Call for papers: OR Spectrum; Special issue on “Advancements in Stochastic DEA and Environmental Efficiency Applications”

Guest editors:

Ali Emrouznejad, Surrey Business School, The University of Surrey, Guildford, UK,
Vincent Charles, School of Management, University of Bradford, Bradford, UK,

Submission deadline: July 31, 2022

The twenty-first century demands an urgent balance of economic and social development, as well as environmental protection and stewardship. Higher energy consumption is required to support the continued growth of the human population and rising living standards. However, this primarily leads to increased pollution and waste from industrial, agricultural, and construction activities alike, to name a few. Moreover, the public‘s attention is increasingly focused on addressing environmental problems caused by pollution, such as GHG emissions, which in turn has increased the focus on environmental efficiency evaluation in recent years.

Stochastic Data Envelopment Analysis (SDEA) can assist in determining how to achieve this balance. Data envelopment analysis (DEA) is a popular tool for analysing and measuring efficiency in both the public and private sectors. DEA considers multiple-input and multiple-output situations and compares the relative efficiency of factor input and output of multiple similar entities, also known as decision-making units (DMUs). In its original configuration, DEA is a non-stochastic tool that assumes all input and output data are quantitative, with specified numerical values; in other words, they are both deterministic and noise-free. In real-world applications, however, inputs and outputs are frequently of a stochastic nature, fluctuating over time, making it difficult to establish precise numbers for them based on the limited historical data. The introduction of SDEA, in which the possibility of noise in the data and the consideration of measurement errors and specification errors are explicitly taken into account, has been found to be efficient in dealing with and measuring the uncertainties in one or more DMUs.

The purpose of an SDEA approach under these circumstances is straightforward. Achieving environmental sustainability is fraught with uncertainty, which can manifest itself in a variety of ways, including process activities, resource consumption, and emissions across the value chain, among other things. The outstanding feature of most existing studies dealing with environmental efficiency analysis is that they model variables such as wasting water, GHG emissions, the production of useless solid materials, etc. as deterministic variables. As mentioned, DEA models with stochastic settings have been developed to accommodate both inefficiency and the presence of noise, measurement errors, and specification errors. But then again, despite substantial progress with SDEA approaches, research focusing on the use of SDEA to address environmental efficiency across domains is rather scarce.

Against this background, this Special Issue aims to compile original research articles of high quality that focus on recent advancements in SDEA and environmental efficiency applications. Theoretical, conceptual, methodological, and empirical research studies are all of interest. Contributions from both the academic and the practitioner communities are encouraged. In order to not only bring together the most recent knowledge but also to close the gap between scientific research and practical impact, we especially encourage contributions that are implementation-focused, i.e., with real-world data experiments, pilots, and industry collaborations.

Specific topics/applications of interest include, but are not limited to:

  • Environmental efficiency of countries and regions
  • Environmental efficiency of energy and carbon dioxide emissions  
  • Environmental efficiency of regional or national energy production
  • Environmental efficiency in the context of the green economy or circular economy and the sustainability of supply chains
  • Environmental efficiency of manufacturing firms
  • Environmental efficiency of construction companies
  • Environmental efficiency of maritime companies
  • Environmental efficiency of container ports
  • Environmental efficiency of (electric) fossil fuels
  • Environmental efficiency of oil and gas companies
  • Environmental efficiency of chemical and pharmaceutical companies
  • Environmental efficiency of regional or national transportation systems
  • Environmental efficiency in agriculture (at farm-level, crop-level, etc.)
  • Environmental efficiency in aviation

Specific methods of interest include, but are not limited to:

  • Stochastic DEA (with a consideration of stochastic inputs and outputs, desirable and undesirable stochastic inputs and outputs, etc.)
  • Stochastic cross-efficiency DEA
  • Stochastic network DEA
  • Chance-constrained DEA
  • Satisficing DEA
  • Stochastic frontier analysis
  • Stochastic programming
  • StoNED DEA
  • State-contingent approaches

Submission Guidelines and Review Process

Papers must be submitted at http://www.editorialmanager.com/orsp/  by July  31st, 2023. Authors should select “S.I.: Stochastic DEA & Environmental Efficiency” during the submission step ‘Additional Information.’ All papers submitted to this special issue should report original work and contribute to the journal OR Spectrum by using a quantitative research paradigm and OR methods. According to the aims of OR Spectrum, high quality papers are sought that match the scope of the journal, demonstrate rigor in the application of state-of-the-art OR techniques, and promise to impact the future work of the OR community.

Papers will be screened by the Editor-in-Chief and one Special Issue Editor. If the paper is deemed to be of sufficient quality, it will be peer-reviewed according to the standards of OR Spectrum by at least two experienced reviewers. We will adopt a rapid and fair review process, striving to provide reviews within three months of submission. Accepted papers will be available online prior to publication of the special issue.

Special Issue Editors:

 Professor A. Emrouznejad a.emrouznejad@surrey.ac.uk  Surrey Business School,
The University of Surrey,
Guildford,
UK  
Professor V. Charles
c.vincent3@bradford.ac.uk
School of Management,
University of Bradford,
Bradford,
UK

Call for Exclusive Book Chapters: Decision Making Optimization Models for Business Partnerships: Data Envelopment Analysis and Parametric Approaches”

Editors:

Gholam R. Amin and Mustapha Ibn Boamah, Faculty of Business, University of New Brunswick, Saint John, Canada

Submission deadline: 30 April 2023

Decision making optimization models for business partnerships are essential, as businesses seldom have all of the resources they need, and thus they require alliances and partnerships with others to enable them to meet their goals. A book on optimal business partnership models, with foundations in management science and economic theory, would therefore be an important resource in understanding and quantifying business partnership gains. Many studies emphasize the importance of business partnerships. However, literature on partnership optimization is quite limited. The proposed new book will cover recent developments in decision making as it relates to business partnerships using data envelopment analysis (DEA), inverse DEA, and other decision-making methods, including parametric approaches. As introduced in Charnes et al. (1978), DEA can be safely viewed as one of the success stories of operations research and management science for evaluating relative efficiency of decision-making units (DMUs) in the presence of multiple inputs and multiple outputs. Research in recent years and outputs required for business partnerships to achieve given efficiency targets.
The objective of this edited book is to develop new DEA and other decision-making optimization models to answer key questions related to business partnerships that would be of interest to business firms, policy makers, and practitioners in areas such as firms’ restructuring, resource allocation, environment and climate change, pollution minimization, and gain maximization from mergers and acquisitions. The book:

  1. will introduce DEA models to estimate the potential gains from mergers and acquisitions for various DMUs and guide them on how to combine their resources to get the maximum benefits out of the resulting synergies.
  2. will introduce advances in DEA optimization and other techniques through the development of models that will define various types of business partnerships between DMUs.
  3. will develop the literature of business partnerships that would guide partners on how redistributing their resources could improve their efficiency score.
  4. will help partners to use each other’s resources in order to reach target efficiency.
  5. will extend the well-known non-parametric performance measurement approach of DEA that would help business firms to collaborate and improve their efficiency.
  6. will guide business firms on how to choose partners to reduce negative externalities such as environmental pollution.
    Submissions may be sent directly to the editors at gamin@unb.ca (Gholam R. Amin) or mboamah@unb.ca (Mustapha Ibn Boamah).

Important Dates
30 April 2023 Chapter submission deadline
30 July 2023 Notification of status and comments for revisions
30 October 2023 Chapter resubmission
31 December 2023 Additional comments if any
30 March 2024 Final chapter submission due
31 August 2024 Book submission to Taylor and Francis CRC Press

Editors Selected References:

  1. Amin, G.R., and Ibn Boamah, M. (2022). Modeling business partnerships: a data envelopment analysis approach, European Journal of Operational Research, Doi: 10.1016/j.ejor.2022.05.036, article in press.
  2. Amin, G.R., and Ibn Boamah, M. (2021). A two-stage inverse data envelopment analysis approach for estimating potential merger gains in the US banking sector, Managerial and Decision Economics, 42(6), 1454-1465.
  3. Amin, G.R., and Ibn Boamah, M. (2020). A new inverse DEA cost efficiency model for estimating potential merger gains: A case of Canadian banks, Annals of Operations Research, 195(1), 21-36.
  4. Amin, G.R., Al-Muharrami, S., and Toloo, M. (2019). A combined goal programming and inverse DEA method for target setting in mergers, Expert Systems with Applications, 115, 412-417.
  5. Amin, G.R., and Al-Muharrami, S. (2018). A new inverse DEA model for mergers with negative data, IMA Journal of Management Mathematics, 29(2), 137-149.
  6. Amin, G.R., and Oukil, A. (2019). Flexible target setting in mergers using inverse data envelopment analysis, International Journal of Operational Research, 35(3), 301-317.
  7. Amin, G.R., Emrouznejad, A., and Gattoufi, S. (2017). Modelling Generalized Firms’ Restructuring using Inverse DEA, Journal of Productivity Analysis, 48(1), 51-61.
  8. Amin, G.R., Emrouznejad, A., and Gattoufi, S. (2017). Minor and Major Consolidations in Inverse DEA: Definition and Determination, Computers and Industrial Engineering, 103(1), 193-200.
  9. Emrouznejad, A., Yang, G., and Amin, G.R. (2019). A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries. Journal of the Operational Research Society, 70(7), 1079-1090.
  10. Gattoufi, S., Amin, G.R., and Emrouznejad, A. (2014). A new inverse DEA method for merging banks, IMA Journal of Management Mathematics, 25(1): 73-87.
  11. Wegener, M., and Amin, G.R. (2019). Minimizing Greenhouse Gas Emissions using Inverse DEA with an Application in Oil and Gas, Expert Systems with Applications, 122, 369–375.

About the editors

Gholam R. Amin is an Associate Professor of Operations Research and Management Science in the Faculty of Business at the University of New Brunswick, Saint John, Canada. He is an Associate Editor of the IMA Journal of Management Mathematics at Oxford University Press. Dr. Amin’s research interests include performance measurement, productivity and efficiency analysis through data envelopment analysis (DEA) and optimization models. Dr. Amin has published over 70 articles in the leading journals such as Operations Research (FT-50), European Journal of Operational Research, Journal of Productivity Analysis, Annals of Operations Research, International Journal of Production Research, Journal of the Operational Research Society, Computers and Operations Research, Computers & Industrial Engineering, Applied Mathematical Modeling, International Journal of Approximate Reasoning, International Journal of Intelligent Systems, ABACUS, and IMA Journal of Management Mathematics among others. Mustapha Ibn Boamah is a Professor of Economics in the Faculty of Business at the University of New Brunswick, Saint John, Canada.

Dr. Ibn Boamah’s research interests include open-economy macroeconomics, monetary economics, international finance, and the economics of financial institutions. He has published in various peer reviewed journals – including publications in the Review of Financial Economics, Atlantic Economic Journal, Strategic Change, Social Responsibility Journal, International Journal of Organizational Analysis, International Journal of Social Economics, Managerial and Decision Economics, Annals of Operations Research, and the European Journal of Operational Research.

Data Envelopment Analysis at IFORS2023, Juy 2023, Santiago, Chile

Dear Colleagues,

I would like to issue an invitation to take part in the Data Envelopment Analysis and Performance Measurement Stream of the IFORS2023 Conference. This conference will be held on July 10 to 14, 2023 in Santiago, Chile. Contributions from both the academic and the non-academic communities are welcome. (see details at http://www.DEAzone.com/en/ifors2023).

This stream covers papers on the theme of efficiency and productivity analysis and performance management. Both parametric and non-parametric papers will be considered. We especially welcome papers on the theory, methodology and application of Data Envelopment Analysis and econometric methods in performance management. Of particular interest are successful applications of performance and efficiency analysis in the real world, for example in banking, healthcare, education, transportation, and so on.

– If you are interested in making a presentation, please submit a title of your talk no later than 31st January 2023 at https://www.euro-online.org/conf/ifors2023/, using the session code “28713198” for DEA&SFA: Theory, Methodology and Applications.

– If you are interested to organise a session on specific topic of DEA please contact me (ae0027@surrey.ac.uk). Each session normally consists of 4 papers.

Important dates:
Deadline for submission abstract: 31 st January 31, 2023.
Early-bird Registration:  April 25, 2023.
Conference date: July 10 to 14, 2023
Conference Venue: Santiago, Chile

Best regards
Ali Emrouznejad
Professor and Chair in Business Analytics
Surrey Business School, University of Surrey, Guildford, UK, UK
Email: a.emrouznejad@surrey.ac.uk
Web: http://www.emrouznejad.com
Linkedin: https://www.linkedin.com/in/emrouznejad
Online DEA course:
https://dataenvelopment.com/course/

https://dataenvelopment.com/course/  

Online DEA & SFA course – 3 days – November 2022

Online Courses on:

Performance Measurement Using Data Envelopment Analysis (DEA) & Stochastic Frontier Analysis (SFA)

Details: https://dataenvelopment.com/course/

DATA ENVELOPMENT ANALYSIS COURSE:
November 28-30, 2022

STOCHASTIC FRONTIER ANALYSIS COURSE:
December 2-4, 2022

For registration and further details please visit: https://dataenvelopment.com/course/


Early registration fee
31st October 2022

Details: https://dataenvelopment.com/course/

Surrey Business School: Management and Business (PhD Studentships – January 2023)

Studentships are available for full-time and part-time study on the Management and Business PhD at Surrey Business School. The studentships are available for January 2023 entry.

Our PhD in Management and Business will train you in critical and analytical skills, research methods, and in discipline-specific knowledge that will give you the knowledge, skills and abilities needed for a career in academia, or as a researcher in a wide variety of settings.  You could also benefit from our choice of writing a traditional dissertation monograph or by following a PhD by publication format, so your work suits your interests. The programme will give you the intellectual foundation to ask cutting-edge questions and then conduct high-quality research to address those questions.

As part of REF2021, Surrey was ranked in the top 10 for the outputs and top 20 for the real-world impact of our business research.

Application deadline: 20 October 2022

Please complete both application form  (CLICK HERE) and Studentship form (CLICK HERE) before the deadline 20 October 2022

Topic of research: Any area in Business and Management, specially Data Envelopment Analysis, Big Data, Energy Efficiency  and NetZero.

Start date: 1 January 2023

Duration: 3 years full-time (pro rata part-time)

Funding source: University of Surrey (FASS studentships)

Funding information: The funding package is for 3 years full-time (pro rata part-time) and is:

  • Stipend at the standard UKRI rate  – £16,062 for 2022-23 (pro-rata for part time)
  • Full UK or Overseas fees waiver
  • Research Training and Support Grant of £750 per annum for three years (pro rata for part-time). To be used during the funded period.

Journal of Business Research: Special issue on “Advancements in Artificial Intelligence-based Prescriptive and Cognitive Analytics for Business Performance”

Journal of Business Research
Special issue on
Advancements in Artificial Intelligence-based Prescriptive and Cognitive Analytics for Business Performance
 

Guest editors:

Vincent Charles, School of Management, University of Bradford, Bradford, UK,
Ali Emrouznejad, Surrey Business School, The University of Surrey, Guildford, UK
Werner H. Kunz, University of Massachusetts Boston, Massachusetts, USA

Submission window: Aug 1 to Nov 1, 2022

Article type to select when submitting: AI for Business Performance

Call for papers

Analytics is probably the most important tool a business has today to improve efficiencies and optimize performance. And while traditionally businesses have focused on descriptive and predictive analytics, in recent times, we witness, more and more, the rise of prescriptive and cognitive analytics. This is because prescriptive analytics provides an advisory role regarding the future rather than merely predicting what will happen, while cognitive analytics replicates human thought and converts it into pure intelligence. Thus, their value lies within. Especially when operating in a competitive business environment, prescriptive and cognitive analytics can help and enhance decision-making, giving companies a real competitive advantage; they can mean a huge boost to profit, productivity, and the bottom line.

Prescriptive and cognitive analytics combine standard analytics techniques with Artificial Intelligence (AI), and intrinsically Machine Learning (ML), characteristics for advanced analytics results. As AI continues to develop, the way we use analytics also continues to grow and change. And while AI-based prescriptive and cognitive analytics have grown in popularity in recent years, we have yet to see them deliver on their true promise.

So far, the full potential of prescriptive and cognitive analytics using AI approaches and tools has only been investigated from a theoretical and conceptual perspective.  There is, therefore, untapped potential to utilize AI to discover more trends and produce actionable recommendations based on those trends. Furthermore, in the majority of research studies, analytics is based on an optimization problem with an expert-created objective function. The growth of data-oriented applications, however, paves the way for the advancement of prescriptive and cognitive analytics models that combine domain expert knowledge and data-driven insights.

Moreover, AI-based analytical models generally operate in a black box, meaning that we do not really know how they work, nor how decisions are being made. This poses an issue in real-life, especially since decision-makers rely on such models for optimizing their decision-making processes. It is for this reason that AI-based analytical models need to have interpretable and explanatory capabilities in order for users to understand how and why certain decisions were made and how these impact on business performance. The literature, however, shows that there is still limited research when it comes to the development of such models that are interpretable and explainable.

This Special Issue aims to compile recent advancements in AI-based prescriptive and cognitive analytics for better business performance. As a result, the Special Issue will cover both theoretical and conceptual approaches that pave the way for the development of more advanced AI-based prescriptive and cognitive analytics models and empirical studies that address specific problems in a business setting, such as ways to better integrate domain expert input in the data analytics lifecycle, innovative implementations, and so on.

Potential AI and AI-related approaches (within a business context) to be covered include but are not limited to:

  • Artificial Neural Network
  • Deep learning
  • Deterministic programming
  • Fuzzy programming
  • Game theory
  • Nature-inspired algorithms
  • Optimization
  • Reinforcement learning
  • Semantics
  • Simulation (Monte Carlo, etc.)
  • Stochastic process
  • Stochastic programming

Instructions for Authors can be found at:

https://www.elsevier.com/journals/journal-of-business-research/0148-2963/guide-for-authors

Authors should submit a cover letter and a manuscript by Nov 1, 2022, via the Journal’s online submission site. Manuscripts submitted after the deadline may not be considered for the special issue and may be transferred, if accepted, to a regular issue.

Please see the Author instructions on the website if you have not yet submitted a paper through Springer’s web-based system, Editorial Manager (. When prompted, please select the special issue’s title, Advancements in Artificial Intelligence-based Prescriptive and Cognitive Analytics for Business Performance, to ensure that it will be reviewed for this special issue.

Papers will be subject to a strict double-blind review process under the supervision of the Guest Editors, and accepted papers will be published online individually, before print publication.

Important Dates

1 November 2022Submission deadline,
submit at: https://www.editorialmanager.com/jobr/default1.aspx (early submission recommended, referee process starts once the paper is received, accepted papers will be published individually online as they are accepted)
31 March 2023Notification of status and acceptance of paper
31 August 2023Revised manuscripts
31 December 2023Final version of paper

Guest Editors

Professor V. Charles
c.vincent3@bradford.ac.uk   
School of Management,
University of Bradford,
Bradford,
UK  
Professor A. Emrouznejad a.emrouznejad@surrey.ac.uk  Surrey Business School,
The University of Surrey,
Guildford,
UK  
Professor W. H. Kunz, werner.kunz@umb.edu
University of Massachusetts Boston, Massachusetts,
USA

Annals of Operations Research Special Issue: Transparent and Responsible Artificial Intelligence: Implications for Operations Research

Guest Editors

Ali Emrouznejad, Surrey Business School, University of Surrey, UK
Soumyadeb Chowdhury, TBS Business School, Toulouse, France

Artificial Intelligence (AI) is one of the most disruptive technologies in this decade that has started to transform both business organisations and societies in ways we could not have envisaged a few years ago. AI has become the key source of business model innovation, process transformation, re-engineering practices for gaining competitive advantage in organisations embracing data-centric, analytics, and digital culture.  The impact of AI in transforming both businesses and societies is comparable to that of the Internet and World Wide Web (1990–2000) that led to the emergence of ecommerce, consume-centric practices, sharing economy, gig economy, digital manufacturing, and data-driven operations.

Despite the benefits offered by AI, machine learning (ML) algorithms currently lack transparency and explainability that makes it difficult for managers (or any other concerned user) to trust the output generated by these algorithms. In the context of transparency, it is a form of ideal that should facilitate revealing how data is integrated into an algorithm, processed by the algorithm and the knowledge that is gained using that data. The issue with explainability is that business managers do not usually understand how AI-based ML algorithms generate the outputs after processing the input data because the algorithm is either proprietary or that the mathematical computational models used in the algorithm are very complex to understand (even with technical expertise). In this context, literature has discussed that the time, effort, and resources invested by the organisations in AI systems has not fully translated into business value and productivity in majority of cases. Limited transparency and explainability of output responses generated by the AI systems has emerged as a key barrier to experiencing anticipated benefits by confidently turning data-centric decisions into effective actionable strategies. 

The primary goal of embedding transparency within AI-based ML and deep learning models is to help the decision-making authorities understand what the AI system is doing, how it is generating the output responses, and why a particular response is generated. This will help these business users to confidently understand and assess the accuracy of the responses based on their own tacit domain expertise that will increase trust in these systems. The ability to get explanations for the output responses generated by AI algorithms can also potentially reduce biases in business processes, operations, and decision-making, thus enhancing both accountability and fairness. For example, gender discrimination in hiring employees and setting up credit card borrowing limits stemming from AI systems has led to mistrust among both businesses and their consumers, demonstrating the need for AI transparency. Furthermore, AI transparency can also aid in identifying and resolving flaws within ML models stemming from improper training datasets (input issues), wrong settings, configurations and hyperparameters (algorithmic issues), and overfitting or underfitting models that can help evolve these systems and therefore enhance the value offered.

While research into AI transparency, interpretability, and trust mechanisms is nascent (within Operations Research [OR]), following streams of research are gradually emerging in the literature: (1) techniques and mechanisms to embed transparency in AI models that can potentially alleviate concerns about the negative impact of AI such as bad decision-making, discrimination, bias, inaccurate recommendations; (2) impact/implications of embedding transparency to data-driven decision making and accountability of people/organisational body making these operational decisions; (3) what are the skills, operational strategies, changes and policies required to facilitate responsible usage of AI applications in operational decision-making; (4) what are the drivers and barriers to embed transparency within AI models and applications from different perspectives (businesses, consumers, employees, supply chain partners and other stakeholders involved, e.g., how enhancing AI transparency can impact supply chain culture and relationships).

There is limited consensus on the best practices, tools, and mechanisms to embed transparency in AI-based systems, and the impact of introducing such mechanisms on organisational operational processes, dynamics, supply chain partnership, supply chain culture, multi-stake holder collaboration in supply chain, and gaining competitive advantage). Embedding transparency in AI algorithms (such as machine learning and deep learning) to enhance interpretability, accountability, and robustness of data-driven decision-making (leveraging AI and analytics) represents an unknown and unexplored territory for both researchers and practitioners. This presents an important challenge and opportunity for the community to advance scholarship of AI transparency and explainability in OR. However, research reported in the field of OR (and wider business and operations management literature) on this theme (conceptual, theoretical, and empirical) is extremely limited.

Considering these knowledge gaps, we invite high-quality submissions addressing theoretical and algorithmic developments, providing evidence-based empirical cases and insights, advancing theory, practice, and methodology of AI transparency scholarship in operations research. The special issue will also consider real-world innovative implementation of tools, techniques, and mechanisms to enhance interpretability of AI outputs for both business organisations and society in areas such as supply chain management, operations management, service operations, transformative marketing operations, industrial management and production systems, supply chain finance, reverse logistics, and sustainability. Review papers and conceptual papers without any empirical evidence are beyond the scope of this special issue. However, the special issue will consider demonstrators relevant to OR (corresponding to the theme) making considerable contributions to both theory and practice. Topics of contributions corresponding to the theme of the special issue, AI Transparency, include (but are not limited to):

  • Transparency in various stages of data analytics
  • Mechanisms to AI transparency (e.g., model-agnostic interpretability methods?)
  • Tools for AI transparency (e.g., LIME, SHAP)
  • Optimising AI algorithms by embedding transparency
  • Models for accountability in AI-based data-driven decision-making
  • Evaluating the performance of AI algorithms through transparency
  • Methods for model interpretability in machine learning
  • Implications of AI transparency on data-driven decision-making
  • Implications of AI transparency on supply chain relationships and culture
  • Implications of AI transparency on supply chain culture
  • AI transparency and social sustainability
  • AI transparency and supply chain resilience, supply chain diligence
  • Role of visualisation in AI interpretability
  • Role of AI transparency to algorithmic fairness and robustness
  •  Dark side of AI transparency for OR and OM

Please note: Manuscripts on the above topics should make a clear and unique contribution to OR through relevant references to OR literature.

Instructions for Authors can be found at: https://www.springer.com/journal/10479/submission-guidelines

Authors should submit a cover letter and a manuscript by March 31, 2023, via the Journal’s online submission site. Manuscripts submitted after the deadline may not be considered for the special issue and may be transferred, if accepted, to a regular issue.

Please see the Author instructions on the website if you have not yet submitted a paper through Springer’s web-based system, Editorial Manager. When prompted for the article type, please select Original Research. On the Additional Information screen, you will be asked if the manuscript belongs to a special issue, please choose yes and the special issue’s title, Transparent and Responsible Artificial Intelligence: Implications for Operations Research, to ensure that it will be reviewed for this issue. ). 

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. Papers will be subject to a strict review process managed by the Guest Editors and accepted papers will be published online individually, before print publication.

Important Date

31 March 2023Submission deadline,
submit at: https://www.editorialmanager.com/anor/  (early submission recommended, referee process starts once the paper is received, accepted papers will be published individually online as they are accepted)

Guest Editors

Professor Ali Emrouznejad
E: a.emrouznejad@surrey.ac.uk  
Professor and Chair in Business Analytics
Surrey Business School
University of Surrey
United Kingdom  
Dr Soumyadeb Chowdhury
E: s.chowdhury@tbs-education.fr
Associate Professor Information Analytics and Sustainability Management
Head of TBS Center of Excellence on Sustainable Development and CSR
TBS Business School, Toulouse, France 

Lecturer/Senior Lecturer in Business Analytics (Teaching Track) – University of Surrey

Location: Guildford
Salary: £42,149 to £61,819
Post Type: Full Time
Closing Date: 23.59 hours BST on Wednesday 06 July 2022
Reference: 039922

CLICK HERE TO APPLY ONLINE

The University of Surrey is a global university with a world-class research profile and an enterprising and forward-thinking spirit committed to research and innovation excellence and to benefitting the economy, society and the environment. Our researchers practise their excellence against the backdrop of our broad spectrum of technological, human, health and social sciences, and their uncommonly strong linkages forged in an integrated campus culture of cooperation.

The Department of Business Transformation at Surrey Business School is seeking applications for a Business Analytics Senior Lecturer/Lecturer (teaching track) position. This is part of an ambitious effort to develop a world-class group of scholars who will make significant contributions to the various facets of business analytics teaching. Our department is deeply engaged with local and international businesses which provide opportunity for meaningful interaction ensuring relevant and current teaching. Our department actively collaborates with scholars across the University such as computer science, engineering, data science and digital health.   

Clear evidence of a commitment to collaboration with academic and non-academic partners will be essential as will be evidence of excellence in the development and delivery of teaching and the promotion of student experience. We are particularly interested in candidates who have taught in the areas that have involved the use of AI, big data, virtual reality, machine learning and visualisations. Applicants should have experience of how technology can contribute to decision making and insights in a business environment.  

Informal enquiries should be directed in the first instance to Professor James Aitken (James.Aitken@surrey.ac.uk), Head of the Business Transformation Department.

CLICK HERE TO APPLY ONLINE

Job Opportunities – Lecturer in Sustainable Supply Chain Management Surrey Business School

Location: Guildford
Salary: £42,149 to £50,296
Post Type: Full Time
Closing Date: 23.59 hours BST on Wednesday 06 July 2022
Reference: 039222

CLICK HEAR to apply

Lecturer in Sustainable Supply Chain Management Surrey Business School

The University of Surrey is a global university with a world-class research profile and an enterprising and forward-thinking spirit committed to research and innovation excellence and to benefitting the economy, society and the environment. Our researchers practise their excellence against the backdrop of our broad spectrum of technological, human, health and social sciences, and their uncommonly strong linkages forged in an integrated campus culture of cooperation.

The Department of Business Transformation at Surrey Business School is seeking applications for a lecturer position. This is part of an ambitious effort to develop a world-class group of scholars who will make significant contributions to the various facets of management research on sustainable supply chains.  Our department is deeply engaged with local and international businesses which provide opportunity for meaningful interaction ensuring relevant research and teaching. We are looking for someone that supports our revenue generation in sustainability (e.g. via bids, Innovate UK funding, etc.). We actively collaborate with researchers across the University such as the Institute for Sustainability and the Centre for Environment and Sustainability.  

Candidates should hold a relevant doctoral degree and have experience of supply chain management especially in terms of sustainability (environmental and social). Candidates should demonstrate their ability and interest to conduct novel empirical research on phenomena that are important to sustainable supply chains. We are particularly interested in candidates who have developed an understanding of how sustainability can contribute to decision making and insights in a business environment.

You will have demonstrated a strong potential for A-journal publications and have experience of teaching in higher education – preferably in a business school context. 

 Sustainability is core to the values of the University which enables researchers to develop in a supportive and exciting environment.

Informal enquiries should be directed in the first instance to Professor James Aitken, (James.Aitken@surrey.ac.uk), Head of the Business Transformation Department. 

Further details:   
Apply online:
Job Description   
CLICK HEAR to apply

Call for Papers: Data Envelopment Analysis and its Applications, September 2022, Warwick University

Call for Papers: Data Envelopment Analysis and its Applications
OR64: Annual Conference, 13-15 September 2022, Warwick University, UK
Abstract submission: CLICK HERE 
(deadline: 15h July 2022)
Dear Colleagues,

I would like to issue an invitation to take part in the Data Envelopment Analysis Stream of the OR64 Conference. This conference will be held in Warwick University, UK from 13rd to 15th September 2022 Contributions from both academic and non-academic communities are welcome.

This stream covers papers on the theme of efficiency and productivity analysis and performance management. Both parametric and non-parametric papers will be considered. We especially welcome papers on the theory, methodology and application of Data Envelopment Analysis and econometric methods in performance management. Of particular interest are successful applications of performance and efficiency analysis in the real world, for example in banking, healthcare, education, transportation, Energy, and so on.

If you are interested in making a presentation, please submit an abstract of your talk no later than 15th July 2022.

Detailshttp://deazone.com/en/or64
Deadline for submission abstract: July 15, 2022
Conference date:  September 13-15, 2022
Conference Venue: WarwickUniversity, UK
Details about the venue: CLICK HERE
Abstract submission: CLICK HERE

Kind regards,
Ali Emrouznejad, Surrey Business School, University of Surrey, UK, a.emrouznejad@surrey.ac.uk
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