Logistics, Volume 7, Issue 4 (December 2023) – 23 articles

Background: Systematic literature review (SLR) is increasingly utilized to maximize the element of rigor and minimize the individual bias of research synthesis. An analysis of the Web of Science (WoS) database indicates that 90% of the literature review studies using SLR have been published between 2012 and 2022. However, this progressive agenda is impaired by the lack of methodological consistency and rigorousness. To fill this gap, this paper aims at mapping the theoretical comprehension and practices of SLR and providing a stepwise approach to employing such a framework. Methods: A comprehensive narrative review is used in this paper to analyze the studies concerning the literature review typology and the structural assessment of the SLR. Furthermore, the methodological approach of the literature review studies that adopted the SLR and were published in the Logistics journal is assessed across a set of vital criteria associated with conducting an SLR. Results: There is a concrete link between the purpose of a review, i.e., to describe, test, extend, or critique, and the literature review type. There are 17 distinct literature review types, e.g., a narrative review, a bibliometric analysis, etc., which must be justified meticulously regardless of the SLR. The ambiguity in conceiving the SLR either as a toolkit or a review type, the lack of justification regarding the review purpose and type, and vague conceptual distinguishment between the bibliometric analysis, as a distinct review type, and the SLR framework, are only a few of the shortcomings observed in the analyzed papers. Conclusions: Given the significant role of SLR in elevating the element of rigor within the literature review studies, it is deemed essential to employ this framework by paying attention to two holistic factors: (1) theoretical distinction between the literature review purpose, the literature review type, and the SLR; (2) strict adherence to the SLR procedure with a high degree of accuracy and explicitness. Full article

Background: Logistics management in the clinical trials industry is a very challenging undertaking because it involves multiple stakeholders, complex processes, diverse software applications, intensive white-collar jobs, and onerous quality standards. Current business practices are inefficient and difficult to automate technologies. Methods: This paper reviews the theories and concepts of clinical trials logistics management. The inefficiencies in current logistics management industry are then addressed by building a conceptual framework based on contemporary software tools and architectures, such as web portals, software agents, business process management system, project cards, and resource cards, all interacting with specialized software applications such as accounting, inventory, and label design software. The framework supports data analysis at multiple levels of decision making. To this end, a project planning tool for facilitating and optimizing the operational planning in this industry is designed and presented. Results: The planning tool also contributes to the literature by contrasting several different resource scenarios such as the shared pool, dedicated resources for each project, and the creation of several work groups with dedicated resources. These are Pareto trade-offs. Conclusions: A framework employing a business process management is proposed for clinical trials logistics management. Different managerial scenarios with shared, dedicated, and work group resources are investigated using a case study. Full article

Background: In container terminals, optimizing the scheduling of external trucks and yard cranes is crucial as it directly impacts the truck turnaround time, which is one of the most critical performance measures. Furthermore, proper scheduling of external trucks contributes to reducing ${\mathrm{CO}}_2$ emissions. Methods: This paper proposes a new approach based on a mixed integer programming model to schedule external trucks and yard cranes with the objective of minimizing ${\mathrm{CO}}_2$ emissions and reducing truck turnaround time, the gap between trucking companies’ preferred arrival time and appointed time, and the energy consumption of yard cranes. The proposed approach combines data analysis and operations research techniques. Specifically, it employs a K-means clustering algorithm to reduce the number of necessary truck trips for container handling. Additionally, a two-stage mathematical model is applied. The first stage employs a bi-objective mathematical model to plan the arrival of external trucks at the terminal gates. The second stage involves a mathematical model that schedules yard cranes’ movements between different yard blocks. Results: The results show that implementing this methodology in a hypothetical case study may lead to a substantial daily reduction of approximately 31% in ${\mathrm{CO}}_2$ emissions. Additionally, the results provide valuable insights into the trade-off between satisfying the trucking companies’ preferred arrival time and the total turnaround time. Conclusions: The integration of data clustering with mathematical modeling demonstrates a notable reduction in emissions, underscoring the viability of this strategy in promoting sustainability in port-related activities. Full article

Background: Transportation demand forecasting is an essential activity for logistics operators and carriers. It leverages business operation decisions, infrastructure, management, and resource planning activities. Since 2015, there has been an increase in the use of deep learning models in this domain. However, there is a gap in works comparing traditional statistics and deep learning models for transportation demand forecasts. This work aimed to perform a case study of aggregated transportation demand forecasts in 54 distribution centers of a Brazilian carrier. Methods: A computational simulation and case study methods were applied, exploring the characteristics of the datasets through autoregressive integrated moving average (ARIMA) and its variations, in addition to a deep neural network, long short-term memory, known as LSTM. Eight scenarios were explored while considering different data preprocessing methods and evaluating how outliers, training and testing dataset splits during cross-validation, and the relevant hyperparameters of each model can affect the demand forecast. Results: The long short-term memory networks were observed to outperform the statistical methods in ninety-four percent of the dispatching units over the evaluated scenarios, while the autoregressive integrated moving average modeled the remaining five percent. Conclusions: This work found that forecasting transportation demands can address practical issues in supply chains, specially resource planning management. Full article

Background: A hospital’s operating theater service system is a large-scale, complicated system that must be carefully managed to offer the best possible results for its patients. Unlike other industries such as manufacturing and logistics, system dynamics (SD) methodologies are not extensively applied in hospital operating theaters. This study deals with the future development and possible future scenarios for hospital operating rooms in Bangladesh. Methods: Due to demographic dynamics and demographic processes, increased pressures on hospital care are expected in Bangladesh. The SD model anticipates possible future scenarios, reconciles service capacities and the variability of patient demand, and reduces patient congestion and waiting times in the hospital area. This study introduces a causal loop diagram to show a causal link between the hospital operating theater system variables. It also introduces a stock flow diagram to understand the dynamic behavior of the system. Results: The model validation testing reports that in extreme conditions, such as a 50% reduction in the patient arrival rate, the model is valid and runs as usual. Conclusions: This first work of SD modeling for hospital operating theater systems can help healthcare managers, decision makers, or researchers of any responsibility level make better predictions in order to reduce patient waiting times and backlogs and make appropriate decisions. Full article

Background: The air cargo logistics industry has been deemed reluctant to adopt new technologies for their business operations. So, this study aims to examine the adoption of technological innovations in the air cargo logistics industry in South Africa. The specific objective is to emphasise the effects of technologies on air cargo logistics operations to address the reluctance to adopt technological innovations in the industry. Methods: A questionnaire survey was conducted randomly on three hundred and seventy-three (373) cargo agents at the OR Tambo International Airport. The data were subjected to a nonparametric test with Kruskal–Wallis, exploratory factor analysis and regression analysis to explore the effects of technologies for prompt adoption and implementation of emerging innovations that enhance quality service delivery. Results: The study found that promptly adopting emerging technological innovations in the air cargo logistics industry promotes efficient operations, improves warehousing, and enhances cargo delivery services for customer satisfaction. Also, the study established that technologically driven operations and warehousing are significant determinants of quality service delivery in the air cargo logistics industry. Conclusions: This study encourages the prompt adoption and implementation of technological innovations for improved quality service delivery, customer satisfaction, and loyalty in the air cargo logistics industry. Full article

Background: As a result of the effort to satisfy unique customer needs, the complexity of production and service processes is constantly increasing. In this context, the requirements for packaging systems, essential for carrying out logistic tasks, are also diversifying, and various quality defects and problems are appearing more and more frequently. Methods: The research used an inductive method. While practical problems were being solved, the need for developing the concept of a packaging inspection framework arose, the lack of which was also supported by a systematic literature review. Results: During the concept’s development, packaging errors found in the literature were identified and methods for detection and solution were systematized. A general framework was developed to identify and eliminate these errors. The applicability of the developed method was demonstrated through a complex case study, and its accuracy was verified. Conclusions: This research is important because, instead of using “island” solutions, in the future, companies will have a general framework available to them for handling all packaging-related errors according to a predefined methodology. This can reduce the time required for problem-solving and increase efficiency, which is a significant competitive factor. Full article

Background: The COVID-19 pandemic is a worldwide threat that has positioned micro-enterprises under enormous tension to persevere. As a result, these businesses are obligated to respond to the epidemic in an efficacious manner. In order to weather this economic storm, micro-enterprises have implemented a variety of digital technologies. Methods: The research investigates the connection between the communications technology of driving schools and the public crisis responses of those driving schools using a data set obtained from a survey administered to those schools. Results: The quantitative findings demonstrate that digitalization has made it possible for driving schools to efficiently and successfully respond to the public dilemma by utilizing their resilient functionality. In addition, digitalization can greatly enhance driving schools’ performance. Conclusions: This paper provides drawings for digitalization and crisis responses for driving schools. Full article

Background: in recent years, automation has emerged as a hot topic, showcasing its capacity to perform tasks independently, without constant supervision. While automation has witnessed substantial growth in various sectors like engineering and medicine, the logistics industry has yet to witness an equivalent surge in research and implementation. Therefore, it becomes imperative to explore the application of automation in logistics. Methods: this article aims to provide a systematic analysis of the scientific literature concerning artificial intelligence (AI) and automation in logistics, laying the groundwork for robust and relevant advancements in the field. Results: the foundation of automation lies in cutting-edge technologies such as AI, machine learning, and deep learning, enabling self-problem resolution and autonomous task execution, reducing the reliance on human labor. Consequently, the implementation of smart logistics through automation has the potential to enhance competitiveness and minimize the margin of error. The impact of AI and robot-driven logistics on automation in logistics is profound. Through collaborative efforts in human–robot integration (HRI), there emerges an opportunity to develop social service robots that coexist harmoniously with humans. This integration can lead to a revolutionary transformation in logistics operations. By exploring the scientific literature on AI and automation in logistics, this article seeks to unravel critical insights into the practical application of automation, thus bridging the existing research gap in the logistics industry. Conclusions: the findings underscore the impact of artificial intelligence and robot-driven logistics on improving operational efficiency, reducing errors, and enhancing competitiveness. The research also provided valuable insights into the applications of various automation techniques, including machine learning and deep learning, in the logistics domain. Hence, the study’s insights can guide practitioners and decision makers in implementing effective automation strategies, thereby improving overall performance and adaptability in the dynamic logistics landscape. Understanding these foundations can pave the way for a future where automation and human expertise work hand in hand to drive logistics toward unparalleled efficiency and success. Full article

Background: Over the past decade, the potential advantages of employing deep learning models and leveraging auxiliary data in data-driven end-to-end (E2E) frameworks to enhance inventory decision-making have gained recognition. However, current approaches predominantly rely on feed-forward networks, which may have difficulty capturing temporal correlations in time series data and identifying relevant features, resulting in less accurate predictions. Methods: Addressing this gap, we introduce novel E2E deep learning frameworks that combine Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for resolving single-period inventory ordering decisions, also termed the Newsvendor Problem (NVP). This study investigates the performance drivers of hybrid CNN-LSTM architectures, coupled with an evolving algorithm for optimizing network configuration. Results: Empirical evaluation of real-world retail data demonstrates that our proposed models proficiently extract pertinent features and interpret sequential data characteristics, leading to more accurate and informed ordering decisions. Notably, results showcase substantial benefits, yielding up to an 85% reduction in costs compared to a univariate benchmark and up to 40% savings compared to a feed-forward E2E deep learning architecture. Conclusions: This confirms that, in practical scenarios, understanding the impact of features on demand empowers decision-makers to derive tailored, cost-effective ordering decisions for each store or product category. Full article

Background: Airfreight transport refers to the shipment of goods by air from one location to another and is often perceived as a contributor to global carbon emissions. The environmental impacts associated with airfreight are of notable and genuine concern. Such concerns have often led to calls for measures to ban or limit air freight as a mode of transportation for goods. Whilst the majority of these calls are perceived to be well placed, it is nevertheless essential to acknowledge the climate justice implications associated with such measures, particularly in the context of perishable products like fresh produce. Methods: The aim of this study is to thoroughly examine the socioeconomic implications of banning air-freighted fresh produce and to recommend practices that can minimize the environmental impacts. Utilizing Blue Skies Holdings Ltd., Pitsford, UK as a case study, this paper undertakes a comprehensive analysis of the potential climate justice ramifications associated with the prohibition of air-freighted fresh produce. Results: The analysis highlights the intricate interplay between the environmental and socioeconomic dimensions of the issue. By investigating the carbon emissions attributed to aviation and air logistics in particular and meticulously scrutinizing the possible consequences of an airfreight ban in relation to vulnerable communities within developing economies that are heavily reliant on fresh produce exports, the study contributes insights to guide policy discourse and the decision-making processes within commercial entities with respect to their carbon emissions reduction strategies. Conclusions: Accordingly, this study provides a number of recommendations for various actors, particularly commercial stakeholders, who deal with air-freighted fresh produce. Full article

Background: What are the cumulative influences of pricing, promotions of commodities, order size-based discount opportunities, and alternative warehousing scenarios on effective decision-making concerning inventory management? It is observed that the prices and promotion of products influence the demand rate. The shortage can be partially backlogged, and the backlogging rate depends on the waiting time. Also, discount and trade credit facilities may be available when purchasing items. This paper describes a novel inventory control model regarding optimal warehousing decision-making scenarios. Methods: This paper includes the facts in its hypothesis and examines the overall impact of the mentioned issues on profitability. The inventory carrying scheme associated with the proposed model consists of both rented and owned warehouse facilities in which the cost increases linearly with time. The numerical and visual simulation succeeds the mathematical approach to analyze the proposed inventory model in Mathematica software. Results: The results show that a price hike enhances profit despite the negative impact on demand creation. Also, promotion frequency favors profitability, suppressing the corresponding costs. Another managerial intuition is revealed through the numerical result that the stock should be held in a rented warehouse when deterioration in the owned warehouse increases, despite the cost of a rented warehouse. Conclusions: Besides several mentioned management insights, this study includes several existing models as particular cases and tackles challenges in the analytical optimization approach. This study leads toward the consequences of future research scopes with industry-based raw data. Full article

Background: This paper revolves around a meticulous exploration aimed at discerning the impact of total quality management (TQM) and service quality (SQ) practices upon the financial performance (FP) of firms within the container shipping industry. Our pursuit extends further, delving into the intricacies of SQ as a potential mediating factor in the TQM–FP relationship. This distinctive study investigated six TQM practices in conjunction with five SQ factors, analyzing their impact on firm FP. Methods: This study used a quantitative research approach. Data were collected through a structured questionnaire from logistics specialists and experts in the shipping field. Results: The findings were interesting and showed that two TQM practices—managerial leadership and employee involvement—have a significant positive effect on SQ levels, and one SQ dimension, namely responsiveness, has the biggest and most significant effect on the FP of container shipping companies. Furthermore, SQ mediates the relationship between TQM and FP. Conclusions: This study provides a theoretical contribution by developing a conceptual model that elucidates the correlation between TQM practices and the degree of SQ on the FP of container shipping firms. Moreover, this study provides managers with a tool for identifying, prioritizing, and improving key TQM and SQ practices that contribute significantly to financial success. Full article

Background: This article identifies and examines key performance indicators (KPIs) related to citizen mobility logistics in smart and sustainable urban areas. It begins with a comprehensive literature review to identify essential KPIs, offering valuable insights for both public and private stakeholders, including policymakers and mobility service providers. Drawing from various mobility projects in smart cities, the study extracts common KPIs and best practices. The focus of the paper then turns to Barcelona, Spain, where KPIs that matter most are analyzed. Methods: Using open data from the city council spanning from 2017 onwards, the study provides insights into the evolving mobility logistics landscape. KPIs from other European cities are also considered by utilizing similar open data sources. This comparative analysis provides valuable benchmarks and reveals disparities in mobility logistics. Throughout this investigation, the paper emphasizes the role of data quality in KPI selection. Results: Reliable open data significantly influence indicator choices and present challenges when comparing cities. Remarkably, the findings consistently highlight environmental data as an area requiring attention in sustainable mobility logistics. Conclusions: This paper makes contributions by identifying and examining KPIs relevant to citizen mobility logistics in smart and sustainable urban areas. It offers insights by applying these KPIs to Barcelona and conducting comparative analyses with other European cities. These findings serve as a valuable resource for policymakers, city planners, and mobility experts. Full article

Background: The Brazilian National Solid Waste Policy establishes the shared responsibility of all actors involved in e-waste generation and management; however, some conflicts of interest need interventions and approaches for preventing them. Objective: This paper proposes using a graph model for conflict resolution (GMCR) decision support system to simulate the analysis and resolution of realistic e-waste management conflicts. Method: A systematic literature review focused on e-waste management, shared responsibility and conflict management was conducted, and a graph model for conflict resolution (GMCR) decision support system was applied to generate a framework to address this context. Results: The need for commitment agreements promoted by government institutions in partnerships with companies involved in the process is essential since the principle of shared responsibility requires educational actions, favoring efficiency in the reverse logistics recovery procedures for e-waste. Understanding the interconnected causes of conflicts and their facets is crucial for effective resolution and prevention, aiding comprehension, focused interventions, and evidence-based decision-making for transformative change amidst conflicting stakeholder objectives in the case of WEEE management. These results can be helpful for academics and practitioners working in this area. Full article

Background: Due to increased globalization and its subsequent rise in competitiveness, the role of supply chain services (3PL) in managing logistics, reducing operational and non-operational costs, and managing customer and supplier relationships, have become of utmost importance. Customer-centric production has led to the development of a close relationship between production processes. Amidst all this, the demand for logistic services has dramatically increased, thus putting more pressure on firms for enhanced operational results, and leading to the outsourcing of their internal and external logistic activities. On the other hand, supply chain firms that provide 3PL services seek to enhance their sustainability and predict their customers’ demand. Methods: The authors collected quantitative data from 81 firms that operate in various industrial sectors in Greece. A questionnaire was sent for completion, in which firms could rate and evaluate various aspects that were discerned as important for deciding to cooperate with a 3PL service provider and maintain this cooperation in the long run. To extract the required outcomes, statistical analyses like categorical regression (CATREG) and MANOVA were utilized. Results: The demand for 3PL services was affected by 3PL service providers’ operational performance based on accuracy, reputation, and IT capabilities, while the customer firms’ intention for maintaining cooperation with 3PL service providers was affected by their reliability level, improved service efficiency, and trustworthiness. Conclusions: 3PL service providers should seek to improve the reputation, IT infrastructure, and accuracy of their firm's operations to have a continuous demand for their services. Apart from that, 3PL service providers to maintain the cooperation with their customers, need to enhance the levels of their services reliability and efficiency, while also creating a bond of trust with their existing customers. Full article

Background: The growth of e-commerce necessitates efficient logistics management to address rising last-mile delivery challenges. To overcome some of the last-mile delivery costs, parcel lockers as a delivery option, can be an alternative solution. This study presents the Capacitated Vehicle Routing Problem with Delivery Options (CVRPDO), which includes locker delivery. Methods: this problem is solved with An Adaptive Large Neighborhood Search (ALNS). The solution suggests some specific destroy and repair operators and integrates them with various selection schemes. The proposed method results are compared with the exact solution of the MIP model of the problem for validation. Results: Objective function values improved by 25%, 30%, 7%, 5%, and 6% for 1000, 800, 600, 400, and 200 customers, respectively, when using a 120-s ALNS runtime compared to the MIP model with a 3-h runtime. Conclusions: the CVRPDO problem involves creating a set of routes for ve-hicles that visit each customer at their delivery location or deliver their parcels to one of the lockers. These routes should respect the capacity of each vehicle and locker while minimizing the total routing costs and the number of utilized vehicles. The problem is resolved by ALNS algorithm, which outperformed the MIP model. Full article

Background: This study aims to explore how domestic entrepreneurs can benefit from the thriving global agribusiness by establishing themselves in agro-industrial segments that can best remunerate capital and labour. The ways in which domestic entrepreneurs in Brazil enter different segments of the agribusiness industry were assessed with specific attention to implications for the development of local agro-industrial capabilities. Methods: We assessed the current market share of domestic companies in relation to foreign multinationals in various segments of the sugar and ethanol supply chain in Brazil. Results: Foreign multinationals are market leaders in the fertilizers, machinery and trading segments (domestic companies market share is 20.3%, 33.3% and 42.9% in those segments respectively). However, Brazilian companies have achieved higher market share in segments such as plant breeding, sugarcane processing and farming (domestic market share is 93.2%, 67.4% and 75.5% respectively). Plant breeding, farming and trading benefit from governmental support in research, subsidized credits and market policies respectively. Conclusions: By investing in agro-industrial sectors developing countries can benefit from agribusiness expansion for their economic growth. Investments in science and technology and domestic regulatory actions can help to build country capabilities, although the impacts are sometimes limited to the agro-industrial sectors where domestic companies are more competitive. These lessons can help other developing countries to assess their opportunities and challenges for agro-industrial development. Full article

Background: By examining the recent advancements in technology, particularly the transformation of material into digital flows and its impact on customer demands, the aim of this paper is to investigate supply chain management (SCM) by analyzing seven key constructs: uncertainty, perspective, topicality, coordination, flow, job to be done, and connection. These constructs were chosen since they reflect evolving SCM practices that motivate this study. Methods: We conducted a broad, structured narrative review to comprehensively address the extensive SCM literature. This approach allowed us to evaluate the current state of SCM research and offer recommendations for overcoming prevailing challenges. Results: Our findings reveal the significant impact of technological advancements on SCM operations, requiring companies to adapt and remain competitive. We envision future supply chains as dynamic networks of networks, necessitating the adoption of a value architecture concept that extends a firm’s business model to an ecosystem business model. Conclusions: Considering these changes, our study recommends exploiting uncertainty, adopting demand-driven systems, offering on-demand customized services and products, utilizing prescriptive analytics, prioritizing information flows and services, and embracing open systems with high interoperability. Summarizing these opportunities and challenges that arise with changes in SCM provides interesting venues for future research and valuable insights for practitioners. Full article

Background: Family farmers’ access to markets is key for rural development. This study seeks to assess to what extent short and long marketing channels promote or inhibit the commercial inclusion of family farmers. Methods: The research was conducted in the Brazilian state of Goiás through questionnaires and interviews with rural outreach agents and family farmers’ leaders. Results: The results reveal that 31.28% of sampled farmers are not included in any marketing channel. High inclusion rates in long channels (such as commodity markets) are related to high inclusion rates in short channels (such as farmers’ markets), with some regions having greater availability of marketing channels than other regions. Conclusions: The high participation of family farmers in long channels linked to the cattle supply chain and agricultural commodities is related to the low participation of this category in other channels. Such results provide lessons for public policies by demonstrating the need to encourage a greater diversity of both short and long channels to greater marketing opportunities for family farmers. Full article

Background: This study presents a comprehensive methodology for enhancing humanitarian logistics planning and management in natural disasters, focusing on earthquakes. Methods: The innovative approach combines a deterministic mathematical model with a simulation model to address the problem from multiple perspectives, aiming to improve efficiency and equity in post-disaster supply distribution. In the deterministic modeling phase, optimal locations for humanitarian distribution centers and points in Popayan, Colombia, were identified, enabling efficient resource allocation for affected families. Subsequently, the simulation model evaluated scenarios based on real earthquakes in Colombia and Latin America, providing a comprehensive view of the logistics system’s response capacity to different disaster conditions and magnitudes. Results: The results demonstrated that the proposed methodology significantly reduced supply delivery time, achieving a 30% improvement compared to traditional humanitarian logistics approaches. Moreover, it led to a more equitable coverage of affected communities, with a 25% increase in families served in previously underserved areas. Expert validation from the Disaster Risk Management Committee of the study area confirmed the methodology’s usefulness for informed and effective decision-making in real situations. Conclusions: This integrated approach of mathematical modeling and discrete event simulation offers valuable insights to address disaster management and support decision-making in humanitarian crises. Full article

Background: The classical mathematical formulation of the vendor-managed inventory (VMI) model assumes an infinite planning horizon, and consequently, the solution derived ignored the impact of the first cycle. The classical formulation is associated with another implicit assumption that input parameters remain static indefinitely. Methods: This paper develops two mathematical models for VMI for a joint economic lot-sizing (JELS) policy. Each model considers investment in green production, energy used for keeping items in storage, and carbon emissions from production, storage, and transportation activities under the carbon cap-and-trade policy. The first model underlies the first cycle, while the second underlies subsequent cycles. Results: The re-start-up production time for subsequent cycles commences only at the time required to produce and replenish the first lot, which implies further cost reduction. Mathematical formulations are perceived as important both for academics and practitioners. For example, the base model of the first cycle (subsequent cycles) generates an optimal produced quantity with 18.42% (4.35%) less total system cost when compared with the pest scenario in favor of the existing literature. Moreover, such a percentage of total system cost reduction increases as the production rate increases. Further, the proposed models not only produce better results but also offer the opportunity to adjust the input parameters for subsequent cycles, where each cycle is independent from the previous one. Conclusions: The emissions generated by the system are very much related to the demand rate and the amount of investment in green production. Illustrative examples, special cases, model overview, and managerial insights are given. The discussion related to the contribution of the proposed model, the concluding remarks, and further research are also provided. The proposed model rectifies the base model adopted by the existing literature, which can be further extended to be implemented in several interesting further inquiries related to JELS inventory mathematical modeling. Full article