Annals of Operations Research最新文献

This study develops a comprehensive policy-making model to diffuse sustainable supply chain management (SSCM) across pharmaceutical supply chains (PSCs), addressing triple bottom line (TBL) concerns—people, planet, and profit—while considering stakeholder dynamics interactions and unexpected events like global pandemics. Initially, the pharma industry's most essential TBL criteria are extracted by systematically reviewing the extant literature. Next, two different types of evolutionary game theory (EGT) (i.e., pairwise contest game (PCG) and tripartite evolutionary game (TEG)) are innovatively integrated to describe the stakeholders' interactions, providing a theoretical basis for assessing strategy stability and sustainable practice diffusion across multi-tiered and multi-echelon PSCs under regulators' interventions. Then, a system dynamics (SD) model is developed based on the EGT models to simulate the targeted policy-making system based on Iran's PSC as a case study. Key findings reveal that COVID-19 health protocols and lockdowns increased work avoidance among Regulatory Enterprises (REs), but long-term adherence to sustainable policies by distributors (Ds) and retailers (Rs) is projected by 2228. Five policies—imposing fines, segregation and public disclosure, increased punishments and incentives, rewards for sustainable practices, and subsidies for sustainable purchases—are recommended to accelerate sustainable adoption by 2056, eliminate RE work avoidance, and achieve significant outcomes: a monthly reduction of over 2,000 expired products, a decrease of around 1,000 TCO2e (Tons of Carbon Dioxide Equivalent) in upstream CO₂ emissions, an increase of over 600 TCO2e in CO₂ neutralisation, and over 100 new monthly job vacancies. This highlights the model's potential to diffuse economic, environmental, and social sustainability in PSCs post-COVID-19 under regulations.

Systems commonly experience competing risks of internal degradation and random shocks. Random shocks may cause damage to the system, and the degrees of the damage affect system degradation differently. In this paper, we consider a system, namely, a damage-resistant system, designed to withstand shocks up to a specified threshold. To consider the system damage resistance, we first propose a new shock classification based on the shock magnitude and define the damage resistance level of the system accordingly. Furthermore, because the system will be more vulnerable to shocks over time, we divide the system degradation process into two stages and propose nonlinear Wiener process degradation models with two stages by incorporating the system damage resilience level with the new shock classification. The closed-form system reliability functions for these two stages are further derived. Lastly, we conduct numerical examples with real-world data to demonstrate the practical application of our proposed model.

In this paper we examine the relation between the “bad outputs as inputs” with other approaches of modeling undesirable outputs in non-parametric production analysis and we end up with six equivalencies among them. Based on these, we argue that the “bad outputs as inputs” is a valid modeling option as long as the assumption of extended strong disposability and thus of reverse outputs is reasonable in a particular setting. In this case, the “bad outputs as inputs” approach can easily be implemented by means of a conventional input-oriented DEA model with variable returns to scale where the translated additive inverse transformation is applied to undesirable output quantities.

We study a loss-averse (LA) retailer with a quantity-oriented reference point (RP) in a piecewise form in a random market. The proposed RP is characterized by a two-fold convex combination of three elements (profits in three environments) at low reference levels, and a standard convex combination of two elements (profits in two environments) at high reference levels. We aim to use prospect theory with the above piecewise-formed RP to characterize the LA retailer’s optimal procurement strategy in maximizing her expected profit, and describe the effect on the procurement strategy from some parameters. We prove that there exists a procurement strategy inducing the LA retailer to make her optimal ordering decision given a random market with a general distribution. We analyze the respective effect of LA degree and reference level on retailer’s optimal procurement strategy, and obtain some intuitive insights, such as, a LA retailer always orders less than a loss-neutral one; increasing LA degree leads to more cautious ordering behavior; a lower RP level leads retailer to over-order, and a higher RP leads to under-order. Moreover, we find that the joint effect from LA degree and RP level on the optimal order quantity is mutually reinforcing. This joint effect also presents results consistent with people’s cognition, that is, the optimal order quantity of a LA retailer with RP is more likely to be lower than a classical retailer, compared to a risk-neutral retailer with RP. We finally provide the sensitivity analysis of parameters on the optimal order strategy.

Circular supply chain management is expected to shift the steel sector from a conventional economic paradigm to a more fruitful circular economy. Furthermore, tracing reusable and recyclable metals across the steel industry’s supply chain necessitates multi-stakeholders’ involvement. Radio frequency identification (RFID) may assist in the governance of the complexity of circular steel supply chain management (CSSCM), strengthening the steel industry’s transparency as well as efficiency. The following research develops a decentralized and service-oriented RFID-enabled blockchain-integrated conceptual framework that incorporates a circular steel supply chain strategy for the B2B steel sector to achieve this goal. Industrial methods, inventory management, and resource utilization are examined in this study. The findings show that RFID-enabled blockchain technology enhanced firm’s capacity to share real-time knowledge with CSSCM stake-holders and aid organizations in the B2B steel sector in understanding the aspects that influence the steel sector’s overall performance. Management must need to understand the value of associated technologies to deliver CSSCM and make choices faster. The implications of adopting RFID-enabled blockchain technology to serve the circular economy are discussed from theoretical, practical, and social perspectives.

This paper primarily focuses on grading barriers that hinder internet-of-things (IoTs) adoption, which eventually promotes sustainable supply chain execution. As countries globally plan to combat climate change, supply chain sustainability is substantial. Digital technology, such as IoT, supports sustainability within supply chains. Still, studies infer that the adoption could be more direct and involve barriers that must be graded for efficient implementation and planning. Previous barrier grading models (i) did not accept natural language-based ratings; (ii) subjective orientation of experts’ weights is not well explored; (iii) hybrid determination of attributes’ weights is lacking; and (iv) personalized grades for barriers are also unexplored. Motivated by these gaps, this article develops an integrated model by considering preferences in the natural language form via double hierarchy fuzzy data (DHFD). Later, the rank sum (RS) approach is presented for determining the weights of experts, and the RS-Cronbach factor is put forward for the hybrid weight calculation of attributes. An algorithm to grade barriers is proposed based on WISP formulation combined with the Copeland method. Finally, a case example from Coimbatore is presented to understand the framework’s usefulness, and sensitivity/comparison reveals the pros and cons of the framework.

In this golden age of artificial intelligence, transparency and responsible decision-making are paramount. While machine learning (ML) and operational research (OR) optimisations are fundamental aspects of AI, the benefits of explainable AI (XAI) for combinatorial optimisations remain underexplored. This study investigates the convergence of XAI and OR, emphasising the importance of transparency in combinatorial optimisations. Using the Knapsack problem as an example, we demonstrate that interpretable ML models can effectively solve combinatorial optimisation challenges and enhance transparency. Additionally, we illustrate the application of post-hoc XAI methods to OR optimisations solved with ML, providing transparent, human-friendly explanations. The key contributions of this work include proposing the application of the SAGE framework for transparent OR, demonstrating the integration of XAI with combinatorial optimisations, and offering practical guidelines for creating transparent explanations. These contributions can aid decision-makers in understanding, communicating, and trusting combinatorial optimisation solutions, paving the way for enhanced transparency in operational research across various sectors.

In recent years, the increasing interest in financial policies in more sustainable economics and the consequent growth of public awareness about environmental, social, and governance (ESG) companies’ issues have modified investors’ portfolio management through ESG considerations in investment decisions. Consequently, the classic Markowitz mean-variance solution based on expected returns and standard deviation has been modified to consider ESG firm characteristics. This study investigates how investors’ ESG preferences influence portfolio choices and decision-making processes. We employ a discrete-time homogeneous Markov model to analyze ESG rating migration patterns, simulate possible configurations of the efficient frontier in portfolios aligned with sustainable preferences, and optimize portfolio asset weights complying with ESG portfolio performance over a time period. The obtained results provide a means of assessing the impact of the investor’s propensity toward sustainability over time on portfolio profitability. This approach provides insights into how ESG considerations may reshape portfolio performance over time, fostering more informed and ethically guided financial decisions.

This editorial synthesizes the principal research trends and prospective directions emphasized in the special issue titled Emerging Trends in the Interplay between Analytics and Operations in MSMEs, published in Annals of Operations Research. The contributions underscore the transformative impact of analytics on Micro, Small, and Medium Enterprises (MSMEs), accentuating the integration of Industry 4.0 technologies, artificial intelligence (AI), machine learning, deep learning, blockchain, and big data analytics into operations management. Furthermore, the discussions illuminate emerging trends concerning the application of these technologies in MSMEs, presenting a future roadmap and directions regarding the interplay between analytics and operations. This also affirms a renewed focus on data analytics capabilities in enhancing operational efficiency, resilience, and sustainability within MSMEs. Prospective research directions encompass the development of transparent and responsible AI models, the addressing of implementation challenges related to business intelligence tools, and the fostering of dynamic capabilities to navigate the evolving digital landscape.