Search

Achievement of sustainability goals is an epic task for developing economies that still strive to fulfil their basic needs. The availability of limited resources in the developing world vis-à-vis the ever-increasing demand poses further challenges to developing economies willing to transition into circular economies. Reverse logistics (RL) can facilitate this transition towards a circular economy (CE) by maximising resource utilisation and minimising waste, contributing to sustainability goals. This paper contributes to emerging literature by analysing the development and comprehensive potential of reverse logistics as a sustainability tool. It explores the significant barriers to the adoption of reverse logistics towards a circular economy, considering long-term sustainability. In the first phase, thirteen barriers have been identified from the past academic literature. Three barriers with a defuzzification number less than the threshold limit are excluded, and the final ten barriers are then prioritised using the decision-making trial and evaluation laboratory (DEMATEL) method. The findings suggest that a lack of strategic plans for returns is crucial for RL adoption towards a circular economy, followed by a lack of visibility for recycling/reuse. Organisations can increase customer satisfaction, promote environmental sustainability, and gain a competitive edge in the market by creating a strategic plan for reverse logistics. Organisations may lower costs and contribute to a more sustainable and ecologically responsible supply chain by improving visibility across the reverse logistics process. The results serve as a framework for decision-making in RL towards sustainable development. Managers and policymakers can formulate more robust and realistic decisions that align with “maximising profits,” “saving the planet,” “social concerns,” and, most importantly, “consumer concerns” in the circular economy ecosystem. Several implications are derived, leading to increased competitiveness and resilient business strategies. The novelty of this work lies in the identification of barriers to reverse logistics adoption towards a circular economy using an integrated fuzzy Delphi-DEMATEL approach, considering long-term sustainability. This approach is studied for the first time in a developing economy context, proposing social, economic, and environmental effects and actions to be taken by organisations for sustainable development.

This study conducted a comprehensive bibliometric analysis to uncover emerging trends, preference patterns in supplier selection, and the important factors considered in the process. This study integrates three interrelated approaches, namely bibliometric analysis, literature review, and expert interviews. This study demonstrates the significant contribution of combining bibliometric analysis and literature review in the development of supplier selection methods. Two primary approaches are commonly employed: Multi-Criteria Decision Making (MCDM) and Optimization. MCDM methods, such as AHP, SWARA, and TOPSIS, are frequently utilized to measure relative preferences and select the most suitable supplier. On the other hand, the optimization approach employs linear programming methods to achieve optimal solutions in supplier selection. The hybridization of methods, particularly the combination of AHP and Linear Programming, is the most commonly employed approach to achieve more effective supplier selection outcomes aligned with the food industry's needs. Furthermore, this study discusses the significance of identifying and developing relevant supplier criteria in the food industry. Notably, environmental criteria emerge as prominent qualitative criteria, while quality and sustainability stand out as primary quantitative criteria.

This paper focuses on addressing the crucial challenges posed by the uncertainty surrounding perishable goods within the supply chain. With staggering socioeconomic costs and significant environmental implications, effective management of perishable goods emerges as a critical imperative. Globally, the annual loss of approximately 14% of food, amounting to a staggering USD 1 trillion, highlights the urgency of the issue. However, Prevailing classification methods for perishable products oversimplify their complexity by dividing them into fixed or variable lifespans, neglecting the random lifespans endured due to fluctuating storage conditions, blurring traditional boundaries between fixed and variable classifications. In response to these pressing challenges, the research proposes a novel approach: the development of a nonlinear mixed-integer programming model for a green closed-loop supply chain. This innovative model seamlessly integrates production, inventory, and routing decisions for both main and secondary products of a manufacturer. Moreover, it optimizes order splitting and transportation modes to efficiently convert perished items into raw materials under conditions of uncertainty. Central to the approach is the adoption of a scenario-based methodology to model uncertainties, particularly focusing on the variability in the lifespan of perishable products. This approach allows for a more nuanced understanding of the complex dynamics inherent in managing perishable goods within the supply chain. To solve the proposed model, a novel hybrid algorithm is introduced: the Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithm, or PSOSA, ensuring robust optimization under uncertainty and enhancing decision-making within the perishable goods supply chain. The research findings underscore the inadequacy of prevailing assumptions regarding the fixed lifespan of perishable products, commonly observed in the literature. By accounting for uncertainty in perishable goods’ lifespan, a more accurate representation of total producer costs is achieved, highlighting the misleading reduction of 25% observed when neglecting such uncertainty.

To manage remotely piloted aircraft system (RPAS) networks effectively, this research presents a multi-objective location-routing optimization model. This model integrates time window constraints, concurrent pick-up and delivery demands, and rechargeable battery functionality, and also introduces a standardized framework to clarify the RPAS CO2 emission model. These integrations significantly decrease battery consumption in Remotely Piloted Aircraft Systems (RPAS) and lower transportation costs, while also optimizing delivery times, reducing operational risks, and minimizing CO2 emissions. The model’s enhancement for optimizing delivery schedules takes into account uncertain traffic conditions, thus improving accuracy in dynamic environments and further contributing to environmental sustainability. Risk assessment employs the Specific Operations Risk Assessment (SORA) standard, adding a third objective function. This combination of the model, further enhance the efficiency and sustainability of RPAS operations, by optimizing delivery schedules, reducing CO2 emissions and battery consumption, and improving accuracy under dynamic conditions. Also, it make RPAS logistics more practical and effective in real-world applications. As result, the NSGA-II algorithm achieves significant reductions across all objectives: 33.3 % in cost, 6.48 % in time, 33.3 % in risk, and 35.7 % in battery usage within 250 generations. The use of the NSGA-II meta-heuristic method for validation enhances the credibility and practicality of the model. The optimization model’s performance over 250 generations shows rapid initial improvements in cost, time, risk, and battery usage, followed by stabilization, indicating efficient convergence and effective evolutionary computation. Also the findings show that with a CO2 emission rate of 3.773 × 104 kg of CO2 per Wh, highlighting the model’s efficiency and effectiveness.

This paper aims to explore the current stakeholder forces for sustainable supply chain management (SSCM) in the Bangladeshi garment industry. The focal point is identifying which stakeholder forces could enhance sustainable practices in the factory premises, and whether there are conflicting forces which might obstruct the sustainable practices. A qualitative research design is adopted, drawing on data from 37 semi-structured interviews with stakeholders related to garment manufacturing. Thematic analysis conceptually guides the analytical process. The findings demonstrate the supporting and hindering forces currently influencing the garment industry in Bangladesh. This paper also identifies three conflicting forces relating to economic gain, employee empowerment and global image, where trade-offs towards SSCM implementation exist. This study extends existing research relating to SSCM within the garment industry of Bangladesh, identifying differences between different stakeholder groups and recognising the conflicting forces that exist.

To facilitate the shift to circular models, supply chains urgently need to be strengthened and made more resilient and robust. The COVID-19 pandemic generated problems in logistics activities, disrupting supply chains, including electronic products flows. In this context, based on the need to keep supply chains reliable in the face of the risks inherent in their processes, this work aims to identify the attributes for a robust supply chain and analyze the interrelationship among them using DEMATEL technique. As a result, it was observed that the agility factor, risk-oriented management, and flexibility are the most important factors, and that Information Technology and Human Resources are the most influential (causal factors) for the design of a robust supply chain in the context of electronic products.

Today, reducing fossil fuel consumption in the transport sector is one of the important requirements to preserve the ecological environment. In this paper, we investigated a logistics problem in small and medium cities. We proposes the time-dependent vehicle routing problem model in small and medium cities considering heterogeneous fleets, time windows, multi-trip, time dependency, and road network. We conducted a numerical analysis on fuel consumption minimizing path selection between two customer nodes and found that the path selection is independent of the payload in most cases. The model is formulated as a mathematical integer linear programming model, and small problems are solved using a commercial optimization solver. For large-scale instances, we proposed a heuristic algorithm based on the variable neighborhood search and evaluated its performance. The proposed algorithm can decrease fuel consumption by 25 % compared to manual routing and scheduling. Furthermore, for provide useful managerial insights, sensitivity analyses were performed considering different vehicle capacities.

In liner shipping, stakeholders are increasingly committed to adopting autonomous and environmentally friendly transportation solutions, especially for truck operations managing container transfers. Beyond reducing labor costs, truck platooning technology—which enables autonomous trucks to operate in close formations, thereby significantly decreasing fuel consumption—promises to revolutionize fleets involved in maritime container transport. However, the potential of these benefits hinges on the process of developing and implementing optimization plans that address the specific challenges of container logistics, particularly in integrating truck platooning plans. In response to this need, this study extends the traditional instant-dispatch strategy by proposing a novel, data-driven dispatch strategy. We develop algorithms for both models and conduct extensive experiments focusing on truck operations for sea freight containers. Our findings reveal significant advantages of the data-driven dispatch strategy: it substantially reduces the total costs and fuel consumption associated with truck deliveries compared to the instant-dispatch strategy.

Global environmental concerns are leading to paradigm shift in the automobile eco-system. This comprehensive study of the automobile ecosystem, incorporates technical, economic, and environmental-social-governance (ESG) dimensions. An “integrated multi-criteria decision-making” (MCDM) methodology using “interpretive structural modelling” (ISM) and “fuzzy analytical hierarchy process” (FAHP) is deployed, The ISM models highlights prominent drivers and critical barriers, establishes their hierarchy, and depicts interrelationships. The FAHP rankings for drivers favours electric vehicles and hydrogen fuel vehicles over others, while barriers promote hybrids and alternate fuels like Biofuel, CNG/LNG. Unlike previous works, this study considers all eco-system factors, both critical drivers and barriers, and demonstrates hierarchy using a unique V-model. This is an innovative extension of V-model from traditional systems engineering to strategic project management domain. This holistic approach, in sync with the natural state of things, will enable government, industry, and academia in formulating a strategic framework towards future automobile sustainability.

Industrial-scale food production systems often comprise of different parties such as farmers, processors and retailers involved in various activities along the supply chain. These activities are aimed at converting growing items such as crops and livestock into processed and packaged food products that are safe for human consumption. This paper develops inventory models for a three-echelon food supply chain comprising of growing items. The items are grown at the farming echelon, then processed at the processing echelon and finally sold at the retail echelon provided that the various activities that take place at each of those echelons result in the release of carbon emissions. Furthermore, the customers at the retail echelon are both price-conscious and environmentally-conscious and therefore, demand is assumed to be a function of the retail selling price and the amount of carbon emissions released. The inventory system is studied under three different carbon emissions regulations, namely, carbon cap, carbon cap-and-trade and carbon tax regulations. The results from a numerical analysis indicate that the carbon cap-and-trade regulation yields the highest supply chain profit. Moreover, while both carbon cap-and-trade and carbon tax regulations are effective at minimising carbon emissions generated across the supply chain, the cap-and-trade regulation is better at incentivising the supply chain members to reduce their carbon footprint.

The growing disparity between food supply and demand requires innovative Digital Agriculture (DA) systems to increase farm sustainability and profitability. However, current systems suffer from problems of complexity stemming from the challenge of integrating diverse, often non-interoperable hardware and software components. In order to tackle these complexities to increase farm efficiency and understand the tradeoffs of these new DA innovations we developed Realtime Optimization and Management System (ROAM), which is a decision-support system developed to find a Pareto optimal architectural design to build DA systems. To find the Pareto optimal solution, we employed the Rhodium Multi-Objective Evolutionary Algorithm (MOEA), which systematically evaluates the trade-offs in DA system designs. Based on data from five live deployments at Cornell University, each DA design can be analyzed based on user defined objectives and evaluated under uncertain farming environments with ROAM. Paired with this, we develop a web interface that allows users to define personalized decision spaces and visualize decision tradeoffs. To help validate ROAM, it was deployed to a commercial farm where the user was recommended a DA architecture design method to increase farm efficiency. ROAM allows users to quickly make key decisions in designing their DA systems to increase farm profitability.

Manufacturing supply chains (SCs) has received great attention in both academia and industries to improve ecological sustainability and achieve net-zero emissions. Governments across the globe are adopting various Carbon Regulatory Policies (CRPs) to curb excessive carbon emissions through SCs and promote sustainable development. Especially among the emerging economies, organizations are under immense pressure from consumers and stakeholders for a transitional shift towards zero-carbon society. Therefore, this research develops a multi-stakeholder framework that identifies and evaluates the drivers of CRP adoption and implementation in manufacturing SC to elevate ecological sustainability. At first through an extensive literature review and Delphi method, relevant drivers and sub-drivers are identified, finalized and categorized under economic, environmental, social, technical and organizational categories. Secondly, fuzzy Best Worst Method (f-BWM) is employed to examine relative importance of all the drivers and sub-drivers. Thirdly, Weighted Influence Non-Linear Gauge System (WINGS) method is used to determine their cause-effect relationships. The proposed framework is illustrated through an industrial case study considering multiple manufacturing organizations in India. The results show that availability of initial funding, economic benefits, and legislative support obtained top three positions in the priority list, whereas, skill training of workers attained the least priority. Finally, critical managerial implications are deduced based on the results of relative importance and inter-relationships analysis. The proposed analytical framework can guide managers and policy makers to the identified drivers to foster effective CRP implementation.

Objective: The growth of e-commerce has transformed last-mile logistics, with direct-to-consumer deliveries and frequent delivery failures. This has exacerbated pollution and congestion issues, negatively impacting urban life. Given this scenario, the present study aimed to develop a taxonomy that enables the design of a sustainable last-mile logistics network specifically tailored to the context of e-retail. Method: A systematic literature review was employed for collecting robust scientific research, with the PRISMA diagram used to represent the steps followed in this process. Content analysis was applied to categorize the sustainability practices identified in the literature, grouping them according to the last-mile logistics process. Relevance: This research contributes to understanding the last-mile logistics field in the e-retail context and provides valuable insights for formulating sustainable strategies and practices to mitigate the negative impacts of e-retail growth on the urban environment and society’s quality of life. Results: The research presents a structured taxonomy with 31 categorized practices across last-mile logistics processes: fulfillment, transportation, and delivery modalities. The results indicate that a sustainable last-mile network includes consolidation centers, shared micro-depots, shared freight transportation, electric vehicles such as bicycles and tricycles, and the implementation of collection and delivery points (CDPs). Social/Management Contributions: The taxonomy offers elements that may contribute to operational efficiency, environmental impact reduction, and improving society’s quality of life by providing practical guidelines for the sustainable management of last-mile logistics in the e-retail context.

Two critical questions arise in the context of mounting pressures to reduce carbon emissions from freight transportation. Is it possible to reconcile the emissions reductions goals of shippers and logistics service providers (LSPs)? How do factors such as consumer preferences, eco-efficiency, carbon tax policy, and sharing of green investment cost affect target emissions levels? We address these questions using game theory to model shippers and LSPs as economic agents, thereby gaining insight into how the carbon intensity of freight delivery operations can be reduced. Our novel contributions include (1) models that consider both the shipper’s and the LSP’s interests, and in particular the gap between their preferred environmental performance levels; and (2) a methodologically rigorous explanation why, as empirical studies suggest, LSPs and shippers rarely agree on how to share responsibility for environmental initiatives, or who should pay a carbon tax.

Given the contradiction between the rapid growth of products and the modest recovery rate of end-of-life products, there is a pressing need to understand the societal significance of establishing a reverse logistics network for end-of-life products. This research constructs an open-loop five-tier reverse logistic network model encompassing customers, centres for collection, disassembly and inspection, remanufacturing, and disposal. A multi-objective mixed-integer nonlinear programming model under uncertainty has been developed. Unlike previous research, this model accounts for surrounding residents' disutility of facilities while simultaneously minimizing economic costs and environmental impact. Besides, uncertainty theory is introduced in solving the proposed model. More specifically, the formulated model converts all uncertain variables into uncertain distributions by implementing the uncertain multi-objective programming method. Furthermore, a customised non-dominated sorting genetic algorithm III (NSGA-III) is proposed and is employed for the first time to address facility selection and recycling volume distribution within the network. The model is then validated using a real-life case study focusing on end-of-life vehicles in Changchun, China. This research could assist decision-makers in both governmental and private sectors in achieving a balanced approach to the interests of the economy, environment, and local communities comprehensively when designing reverse supply chains.

The aim of this paper is to decipher and investigate the relationship between artificial intelligence implementation (AII), healthcare social innovation (HSI) and hospitals’ green image (HGI) in an Egyptian emerging market. An interdisciplinary research with a mixed-methods approach was conducted to add bricks, conceptually and empirically, that fill a literature gap between using this evolving AI-technology and sustaining socially-innovative medical operations and supply-chain management (OSCM) practices in a dynamic green healthcare industry. After scanning the relevant transdisciplinary literature, the authors developed and tested a conceptual model through analysing data collected from 116 quantitative questionnaires answered by healthcare managers/leaders in 46 Egyptian hospitals. After applying structural equation modeling using SmartPLS-v4, the results illustrated that AII has a positive significant impact on HSI, which has a positive significant influence on HGI. Furthermore, AII-HGI relationship can be fully-mediated significantly by HSI, which confirms the significant role of AI-powered technology in reinforcing socially-innovative medical OSCM processes to sustain an environmental-friendly image of Egyptian hospitals. Based on these quantitative findings and qualitative fruitful interviews with healthcare leaders and technology experts, this article used a comprehensive approach to contribute to AI-enhanced healthcare OSCM literature in interdependent ways. It encapsulated the benefits and obstacles of using AI-driven socially-innovative green medical OSCM practices in emerging healthcare markets similar to Egypt. Moreover, it proposed a balanced scorecard map for communicating and evaluating socially-responsible ecofriendly OSCM strategy of AI-powered hospitals. As for the practical implications, the medical operations and supply-chain managers of similar emerging healthcare markets can benefit from the exemplars highlighted throughout this paper on how their hospitals can invest in AI-based healthcare processes/services, which can provide creative and scientific solutions for social, educational, environmental and economic problems within their countries. Thus, reflecting on the social implications, these digitally-transformed hospitals can contribute innovatively to the social wellbeing of their communities and promote their green image among their supply-chain stakeholders.

In light of the recent developments in recycling technologies and commercialization, supply chain capabilities are decisive for large-scale textile-to-textile recycling practices. Despite this, the current status and landscape of these practices is surprisingly little researched from a logistics and supply chain management point of view. Therefore, the purpose of this study is to explore supply chain capabilities required in the textile-to-textile recycling supply chain. The paper is based on an interview study with 20 stakeholders along the textile recycling supply chain, covering various types of collectors, sorters, and recyclers in Sweden and mainland Europe.The findings present eleven specific supply chain capabilities distributed over the three main supply chain activities of collection, sorting, and recycling. By doing this, the study seeks to provide a more thorough and comprehensive understanding of the role of logistics and supply chain management in textile-to-textile recycling practices. The findings also provide insights into more general industry-related design issues, including the matching of product supply and demand, the establishment of new supply chains, and balancing global and local interests.

The integration of sustainability dimensions as part of supply chain management represents a constant challenge for companies. This happens because to address supply chain sustainability (SCS), companies need to know how collective behaviours are present in their supply chain practices (e.g. cooperation, coordination, collaboration). However, this is easier said than done. Therefore, through the lens of social capital theory (SCT), this paper elaborates on how SCS practices among supply chain members can enhance sustainability outcomes. Through an iterative theory-building process, we explored the integration between SCT and SCS in this conceptual paper. The argumentation shows that beyond direct causality relations employed, social capital can strengthen SCS practices to create value among supply chain members. This becomes possible through a combination of collaboration initiatives and supply chain learning. This paper contributes to the theory by offering a conceptual framework and a set of propositions for an alternative way to understand SCS practice. Additionally, this paper provides practical contributions by showing how managers can integrate sustainability into their daily operations.

Ports, as nodes in maritime transportation, frequently face disruptions leading to congestion, adversely affecting the efficiency and sustainability of the global supply chain. This study considers the speeding-up strategy to alleviate port congestion. We model the transportation network as a closed Jackson network and adopt a tailored mean-value analysis algorithm to solve the model. To deliver the increased sailing speed, we further put forward a bi-section search algorithm. Our numerical analysis results demonstrate the feasibility of increasing speed to alleviate port congestion. However, when port congestion exceeds a certain threshold, the system faces collapse, rendering the increase in speed ineffective. Additionally, we recommend shipping companies consider using clean energy when employing the speeding-up strategy to mitigate congestion, thus safeguarding the environment. Our study combines theoretical methodologies and analytical models, providing insights regarding speeding up the vessels. The findings of this study offer guidelines for real-world sustainable maritime practice.

Recent major disruptions such as natural disasters, geopolitical tensions, Covid-19 pandemic have led supply chain managers to rethink strategies for achieving significant levels of sustainability and resilience. At the same time, new digital technologies are offering significant opportunities to increase company performance, while maintaining a high level of customer service. In this challenging context, the Supply Chain Digital Twin (SCDT) represents an emerging and promising concept, which deserves to be explored. In this paper, a holistic and cyclical methodology, based on simulation, is proposed to enable and exploit the SCDT Paradigm, with the main aim to increase sustainability and resilience of the chains. The proposed methodology is successfully tested and validated on a real case study in the agri-food sector, through the use of the anyLogistix software. The results return valuable information on the level of resilience of the supply chain in the face of any considered disruption and provide useful managerial insights on the actions to be taken to improve some performance indicators.

The surge in e-commerce has led to an increased demand for urban express services, requiring the strategic development of delivery networks that are both efficient and cost-effective. This study addresses a practical vehicle routing problem (VRP) in an urban express delivery network to minimize transportation costs. Specifically, it considers the implementation of backhaul discounts, a factor disregarded in the existing literature. This VRP is further complicated by various realistic constraints, including pickup and delivery, time windows, multiple trips, heterogeneous fleets, and docking capacity limitations, which make most general VRP solvers inapplicable. This study proposes a trip-based formulation to overcome this challenge and develop a tailored branch-and-price algorithm. Feasible trips are classified into four types to simplify the computation of backhaul discounts, thereby enhancing solution efficiency. Validation with real-world data from SF Express substantiates the efficacy of our method and yields insights for sustainable city logistics management. Moreover, our simplified column generation algorithm exhibits competitive performance, achieving optimal solutions expeditiously for the tested instances.

Advanced biofuels, such as biomethane, could contribute to environmental sustainability in increasingly interrelated sectors, such as energy and waste management. Accordingly, innovative biomethane production technologies are argued to be the enablers of circular and waste-to-energy concepts, for example, at wastewater treatment plants (WWTPs). Nevertheless, their integration into biofuel supply chain (BSC) research is overlooked from a circular business model innovation (BMI) perspective. This study aims to address this research gap by focusing on an innovative biomethane production approach (power-to-gas, P2G) and related circular business model innovation opportunities in the context of WWTPs. We carried out lab-scale research and a case study at a mid-sized European WWTP to establish an empirical basis for large-scale techno-economic calculations. Despite the explored technological opportunity to increase advanced biofuel supply and decrease carbon dioxide emissions at WWTPs, current market risk levels challenge the economic prospects of the system concept. These empirical results demonstrate the necessity of policy interventions in different combinations (e.g., investment support, favourable taxation, feed-in tariffs). This study is one of the first to combine technological and business modelling aspects to support BSC planning, and supplement optimization-focused BSC research with explorative techno-economic analyses based on empirical data.

Sustainable mining practices is a concept that embeds the principles of sustainable development into the whole mine life-cycle, from exploration, extraction and processing through to mine closure. The optimization of coal mine planning and the developing a standardized design for its sustainable development is very challenging and requires more effort. The present research attempts to address the conditions of sustainability and necessary measures for sustainable development, thereby providing appropriate solutions for each stage of mining operation besides expressing the necessity of sustainable development integration at different stages of mining life cycle (MLC). The approach of systems engineering is essential to assist the sustainability goals which are integrated with the expected results. Hence a method depending more on systems engineering principles and optimization can be incorporated to attain better results. Several socio-environmental factors associated with sustainability depends on the geographic condition and few mining engineering considerations such as mine location, topography, coal seam characteristics and so on. These systems engineering approach can be further enhanced by incorporating tools like Geographic Information System (GIS), which provides more accuracy and precision of the geographic conditions of the site identified for the coal mining plan. In order to begin this way of approach towards the sustainability development and mining planning, the appropriate optimization parameters should be identified. The outcome of these optimization parameters can be also achieved by optimizing coal mining system models.

Due to increasing last-mile logistics complexity, the e-commerce business's last-mile delivery has emerged as an imperative issue that impedes the advancement of contemporary last-mile logistics practices. The paper attempts to identify last-mile logistics associated risk hurdles from e-commerce literature and prioritize them on their significantly affecting logistics operations. The factors were identified by reflecting on the recent literature and internet resources coupled with in-depth brainstorming with experts in the field of Logistics Company including, practitioners, and employers. Two ranking methods namely TODIM (TOmada de Decisao Interativa Multicriterio in Portuguese “interactive and multicriteria decision making” in English) and ARAS (Additive Ratio ASsessment method) are used together to rank order the major risk hurdles and criteria. The results of this study present that lack of visibility (LV), low-efficiency levels (LEL), and unpredictable elements (UE) are ranked as the top three major risk hurdles whereas real-time information on a package’s location (LV1), putting a GPS tracking system to track last- mile journey (OT3) and users wants on time location of their package (LV2) are ranked as top three most significant criteria affecting the practices of modern last-mile logistics in e-commerce businesses. The findings can be used by logistics firms, organizations, and policymakers to better understand the issues and develop strategies to address them, making last-mile delivery a more efficient also better form of awareness for practitioners who are engaged in third-party logistics firms. The novelty of this study is to identify the primary risk hurdles affecting the practices of last-mile logistics and to prioritize them using ranking methods such as TODIM and ARAS. This offers advice for managers and researchers to consider how to handle the logistical uncertainty in e-commerce enterprises.

The increasing public awareness of environmental protection and the scarcity of rare earth elements have made closed-loop supply chains a necessity in many sectors. In particular, recycling components and parts from end-of-life consumer electronics have drawn the attention of both academics and practitioners. Disassembly line balancing improves the cost efficiency of recycling operations and hence helps waste management businesses that operate on very narrow profit margins. This study proposes a new mathematical formulation and hybrid metaheuristics to solve the Disassembly Line Balancing Problem (DLBP) considering multi-manned workstations and resource constraints. The transformed AND/OR graph is used for prioritizing disassembly tasks in the modeling process. The method is applied for optimizing a real-world case of laptop disassembly to showcase the usefulness of the proposed approach. The performance of the developed metaheuristics is evaluated for minimizing the number of workstations, operators, and machines involved in the disassembly operations. Further, the results are analyzed through sensitivity analysis. This study is concluded by providing practical insights and suggestions for the future development of DLBPs.

Increasing concern about the environmental impact of industrial activities has prompted a shift to renewable energy sources and the development of environmentally conscious supply chains. In this regard, electrochemistry has shown promise for converting biomass into specialty chemicals in distributed facilities that exploit renewable energy resources. To examine the impact of electrochemistry technology on optimal supply chain configuration, we formulate a mixed-integer linear programming model to optimize the locations and capacities of distributed facilities for converting biomass to chemicals. The economic objective of the supply chain design model is to minimize the total annual cost of producing chemicals from biomass-derived glucose and delivering them to market. To analyze the trade-off between environmental and economic considerations, we also consider an environmental objective of minimizing greenhouse gas (GHG) emissions. The results of a US case study indicate that, while cost is minimized by constructing one large facility, GHG emissions are lowered by a distributed configuration. Varying the setting of a process design parameter expands the Pareto frontier along which decision-makers can choose a configuration according to their preferences between economic and environmental criteria.

The Vehicle Routing Problem (VRP) represents a thoroughly investigated domain within operations research, yielding substantial cost savings in global transportation. The fundamental objective of the VRP is to determine the optimal route plan that minimizes the overall distance traveled. This study employs VRP to address the challenge of distributing fresh agricultural products within retail chains. It introduces an integrated bi-objective VRP model that concurrently optimizes resource allocation, order scheduling, and route planning. The proposed model incorporates two objective functions with the goals of minimizing total distribution costs and ensuring timely product deliveries to retail outlets. Real-world characteristics are integrated to enhance practical applicability. All solution algorithms and the developed VRP model undergo testing using data from one of Sri Lanka's largest retail chains. Numerical experiments showcase the efficiency of the proposed algorithm in solving real-world VRP problems. Moreover, the proposed VRP model achieves a 19% reduction in daily distribution costs, including a 24% saving in fuel costs. This not only provides financial benefits but also contributes to the reduction of the carbon footprint of retail chains. The model ensures on-time deliveries to 95% of retail outlets, which is crucial for maintaining the quality of fresh food. The findings of this study underscore the significant cost savings, enhanced sustainability, and improved quality associated with the efficient distribution of fresh agricultural products within retail chains.

While electrification of freight deliveries has the potential to heavily reduce the sector’s negative impact, there is a clear lack of understanding how logistics systems are affected when electric freight vehicles are implemented. The purpose of the paper is to present an overview of the area and identify major gaps in the literature. A systematic literature review was conducted, with an initial sample of 353 papers, finally resulting in 62 papers after several exclusion steps. A descriptive analysis showed that the area is growing fast and that a vast majority of the papers applied mathematical methods with transport data. Further, five categories were deemed of interest in relation to logistics and electrification in the analysed literature: transport operation, charging, cost, actors, and sustainability. Each category is elaborated on and described in the paper. Additionally, the discussion includes several research gaps that the paper proposes need to be addressed to achieve viable electrified logistics systems and the gaps resulted in a research agenda with seven bullet points aiming to guide future research. The research agenda include, how planning of logistics systems is affected when charging is included; how logistics performance is affected when adopting electric trucks, and there is a need to include actors and their perspectives to a larger extent than what is done in the current literature. By highlighting what is missing in the literature, this paper strives to be an important addition to achieve viable electrified logistics systems, which can ultimately lead to a reduction of the freight sector’s negative climate impact.

Literature on garment reuse focussing on consumer behaviour and end-of-life products is scarce. Our study addresses this gap by exploring significant predictors of end-of-life garment reuse by Australian consumers. Subsequently, this study extends the theory of planned behaviour (perceived behavioural control, attitude, and subjective norms) by general recycling behaviour, self-identity, quality consciousness, eco-literacy, and self-efficacy as predictors of reuse intention and behaviour. Structural equation modelling is conducted to analyse data from a sample of 428 questionnaire responses retrieved between 16 and 22 March 2022 from Australian consumers. Our findings support the notion that the fundamental predictors of the theory of planned behaviour, along with self-efficacy, and eco-literacy, are significant predictors in understanding sustainable behaviour such as reuse. However, no significant relationships were observed between self-identity, general recycling behaviour and quality consciousness. We made theoretical contributions to literature by addressing a gap in reuse, focusing on consumer behaviour and end-of-life garments. Extending on the theoretical implications, our study contends that engaging in reuse practices must be in collaboration with the entire supply chain. This study also provides a cultural context for reuse intention and behaviour amongst Australian consumers. In a broader context, the findings from this study could reduce the number of garments sent to landfills and promote the utilisation of garments beyond one lifecycle.

The road transport systems have a direct impact on both passenger and freight movements, leading towards negative environmental effects plus economical and societal costs. It has reached an alarming level. Afford to purchase more than one vehicle by an individual who can pay for it may solve the transportation problem for them; however, not everyone has a personal vehicle for movement. Moreover, providing public transport facilities to everyone on a demand basis or on time basis is not possible for several reasons. Further, personal or commercial vehicles are not being used all the time or not carrying passengers in full though it can afford with the number of seats available. One possible solution is to share these vehicles for minimising the number of vehicles and to ensure the effective use of vehicles. Thus, the shared transport systems, such as ride-sharing, car-sharing, car-pooling, or bike-sharing are the promising choice to facilitate good accessibilities to the city or urban living population, especially in a densely populated area. In this paper, we have worked with such shared transport options by collecting data through an online survey and analysed data in various ways. We have also designed and developed a decision support system for this data to offer the city planners in Western Australia to make informed decisions about the future transportation systems for their people. Finally, we have predicted on the use of rail transport, for example, how a train station can be used to facilitate the shared transport options for better accessibility.

Closed-loop supply chains (CLSCs) are an important response to pressing sustainability challenges. However, they bring about tradeoffs like increased resource use in logistics. Addressing these complexities, digital platforms emerge as promising, yet under-researched, tools for efficiency enhancement in CLSCs. This study investigates three digital platforms facilitating furniture and household item trade in relation to CLSCs. A comprehensive framework, rooted in literature on CLSCs, digital platforms, and logistics, guides the analysis. The findings reveal diverse sustainability potentials within these platforms, primarily driven by system design, scale, and actor engagement. The efficacy of the digital platform varies based on usage patterns, their geographical scope, logistic solutions, and providers. Crucially, environmental and social sustainability tradeoffs and benefits come to light, offering insights into their emergence based on system design. This study provides insights for practitioners and scholars, regarding potentials/risks of trade-offs in environmental and social sustainability dimensions related to the use of digital platforms for trading secondhand furniture. It provides a nuanced understanding of how these differ depending on system design.

Summary: Fostering green innovations in the supply chain which are simultaneously more sustainable and economically successful requires respective sustainability and innovation management skills from purchasers. However, currently, these two requests for sustainability and innovation often appear to be treated as two separate procurement targets. This paper provides a framework of specific purchasing and supply management (PSM) competencies needed to stimulate green innovation. Based on job ad analysis, we contribute to the literature by opening the black boxes “innovation purchaser” and “sustainability purchaser” and identifying about a dozen specific skills needed. Findings indicate that, as of now, job descriptions in job ads for sustainability and for innovation managers are almost antagonistic. In this way achieving green innovation in the supply chain is hampered. As a solution, this paper provides initial insights into merging the two roles into a “sustainability innovation purchaser”. It thus enables procurement to operationalise the Porter hypothesis (implementing a simultaneously cost-efficient and environmentally friendly supply chain).

The purpose of this study is to investigate the impact of Green Human Resource Management (GHRM) and Green Supply Chain Management (GSCM) on the sustainable performance of the Bangladeshi textile sector. Specifically, the study focuses on environmental and employee-related aspects. Additionally, we examine how environmental performance and employee performance mediate the relationship between GHRM and GSCM. This study draws upon data collected from 450 employees across various textile enterprises in Bangladesh. Structural Equation Modeling is employed using the Amos 24 software to analyze the relationships and interactions among these variables. These findings demonstrate that using environmentally sustainable practices in human resource management and supply chain management results in enhanced sustainability. The study indicates that environmental performance significantly influences the relationship between GHRM and GSCM regarding sustainable performance. The study findings indicate that firms operating in the textile industry should implement GHRM and GSCM practices to enhance their sustainability performance. Additionally, it is recommended that these organizations prioritize the well-being and engagement of their employees. Implementing such a strategy can bolster the organization's comprehensive sustainability initiatives and raise its standing among stakeholders. This study contributes to the expanding body of literature on textile sustainability by investigating the mediating role of employee and environmental performance. It emphasizes the significance of GHRM and GSCM techniques in improving sustainable performance. The findings provide valuable insights for firms seeking to develop more effective sustainability initiatives.

This study aims to discuss how literature explores the relationship between Industry 4.0 and Industry 5.0, operations management, and sustainability. A systematic literature review was conducted to investigate this relationship, and 30 articles were identified. The data from these articles was discussed in a structured summary, which facilitated its analysis. It was identified that the Internet of Things (IoT) and Big Data are the most commonly cited technologies in the current literature. However, it was realised that most of the literature analysed in this study focuses mainly on Industry 4.0 instead of Industry 5.0. This represents a concern and also an empirical and theoretical opportunity for a deeper analysis of topics such as people, sustainability, and organizational resilience related to Operations Management & Supply Chain. These opportunities can provide insights and assist researchers in future operations management practices and further research.

The importance of TQM in cleaning companies in Finland is investigated in this study. The theoretical framework, TQM practices, and the relationship between TQM implementation and customer satisfaction among cleaning companies in Finland were all examined. It was discovered that employee and communication management increases service quality and boosts organisational performance. The study also discovered that in cleaning organisations, organsational performance has a favourable and significant relationship with customer satisfaction, as well as a positive correlation between employee, communication management, and customer satisfaction. Meanwhile, according to the results of a correlation analysis based on the Pearson correlation coefficient, the only factor limiting the implementation of Total Quality Management in Finnish cleaning organisations was resistance to change, as there were no preliminary signs of multicollinearity based on the two variables of the correlation coefficient criteria (r 0.70). Furthermore, the study adopted hierarchical multiple regression analysis to analyse the relationship between Total Quality Management and Organisational Performance. The results revealed that the two entities have a substantial association, providing valuable insights into the cleaner aspects of the study, particularly when considering organisational performance and customer satisfaction in the Finnish cleaning industry.Subject classification: Total Quality Management, Organisational Performance, Customer Satisfaction.

The increasing global concern for sustainability in supply chain management is driven by stricter government regulations addressing environmental pollution and social injustice. This has led to a growing emphasis on integrating sustainability into supply chain practices. However, there is limited research on incorporating all three dimensions of sustainability (economic, environmental, and social) into supply chain management. This study presents a mixed-integer linear programming model for designing an uncertain supply chain network design that aims to minimize overall costs (establishment, production, and transportation/routing costs) while considering carbon emissions and a few social factors simultaneously. The study considers sustainable aspects of decision-making process and utilizes chance-constrained programming to address uncertainties. The proposed model attempts to maintain balanced flow levels across all stages of the network, optimizing the utilization of raw materials and production. The proposed optimization model is a cost minimization model that also tries to minimize greenhouse gas emissions throughout the entire network. A greedy based heuristic is provided for dealing with larger instances of the given decision making problem. Additionally, sensitivity analysis has also been carried out to explore the impact of various parameters involved.

The emerging institutional concerns regarding the environmental crisis over the past decades have promoted the implementation of green initiatives within the supply chain, aiming to reduce environmental harm while maintaining efficiency. This study employed the BWM from a double perspective, aiming to identify and rank institutional pressures for GSCM practice implementation and the importance of these practices for sustainable performance in the Moroccan manufacturing industry. The study used purposive sampling to identify respondents with supply chain management expertise. A survey was distributed to 124 potential respondents, resulting in 55 responses. Data collection occurred between April and August 2022, with an effective response rate of approximately 38%. The results indicate that institutional pressure increases the likelihood of firms improving their GSCM activities. Normative pressures are found to be the main influencing factor for businesses to implement GSCM practices, while mimetic pressures are the least important. However, certain GSCM practices, such as GP, ECO-D, and IEM, remain among the top three most affected by isomorphic pressures. The study also highlights the importance of certain GSCM practices, such as IEM, GP, and ECO-D, for firms' sustainable performance. Economic outcomes are perceived as the experts' priority, followed by environmental and social performance. This research provides a theoretical and practical foundation for decision-makers seeking to identify the pressures affecting the implementation of GSCM practices along with the activities that most likely contribute to improving various aspects of sustainable performance.

Maritime transport is responsible for producing a considerable amount of environmental pollution due to the reliance of ports and ships on the carbon-based energy sources. With the increasing trend towards port electrification to reduce carbon emissions, the operation of ports will be increasingly relying on the electricity network. This interconnection creates multiple challenges due to the complexity of power flow in the port network, uncertainty of vessel arrival time and fluctuation of power generation of renewable energy sources. These uncertainties can lead to an overload in electricity networks and delays in cargo-handling activities, resulting in increased vessel handling times and environmental emissions. This paper presents a joint logistics-electric framework for optimal operation and power management of electrified ports, considering multiple uncertainties in the arrival time of vessels, network demand, and renewable power generation. An optimal power flow method is developed for a real-life port, with consideration for multiple port logistic assets such as cargo handling equipment, reefers, and renewable energy sources. The proposed model ensures feasible port operation for all uncertainty realisations defined by robust optimisation, while minimising operational costs. Simulation results demonstrate that the probability of a network constraint violation can be as high as 70% for an electrified major UK port if the uncertainty in the port operation is neglected, presenting an unacceptable risk of disruption to port activities. Furthermore, such uncertainty can cause 150% increase in emissions if the ships use their auxiliary engine instead of using shore power. The numerical study shows that such challenges can be handled by a 0.3% increase in the robustness in face of uncertainty, while the cost increase in the worst case does not exceed 4.7%. This shows the effectiveness of the proposed method enhancing robustness against uncertainty at the minimum cost.

Context: Medical devices fall under the broad topic encompass everything from basic hardware to integrated software systems. The integration of software into hardware devices is not simple due to requirements of regional regulatory bodies. Therefore, medical businesses need to oversee not only the creation of devices but also the observance of guidelines and standards established by regulatory bodies. While plan-driven methodologies prevented software from evolving or changing, agile methodologies have inherent characteristics of insufficient planning and documentation. Objectives: The objective of our research is to propose a suitable process model for medical device development, keeping in mind the regulatory requirements. Methods: First, based on the detailed analysis of literature and McHughs proposed model, we suggested the Enhanced Agile V-Model (EAV), which combines plan-driven and agile approaches. Second, we mapped the proposed model to the MDEVSPICE framework to confirm that it adhered to the rules outlined in the standard IEC62304. Finally, the proposed model is evaluated through implication to case study of wave therapeutic medical device. Results: The support of both agile and waterfall approach in EAV model helps in accommodating new requirements in the medical devices and the proposed systems engineering approach helps in hardware and software integration. The mapping of the EAV model to the MDEVSPICE shows complete compliance. Moreover, the implication of the proposed model has been clearly shown statistically and successfully implemented in our case study. Further, device usability and efficiency metrics showed confidence of P < 0.05 and for device safety and efficiency, we conducted an experiment which shows significant improvement in selected parameters. Conclusion: The proposed model shows conformance to regulatory standards, and successfully implemented in development of wave therapeutic device. However, its applicability to more compact and straightforward medical products is unknown and can be determined by using this model to analyze the performance of other medical products.

In this paper, the authors have developed a quantitative mathematical model to assess the performance of a smart trash bin. This model takes into account six hardware components of the trash bin, including Arduino Uno, ultrasonic sensor, servo motor, switch, battery, jumper wires utilizing Markov modelling and reliability-based approach. The objective of this model is to facilitate timely planning of repair and maintenance activities, ensuring prolonged availability of the smart trash bin after identifying the weakest component/components of the system.The efficient functioning of all components in the smart trash bin is imperative for the timely transmission of the garbage level data to the municipal corporation. To achieve this, the paper focuses on evaluating key reliability metric for smart trash bin system, specifically reliability, unreliability, and Mean time to Failure (MTTF). The modelling approach employs a state-based Markov model, from which Kolmogorov differential equations are derived and subsequently solved using Laplace transformation. After collecting data from organization and experts of the field explicit expression for system reliability, unreliability and MTTF have been obtained.Furthermore, this study includes sensitivity analysis of the system to pinpoint critical components which is switch and servo motor, requiring attention for proper maintenance. The research finding indicates that reliability and MTTF of the system diminish as the failure rate of the components increase under certain conditions.

This study investigated the combined effects of inventory, green, supply and lean manufacturing practices on the sustainable performance (environmental [EP], social [SP] and economic [EcP] performances) of food manufacturing enterprises in emerging economies like Ghana. The study adopted the quantitative approach and explanatory research design. Primary data was obtained from 247 owners and managers of food manufacturing enterprises in Ghana through structured questionnaires. Twelve hypotheses were developed and tested using the partial least square-structural equation modelling (PLS-SEM) analytical tool. The study’s results revealed that inventory and green manufacturing practices directly influenced sustainable performance (SP, EP, and EcP), while lean manufacturing practices had an insignificant influence on economic performance. Supply management, on the other hand, significantly improved only economic performance. The outcome of the study provides valuable information for food manufacturers in Ghana and sub-Saharan Africa. The outcome will provide more robust support for food manufacturers to invest heavily into sustainable supply chain management practices such as inventory, green and lean while establishing stronger relationships with key suppliers to guarantee a consistent supply of raw materials; thereby, promoting sustainable performance. This study contributes to the current literature on sustainable supply chain management and sustainable performance with concrete evidence from food manufacturing enterprises within an emerging economy.

Innovative control systems regulate and adjust a lot of operational conditions of the modern ship’s functions. A comprehensive Ship Integrated Control System (SICS), for the operation of the propulsion plant, the power management of the auxiliary engines, the operation of the auxiliary machinery, the loading and unloading of cargo, the navigation, the management of maintenance and the purchase of spare parts is implemented in the paper. A strategy where preventive maintenance is carried out and different repair actions can be considered is proposed. The SICS is subjected to various levels of degradation, and if so, a repair process will be undertaken to restore the system to its full working state. The aim of the paper is to optimize the best strategy for the different sensors of the SICS where only maintenance actions are performed or only repair actions need to be carried out. The model is developed and applied to the sensors of a ship and the objective is to determine the optimal asymptotic availability, and in particular in the main propulsion plant of a Liquefied Natural Gas (LNG) carrier. The model is illustrated by a numerical example based on empirical data.

In winter road maintenance, abrasives are spread on roads to ensure user safety. These abrasives must be swept in the spring and often end up in landfills. To reduce landfilling and the consumption of non-renewable resources, previous work has demonstrated the potential of reusing collected street sweepings for the production of abrasives. However, the current contractual approach of the linear supply chain requires revision to enable the sharing of financial gains between the road authority and the service provider to achieve a win–win situation, primarily considering the uncertainty in the quantity of abrasives spread, which directly impacts the service provider’s profit. This study proposes two closed-loop supply chain structures and analyzes three contract models. Results from a case study in a highway context in Quebec, Canada reveal that transitioning from a linear supply chain to a closed-loop supply chain generates an average systemic financial gain of 9%. Furthermore, a sensitivity analysis on the average quantity of abrasive spread demonstrates that when the road authority buys back reusable sweepings from the service provider at market value, it enables the sharing of a portion of the gain. Consequently, compared to a linear approach, adopting circular economy strategies in the winter road maintenance supply chain mitigates the potential profit loss of the service provider caused by uncertainty.

This study investigates an integrated yard planning and module transportation problem in modular integrated construction (MiC). A Stackelberg game model is proposed to characterize the relations between the government and contractors. The government is the leader and decides the locations and areas of temporary storage yards to facilitate the cost-effective and eco-friendly transportation of MiC modules. The contractors are the followers and decide the transport mode (i.e., road transport or intermodal transport) for modules to minimize their total transportation costs. This study provides an in-depth analysis of model properties to derive a number of useful managerial insights. The economical and environmental trade-offs between costs and emissions are observed. Our study further proves that an optimal solution is likely to simultaneously reduce the overall costs and carbon emissions, in comparison to choosing the location with the largest area to construct a temporary storage yard. These insights offer a comprehensive framework for understanding the interactions between government and contractors. Overall, this paper aims to promote the implementation of MiC by achieving the economical and sustainable transportation of MiC modules.

This paper introduces a resilience assessment methodology for sustainable autonomous maritime transport networks developed by the European project entitled “Advanced, Efficient, and Green Intermodal Systems” (AEGIS). This problem being addressed in this paper concerns the investigation of threats, incidents, and risks in an autonomous- and sustainable shipping context, and the research question is the development of both preventive measures and reactive actions to maintain an acceptable level of operational constraints. The paper’s methodology aids in designing sustainable logistics systems for highly automated waterborne transport, identifying threats and barriers to mitigate event consequences, thereby facilitating a seamless green transition. To examine the usability, this methodology is applied in a case study for cargo transportation, where we in this paper consider the maritime corridor between Trondheim and Rotterdam. The findings encompass the spectrum of possible actions to prevent and mitigate unwanted events and enhance resilience and flexibility. This can be used as a tool to respond to unwanted threats, enhance safety, and introduce new strategies. These results are deemed important as resilience is one of the prerequisites for the development of a sustainable transport system. This is true both for the companies that are engaged in the operation of such systems and for policymakers.

The resource and environmental problems caused by excessive packaging of e-tailing goods have attracted widespread attention, and its management has become an urgent task. To this end, we establish a tripartite game model of merchants, logistics enterprises and consumers to analyze the dynamic evolution process of behavioral choices. The results show that (1) the stage of environmental pollution positively promotes the behavioral choices of merchants, logistics enterprises and consumers. (2) Cost expenditures have a greater impact on the evolutionary path of merchants than the benefits generated by excessive packaging of e-tailing goods. (3) Penalties from online platforms have a greater impact on merchants’ evolutionary paths than rewards. This study provides a new perspective for the study of recycling of excessive packaging of e-tailing goods, and the conclusions of the study can provide a reference for governments to formulate effective policies on the management of excessive packaging of goods.

With the rise of technology, increasing competitiveness, market globalization and the fourth industrial revolution, companies are forced to rethink the way they do business to create or maintain a competitive advantage. Consumers, who are increasingly informed, demanding and concerned about sustainable development, are forcing companies to adapt to their needs to respond adequately to personalized demand. Small and medium-sized enterprises (SMEs) in the manufacturing sector must adjust to this new context. The move towards mass customization is one way of meeting customer requirements. However, no strategy for making this shift currently exists in the literature. The aim of this article is to present a strategy for operationalizing mass customization using modular tools. Action research is used to test the proposed strategy. The paper proposes 4 transformation axes to migrate towards mass customization: Modular product design, Modular process design, Technology use, Collaboration network. This article also highlights the need to tackle modular product design first to migrate to mass customization, by proposing a 3-stage strategy: modular product architecture, standardization of interfaces and definition of configuration rules. A case study is used to test the proposed strategy.