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Climate change has exacerbated the occurrence of extreme storms, resulting in urban flooding. However, rainwater is a potential source of water for various end uses of municipal water. In this context, permeable pavements are promising for mitigating floods and alleviating water stress in cities. Herein, a process-based life-cycle inventory modeling approach was used to quantify the life-cycle greenhouse gas (GHG) and water footprints of different road pavements in 28 pilot sponge cities in China. Three asphalt pavements were considered in this study: permeable-surface, permeable-base, and conventional nonpermeable pavements. Moreover, life-cycle cost analyses were performed to reveal the cost-effectiveness of these pavements. Results showed that permeable-base pavements exhibited the highest levelized GHG emissions (5.9 kg CO 2e /m2/yr), followed by nonpermeable (5.5 kg CO 2e /m2/yr) and permeable-surface pavements (3.8 kg CO 2e /m2/yr). In terms of levelized water footprint, permeable-base and permeable-surface pavements enabled water savings (433.8 and 304.1 kg/m2/yr, respectively), while nonpermeable pavements consumed 30.6 kg/m2/yr of water. The levelized life-cycle costs of permeable-surface, permeable-base, and nonpermeable pavements were 32.8, 35.4, and 40.5 yuan/m2/yr, respectively. With regard to transitioning from nonpermeable to permeable pavements in urban road construction, Chongqing and Shenzhen presented favorable potential for GHG mitigation (22 and 26 kt CO 2e /yr, respectively) and water savings (196 and 187 Mt/yr, respectively). Contrarily, Hebi, Yuxi, Sanya, Qianan, Chizhou, and Xining were not cost-effective in terms of GHG emission reduction. Moreover, in this study, GHG–water–cost tradeoffs were identified among different pavements, highlighting directions for unlocking the green benefits of installing permeable asphalt pavements in China. • Estimated life-cycle GHG emissions, water consumption, and cost of pavements. • Considered permeable and nonpermeable pavements in 28 pilot sponge cities. • Permeable-base pavement has the highest levelized GHG emission (5.9 kg CO 2e /m2/yr). • Shenzhen has the largest GHG mitigation (26 kt CO 2e /yr), and Chongqing has the largest water-saving potentials (196 Mt/yr). • GHG–water–cost tradeoffs exist among different pavements. [ABSTRACT FROM AUTHOR]

There has been limited progress in addressing the demand uncertainty inherent over time and the sustainability impact on food logistics. This paper aims to fill this gap, addresses the sustainability of cold food supply chains, and proposes a mixed-integer programming model to solve a multi-period inventory routing problem (IRP) under non-stationary stochastic demand, route-dependent costs, and environmental concerns. The study investigates the replenishment and routing plans to minimise the total expected cost while producing a minimum amount of CO 2 emission. Due to the uncertainty of demand, we apply the static-dynamic strategy and propose a mathematical model under the (R , S) replenishment policy to maintain flexibility in ordering decisions under a pre-determined replenishment schedule. Our numerical experiments suggest that the (R , S) policy reduces inventory costs significantly, since it solves the excess inventory issue caused by the higher buffer stock levels due to the pre-determined order quantities in the (R , Q) policy. However, the resulting CO 2 emission levels and routing costs remain similar in both models. Due to the reduced inventory costs, the (R , S) policy makes a significant improvement on the total cost. Moreover, our numerical experiments show that the difference between the cumulative ending inventory levels for the (R , Q) and (R , S) policies increasingly grows as the time horizon gets longer, and it results in increasingly larger differences in the total cost values for both policies. [ABSTRACT FROM AUTHOR]

Europe 2020 strategy highlighted that sustainable consumption and production requires resource and energy efficiency. In terms of sustainability in energy, carbon footprint, conservation, efficient energy use, and emissions trading are some major concerns. Under the Kyoto Protocol, a trading market mechanism is developed as a policy of reducing the emissions of greenhouse gases in each country. Firms are always considering whether the investment cost on emission reduction matches the benefit. One possible practice is to look for energy savings that are able to offset additional costs from the sustainability investment. This paper aims to establish such a sustainability investment decision model, which uses dynamic programming for solutions to minimize cost. The purpose is to effectively reduce emissions produced during production through investment in energy-saving, high productivity equipment and allocation of production capacity considering emissions (or so-called carbon) trading market price, greenhouse gas emission credits or caps given by the government, inventory cost, and production cost. Management-oriented implications are proposed following analysis of the results of detailed numerical examples. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) brings new opportunities for creating intelligent and streamlined supply chains that have better environmental and cost performance as compared to conventional ones. In this paper, we quantify such improvements for a specific logistics chain case. To support the inventory of cost and emission data, we utilize system dynamics (SD) and agent-based modeling (AB) to define the structure of the two logistical systems, simulating and estimating differences in e.g., required storage levels, efficiency of transport, etc. In particular, we assess the difference in carbon emissions, cost, and market performance of a battery delivery chain in the delivery process between a two-tier IoT-supported supply chain (users are served by an IoT retailer directly connected to the producer) and a conventional three-tier supply chain (include an additional wholesaler to connect retailer and producer). The results demonstrate that IoT supply chains have significant advantages in minimizing average product storage and shipment fluctuations. IoT suppliers can estimate market demand to adjust production and transportation strategies for new orders. Consequently, the overall profitability of the IoT supply chain increases by more than 30%. Heating and lighting emissions in the storage process and direct emissions in transportation per functional unit (one unit of a Li-ion cell module) are reduced by 60%–70% under middle- and low-demand scenarios, and by at least 50% under high-demand scenario. However, the increasing use and higher loading rates of heavy trucks will weaken the advantages of IoT. Moreover, IoT products occupies a 10% lower market share compared to conventional ones under the same pricing strategy but achieves similar market share under the same value-added strategy. • Shipment is the dominant contributor in both supply chain emissions. • IoT enables a two-tier supply chain with less required warehouse space and waste. • IoT supply chain can reduce carbon emissions by 50%–70% in most scenarios. • IoT helps enterprises to acquire higher profits with lower market occupation. • Reducing the supply frequency through heavy trucks will weaken IoT's advantage. [ABSTRACT FROM AUTHOR]

The use of food additives and greenhouse gas (GHG) emissions are major factors that affect consumer health, which cannot be ensured, therefore, unless the food supply chain is controlled. This paper presents a bi-level optimization approach to address competition between the members of a food supply chain, including two suppliers and one manufacturer. The manufacturer seeks to determine prices, production, and investment in reducing GHG emissions to maximize profits at the upper level. A multi-period model is presented, in which the manufacturer seeks to reduce production costs by maintaining inventory and, of course, accepting the risk of waste. At the lower level, the suppliers compete to determine the price of raw materials and the amount of additives to maximize their profits. Two linear approximation methods are proposed to linearize the nonlinear model. According to the results, the lowest relative gaps obtained from solving the model for two numerical examples using the second linear approximation method are lower than those obtained by the first method. A sensitivity analysis of the effective parameters demonstrates that an increase in the dependence of demand on price leads to a 30% decrease in the profit of the entire supply chain. The manufacturer faces an average 3% drop in demand with an increase of 4%–7% in the rate of production waste. An 18% increase in the cost of GHG emission reduction leads to a 5% decrease in total supply chain profit. Hence, the increase in the cost of reducing GHG emissions is not economical and environment-friendly for the supply chain members. A budget increase is appropriate in terms of the economic and environmental goals, but its impact on the manufacturer's decision variables varies by period. [ABSTRACT FROM AUTHOR]

This paper investigates the joint decision on inventory and refurbishing strategies in a closed-loop supply chain, consisting of a manufacturer responsible for the production and first-market sales and a third-party refurbisher responsible for the refurbishment and second market. Based on a game-theoretic model, we investigate the effects of supply chain structures on the production operations, sales price of a refurbished product, profits and environmental performance. Through numerical analyses, we reveal that the integrated system yields higher first-market sales and a larger production lot size, while the decentralized one shows higher second-market sales and a larger refurbishing lot. Interestingly, the integrated system sets the higher price for the refurbished item due to channel conflict issues. Efforts to increase the second-market sales can harm the overall supply chain profit by leading to the decrease in the higher-margin first-market sales. We also show that the difference in pricing decisions and inventory holding cost components allows a better operational performance of the third-party refurbisher's process in the decentralized system. Then, it leads to the better environmental performance in the decentralized system, enhancing the refurbishing rate and sales of refurbished items while reducing scrap costs. By investigating the overall performance of integrated and decentralized supply chains, we offer important implications to practicing supply chain managers by revealing how the third-party refurbisher can help the sustainable operations of a closed-loop supply chain. We also contribute to the body of knowledge by incorporating pricing and lot sizing issues into the context of closed-loop supply chain management. • We investigate inventory and refurbishing strategies in a closed-loop supply chain. • We consider integrated and disintegrated systems with a third-party refurbisher. • The integrated supply chain yields better profit with higher first-market sales. • The disintegrated system induces better environmental performance. • The third-party refurbisher improves the sustainable operations of a supply chain. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a ship routing and speed multi-objective optimization model that considers ECA (Emission Control Area) regulations, which can realize the minimization of both shipping costs and emissions. This proposed model can help maritime regulators, such as those who work in maritime departments, to trade-off shipping costs for the minimization of CO 2 and SO x emissions by selecting appropriate routes and speeds. The total costs are the sum of the fuel costs, fixed time costs, and cargo inventory costs. The difference between the significance of SO x emissions inside and outside ECAs was considered. The Non-dominated Sorting Genetic Algorithm II (NSGAII) was used to obtain the Pareto-optimal set of the multi-objective optimization model and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to find the trade-off solution from the Pareto-optimal set. The model was applied for a case study of a service route from Halifax, Nova Scotia, Canada to Brunswick, Georgia, United States. Compared to an actual scenario without any optimization, the results show that the model can simultaneously reduce a ship's total costs and emissions, as well as avoid increase in the total costs that accompany increase in fuel prices. Moreover, the model can provide different optimal routes and speeds for different levels of ship emissions (CO 2 and total SO x inside ECAs). • We propose a multi-objective ship route and speed optimization method to reduce total costs and emissions simultaneously. • Different ship emission objectives require different optimal routes and speeds. • The proposed method can help the shipping companies reduce the pressure to increase Fuel prices. [ABSTRACT FROM AUTHOR]

This paper provides a mathematical model to analyse the impact of a negative corporate social responsibility (CSR) parameter that for 'forced labour', on supply chain profitability based on coordination contracts. Four types of supply chain coordination contract are developed and benchmarked against set performance indices employing sensitivity analysis and non-linear optimization techniques. Nash 'static' and Stackelberg 'dynamic' models are employed to represent the 'gameplay' between the 'manufacturer' and 'retailer' in a supply chain. The key inputs are average demand and 'forced labour' ratios, whereas key performance indicators are advertising, inventory and pricing costs. Our results indicate that by reducing the proportion of 'forced labour' for both members of the supply chain, the overall profitability increases in a Nash static gaming context. However, the contractual relationships in a Stackelberg dynamic situation provide a different picture. Here, if the 'retailer' assumes the leadership role, cost-sharing contracts increase the profitability for both partners. However, if the 'manufacturer' assumes the leader, the profit-sharing contract leads to the highest overall profit. Furthermore, sensitivity analysis confirms that an ideal situation is one in which the 'forced labour' ratio approaches 'zero', where the profitability of the retailer is usually higher than that of the manufacturer. Previous researchers have used 'survey-based' methods to develop proportional or 'mediating' relationships to measure the impact of CSR on supply chain profitability. This paper addresses the associated gap by providing a game theoretic-based non-linear mathematical model to assess the direct impact of negative CSR on supply chain profitability and benchmarks the performance of different supply chain contracts. [ABSTRACT FROM AUTHOR]

In this paper, by assuming a B2C e-business company with several regional distribution centers (DC s), an extension of multi-item joint replenishment-distribution problem (JRD) is raised with stochastic lead-time and demands to investigate their mutual impacts to the JRD system. For the proposed JRD , the objective comprising four types of costs, namely, the ordering cost, the inventory holding cost, the lost sale penalties and the transportation cost is minimized by jointly deciding the basic cycle time, the replenishment frequencies and safety stock factors of all items. A hybrid differential artificial bee colony (DABC) algorithm, which combines the superiorities of the differential evolution (DE) algorithm in global search and the artificial bee colony (ABC) algorithm in fine search, is presented to solve the proposed JRD model. Numerical experiments and parameter sensitivity analyses are conducted. The computational results have testified that DABC is faster than that of DE (16%) and two hybrid ABC-DE s (18% and 9%), more effective than that of genetic algorithm (GA , 0.15%), ABC (0.03%) and ABC-DE1 (0.015%) and robust than that of GA (99%), DE (71%), ABC (0.97%) and two hybrid ABC-DE s (86% and 88%). Most importantly, different uncertainty significance of lead-time and demands to JRD are revealed and analyzed. Furthermore, management insights, such as the magnitudes of the problem-related parameters and the mutual effects of the two stochastic variables to the system cost, are indicated in the replenishment process. Image 1 • A model is presented with stochastic lead-time and demand. • Synthetically optimize ordering cost, holding cost, lost-sale cost and transportation cost. • A new algorithm is redesigned to solve the proposed model • Algorithm performance is verified through the numerical experiments. • Analyses of two stochastic factors on the total cost are performed. [ABSTRACT FROM AUTHOR]