Post-disaster relief depends on the rapid clearance of road debris to restore accessibility for emergency response and relief distribution. However, debris demand, clearance efficiency and transport conditions are often uncertain, while available clearance personnel are limited and differ in attributes such as operating efficiency. To address these challenges, this study formulates a novel multiobjective personnel allocation problem for post-disaster debris clearance under uncertainty, considering heterogeneous workforce characteristics. Trapezoidal fuzzy numbers are used to represent uncertain parameters, and the objectives are to minimize the total clearance time, number of deployed personnel and operational cost. To solve the problem, a multiobjective adaptive large neighborhood search algorithm is developed. A simulated post-earthquake case study is used to evaluate the proposed model and solution approach. The results indicate that the proposed framework can provide effective decision support for generating robust and cost-efficient debris clearance plans under uncertain post-disaster conditions.
Industry 4.0 technologies are reshaping production and inventory management through real-time sensing and decision intelligence. Conventional inventory models, built on stochastic forecasts, ignore reverse logistics, carbon constraints, and energy structures, rendering them unsuitable for modern circular manufacturing. This study proposes a novel closed-loop deterministic inventory optimization model driven by IoT demand sensing, which captures real-time consumption data to eliminate forecast uncertainty and enable dynamic zero-stockout planning. The model jointly optimizes forward production and reverse remanufacturing flows, incorporating deterministic return-lag and recovery yield, renewable-grid energy allocation, carbon emissions pricing, ramping stability, and waste disposal costs. A linear trust-band mechanism preserves robustness against sensor noise by penalizing deviations from IoT measurements. The resulting scalable linear program supports real-time rolling-horizon execution. This work generalizes classical EOQ/EPQ and closed-loop formulations into a unified deterministic framework embedding sustainability, energy decarbonization, and digital supply-chain intelligence, offering actionable insights for smart, circular, low-carbon factory operations.
Consumers are increasingly demanding transparency in food origins and ethical production practices, yet the opaque nature of the Agri-Food Supply Chain (AFSC) hinders the confidence of consumer. The Blockchain Technology (BT) offers transformative solutions for food safety, ethical sourcing, and traceability with its decentralized and transparent framework. However, its adoption in the AFSC faces significant barriers. This study aims to identify the barriers of BT adaptation in the AFSC using literature survey and models them using the Fuzzy Total Interpretive Structural Modeling (F-TISM) and Fuzzy Matrice d'Impacts Crois & eacute;s Multiplication Appliqu & eacute;e & agrave; un Classement (F-MICMAC) to analyze the relationship between barriers and prioritize them based on influence and dependence. This study identifies the immature stage of development of BT and its complex system design as the primary barriers to implementation in the AFSC. This study helps policymakers, developers, and decision-makers overcome key barriers and enable successful blockchain integration in the AFSC. [GRAPHICS] .
This study examines whether the relationship between digital transformation and digital supply chains enhances sustainability. Given fragmented evidence and inconsistent findings in prior research, it aims to provide a clearer understanding of this linkage. A two-phase research design was adopted. First, a systematic literature review using the Scopus database explored how digital transformation contributes to supply chain sustainability. Second, a meta-analysis was conducted to evaluate whether digital transformation influences sustainability outcomes directly or indirectly. The findings indicate a significant positive relationship between digital transformation and sustainable supply chain performance. However, existing empirical studies report mixed results, limiting the ability to draw definitive conclusions. Although the evidence suggests that digital transformation can strengthen sustainability, current support remains largely theoretical and insufficiently validated. This study highlights the need for more robust empirical investigations from diverse perspectives to confirm the causal mechanisms and advance the development of sustainable digital supply chains in practice.
This research proposes a robust optimization model for a hybrid truck-drone delivery system under uncertain drone battery performance. The model introduces a one-to-one truck-drone pairing structure and formulates a hierarchical bi-objective mixed-integer linear program to (1) minimize the required fleet size and (2) minimize the total return time. A novel data-driven uncertainty modeling approach is employed, leveraging piecewise linear support vector clustering to formulate realistic uncertainty sets and mitigate over-conservatism. Findings from a real-world case study illustrate the model's ability to maintain solution quality under uncertainty. Eliminating drones increases both truck count and delivery time by over 23%, highlighting the operational benefits of drone integration. This work advances last-mile logistics research by combining machine learning and robust optimization to address performance-risk trade-offs in hybrid delivery systems.
Although Industry 4.0 provides opportunities to increase efficiency, optimize processes, and enhance sustainability, prior studies largely rely on maturity models and implementation frameworks focusing on discrete manufacturing, hindering the application of these frameworks to process-intensive industries such as cement. This study addressed this shortcoming by proposing a novel implementation-oriented, industry-specific framework tailored to the structural, technological, and operational characteristics of the cement industry. Using a systematic literature review based on the ProKnowC method, we identified that maturity models lack actionable pathways for transitioning toward structured technology deployment. Similarly, existing implementation frameworks do not adequately address the specificities and constraints of the cement industry, such as continuous production flows, high energy consumption, and legacy systems. This study contributes by proposing a novel framework that integrates maturity assessment concepts with implementation-oriented mechanisms adapted to continuous-process industries and provides practical guidelines. However, the framework remains untested, although testing scenarios are proposed.
Integrating sustainability is vital for the commercial furniture industry, yet business-to-business enterprises often struggle with fragmented value prioritization during green innovation. This study develops a synergy-based framework to evaluate value drivers using a hybrid approach integrating the Grey-Decision Making Trial and Evaluation Laboratory and the Analytic Network Process. Key insights include: (i) knowledge sharing, green brand image, and supply chain collaboration are top strategic priorities; and (ii) sustainable transformation is driven by the formal institutionalization of user demands through mandatory standards. Discussions highlight that collaborative knowledge sharing resolves inherent conflicts between production costs and aesthetic requirements. Consequently, manufacturers should shift toward proactive regulatory anticipation, while designers utilize quantitative assessment tools for empirical justification. The scientific value of this paper lies in providing a quantifiable decision-making model that bridges institutional theory and industrial design. Findings offer a tool for formulating data-driven sustainable strategies.
The COVID-19 pandemic and recent geopolitical disruptions have exposed the vulnerability of small and medium-sized enterprises (SMEs), highlighting the need for resilient and collaborative supply chain frameworks. This study presents an optimization model for a multi-SME, single-manufacturer system operating under stochastic demand and a (Q, R) inventory policy. Independent SMEs, located at varying distances from the manufacturer, deliver raw materials asynchronously but make an equal number of trips per production cycle to ensure sustained coordination. Each SME operates with its own production capacity and dispatch schedule governed by the manufacturer's replenishment plan. Two coordination strategies are examined: an integrated model that minimizes the total system cost and a decentralized Stackelberg approach. Numerical results show that the integrated strategy yields greater cost savings when the consumption of raw materials per unit of production is high. The findings provide theoretical and managerial insights for enhancing resilience in SME-driven supply chains.
A min-max-min two-stage robust optimization model is established to minimize the average daily total cost and configure the integrated photovoltaic (PV) storage and charging system for microgrid and the source and the load. Many studies are neglecting the potential of electric vehicles (EVs) to enhance the stability of microgrid systems by serving as dispatchable reserve power sources. The proposed model realizes multi-scenario application such as smoothing the fluctuation of PV output and participating in net load peak shaving. A hybrid supercapacitor-lithium-ion battery storage system is on the power side. This study considers the demand response mechanism guided by time-of-day tariffs to promote the balancing of the EVs' supply and demand. The improved snow goose optimization algorithm is used to the proposed model characteristics. The result shows that reasonably optimize the configuration of microgrid optical storage and charging capacity to guide the construction of microgrid green energy system.
This study proposes a sustainable joint economic lot size (JELS) model for tuna supply chains under stochastic demand. The model considers a multi-echelon network consisting of a processor, a distribution center, multiple retailers, and end customers. Its novelty lies in integrating battery-constrained electric vehicles and drone-based delivery with stochastic demand and variable lead time, aspects rarely addressed simultaneously in previous studies. Lead time is formulated as a function of production, transportation, loading and unloading, transit, charging, and courier delivery activities. To solve the proposed model, an algorithm is developed and validated through a real-world case study. The results show that electric motorcycles achieve annual cost savings of IDR 8.469 billion (80.61%) compared to drones. Conversely, drones reduce delivery time by 2.32 days (55.42%) but increase carbon emissions by 3.13 t.CO(2)e (2.65%). Sensitivity analysis identifies product weight, vehicle load capacity, speed, and transport distance as key factors affecting system performance.
Under carbon cap-and-trade, capital-constrained manufacturers can leverage carbon quotas via carbon quota pledge financing (CQPF) and carbon quota repurchase financing (CQRF). Using a Stackelberg game model, this study analyzes the optimal financing choice. Key findings reveal: 1) From both the profit and social welfare perspectives, the optimal financing approach is primarily determined by initial capital and financing costs, with CQPF preferred only when its financing cost is lower than that of CQRF. 2) From an environment perspective, financing is avoided at moderate R&D cost coefficient but becomes attractive beyond thresholds, with preference hinging on the R&D cost coefficient and financing-cost differences. 3) Higher average technology levels boost carbon emission reduction (CER) efforts, sales, and profits across all models, while greater technological volatility improves both economic and environmental outcomes. We also examine the effects of retailer risk aversion and two-manufacturer competition, providing new insights for carbon-financing strategies and related policy design.
This article addresses the parallel machine scheduling problem in an additive manufacturing environment to minimize makespan. This production scheduling problem involves both batching decisions and sequencing decisions, resulting in a complex combinatorial optimization problem. Given these characteristics, we propose an approximation algorithm that combines mathematical programming and heuristic techniques. At a high level, a batch subproblem is solved using a mixed-integer linear programming model, which serves as a warm start for the entire problem. Subsequently, a local branching procedure is applied to improve the first integer solution obtained in the previous step. We evaluate our approach using 126 test instances. Our solution is compared with four variants of an iterated local search algorithm, which represents the state-of-the-art approximate procedure for the problem. According to two performance indicators, our matheuristic approach outperforms all other algorithms in the comparison with statistical significance.
Post-harvest fish losses remain a persistent barrier to food security, profitability, and sustainability in tropical aquaculture supply chains. This study develops a novel fuzzy-integrated decision-support framework that incorporates both efficiency and stakeholder influence into the House of Risk 2 model, extending it into a multidimensional prioritization tool. The framework combines the Fuzzy Delphi Method to validate expert-driven mitigation options, Fuzzy Data Envelopment Analysis to measure strategy efficiency, and Fuzzy Interpretive Structural Modeling to quantify actor interdependence and influence, integrated through a fuzzy-modified House of Risk 2 model under a triangular fuzzy set environment. Eight mitigation strategies were validated, with capacity building, ice-use training, and market-facility improvement identified as the most effective and feasible. The model offers a data-driven approach for designing resource-efficient and institutionally coordinated mitigation strategies, highlighting the need for synergistic technical, infrastructural, and governance actions to achieve Sustainable Development Goal targets 12.3 and 14.7.
Production lines utilize robotic arm conveyor systems for sorting and packing items. The continuously moving conveyor belt requires fast decisions based on the current system state. The interplay between information availability, system layout configuration, and performance has not been explored, and is thus studied for a multi-conveyor system with strong sequence dependency. Introducing a probabilistic control scheme utilizing varying degrees of information about future system states, and a continuous time problem formulation, this study evaluates the performance of the real-time scheduling problem solution under different levels of information. Results from extensive testing show that increased future state information asymptotically reduces give-away and improves performance for the studied type of system. The findings highlight a relationship between information access, arm coverage configurations, and system performance. This research provides insights into systems of similar type and configuration, while also offering a framework for estimating performance under different constraints and objectives.
This paper presents an integrated framework that treats an ergonomic rotation strategy while jointly optimizing production planning and workforce conditions to ensure demand satisfaction under a service level constraint and to preserve operator well-being. The model accounts for ergonomics-driven variability in worker productivity across multi-severity workstations and adjusts rotations to balance workloads. Individual ergonomic scores, dynamically updated according to rotation decisions, guide task allocation to improve exposure distribution and align operator capabilities with production targets while minimizing costs. A comparative analysis shows that the ergonomic rotation strategy outperforms a random rotation baseline, achieving 0.22% reduction in total production cost and a 15% improvement in workload balance under identical conditions. Sensitivity analysis confirms the robustness of the framework across cost structures. The model is evaluated with two metaheuristics: Random Search and Genetic Algorithm. Results showed that the Genetic Algorithm yields a 1.54% lower total cost, demonstrating the framework's practical relevance.
This study introduces an integrated energy-aware Economic Production Quantity model with stochastic demand (EEPQ$_S$ S), incorporating four key factors: Poisson-distributed demand, imperfect production systems with defects, shortage handling, and energy consumption in both production and storage. The model jointly optimizes production rate and production time to maximize profit while reducing energy and defect-related costs. Given the complexity of the objective function, solution procedures based on Differential evolution, Genetic algorithm, and Grid search are applied. Numerical analysis confirms that EEPQ $_S$ S consistently outperforms traditional models, achieving the highest profit and significantly lowering energy costs through a controlled single production run strategy. Sensitivity analysis identifies energy price, equipment capacity, and environmental conditions as the most influential parameters, emphasizing the importance of energy efficiency, right-sizing, and quality control. The findings contribute to inventory optimization theory, offering practical guidance for enterprises seeking to enhance competitiveness, reduce energy consumption, and achieve sustainable production under volatile conditions.
The rapid adoption of electric vehicles (EVs) is essential for reducing air pollution and achieving environmental sustainability. However, the inefficient placement of electric vehicle charging stations (EVCSs) can limit accessibility, increase costs, and reduce overall system efficiency, thereby slowing EV adoption. This study addresses the complex challenge of optimally locating EVCSs by evaluating and ranking candidate sites using a comprehensive multi-criteria framework. To select the optimal site location, 66 criteria were considered, comprising 55 factors identified from the literature and 11 additional criteria proposed by experts. A novel hybrid multi-criteria decision-making (MCDM) method was introduced to prioritize candidate locations systematically. Additionally, fuzzy theory was applied to handle uncertainty in decision-making processes. The proposed MCDM was validated through a real-world case study. The results indicate that "Income Rate," "Greenhouse Gases Emission Reduction," and "Investment Pay-back Period" are the most influential factors in EVCS site selection.
