OPSEARCH - Call for Papers: Special Issue on Future of Circular Economy and Decision Making Models and Techniques: Research Opportunities in the Post-COVID Scenario

Circular economy (CE) advocates a reduction in the use of resources and extends long-term value through reusing/remanufacturing/recycling activities to gain environmental and socio-economic benefits (Morseletto, 2020). However, during uncertain times, such as pandemics, the end-to-end supply chain is highly stressed due to lack of adequate information, unavailability of resources, limited response time to address the challenges. The adaptability of the “new normal” in a CE is a key challenge where decisions are made based on viability and long-term planning. In particular, isolation and physical distancing are the common ways to break the chain of COVID-19 spread, which forces the entire paradigm of logistics and business to reimagine the network planning and decisions from an integrated approach to a piecemeal approach. In tandem, there is an invisible but predominant mandate for global supply chains to shift towards a hybrid mode of operation. Given this background, the question haunts the researchers that what would be the optimal parameters and performance indicators within the hybrid mode of operations. Thus, the epistemic and stochastic modelling of "old normal" as compared to "new normal" is significantly different. Therefore, there is a strong need for the development of decision models on how to eliminate waste and overuse of resources in the “new-normal” hybrid mode of operation.

Well-known strategies within CE are used to recover, recycle, repurpose, remanufacture, refurbish, repair, reuse, reduce, rethink, and refuse. In terms of strategic and operational level objectives, there are three levels in the circular economy: i) macro (global, national, regional, city), ii) meso (industrial symbiosis, eco-industrial parks) and iii) micro (single firm, product) (Kristensen & Mosgaard, 2020). Overwhelming studies within modelling literature addressed at the micro-level were based on the practices related to recycling, end-of-life management or remanufacturing, while a few other studies considered disassembly, lifetime extension, waste management, resource-efficiency or reuse at the firm level. However, decisions concerning adoption, recovery, performance, preparedness and resilience of the closed-loop supply chains are traditionally treated at the strategic level. The recent COVID-19 pandemic taught us how to align strategic and operational level objectives in all three levels in the CE. Hence given the concurrent disruption and isolated business operations, there is an increasing risk in the reuse or acquisition of end-of-life products within a short or medium period. Additional parameters need to be taken while modelling to avoid health hazards for a product return. Typically, reverse logistics is currently under the pressure due to the high cost of operation and the lack of a coordination mechanism.

To address the above challenges operations management researcher should think of developing integrated decision models and explore the use of different techniques as follows: i) possibilistic optimization (Dehghan et al., 2018; Pishvaee & Torabi, 2010; Tosarkani & Amin, 2018), ii) robust convex optimization (Keyvanshokooh et al., 2016; Pishvaee et al., 2011), iii) chance-constrained optimization (Ahmadi & Amin, 2019; Zandkarimkhani et al., 2020; Mohanty et al., 2020) and iv) probabilistic modelling and artificial intelligence-based algorithms (Amin & Zhang, 2013; Yang et al., 2021; Choi et al., 2021).

In recent days, Decision Sciences and AI-based modelling exploit quantitative techniques and Operations Research (OR) tools like Mixed-Integer Linear Programming (MILP), Dynamic Programming (DP) and Non-Linear Programming (NLP) to solve complex and dynamic Business problems. Especially, after the COVID-19 outbreak, the prescriptive analytics-based modelling exercises are repurposed to solve new and the existing business problems in new normal conditions using decision tree or ensemble techniques (like Random Forest) for MILP; Bender's Cut Decomposition Algorithm (Rebennack, 2016) or Danzig Wolf's Decomposition Algorithm (Wu et al., 2020) for DPs; and Meta-Heuristic Algorithms for NLPs (Hosseini, 2017). Even predictive analytics now adheres to Reinforcement Learning  (RL) (Magazzino et al., 2021), Deep Learning (DL) techniques (Khan et al., 2021), like Long-Short-Term Memory (LSTM) (Uribe-Toril et al., 2022), Facebook Prophet (Toharudin et al., 2020), Neural Prophet (Pontoh et al., 2021), and so forth.

Supply chain related analytical problems are being solved by various quantitative approaches. Researchers are encouraged to build optimization models for supply chain planning and decision-making. The Multi-Criteria-based Decision-Making (MCDM) models are highly effective to evaluate system performance, ranking of alternatives, strategic trade-offs etc for various open-loop and closed-loop supply chains. Supply chain network design has become very critical in the case of new normal situations. For tactical and operational changes optimization of supply chain variables would ensure sustainability. Business problems modelling related to Inventory Management, Production Planning, Logistics and Sourcing are redefined with new-normal attributes. There is a paradigm shift in supply chain coordination and contract modelling also. Incentive designing and auction mechanism designing using quantitative models in the supply chain require substantial revision to accommodate the COVID-19 scenarios.

Although there are many challenges, we have listed below a few of them based on the review which needs to be addressed.

New normal decision models:

• What models are used in the digitization process to deal with consumerism post-COVID-19?
• What type of hybrid models could reflect the agility of the manufacturers under the unusual purchasing behaviour of customers?
•  How AI-based modelling will improve the traceability and efficiency of supply chains?
• How prescriptive modelling will exploit push-pull boundary situations under uncertain scenarios, like COVID-19?

Macro-level challenges

• Explore the potential applications of probabilistic modelling and artificial intelligence-based algorithms in solving problems related to the distribution channel concerning place, trade and customer?
• What are emerging paradoxical decision models that will be suitable to solve sustainability challenges in the closed-loop supply chain?

Meso-level challenges

• How to model the tension between the Micro-Meso-Macro level in the post-pandemic world of CE and apply nascent techniques?
• How to model the power dynamics evolving in the closed-loop supply chain synchronising forwarding logistics and reverse logistics conundrum versa?
• To what extent the OR community could engage in developing newer modelling techniques beyond Long-Short-Term Memory (LSTM), Facebook Prophet, Neural and Prophet to handle Environmental and Ethical challenges?

Micro-level challenges

• How to model chain coordination and contracts including micro-level metrics? 
• New normal decision models, to estimate the carbon footprints and social sustainability in CE?
• What are the emerging techniques to standardise processes in the circular economy?

The uniqueness of this special issue

Relevant studies on the economic planning and execution in post COVID scenario is piling up but we did not see studies related to the circular economy and decision making considering the new normal of operations. On this global need, this special issue focuses to accumulate the idea with potentials on predicted future of the CE.

All potential papers should address the OPSEARCH journal’s core aims, including prominent under-represented research topics, interdisciplinary approach, quantitative and applied research problems. The potential researchers are likely to reflect contemporary supply chain issues because of the closed-loop environment and propose appropriate models to address the issues for a speedy recovery from complex situations in the future. Concepts and tools adopted in these works can have a large spectrum from mathematical models to simulations with suitable case studies and real-time data.

Manuscripts should conform to the OPSEARCH journal format (see https://www.springer.com/journal/12597/submission-guidelines (this opens in a new tab)). Please submit your article via the manuscript central Editorial Manager at https://www.editorialmanager.com/opse/default1.aspx, and select “Special Issue: Future of circular economy and decision making: Research opportunities in the post-COVID scenario” when it prompts to indicate the “Article Type” in the submission.

Important Dates:

• Deadline for manuscript submission: 31 May 2023
• Review report due: 30 September 2023
• Likely publication: Q1 2024

Guest Editors
Nachiappan Subramanian, University of Sussex Business School, University of Sussex, United Kingdom, email: N.Subramanian@sussex.ac.uk (this opens in a new tab)
Vandana Sonawaney, Symbiosis Institute of Operations Management, India, email: director@siom.in (this opens in a new tab)
Ramkrishna Manatkar, Symbiosis Institute of Operations Management, India, email: ramkrishna.manatkar@siom.in (this opens in a new tab)
Shisam Bhattacharyya, Operation Research and Data Science Practice Lab, WIPRO LTD, Kolkata, West Bengal, India, email: shisam.bhattacharyya@wipro.com (this opens in a new tab)
Sobhan Sarkar, Information Systems & Business Analytics, IIM Ranchi, Ranchi-834008, India, email: sobhan.sarkar@iimranchi.ac.in (this opens in a new tab)
Rupak Bhattacharjee, Faculty Council for PG Studies, Arts, Commerce and Law, University of North Bengal, India, email: rupakb@nbu.ac.in (this opens in a new tab)

References

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Hosseini, E. (2017). Laying chicken algorithm: A new meta-heuristic approach to solve continuous programming problems. Journal of Applied & Computational Mathematics, 6(1).

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Khan, S. A. R., Ponce, P., Tanveer, M., Aguirre-Padilla, N., Mahmood, H., & Shah, S. A. A. (2021). Technological innovation and circular economy practices: Business strategies to mitigate the effects of COVID-19. Sustainability, 13(15), 8479.

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