Search Results (9)

This study proposes a bi-objective optimization model for the inventory routing problem with pickup and delivery (IRP–PD) in a closed-loop supply chain, addressing the growing demand for sustainable logistics solutions. The model simultaneously minimizes transportation costs and inventory costs and enhances driver well-being by incorporating regular rest breaks. The network operates within a circular economy framework, where pallets are both delivered and returned for reuse, contributing to waste reduction. A normalized weighted-sum method is initially used to balance the conflicting objectives. However, since the model cannot efficiently solve large-scale instances, we adopt the NSGA-II metaheuristic to generate a Pareto front, enabling decision-makers to explore trade-offs between objectives. The model is tested on a single instance, and the results demonstrate a promising compromise between economic and social goals. Full article

This paper presents a joint optimization framework for multi-period planning in a Manufacturing–Remanufacturing–Transport Supply Chain (MRTSC), focusing on carbon emission reduction and economic efficiency. A novel Mixed Integer Linear Programming (MILP) model is developed to coordinate procurement, production, remanufacturing, transportation, and returns under environmental constraints, aligned with carbon tax policies and the Paris Agreement. To address uncertainty in future demand and the number of returned used products (NRUP), a two-stage approach combining forecasting and optimization is applied. Among several predictive methods evaluated, a hybrid SARIMA/VAR model is selected for its accuracy. The MILP model, implemented in CPLEX, generates optimal decisions based on these forecasts. A case study demonstrates notable improvements in cost efficiency and emission reduction over traditional approaches. The results show that the proposed model consistently maintained strong service levels through flexible planning and responsive transport scheduling, minimizing both unmet demand and inventory excesses throughout the planning horizon. Additionally, the findings indicate that carbon taxation caused a sharp drop in profit with only limited emission reductions, highlighting the need for parallel support for cleaner technologies and more integrated sustainability strategies. The analysis further reveals a clear trade-off between emission reduction and operational performance, as stricter carbon limits lead to lower profitability and service levels despite environmental gains. Full article

Freight mode choice and the resulting inventory implications significantly influence a product’s carbon footprint. This paper investigates mode selection under a voluntary carbon emissions constraint. Slower modes such as inland waterways and ocean freight are less expensive and emit less greenhouse gas (GHG), but they require higher inventory levels due to longer lead times. In contrast, faster modes like less-than-truckload (LTL) shipping reduce inventory needs but incur higher transportation costs and emissions. Mode choice thus involves trade-offs between transport cost, inventory holding, lead time uncertainty, and GHG emissions from transportation and warehousing. This paper develops a comprehensive inventory-transportation model under the stochastic demand and lead time to evaluate these trade-offs and guide sustainable freight decisions. The model is a practical toolbox that enables managers to evaluate how freight mode choice and inventory policy affect costs and emissions under different operational scenarios and carbon constraints. Full article

The integration of Artificial Intelligence (AI) into Supply-Chain Management (SCM) has revolutionized operations, offering avenues for enhanced efficiency and decision-making. AI has become pivotal in tackling various Supply-Chain Management challenges, notably enhancing demand forecasting precision and automating warehouse operations for improved efficiency and error reduction. However, a critical debate arises concerning the choice between less accurate explainable models and more accurate yet unexplainable models in Supply-Chain Management applications. This paper explores this debate within the context of various Supply-Chain Management challenges and proposes a methodology for developing models tailored to different Supply-Chain Management problems. Drawing from academic research and modelling, the paper discusses the applications of AI in demand forecasting, inventory optimization, warehouse automation, transportation management, supply chain planning, supplier management, quality control, risk management, and customer service. Additionally, it examines the trade-offs between model interpretability and accuracy, highlighting the need for a nuanced approach. The proposed methodology advocates for the development of explainable models for tasks where interpretability is crucial, such as risk management and supplier selection, while leveraging unexplainable models for tasks prioritizing accuracy, like demand forecasting and predictive maintenance. Through this approach, stakeholders gain insights into Supply-Chain Management processes, fostering better decision-making and accountability. Full article

We study an online distribution problem in which a producer has to send a load from an origin to a destination. At each time period before the deadline, they ask for transportation price quotes and have to decide to either accept or not accept the minimum offered price. If this price is not accepted, they have to pay a penalty cost, which may be the cost to ask for new quotes, the penalty cost for a late delivery, or the inventory cost to store the load for a certain duration. The aim is to minimize the sum of the transportation and the penalty costs. This problem has interesting real-world applications, given that transportation quotes can be obtained from professional websites nowadays. We show that the classical online algorithm used to solve the well-known Secretary problem is not able to provide, on average, effective solutions to our problem, given the trade-off between the transportation and the penalty costs. Therefore, we design two classes of online algorithms. The first class is based on a given time of acceptance, while the second is based on a given threshold price. We formally prove the competitive ratio of each algorithm, i.e., the worst-case performance of the online algorithm with respect to the optimal solution of the offline problem, in which all transportation prices are known at the beginning, rather than being revealed over time. The computational results show the algorithms’ performance on average and in the worst-case scenario when the transportation prices are generated on the basis of given probability distributions. Full article

Background: Agro-food supply chains possess specific characteristics due to the diverse nature of products involved and contribute to all three pillars of sustainability, making the optimal design of a sustainable agro-food supply chain a complex problem. Therefore, efficient models incorporating the unique characteristics of such chains are essential for making optimal supply chain decisions and achieving economically and environmentally sustainable agro-food supply chains that contribute to global food security. Methods: This article presents a multi-objective mixed-integer linear programing model that integrates agricultural-related strategic decisions into the tactical design of an agro-food supply chain. The model considers transportation, inventory, processing, demand fulfilment, and waste disposal decisions. It also accounts for seasonality and perishability, ensuring a comprehensive approach to sustainability. The model aims to maximize the total generated profits across the supply chain while simultaneously minimizing CO₂ emissions as a measure of environmental impact. Results: By implementing the model on a sugar beet supply chain in the Netherlands, strategic crop rotation farm schedules for the crop rotation cycle and the optimum supply network decisions are obtained. Furthermore, different objectives are analyzed and the Pareto-efficient frontier is investigated to analyze the underlying trade-offs. Additionally, the model serves as a decision support tool for managers facilitating informed investment decisions in technologies that prolong product shelf life while maintaining profitability. Conclusions: The proposed multi-objective model offers a valuable framework for designing economically and environmentally sustainable agro-food supply chains. By aligning with sustainability goals and providing decision support, this research contributes to enhancing global food security and promoting sustainable resource utilization. Full article

Cost-effective monitoring of forest carbon resources is critical to the development of national policies and enforcement of international agreements aimed at reducing carbon emissions and mitigating the impacts of climate change. While carbon monitoring systems are often based on national forest inventories (NFI) utilizing a large sample of field plots, in remote regions the lack of transportation infrastructure often requires heavier reliance on remote sensing technologies, such as airborne lidar. The challenge motivating our research is that the efficacy of estimating carbon with lidar varies across the various carbon pools within forest ecosystems. Lidar measurements are typically highly correlated with aboveground tree carbon but are less strongly correlated with other carbon pools, such as down woody materials (DWM) and soil. Field measurements are essential to both (1) estimate soil and DWM carbon directly and (2) develop regression models to estimate tree carbon indirectly using lidar. With limited budgets and time, however, decision makers must find an optimal way to combine field measurements with lidar to minimize standard errors in carbon estimates for the various pools. We introduce a multi-objective binary programming formulation that quantifies the tradeoffs behind the competing objectives of minimizing standard errors for tree carbon, DWM carbon, and soil carbon. Using NFI and airborne lidar data from a remote boreal forest region of interior Alaska, we demonstrate the operational feasibility of the method and suggest that it is generalizable to other carbon sampling projects because of its generic mathematical structure. Full article

Urban freight transport is essential for supporting our society regarding providing the daily needs of consumers and local businesses. In addition, it allows for the movement of goods, is distributed within urban environments, provides thousands of jobs, and supports economic growth. However, a number of issues are associated with urban freight transport, including environmental impacts, road congestion, and land use of freight facilities that conflicts with residential land use. Electric freight vehicles create zero emissions and provide a sustainable delivery system in comparison with conventional freight vehicles. In this study, a novel dynamic inventory routing and pricing problem under a mixed fleet of electric and conventional vehicles was formulated to minimize the total travel and charging costs. The proposed model is capable of deciding on replenishment times and amounts and vehicle routes. We aimed to determine the maximum social welfare (SW) capable of providing an optimal trade-off between the supplier cost and customer delay that uses a mixed fleet of vehicles. Our computational study was conducted on real data generated from a delivery dataset in Tehran. Under the proposed policy with a fleet of only electric vehicles, the SW increased by 3% while the average customer delay reduced by 15% compared with a fleet of conventional vehicles. The results show that the number of served customers and customer delay would be affected by transitioning conventional urban freight vehicles to electric vehicles. Therefore, the proposed delivery system has a significant impact on energy savings and emissions. Full article

In recent decades, due to market growth and use of synthetic fiber, the fashion industry faces a rapid increase of CO₂ emission throughout the production cycle and raises environmental issues in recovery processing. This study proposes a closed-loop supply chain (CLSC) structure in fashion industry and develops its planning model as multi-objective mixed integer linear programming to find an optimal trade-off between CLSC profit and CO₂ emission. The planning model is associated with the profit analysis of each member in CLSC to find the optimal price of products on CLSC network. The model determines optimal production, transportation, and inventory quantities on CLSC network. The proposed models are validated using numerical experiments and sensitivity analyses, and from the results some managerial insights are addressed. Full article