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Operations Research: Optimization, Resilience and Sustainability

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: 20 September 2024 | Viewed by 13931

Special Issue Editors


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Guest Editor
School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK
Interests: complex systems; optimization; agent-based modeling; sustainability

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Guest Editor
1. Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry CV1 5FB, UK
2. School of Computing, Electronics and Mathematics, Faculty of Engineering, Environment and Computing, Coventry University, Coventry CV1 5FB, UK
Interests: machine learning; AI; Bayesian network; risk assessment; reliability analysis; maintenance strategies; predictive modelling; uncertainty quantification; digital twins; optimisation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to announce a new Special Issue, “Operations Research: Optimization, Resilience and Sustainability”, of the journal Sustainability.

Operations research (OR) is a scientific approach for implementing advanced analytical methods to enhance decision-making in complex systems management. In other words, OR is a systematic tool for solving competitive challenges within industry, operations and businesses. Operations research can be applied to every sector and industry that pursues continuous improvement, advancements and quality. OR applies several mathematical modeling and simulation techniques to design decision-making support tools to support organizations. In addition, operations research embraces certain principles to enable value creation for businesses and finding optimal solutions for different operational problems.

With this Special Issue, we aim to explore the innovative development and applicability of operations research for optimization, resilience and sustainability resolutions using advanced data analytics, simulation and modeling techniques. This research stream cuts across several research areas and disciplines, such as complex systems, data science, problem solving, decision making, system analysis, cost, strategic planning, net-zero, Industry 4.0 and 5.0, etc.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Complex systems and optimization.
  • Sustainability and net-zero.
  • Industry 5.0 and social values.
  • Business systems and operation.
  • Systems thinking and decision making.
  • Analytical modelling and simulation.
  • Resilience and operational excellence.
  • Data science and AI.
  • Proactive/preventive maintenance.

Dr. Maryam Farsi
Dr. Alireza Daneshkhah
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • operations research
  • sustainability
  • resilience
  • optimization
  • AI
  • net-zero
  • systems thinking
  • digitalization
  • digital twin

Published Papers (7 papers)

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Research

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23 pages, 2914 KiB  
Article
Cradle-to-Grave Lifecycle Environmental Assessment of Hybrid Electric Vehicles
by Shafayat Rashid and Emanuele Pagone
Sustainability 2023, 15(14), 11027; https://doi.org/10.3390/su151411027 - 14 Jul 2023
Viewed by 2724
Abstract
Demand for sustainable transportation with a reduced environmental impact has led to the widespread adoption of electrified powertrains. Hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) produce lower greenhouse gas (GHG) emissions during the use phase of their lifecycle, compared to [...] Read more.
Demand for sustainable transportation with a reduced environmental impact has led to the widespread adoption of electrified powertrains. Hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) produce lower greenhouse gas (GHG) emissions during the use phase of their lifecycle, compared to conventional internal combustion engine vehicles (ICEVs). However, a full understanding of their total environmental impact, from resource extraction to end-of-life (EOL), of a contemporary, real-world HEV and PHEV remains broadly elusive in the scientific literature. In this work, for the first time, a systematic cradle-to-grave lifecycle analysis (LCA) of a Toyota Prius XW50, as a HEV and PHEV, was used to comprehensively assess its environmental impact throughout its entire lifecycle using established lifecycle inventory databases. The LCA revealed that the gasoline fuel cycle (extraction, refinement, and transportation) is a major environmental impact “hotspot”. The more electrified PHEV model consumes 3.2% more energy and emits 5.6% more GHG emissions within the vehicle’s lifecycle, primarily owed to the manufacturing and recycling of larger traction batteries. However, when factoring in the fuel cycle, the PHEV model exhibits a 29.6% reduction in overall cradle-to-grave life energy consumption, and a 17.5% reduction in GHG emissions, in comparison to the less-electrified HEV. This suggests that the higher-electrified PHEV has a lower environmental impact than the HEV throughout the whole lifecycle. The presented cradle-to-grave LCA study can be a valuable benchmark for future research in comparing other HEVs and PHEVs or different powertrains for similarly sized passenger vehicles. Full article
(This article belongs to the Special Issue Operations Research: Optimization, Resilience and Sustainability)
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20 pages, 2940 KiB  
Article
Planning an Integrated Stockyard–Port System for Smart Iron Ore Supply Chains via VND Optimization
by Álvaro D. O. Lopes, Helder R. O. Rocha, Marcos W. J. Servare Junior, Renato E. N. Moraes, Jair A. L. Silva and José L. F. Salles
Sustainability 2023, 15(11), 8970; https://doi.org/10.3390/su15118970 - 01 Jun 2023
Cited by 1 | Viewed by 1321
Abstract
Stockyard–port planning is a complex combinatorial problem that has been studied primarily through simulation or optimization techniques. However, due to its classification as non-deterministic polynomial-time hard (NP-hard), the generation of optimal or near-optimal solutions in real time requires optimization techniques based on heuristics [...] Read more.
Stockyard–port planning is a complex combinatorial problem that has been studied primarily through simulation or optimization techniques. However, due to its classification as non-deterministic polynomial-time hard (NP-hard), the generation of optimal or near-optimal solutions in real time requires optimization techniques based on heuristics or metaheuristics. This paper proposes a deterministic simulation and a meta-heuristic algorithm to address the stockyard–port planning problem, with the aim of reducing the time that ships spend in berths. The proposed algorithm is based on the ore handling operations in a real stockyard–port terminal, considering the interaction of large physical equipment and information about the production processes. The stockyard–port system is represented by a graph in order to define ship priorities for planning and generation of an initial solution through a deterministic simulation. Subsequently, the Variable Neighborhood Descent (VND) meta-heuristic is used to improve the initial solution. The convergence time of VND ranged from 1 to 190 s, with the total number of ships served in the berths varying from 10 to 1000 units, and the number of stockyards and berths varying from 11 to 15 and 3 to 5, respectively. Simulation results demonstrate the efficiency of the proposed algorithm in determining the best allocation of stockpiles, berths, car-dumpers, and conveyor belts. The results also show that increasing the number of conveyor belts is an important strategy that decreases environmental impacts due to exposure of the raw material to the atmosphere, while also increasing the stockyard–port productivity. This positive impact is greater when the number yards and ship berths increases. The proposed algorithm enables real-time decision-making from small and large instances, and its implementation in an iron ore stockyard–port that uses Industry 4.0 principles is suitable. Full article
(This article belongs to the Special Issue Operations Research: Optimization, Resilience and Sustainability)
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34 pages, 2425 KiB  
Article
The Adoption of Robotic Process Automation Considering Financial Aspects in Beef Supply Chains: An Approach towards Sustainability
by Khushboo E-Fatima, Rasoul Khandan, Amin Hosseinian-Far and Dilshad Sarwar
Sustainability 2023, 15(9), 7236; https://doi.org/10.3390/su15097236 - 26 Apr 2023
Cited by 2 | Viewed by 2822
Abstract
Sustainable beef production is a global challenge in present times. This research paper aims to investigate the financial risks and barriers in the adoption of robotic process automation (RPA), which has emerged as a strategic catalyst for achieving sustainability in the beef sector. [...] Read more.
Sustainable beef production is a global challenge in present times. This research paper aims to investigate the financial risks and barriers in the adoption of robotic process automation (RPA), which has emerged as a strategic catalyst for achieving sustainability in the beef sector. Beef manufacturers constantly strive to achieve sustainability and a competitive advantage in order to gain enhanced beef productivity at low operational costs. There is a gap in the research, as there is a lack of knowledge about the financial aspects, barriers, and challenges influencing the RPA adoption process in the beef supply chain. To bridge this gap, secondary research is used to extract statistical data and information relevant to the RPA adoption process in beef supply chains, considering financial aspects. This study utilises a simulation method adopting a process model created in previous research and analyses different scenarios based on financial parameters using values or variables in Simul8 software. The scenario analysis allows for the identification of financial risks in the adoption of RPA and evaluates the simulation results from a sustainability perspective. The scenario analysis highlights the financial risks and barriers in the adoption of RPA in beef supply chains through process simulation, using financial parameters as a basis. KPI values, income statements, and carbon emission reports are generated to evaluate the main bottlenecks at various beef supply chain stages, thus allowing business users to conduct a thorough cost analysis. Successful adoption of RPA can lead to reduced supply chain complexity, thus improving financial and operational efficiency, which results in increased beef productivity, quality, and shelf life. This study is extremely important as it assesses scenarios from a sustainability perspective and contributes to academic knowledge and professional practice. It provides a process model to support the financial and ethical decision-making of managers or stakeholders, while helping the beef sector adopt RPA with greater ease. The process model can be adopted or modified according to the financial circumstances and individual requirements of business users. Furthermore, it provides decision-makers with the knowledge to eliminate or prevent financial barriers, thus advancing and accelerating the adoption of RPA. Robust adoption of RPA assists beef supply chains in gaining higher productivity at reduced costs, thus creating sustainable value. Full article
(This article belongs to the Special Issue Operations Research: Optimization, Resilience and Sustainability)
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25 pages, 2341 KiB  
Article
An Integrated MCDM Model for Sustainable Course Planning: An Empirical Case Study in Accounting Education
by Min Tao and Xiong Wang
Sustainability 2023, 15(6), 5024; https://doi.org/10.3390/su15065024 - 12 Mar 2023
Cited by 2 | Viewed by 1123
Abstract
As an essential element of higher education, course planning at the program level is a complicated multi-criteria decision making (MCDM) problem. In addition, a course planning process tailored to sustainable development is exceptionally important to sustaining the quality of academic programs. However, there [...] Read more.
As an essential element of higher education, course planning at the program level is a complicated multi-criteria decision making (MCDM) problem. In addition, a course planning process tailored to sustainable development is exceptionally important to sustaining the quality of academic programs. However, there is a scarcity of research on the program course planning problem at the operational level due to a diverse set of stakeholder requirements in practice. Motivated by the challenge, this study proposes an innovative MCDM model for sustainable course planning based on He-Xie management theory. In the introduced framework, the best worst method (BWM) can obtain the optimal weights of sustainability competencies, which are then embedded into the fuzzy filter ranking (FFR) method to generate the ranking of candidate courses by each course module, considering the connectivity between courses and the development of sustainability competencies. Finally, multi-choice goal programming (MCGP) is adopted to allocate each selected course to a semester, aiming to balance total credits and average difficulty level among semesters as much as possible. The practicability and reliability of the proposed course planning model is validated through a case study of an undergraduate accounting program. Results show that the proposed framework is a feasible tool for course planning. This research extends the existing literature on course planning by explicitly capturing the fuzzy nature of human decision making and avoids underestimation of the decision. The implications of the paper are not restricted to developing a sustainable course plan for an accounting program. Full article
(This article belongs to the Special Issue Operations Research: Optimization, Resilience and Sustainability)
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19 pages, 5074 KiB  
Article
Multi-Scale Remaining Useful Life Prediction Using Long Short-Term Memory
by Youdao Wang and Yifan Zhao
Sustainability 2022, 14(23), 15667; https://doi.org/10.3390/su142315667 - 24 Nov 2022
Cited by 5 | Viewed by 1264
Abstract
Predictive maintenance based on performance degradation is a crucial way to reduce maintenance costs and potential failures in modern complex engineering systems. Reliable remaining useful life (RUL) prediction is the main criterion for decision-making in predictive maintenance. Conventional model-based methods and data-driven approaches [...] Read more.
Predictive maintenance based on performance degradation is a crucial way to reduce maintenance costs and potential failures in modern complex engineering systems. Reliable remaining useful life (RUL) prediction is the main criterion for decision-making in predictive maintenance. Conventional model-based methods and data-driven approaches often fail to achieve an accurate prediction result using a single model for a complex system featuring multiple components and operational conditions, as the degradation pattern is usually nonlinear and time-varying. This paper proposes a novel multi-scale RUL prediction approach adopting the Long Short-Term Memory (LSTM) neural network. In the feature engineering phase, Pearson’s correlation coefficient is applied to extract the representative features, and an operation-based data normalisation approach is presented to deal with the cases where multiple degradation patterns are concealed in the sensor data. Then, a three-stage RUL target function is proposed, which segments the degradation process of the system into the non-degradation stage, the transition stage, and the linear degradation stage. The classification of these three stages is regarded as the small-scale RUL prediction, and it is achieved through processing sensor signals after the feature engineering using a novel LSTM-based binary classification algorithm combined with a correlation method. After that, a specific LSTM-based predictive model is built for the last two stages to produce a large-scale RUL prediction. The proposed approach is validated by comparing it with several state-of-the-art techniques based on the widely used C-MAPSS dataset. A significant improvement is achieved in RUL prediction performance in most subsets. For instance, a 40% reduction is achieved in Root Mean Square Error over the best existing method in subset FD001. Another contribution of the multi-scale RUL prediction approach is that it offers more degree of flexibility of prediction in the maintenance strategy depending on data availability and which degradation stage the system is in. Full article
(This article belongs to the Special Issue Operations Research: Optimization, Resilience and Sustainability)
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17 pages, 2919 KiB  
Article
A Green Approach—Cost Optimization for a Manufacturing Supply Chain with MFIFO Warehouse Dispatching Policy and Inspection Policy
by Santosh Shekhawat, Nazek Alessa, Himanshu Rathore and Kalpna Sharma
Sustainability 2022, 14(21), 14664; https://doi.org/10.3390/su142114664 - 07 Nov 2022
Cited by 3 | Viewed by 1768
Abstract
The present paper considers a manufacturing supply chain of deteriorating type inventories. The problem addresses the extra rented warehouse (RW) to store extra inventories if the manufacturer is producing more inventories than their owned warehouse (OW) capacity. Now, the problem is which inventories [...] Read more.
The present paper considers a manufacturing supply chain of deteriorating type inventories. The problem addresses the extra rented warehouse (RW) to store extra inventories if the manufacturer is producing more inventories than their owned warehouse (OW) capacity. Now, the problem is which inventories should be used first with minimum cost and minimum deterioration. To solve this problem, we have assumed a MFIFO (mixed first in first out) dispatching policy and constant demand rate over a finite time horizon. Along with these we have also assumed an inspection policy during the supply chain to separate deteriorated items and a carbon tax policy is also considered to control carbon emissions. The rate of deterioration depends on the number of inspections. If the number of inspections increases, it minimizes the rate of the decaying process. Due to the adoption of the inspection policy, the supply chain moves toward a green supply chain as it removes deteriorated inventories that minimize further decay by contact, and simultaneously separated deteriorated products can be utilized for other purposes that solve the problem of the disposal of deteriorating inventories and reduce emission generation. We have also established the uniqueness of the established model. The motto of solving the mathematical model is to find the values of the optimum value of N, the number of cycles, and n, the number of inspections that helps to minimize total cost. At last, we illustrate the result with the help of a numerical example. Full article
(This article belongs to the Special Issue Operations Research: Optimization, Resilience and Sustainability)
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Review

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27 pages, 2371 KiB  
Review
Expanding Fundamental Boundaries between Resilience and Survivability in Systems Engineering: A Literature Review
by Kenneth Martínez and David Claudio
Sustainability 2023, 15(6), 4811; https://doi.org/10.3390/su15064811 - 08 Mar 2023
Cited by 1 | Viewed by 1496
Abstract
The pressures of an everchanging world have impacted the ways in which service-based systems operate, along with their forms and boundaries. Resilience and survivability have been treated interchangeably when readying a system to remain true to its functions despite disturbances. Some situations prove [...] Read more.
The pressures of an everchanging world have impacted the ways in which service-based systems operate, along with their forms and boundaries. Resilience and survivability have been treated interchangeably when readying a system to remain true to its functions despite disturbances. Some situations prove the concepts may not always be the equivalent of the other, not even the consequence of the other. There may come scenarios where system components fail to adhere to certain predefined thresholds and cross a breaking point. It is therefore proposed in this study that systems can be survivable, instead of resilient, when they comply in time with the resurgence property. This property signifies the systematic behavior of overcoming a certain stagnation period and, after a time range, return as a transformed system with new functions and challenges. Through this study, it was detected that the symmetries between resilience and survivability are only superficial if systems suffer breakages after misconceiving the true causes of failure. Still, a lack of consensus among scientists and practitioners remains an issue when applying resilience and survivability in their own problems. Although workful, pushing to achieve a greater consensus would signify optimal performance in multifaceted systems involving technical, social, and economic challenges. Full article
(This article belongs to the Special Issue Operations Research: Optimization, Resilience and Sustainability)
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