Mohamad, Ashraf Osman Ibrahim, "Fuzzy Modelling using Firefly Algorithm for Phishing Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 291–296, 2019. doi: 10.25046/aj040637
36. Ahmad Yusairi Bani Hashim, Silah Hayati Kamsani, Mahasan Mat Ali, Syamimi Shamsuddin, Ahmad Zaki Shukor, "Simulation and Reproduction of a Manipulator According to Classical Arm Representation and Trajectory Planning", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 158–162, 2019. doi: 10.25046/aj040619
37. Pablo Pérez-Gosende, "LED Lighting Implementation as a Strategy for Energy Saving in Energetically Sustainable Campus", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 360–368, 2019. doi: 10.25046/aj040547
38. Anton Kuzmin, "Exchange Rate Modeling: Medium-Term Equilibrium Dynamics", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 251–255, 2019. doi: 10.25046/aj040431
39. Nindhia Hutagaol, Suharjito, "Predictive Modelling of Student Dropout Using Ensemble Classifier Method in Higher Education", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 206–211, 2019. doi: 10.25046/aj040425
40. Zdenek Kolka, Viera Biolkova, Dalibor Biolek, Zdenek Biolek, "Emulation of Bio-Inspired Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 21–28, 2019. doi: 10.25046/aj040403
41. Bayan Sapargaliyeva, Aigul Naukenova, Bakhyt Alipova, Javier Rodrigo Ilarri, "Flame Distribution and Attenuation in Narrow Channels Using Mathematical Software", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 53–57, 2019. doi: 10.25046/aj040308
42. Aymen Lachheb, Jalel Khediri, Lilia El Amraoui, "Nonlinear Analytic Modeling for Novel Linear Variable Reluctance Motors", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 190–196, 2019. doi: 10.25046/aj040225
43. Anas Abouzahra, Ayoub Sabraoui, Karim Afdel, "A Practical Approach for Extending DSMLs by Composing their Metamodels", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 358–371, 2018. doi: 10.25046/aj030644
44. Yoshiyuki Higashi, Soonki Chang, "Attitude Control Simulation of a Legged Aerial Vehicle Using the Leg Motions", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 204–212, 2018. doi: 10.25046/aj030627
45. Mouhamad Chehaitly, Mohamed Tabaa, Fabrice Monteiro, Juliana Srour, Abbas Dandache, "FPGA Implementation of Ultra-High Speed and Configurable Architecture of Direct/Inverse Discrete Wavelet Packet Transform Using Shared Parallel FIR Filters", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 116–127, 2018. doi: 10.25046/aj030516
46. Kamel. Idris-Bey, Sofoklis S. Makridis, "Complete Modeling of the Hydrogen Stored in a Spherical Cavity", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 4, pp. 122–129, 2018. doi: 10.25046/aj030413
47. Feim Ridvan Rasim, Sebastian M. Sattler, "Structure-Preserving Modeling of Safety-Critical Combinational Circuits", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 472–482, 2018. doi: 10.25046/aj030158
48. Reem Izzeldin Salim, Hassan Noura, Abbas Fardoun, "The Use of LMS AMESim in the Fault Diagnosis of a Commercial PEM Fuel Cell System", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 297–309, 2018. doi: 10.25046/aj030136
49. Alain Brillard, Patrick Gilot, Jean-Francois Brilhac, "Models accounting for the thermal degradation of combustible materials under controlled temperature ramps", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 5, pp. 78–87, 2017. doi: 10.25046/aj020513
50. Sung-Uk Zhang, "Numerical evaluation of ABS parts fabricated by fused deposition modeling and vapor smoothing", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 6, pp. 157–161, 2017. doi: 10.25046/aj020620
51. Mouhamad Chehaitly, Mohamed Tabaa, Fabrice Monteiro, Abbas Dandache, "A Novel Ultra High Speed and Configurable Discrete Wavelet Packet Transform Architecture", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1129–1136, 2017. doi: 10.25046/aj0203142
52. Arsen Klochan, Ali Al-Ammouri, Viktor Romanenko, Vladimir Tronko, "Aviation Navigation with Use of Polarimetric Technologies", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 67–72, 2017. doi: 10.25046/aj020310
53. Lubna s. Ben Taher, "Evaluation of Three Evaporation Estimation Techniques In A Semi-Arid Region (Omar El Mukhtar Reservoir Sluge, Libya- As a case Study)", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 2, pp. 19–29, 2017. doi: 10.25046/aj020204
54. Imran Arshad, Muhammed Muneer Babar, Natasha Javed, "Numerical Analysis of Drawdown in an Unconfined Aquifer due to Pumping Well by SIGMA/W and SEEP/W Simulations", Advances in Science, Technology and Engineering Systems Journal, vol. 1, no. 1, pp. 11–18, 2016. doi: 10.25046/aj010102