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• Offering a novel fuzzy robust approach for closed-loop supply chain network design. • Examining the hybrid uncertainties and flexibility of constraints in the problem. • Considering economic, responsibility, and environmental subjects in modeling. • Using the interactive fuzzy programming approach to solve the multi-objective model. • Introducing a new application for stone paper closed-loop supply chain network design. Nowadays, the production of stone paper, in addition to its widespread utilization in various fields, does not require water consumption, cutting down trees, and stone paper products are easily recyclable and recoverable. Due to the importance of developing and using stone paper and paying attention to environmental matters, Closed-Loop Supply Chain Network Design (CLSCND) is very important for stone paper products. Moreover, due to epistemic, randomness uncertainties, the uncertainties in Objective Function (OF), and the flexible constraints for CLSCND in the real world, this paper introduces a novel Mixed Robust Stochastic, Possibilistic, and Flexible Programming (MRSPFP) approach based on credibility theory. The different attitudes of the Decision-Makers (DMs) are addressed by a more flexible measurement of the optimistic and pessimistic parameters using the criterion of credibility. In the study, a comprehensive procedure is proposed for stone paper CLSCND, to minimize costs, increase responsiveness by minimizing transit time between different facilities, and regarding environmental concerns into account through minimizing carbon emissions. The model is solved utilizing an interactive fuzzy programming solution procedure and the Best-Worst Method (BWM). The results of sensitivity analysis, the effect of changing the problem parameters, and the performance of the proposed model are investigated and compared. The results show that the MRSPFP model has a better performance compared to other previous models. The MRSPFP model performs better in strategic decisions that require high investment costs due to the minimization of the absolute deviation of OF from its mean. Also, the applied results of the study show that CLSCND has a good capability and potential for sustainable development in the field of stone paper. [ABSTRACT FROM AUTHOR]

Load prediction is crucial to unit commitment and scheduling in electricity systems; however, load prediction models consume considerable electricity. Most conventional prediction methods are based on deep learning (DL) models, such as long short-term memory and gated recurrent units. The nonlinear activation functions and backpropagation mechanisms used in these methods result in high computational complexity and correspondingly high energy consumption. This paper proposes a prediction model based on a green learning (GL) framework aimed at reducing energy consumption. The proposed GL model replaces the activation functions conventionally used for feature extraction with a hybrid scheme combining categorical and numerical features, such as seasonal climate features and multi-autoregression terms. The proposed GL model also eliminates the backpropagation methods used to optimize hyperparameters by employing seasonal centroids and the quantile auto-regression forest algorithm for classification/regression. In a case study, the proposed GL model significantly outperformed conventional DL models in terms of energy consumption without compromising accuracy. The energy savings of the proposed GL model are equivalent to the annual electricity consumption of 211 ∼ 565 households, increasing with model penetration. This paper demonstrates the importance of energy-saving models in mitigating the linkage effect of industrial carbon emissions. It also explores policy implications for the implementation of GL models. [ABSTRACT FROM AUTHOR]

With global climate change posing a major threat to human society, a growing number of countries have taken "carbon-neutral" as a national strategy and proposed a vision of carbon-free future. As an important supplement to traditional fossil energy, renewable energy is the main force to reduce the use of high-carbon energy and carbon dioxide emissions, which will become the trend of social development in the future. Finding the optimal renewable energy source is of particular significance for achieving the net zero emissions. However, the existing evaluation methods of renewable energy sources have obvious shortcomings. In terms of weight calculation methods, such as the randomness of the subjective method is strong and the index weights do not reflect the small changes of the evaluation matrix, which affect the reliability and accuracy of the evaluation result. The existing ranking methods can only achieve the complete ranking of the different objects, but cannot classify the renewable energy technical alternatives into different grades. Given this background, this paper proposes a novel evaluation method for renewable energy plans based on improved sparrow search algorithm and projection pursuit model. Firstly, this paper improves the traditional sparrow search algorithm from three aspects: population initialization, population update and population variation. Then, the projection pursuit model is constructed, and the improved sparrow search algorithm is applied to optimize the projection target to find the optimal projection direction, so as to determine the weight values of each evaluation index. Finally, the weighted rank-sum ratio method is used to select the best renewable energy technical plan, which can not only realize the complete ranking of different plans, but also classify the technical plans into different levels. Based on the actual renewable energy development data from a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some existing evaluation methods of renewable energy technical plans. [ABSTRACT FROM AUTHOR]

The focus of this paper is on achieving a win-win situation regarding the economic, environmental, and social impacts of the cold chain logistics terminal distribution system. This paper proposes three multi-objective models to investigate the above effects by incorporating soft time windows, heterogeneous fleets, and path flexibility, with defining the objectives of examining logistics costs, fuel consumption, carbon emissions, quality damage to perishable commodities, and customer satisfaction using six evaluation functions. To solve the proposed models, an efficient optimization framework is developed by combining domain operators with versatile multi-objective evolutionary algorithms (MOEA) to obtain Pareto solutions. Extensive experiments are conducted to test the validity of the concerned model and algorithms. The results demonstrate that: (1) the proposed algorithm is effective in solving the proposed model; (2) the proposed multi-path strategy can effectively improve the performance of cold-chain logistics systems compared to single-path strategies; (3) evaluation functions that assess customer satisfaction greatly affect the performance of cold-chain logistics systems; and (4) the trade-off relationship between the objectives should be investigated to define the model. The paper also provides valuable managerial insights for improving the efficiency and sustainability of cold-chain logistics operations. [ABSTRACT FROM AUTHOR]

Under dual constraints of green production responsibility and recycling responsibility, the optimal contract between upstream and downstream firms is crucial to achieve low-carbon development, but information asymmetry poses a great challenge to the above contract design. Based on Stackelberg game theory, this paper develops a contract model in a supply chain with information asymmetry under dual environmental responsibility constraints, in which the producer invests in green technology while the retailer exerts green marketing efforts. The paper aims to explore how the producer designs optimal contracts under the different information structures to improve environmental performance while ensuring profitability. Results indicate that: (i) in the full information scenario, the two-part optimal contract makes the decentralized supply chain to achieve the same channel profit and carbon emissions as the centralized scenario. (ii) In the information asymmetry scenario, the producer should design differentiated contract configurations for different types of retailers to eliminate double marginalization. That is, the producer designs low wholesale price and high side payment for the high-type retailer, which enables the producer to earn high profit with low carbon emissions and makes the retailer to obtain additional information profit. While under some conditions, the producer considers the possibility of terminating cooperation with the retailer of low-type, as such a cutoff policy not only guarantees the profitability of the channel members but also improves environmental performance. Furthermore, the impact of some key factors on firms' decisions, profits, and equilibrium carbon emissions is summarized to guide management decisions. [ABSTRACT FROM AUTHOR]

Forest carbon sink project (FCSP) has received growing attention for its outstanding advantages in carbon emission reduction, environment protection, and achieving the carbon neutrality objective. Using a government-social capital cooperation model, FCSP helps to revitalize the ecological and economic benefits of the forest with standard and efficient management. However, the management and operation barriers of FCSP implementation are complex, multiple and indefinite in the long run, which requires a comprehensive barrier evaluation framework. Hence, this paper is devoted to constructing a barrier evaluation framework for FCSP implementation in China with an integrated multi-criteria decision-making (MCDM) method. To this end, critical barriers are identified and the criteria system is constructed with a two-stage systematic literature review and interaction with the experts. Later, an applicable and comprehensive barrier evaluation framework is built, wherein the interval type-2 fuzzy set (IT2FS) is used to process fuzziness and uncertain information, the best-worst method (BWM) is adopted to determine the criteria weight, and the Preference Ranking Organization Method for Enrichment Evaluations II (PROMETHEE II) is introduced for comprehensive barrier evaluation. The criteria weighting results indicate that FCSP implementation encounters challenges taking into account institutional and governance barriers (criteria weight: 0.3592), management barriers (0.2384), economic and market barriers (0.1637), knowledge barriers (0.0747), technical and infrastructure barriers (0.1639), C1 "Lack of clear leadership and policies" and C5 "Forest management performance" were determined the most significant criteria, A5 and A4 respectively performed the lowest and the highest barrier level. In sensitivity analysis, the ranking results were stable when the criteria changed, and Spearman's rank correlations of B2, B4, and B5 are significant. The results were validated by a comparative analysis with other methods with the help of Spearman's rank correlation coefficient, which reflects accepted high correlations (Spearman's pro values of BWM-IT2F-PROMETHEE II (Gaussian rule), BWM-IT2F-TOPSIS, and BWM-TFN-VIKOR are 1.00, 1.00, 1.00). The findings of the paper help the decision makers to develop more rational strategies and successfully implement the FCSP. [ABSTRACT FROM AUTHOR]

Traditional production systems have a sole focus of maximising economic outcomes but due to the world's ever-growing awareness about environmental issues, circular production systems, with a dual focus of maximising economic output and minimising environmental impact, have become prevalent in recent years. These types of production systems incorporate circular economy initiatives in their operational processes with the aim of reducing their carbon footprints. However, this is a voluntary measure to reduce carbon footprints and for this reason, various governments have introduced regulatory measures to involuntarily reduce carbon footprints of production systems. These regulatory measures include carbon tax, cap, cap-and-trade and cap-and-offset policies that are designed to reduce carbon emissions from production processes. With this in mind, this paper proposes an integrated sustainable production–inventory model for a three-echelon supply chain for imperfect quality deteriorating items with a circular economy indicator under various carbon emissions regulations. Using four different combinations of functions for both the demand rate and unit gross profit and four different carbon emissions regulations, 16 variants of the model are solved and analysed using heuristic algorithms. The model variants are not only aimed at optimising inventory replenishment decisions but also the circularity economy indicator of the deteriorating inventory. Of the 16 variants, the highest profit is achieved for the logistic demand and linear profit functions combination under cap-and-offset, despite a circularity indicator of 81.75%. The results also show that carbon emissions policies are effective at reducing the supply chain's carbon footprint, albeit with a slight impact on the profit. • Inventory model for a three-echelon supply chain of deteriorating and circular items. • Optimisation of circular economy indicator and inventory replenishment decisions. • Different carbon emissions policies, demand and unit profit functions are considered. • 16 variants of the model are proposed and solved via heuristic algorithms. • Emissions policies are effective at reducing the supply chain's carbon footprint. [ABSTRACT FROM AUTHOR]

This research introduces Automated Cost-Duration Variance Prediction (CDVP) system to empower project managers to proactively predict project budget, duration, cost variance and duration variance. The research involved the development and comparison of a total of 48 machine learning (ML) algorithms, with 12 algorithms dedicated to each predictive model. To conduct the study, a dataset comprising 1277 historical cases of Field Canals Improvement Projects (FCIPs) was utilized. Factor analysis and stepwise regression analysis were employed for feature selection where five key drivers were identified from the initial 25 parameters. The paper optimized the performance of each algorithm based on Bayesian optimization. The proposed system produces four outputs: project cost, project cost variance, project duration, and duration variance. The developed ML models were able to predict all of the system's outputs with an adjusted determination coefficient R2 score greater than 0.93 and Mean Absolute Percentage Error (MAPE) less than 8.97. Environmentally, this research recommends to assess CO 2 emissions as a performance criterion to select the low environmental impact algorithms. As a result, Light Gradient Boosting Machine (LightGBM) and AdaBoost Regressor were green sustainable algorithms that emitted on average 58 and 69 CO 2e grams per computational hours. To enhance interpretability, the study employed the SHapley Additive exPlanations (SHAP) technique to provide explanations for the key drivers. [ABSTRACT FROM AUTHOR]

Population aging and a shortage of medical resources are driving the growth of home health care (HHC). Many HHC studies have focused on developing an optimal routing plan to reduce operation costs; however, few have addressed the attendant long-term sustainability concerns, such as HHC center locations and carbon emissions from transportation. This paper proposes a home health care location-routing problem with a mixed fleet of electric and conventional vehicles that considers battery swapping stations. It aims to simultaneously determine the locations of HHC centers, the scheduling of caregivers with respect to skill requirements, and a routing plan for a mixed fleet under specific time windows, load capacities, synchronized visits, and driving ranges. To address this problem, the paper proposes a novel competitive simulated annealing (CSA) algorithm in which a series of problem-specific effective local search operators expand the solution space of the CSA algorithm, with a competitive mechanism to adaptively adjust these operators to accelerate convergence speed and improve exploration ability. To enhance the exploitation ability, it employs a modified simulated annealing algorithm with a heating strategy and variable neighborhood descent. Comparison experiments are conducted to verify the superior performance of the CSA algorithm in comparison to existing algorithms and the Gurobi solver. Finally, sensitivity analyses are performed on key factors and provide some new managerial insights. [ABSTRACT FROM AUTHOR]

With the implementation of the carbon tax policy, the carbon emission reduction (CER) actions taken by leading enterprises in energy-intensive industries will significantly influence the CER decisions of their following firms. To delve into the boundary conditions of leading and following enterprises' CER strategies, this paper constructs three tailored CER decision-making game models, specifically targeting leading and following enterprises in these industries. These models are designed to compare and analyze the impact of carbon tax on various CER decision modes. The study mainly derives: (1) Carbon tax is not always conducive to boosting the carbon reduction efforts of both corporations. Only when the carbon tax is smaller than a certain threshold will the carbon abatement efforts of leading and following corporations be proportional to the tax. Moreover, only when the lead firm abates its carbon emission does it contribute to the degree of carbon abatement effort. (2) The overall profitability of the supply chain (SC) is optimized when only the lead company reduces its carbon emissions. In this scenario, not only does the lead corporation's carbon reduction efforts increase, but the lead corporation and the followers also realize a win–win profit. (3) The model with the highest carbon abatement effort is not necessarily conducive to the environment. When the unit product carbon abatement emissions of the two corporations are within a certain threshold interval, the ambient impact of carbon abatement by only the leading firm is greater than that of no abatement by both corporations. The effect on the environment is minimized when both corporations make CER. [ABSTRACT FROM AUTHOR]

The development of data science and sensing technologies provides opportunities to mitigate uncertainty in hybrid manufacturing–remanufacturing systems, and drives manufacturers to invest in intelligent manufacturing (IM). This paper considers the adoption of IM to mitigate classification errors in the end-of-use (EoU) products' acquisition while improving the operational efficiency of manufacturing–remanufacturing systems. By constructing game models, we jointly investigate the investment, pricing, production and order quantities, and collection decisions in a closed-loop supply chain. Interestingly, we find that IM does not always increase manufacturers' enthusiasm to engage in remanufacturing, firms' profits or consumer surplus. It does not always reduce environmental impact either. Analytical results show that IM reduces manufacturers' enthusiasm to engage in remanufacturing if the initial core misclassification rate is low. Furthermore, we identify key factors that impact the firms' profits, consumer surplus, and carbon emissions. For a manufacturer producing high-cost new products, investing in IM hurts its profits when the remanufacturing cost savings due to intelligent manufacturing is small and the manufacturing benefit due to IM is not significant. Moreover, investing in IM reduces consumer surplus if the cost of new products is high, and the IM does not lead to significant remanufacturing cost saving and manufacturing benefit increase. The results reveal that investing in IM is preferred from the environmental point of view if the IM-induced manufacturing cost saving is small. Our research provides management insights into the intelligent transformation of manufacturers with hybrid manufacturing–remanufacturing systems, as well as policymakers concerned with value creation for customers and environmental impacts. • We incorporate discrete probability distributions of misclassification into models. • Assume investing in IM mitigates misclassification while reducing operational costs. • We investigate optimal strategies in both scenarios respectively with and without IM. • We study the impacts of IM on firms profits, consumer surplus and environmental. • Lower misclassification rates reduce the willingness to engage in remanufacturing. [ABSTRACT FROM AUTHOR]

This paper aims to propose a novel framework for designing data-driven strategies for online problems, which utilizes historical data to enhance the actual execution effect of online strategies. It integrates the concepts of competitive ratio and competitive difference to form a comprehensive competitive power concept. Using this framework, a threat-based strategies, T B S λ (threat-based strategy with preference λ) is proposed as an optimal strategy in the sense of competitive power for the one-way trading problem, and a data-driven online strategy D D O S (data-driven online strategy) is further proposed. By the discussion of special cases, it is found that T B S 0 , which is based on competitive difference, is quite different from C D A (competitive difference analysis) in former literature but is also an optimal strategy for the one-way trading in the sense of competitive difference. It is further found that strategy T B S 0 performs more aggressively than strategy T B S 1 which is based on competitive ratio through special case analysis. Additionally, numerical experiments based on China's carbon emission trading markets are presented to further validate the proposed online strategy. The results demonstrate the superiority of D D O S over the ones based solely on competitive ratio or competitive difference. • A new competitive index called competitive power. • Data-driven online strategy for one-way trading. • A framework for data-driven online strategies based on competitive power. [ABSTRACT FROM AUTHOR]

The platform is a comprehensive digital technology carrier that can facilitate the digitization of sales and make it possible to directly connect farmers and consumers. Multi-owner collaboration in logistics may considerably improve efficiency and sustainability, which is crucial for agricultural logistics. This paper introduces a logistics collaboration mechanism for agricultural products of 'Farmer + Consumer Integration Purchase' based on platform information openness and resource sharing. First, the Multi-owner Collaborative Vehicle Routing Problem with Heterogeneous Fleets and Time Windows (MOHFCVRPTW) model was designed. Second, the developed solution is the Adaptive Large Neighborhood Search Heuristic Algorithm (ALNS), which can solve 2 n -1(n is the number of enterprises) sub-alliances corresponding to the collaborative problem at once and produce the scheme and cost. Third, the Shapley value method is used to design a scientific and reasonable cost-sharing mechanism to allocate the total cost of the alliance to each logistics enterprise. Finally, the validity of the mechanism and the model are verified in the context of real logistics networks in Qingdao and Shanghai, and the profit of farmers is quantified by considering the cost of loss of agricultural products. The research found that the logistics collaboration mechanism has achieved cost savings for logistics enterprises, reduced carbon emissions, and improved transportation timeliness. At the same time, losses on agricultural products have been significantly reduced, and farmers' profits have increased. In addition, with the expansion of the scale of the alliance, the increase in the diversity of transport vehicles, and the improvement in the distribution aggregation degree of farmers, the benefits of the collaborative operation mode are more significant. The platform enables the exchange matching and collaborative transportation of agricultural products rapidly and has significant sustainable benefits, which has guiding significance for the practical development of 'Farmer + Consumer Integration Purchase'. [ABSTRACT FROM AUTHOR]

Channels and locks are crucial resources for ships of inland waterways. This paper addresses the ship scheduling problem based on channel-lock coordination, where ships entering the channel are affected by water discharge during flood season. A ship scheduling optimization model is formulated to minimize the total waiting time of ships at the anchorage. The model takes into account the decision-making of the departure time of ships, the change of water discharge in different scenarios, and the coordinated allocation of channels and locks. A dynamic multi-population particle swarm optimization algorithm with recombined learning and hybrid mutation (DMPSO-RH) is proposed to solve the model of ship scheduling. Numerical experiments conducted for the Three Gorges Dam (TGD) demonstrate that the DMPSO-RH can obtain nearly optimal solutions for practical large-sized ship scheduling problems within a reasonable time. By optimizing the departure time and sequence of ships, the scheduling scheme generated by the DMPSO-RH can effectively match the channel operation time and the lockage starting time, which helps to enhance the coordination of channels and locks at the TGD in different scenarios. Due to these improvements, the total waiting time of ships in the two scenarios is reduced by 29.08% and 28.59%, respectively, and the carbon emissions of ships are reduced accordingly. This approach can avoid navigation risks and improve the efficiency of ship scheduling in flood season. Moreover, the result of this study is very useful to reduce the waiting time and improve the environmental pollution of ships at the TGD. [ABSTRACT FROM AUTHOR]

• A multi-attribute group decision method based on the interval-valued intuitionistic fuzzy set is proposed. • A sustainability evaluation of an agricultural water-land-energy-carbon nexus is provided. • Multidimensional indicators are combined through stability, coordinativity and connectivity. • The water, land, energy and carbon subsystems of the agricultural system are quantified based on dual uncertainty. • The model is shown to solve real-world relationship management problems. The interactions between agricultural subsystems, resource scarcity and ecological degradation increase the need for agricultural sustainability assessments in relation to water, land, energy and carbon emissions. In addition, uncertainties caused by natural factors and human activities complicate sustainability assessments. Therefore, this study proposes a regional agricultural sustainability assessment method based on the water-land-energy-carbon (WLEC) nexus system under high uncertainty. This method couples the interval-valued intuitionistic fuzzy set (IVIFS) and multi-attribute group decision making (MAGDM) methods to quantitatively characterise and assess element management and corresponding relationships within the agricultural WLEC nexus system in a multidimensional manner. The paper is able to (1) clarify the role of the coupled agricultural "water-land-energy-carbon" nexus and (2) address the double ambiguity and uncertainty in the indicators and more objectively reflect the interval fluctuations of natural resources and social activities in both positive and negative scenarios. (3) Sustainability management of agricultural resources is carried out according to different regional conditions. The method was applied to Heilongjiang Province in northeast China, and the coupling of agricultural WLEC nexus system in 13 administrative regions of the study area was quantitatively analysed. The degree of agroecological sustainability from a multidimensional perspective is higher in Daxinganling and lower in the provincial capital, Harbin. The recommended range of groundwater use in the study area is [16%,28%] to ensure ecological sustainability when total agricultural output fluctuates between 285,395.18 and 404,495.33 billion yuan. This methodology helps policy makers understand the overall dynamics of agricultural sustainability in an environment of multiple uncertainties and gives suggestions for better assessing management. [ABSTRACT FROM AUTHOR]

In recent decades, global climate change has become one of the most critical environmental issues, leading to increased environmental and social concerns about the sustainability of logistics networks. This study proposes a novel multi-objective, multi-facility location–allocation model for a two-stage solid logistics network that supports green practices. To account for uncertainty in real-world problems, some parameters of the model are represented as triangular type-2 neutrosophic numbers to deal with hesitant situations. The fuzzy model is then converted into a deterministic one using a new ranking approach. Three conflicting objective functions – minimizing total financial costs, maximizing customers' satisfaction levels, and ensuring sustainable and effective conveyances – are simultaneously optimized using the ϵ -constraint approach. The proposed model and solution approach are tested with two numerical examples. A comparative study is also presented, comparing the proposed approach with other Pareto-based multi-objective approaches, such as the global criterion approach, multi-objective fuzzy programming, and weighted Chebychev programming. Finally, the paper concludes with some remarks. • A model integrating location–allocation, two-stage, and solid logistics problems. • Analyzed mixed-integer non-linear multi-objective model under two-fold uncertainty. • De-neutrosophic based on a novel ranking function for sustainable decision-making. • Conducted a comparative analysis of the proposed model against existing approaches. • Demonstrated the impact of carbon emissions constraint with two applications. [ABSTRACT FROM AUTHOR]

Decarbonizing innovation plays a significant role in curbing carbon emissions, allowing a firm to meet governmental environmental regulations and gain a competitive edge. However, innovation uncertainty and technology spillovers deter a firm from doing so. This paper studies the issue of firms' decarbonizing innovation investment in a competing market under carbon emission regulation. We construct game-theoretic models of two competing supply chains, each consisting of a manufacturer and a retailer. Either manufacturer has the opportunity to invest in decarbonizing innovation, but she may fail in the end. According to the manufacturers' innovation investment decisions, several subcases with no investment, unilateral decarbonizing investment, and bilateral decarbonizing investment are explored. The results show that when both manufacturers have a strong absorbing capacity, neither invests in decarbonizing innovation; when they are weak in absorbing external technology, bilateral decarbonizing investment exists. The findings also reveal that unilateral decarboning investment dominates when the probability of innovation success is moderate. There is a case in which both manufacturers do not conduct decarbonizing investment, although the probability of innovation success is high. In addition, we also find that a retailer can make more profit if his upstream manufacturer invests in decarbonizing innovation than in the no-investment scenario. However, he is harmed by bilateral decarbonizing investment if his upstream manufacturer is weak in absorbing external knowledge relative to the case of unilateral decarbonizing investment. Our results elucidated how technology spillovers and the stochastic nature of innovation affect a manufacturer's decarbonizing innovation investment strategy in a competitive environment. [ABSTRACT FROM AUTHOR]

In this paper, we present a multi-objective low-carbon multimodal transportation planning problem with fuzzy demand and fuzzy time (MOLCMTPP-FDFT) that minimizes both cost and time while incorporating mandatory carbon emission, carbon tax, carbon trading, and carbon offset policies. Chance constrained programming and interval programming are introduced to formulate the fuzzy demand chance constrained programming model and the fuzzy time interval programming model, respectively. Then, the mathematical model of uncertain MOLCMTPP-FDFT is transformed into a deterministic model. Based on the sparrow search algorithm, t distribution and the concept of Pareto optimality for multi-objective optimization, we also propose a solution strategy for the proposed model. In this algorithm, the number of iterations is used as the degree of freedom of - to update the sparrow location, which strikes a balance between the capabilities of global search and local search. Finally, the proposed algorithm and MOLCMTPP-FDFT are applied to a real case, resulting in a minimum cost of 260730.48 and a time duration of 13.044, which outperform the minimum cost of 268874.88 and minimum time of 18.32 obtained using single-mode transportation. The carbon emissions resulting from the lowest-cost solution obtained using single-mode transportation are 3,198.48, which are significantly more than the allowed emissions. Therefore, the proposed algorithm and mathematical model of MOLCMTPP-FDFT are valuable tools for optimizing multimodal transportation route. Additionally, the experimental results not only validate the superior efficiency and energy-saving benefits of the proposed multimodal transportation routes in comparison to the actual single-modal transportation, but also demonstrate the applicability of different low-carbon policies. [ABSTRACT FROM AUTHOR]

• Employs non-identical parallel machines in manufacturing with multiple energy sources. • Designs a bi-objective model for a high-mix, low-volume production scheduling problem. • Provides Pareto-optimal solutions that minimize both total cost and carbon emissions. • Validates a model with real-world application to a manufacturer in Ulsan, South Korea. • Analyzes the effects of price fluctuations, different combinations of energy sources. This paper addresses the production scheduling problem with non-identical parallel machines in a high-mix, low-volume production environment with due dates, considering carbon emissions savings and diverse types of energy sources for machine operation. To this end, we present a bi-objective mixed-integer programming model that minimizes both the total production-related cost and the total carbon emissions in the production process to determine the optimal production strategy. For each machine, we consider various types of energy sources with different electricity generation costs and different amounts of carbon emissions. The proposed model is validated with an application to a manufacturer in Ulsan, the industrial capital of the Republic of Korea. Our results showcase the potential use of non-identical parallel machines to minimize the total cost and carbon emissions in green manufacturing. Furthermore, the results identify the trade-off between different energy sources and the total cost under different carbon emissions limits. Generally, as the carbon emissions limit increases, our proposed model tends to replace natural gas with coal to minimize the total cost. Also, we perform sensitivity analyses with respect to the energy price of different types of energy sources combined with nuclear and renewable energy sources. We find that coal provides more stability than natural gas in terms of the total cost, particularly when there are fluctuations in the price for coal and natural gas. Additionally, we determine the optimal combination of energy sources for various carbon emissions limits, aiming to minimize the total cost while simultaneously satisfying all production-related and environmental constraints. [ABSTRACT FROM AUTHOR]

Grey system theory uses differential equations to model small sample time series to predict the short-term development law of things in the future. Since the most classical GM(1, 1) model was proposed, many scholars have improved it and derived various grey models that can predict linear or nonlinear time series. However, the existing grey models are still not accurate enough for constructing background values and parameter estimation methods. In order to improve the accuracy and applicability of the grey model, a novel neural grey model with accumulated time delays is proposed in this paper. In this model, the whitening equation of the grey model is constructed by combining the long short-term memory network to add time-delay variables to the original grey model. Second, the neural ordinary differential equation (NODE) is used to train the model to determine the time delays, the time-delay weights, and the model parameters to obtain better predictions. Finally, the Euler method is used to get the final prediction series. The new model's performance is tested through experiments on energy consumption and CO2 emissions prediction. The results were compared with some grey models, support vector regression (SVR), and autoregressive integrated moving average models (ARIMA). It can be concluded that the new model has better generalization performance and higher prediction accuracy than the selected comparative models. • A novel time-delay neural grey model is proposed combined with LSTM networks. • The model determines the time-delay variables and parameters by network training. • The model outperforms part of the grey models, SVR and ARIMA in the case studies. [ABSTRACT FROM AUTHOR]

[Display omitted] The tourism industry is a key driver of economic growth and contributes to the achievement of sustainability goals. This paper presents a multi-objective group tourist planning problem that considers economic, environmental, and social dimensions simultaneously. The proposed model minimizes total cost and environmental impacts while maximizing the total collected prizes from tourists' interests. We introduce the lost profit opportunity for the cost of tours from an economic perspective for the first time. From an environmental perspective, the model minimizes both carbon emissions for transportation and the waste produced by tourists. Social satisfaction is addressed by considering tourists' preferences for visiting tourist sites and their interests in participating in group tours, maximizing total collected prizes. Uncertainties in travel time and prize values are addressed by using a fuzzy programming approach. A multi-objective adaptive large neighborhood search (ALNS) algorithm is developed to solve the proposed multi-objective group tourist planning problem, offering various removal, insertion, and local search heuristic procedures. Extensive analyses and computations are conducted to demonstrate the performance of the proposed multi-objective optimization model and the ALNS metaheuristic algorithm in solving large-scale instances. The results demonstrate the effectiveness of our approach in aiding tourism managers to make informed decisions that balance economic, environmental, and social objectives. [ABSTRACT FROM AUTHOR]

Most previous studies on the scheduling problem in remanufacturing systems have focused on single or two production stages and economic criteria such as makespan or tardiness. However, environmental criteria like energy consumption or carbon emissions, as well as the lot-streaming production mode, have not been adequately addressed. This paper presents a novel approach to the energy-efficient remanufacturing system scheduling problem with lot-streaming production mode (ERSSP-LS) for the first time, which integrates disassembly, reprocessing, and reassembly three pivotal production stages. Firstly, a multi-objective mathematical model aimed at simultaneously minimizing the total energy consumption (TEC) and makespan (C max) is established and presented. To further reduce the TEC, the well-accepted energy-saving measure, known as the turn off and on strategy, is also integrated. Subsequently, a hybrid multi-objective optimization algorithm called HMOFFO, which combines the fruit fly optimization algorithm and simulated annealing mechanism is developed to seek the promising Pareto solution set. Finally, several experiments on a real-life case and a group of ten random test instances are carried out and the computational results affirm the feasibility and effectiveness of the proposed HMOFFO in solving the ERSSP-LS. [ABSTRACT FROM AUTHOR]

This paper analyses a new hybrid paradigm resulting from the integration of unmanned aerial vehicles (UAV), commonly referred to as drones, in logistics and distribution processes. This work is motivated by a real application, where the company Connect Robotics, the first drone delivery provider in Portugal, made a partnership with a pharmacy located at a rural region to start implementing the delivery of medicines by drone. The pharmacy receives orders throughout the day and has to deliver in the same day with tight lead-times. The resulting problem is modelled as a Dynamic Parallel Drone Scheduling Vehicle Routing Problem with Lead-Time. A solution method is devised to solve it, thus helping the pharmacist to plan the car and drone delivery routes during the day. The results obtained on real instances revealed that the solution method is effective when compared to the optimal solutions of the static version of the problem, since the dynamic solution only differs, on average, about 7% from the static one. Moreover, some managerial insights about the impact of adding drones to the distribution operation are discussed, namely the economic and environmental impacts with cost savings up to 41% and reduction of monthly CO 2 emissions of 310 kg, the use of spare batteries which increase the benefit from 16% to 41%, and same-day versus next-day delivery. [ABSTRACT FROM AUTHOR]

Carbon emission reduction has become increasingly important for limiting global warming throughout the world. Cap-and-trade regulation as an effective market-based mechanism is adopted to motivate firms to invest in decarbonization technologies toward sustainable supply chains. In practice, sustainable supply chains operate in the long term and decarbonization investment decisions could evolve as time passes. Distinguished from previous research, this paper formulates an evolutionary game model to study the investment decisions of suppliers and manufacturers under cap-and-trade regulation. We first analyze the strategic stability of suppliers and manufacturers and subsequently discuss the evolutionary stable strategies (ESSs) of the game with related conditions. The free-rider problem might occur in the investment game, and discourages one who would have invested from making efforts into carbon emission reduction. As a result, we explore the impact of key parameters on the ESSs for reducing free-riding behaviors. Numerical studies are conducted to simulate the evolution and stability of the game. The results show that revenue-related coefficients have a great impact on free-riding behaviors and cap-and-trade-related parameters affect their willingness to invest in carbon emission reduction. Based on findings and observations, some useful managerial implications are provided for regulators to make policies. [ABSTRACT FROM AUTHOR]

Multi-objective optimal active dispatching (MO-OAD) is a high-profile topic in power grid optimizations. Traditional methods are insufficient for the MO-OAD problem due to its nonconvexity and strict constraints. Therefore, an improved multi-objective gravitational search algorithm (IMGSA) with better population diversity and search capability is put forward in this paper. Five MO-OAD experiments considering various user requirements demonstrate that the IMGSA achieves preferable Pareto-front (PF) over the non-dominated sorting genetic algorithm-II (NSGA-II) and other recently published algorithms. More importantly, to further explore potential elite schemes after obtaining the best compromise scheme (BCS) of IMGSA, an efficient power flow prediction model based on the radial basis function (RBF) network is proposed for the first time. By accurately mapping the nonlinear relationship between the control variables of MO-OAD and optimization objectives, more than five elite dispatching schemes are determined by the suggested RBF prediction model. These elite schemes greatly reduce the fuel cost, power loss and exhaust emission of different IEEE power systems. Quantitative evaluations such as generational distance (GD) and hyper-volume (HV) indicators prove that the proposed IMGSA with RBF prediction model (IMGSA-RBF) is more competitive than many existing algorithms. Detailed MO-OAD experiments show that IMGSA-RBF method has significant advantages in PF-uniformity, PF-diversity, and the quality of optimal dispatching schemes. In general, the innovative IMGSA-RBF method provides a valuable technology to realize desirable power grid operation with less carbon emission and better economy. [ABSTRACT FROM AUTHOR]

In urban areas, delivery of the products necessary to survive to the desired points has vital importance, and public distribution centers (PDCs) are one of the most important components of this system. In this paper, we present a hybrid method to analyze the locations of F&VMHs which is one of the PDCs types. Firstly, the alternative numbers have been decreased by interval-valued trapezoidal fuzzy grey relational analysis (IVTFGRA) method. Secondly, we develop a binary fuzzy goal programming (BFGP) model with fuzzy parameters to determine the most appropriate F&VMH locations by taking into consideration costs, arrival times and CO 2 emission goals. Then, the BFGP model is converted to standard FGP model. In the last stage, the constraints with fuzzy parameters are transformed to crisp constraints with Peidro et al. (2010) 's methodology. The results show that current F&VMHs need to be moved to the locations that are more cost effective and out of the city center. [ABSTRACT FROM AUTHOR]

• Developing a novel optimization model for a closed-loop supply chain network. • Applying the model for a cellphone network in the Peel region, Ontario, Canada. • Extending the model to a multi-objective one by adding two new objectives. • Using a fuzzy QFD method to rank the qualitative factors in the third objective. • Finding the efficient solutions by the weighted-sums and ε -constraint methods. A Closed-Loop Supply Chain Network (CLSCN) includes forward and reverse supply chains. In this research, a dynamic cellphone closed-loop supply chain is considered, which contains multiple plants, retailers, demand markets, and collection centers. The returned cellphones are significant parts of electronic waste, and there is an increasing demand for this product due to the technology advancement and population growth. In this investigation, a Mixed-Integer Linear Programming (MILP) model is proposed and solved with the GAMS software to configure a CLSCN in the Peel Region in Ontario, Canada. The results of the optimization model show which facilities should be open for minimization of the total cost of the network. Then, the model is extended to a multi-objective model by adding two objectives considering CO 2 emissions and the quality aspects of the CLSCN. To handle the qualitative factors, a fuzzy Quality Function Deployment (QFD) model is discussed in this paper. Then, weighted-sums and ε -constraint methods are utilized to detect efficient solutions for the CLSCN. Finally, the results are discussed and the conclusions are provided. Based on the results, the weights of the objective functions have significant effects on both the selection of the facilities and the flows of the products between them. [ABSTRACT FROM AUTHOR]

• A sustainable supply chain (SSC) under the cap-and-trade regulation is studied. • Double-level sustainability effort is proposed in the SSC. • Effects of double-level sustainability effort on the SSC performance are explored. • Optimal levels of sustainability, quality, and green effort are determined. • A compensation-based wholesale price contract is developed to coordinate the SSC. In recent decades, due to the growing sustainable development concerns, the supply chains members, such as manufacturers and retailers, are interested in investing in environmental sustainability and social sustainability issues to gain more market share in today's competitive business environment. This paper contributes to the literature by proposing double-level sustainability effort in a sustainable supply chain consists of one manufacturer and one retailer under the cap-and-trade regulation. The manufacturer invests in the technology to reduce carbon emissions and improve the quality of products. On the other hand, the retailer invests in green effort while decides on its periodic review order-up-to-level replenishment policy. Under such a case, both the manufacturer's and the retailer's decisions not only affect their individual profitability but also impact on the whole supply chain performance and sustainability level. Using game-theoretic mathematical modeling, the double-level sustainability effort is first analyzed under the decentralized and centralized structures. Then, a compensation-based wholesale price contract is introduced to coordinate the sustainable supply chain under the cap-and-trade regulation. The surplus profit obtained under the coordination plan is shared between the manufacturer and retailer depending on their bargaining power. The data generated based on the assumptions of a pharmaceutical case study is applied to indicate the applicability of developed models. The results illustrate that the developed coordinated system simultaneously enhances the whole sustainable supply chain performance from the social, environmental, and economic points of view. [ABSTRACT FROM AUTHOR]

• The sequence model of energy-efficient DAPFSP is first established. • A heuristic and a hybrid method are presented to generate initial solutions. • A multidimensional probabilistic model is designed to learn the promising patterns. • A critical path-based local search is used to improve the quality of solutions. • Two speed adjustment strategies are proposed to enhance the performance of MCEDA. In this paper, a matrix-cube-based estimation of distribution algorithm (MCEDA) is proposed to solve the energy-efficient distributed assembly permutation flow-shop scheduling problem (EE_DAPFSP) that minimizes both the maximum completion time (C max) and the total carbon emission (TCE) simultaneously. Firstly, a high-quality and diverse initial population is constructed via a hybrid initialization method. Secondly, a matrix-cube-based probabilistic model and its update mechanism are designed to appropriately accumulate the valuable pattern information from superior solutions. Thirdly, a suitable sampling strategy is developed to sample the probabilistic model to generate a new population per generation, so as to guide the search direction toward promising regions in solution space. Fourthly, a problem-dependent neighborhood search based on critical path is provided to perform an in-depth local search around the promising regions found by the global search. Fifthly, two types of speed adjustment strategies based on problem properties are also embedded to further improve the quality of the obtained solutions. Sixthly, the influence of the parameters is investigated based on the multi-factor analysis of variance of Design-of-Experiments. Finally, extensive experiments and comprehensive comparisons with several recent state-of-the-art multi-objective algorithms are carried out based on the well-known benchmark instances, and the statistical results demonstrate the efficiency and effectiveness of the proposed MCEDA in addressing the EE_DAPFSP. [ABSTRACT FROM AUTHOR]

• Our model considers timetabling and passenger assignment simultaneously. • The original nonlinear model is converted to a MILP model which is tractable. • Two families of valid inequalities are put forward to accelerate the solution process. • A customized genetic algorithm is designed to cope with the large-scale problem. • Real case study is conducted, the results verified the applicability of our approach. Autonomous bus (AB), as a major component of public transport systems in the future, are expected to positively impact safety, congestion, private car ownership, energy consumptions, and CO 2 emissions. The adoption of ABs will inevitably bring a magnitude of changes and revolutions for the traditional bus operations. This paper investigates the AB timetable synchronization problem (AB-TSP), which involves both the AB timetabling and the passenger assignment. The AB-TSP is first formulated as mixed integer nonlinear programming (MINLP) and then converted to an equivalent mixed integer linear programming (MILP). Two families of valid inequalities are proposed based on the property of the developed model in order to accelerate the solution process. A numerical example on a medium-size AB network shows the effectiveness and efficiency of the valid inequalities, and we also make a detailed comparison between the AB-TSP and the traditional bus timetable synchronization problem (TB-TSP). Finally, a genetic algorithm (GA) framework is designed to cope with the large-scale AB-TSP, and the results of a case study based on the Tower Transit SG bus network show that the intractable real-world problem can also obtain satisfactory solutions within a reasonable computation time. [ABSTRACT FROM AUTHOR]

• A vehicle routing problem for low-carbon connected and automated vehicles is modeled. • Carbon emissions and time-varying speeds in different grade roads are considered. • To tackle the nonlinear of the model, the outer-approximate method is used. • A hybrid particle swarm optimization method is proposed and validated. • Some inspiring insights are obtained through numerical experiments. Environmental protection and intelligence have become the inevitable development trend of future transportation. Connected and automated vehicles (CAVs) are expected to be applied in the near future. In this context, how to schedule CAVs to meet customer demands with carbon emissions minimization has become a new green vehicle routing problem (VRP). Due to the fact that carbon emissions are tremendously influenced by vehicle speed, this paper considers vehicle speed as a decision variable in the above low-carbon VRP for CAVs. In addition, the differentiation on speed limits in each time period and each type of road are also taken into account. This study formulates a nonlinear mixed-integer programming model for this problem. The outer-approximate method is used to linearize the proposed model. Moreover, a hybrid particle swarm optimization (HPSO) algorithm is developed to solve this problem. Extensive numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution method. Some implications are also drawn out for reducing carbon emissions in logistics activities. [ABSTRACT FROM AUTHOR]