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In response to global warming, it is urgent to control and reduce greenhouse gas (GHG) emissions, especially from the supply chain perspective. The most often discussed topic in this domain indicates that emission trading scheme (ETS) could be employed as one of policy instruments for supply chain emissions management. Via mathematical models, scholars have shown that the employment of emission trading is affecting supply chain performances (both environmental and economic) in different levels. However, previous literature focus on the cost-effectiveness analysis only from the mathematical aspect. To provide policy-makers and companies with the theoretical bases on this hot topic, it is significant to discuss how ETS, one of market-based instruments, could be imposed in the context of supply chain from the mechanical point of view. By incorporating ideas from relevant literature, this paper generates two concepts/modes in imposing ETS on supply chain: jointly imposed supply chain emission trading (e.g., jointly imposed SC-ETS) and separately imposed supply chain emission trading (e.g., separately imposed SC-ETS). It illustrates the working principles of both concepts/modes and conducts a sandbox example to analyze the cost-effectiveness of them. Furthermore, this paper discusses the challenges and opportunities of implementing two modes from practical aspects. Results of quantitative analysis and discussion show that for the sake of both policy-makers and supply chain companies, the separately imposed SC-ETS allowing selling permits would be so far the better mode to manage supply chain GHG emissions compared to the jointly imposed SC-ETS. [ABSTRACT FROM AUTHOR]

In this paper, an improved mathematical model is proposed to solve the multi-echelon biomass supply chain synthesis problem, including processing hubs selection, biomass allocation design and transportation mode selection, with the consideration of vehicle capacity constraint (weight and volume). On top of that, carbon emission penalty is introduced in the model in order to evaluate the environmental impact in the supply chain. The entire problem is modelled through mixed integer non-linear programming with the aim of maximising the overall profit, at the same time ensuring the minimal CO 2 emission. A comparative study between the model developed in the previous work (vehicle capacity constraint and environmental performance are not considered) and the current improved model is carried out to compare the reliability of the outcomes. Both models are illustrated by using a same case study in Johor, Malaysia. In order to fill the gap of lacking user-friendly decision-making tool for the transportation design in supply chain management, a novel graphical decision-making tool, called smart vehicle selection diagram is proposed in this paper. The diagrams are constructed based on the optimised results obtained from the formulated model. The user manual for the proposed decision-making tool is given in this paper. Besides, five sets of sensitivity analysis are conducted to identify the sensitivity of the assumed realistic factors (i.e. terrain profile, weather changes, traffic congestion, fuel price fluctuation and individual environmental preference) to the optimal results obtained from the proposed tools. This paper shows the potential of the proposed tools in providing rigid optimal solution for the proposed research problem. Finally, several potential future works is suggested in this paper to fill up some of the remaining research gaps. [ABSTRACT FROM AUTHOR]

Abstract Given the high variability of both quantity and condition of the returns, closed-loop supply chains depend on a mix of remanufactured and new components. Effectively managing the combined flow of returned and new components is imperative to minimize total system costs. This paper proposes a model that helps manage the flow of returns by determining the incentives to offer returners, while simultaneously determining a capacity contract with the supplier of new components. The model considers returns from multiple sources, where each source has different characteristics in terms of the quantity and condition of its returns. The model also accounts for uncertainties from the return sources, as well as the new components supplier. Such uncertainties result in a failure to meet the demand for components, incurring loss costs. Sensitivity analyses of a numerical example were conducted to illustrate the model and gain further insights. The results demonstrate noteworthy relationships between supplier-related costs and those associated with not meeting the required demand, as well as their influence on the incentives to offer and the supplier's capacity contract. Highlights • Components can be sourced new or remanufactured from multiple return sources. • Model determines the capacity to contract from the new component supplier. • Model determines the optimal incentive for each return source. • Costs include losses due to a lack of remanufactured components. • Major interactions exist among incentives, contract capacity and loss costs. [ABSTRACT FROM AUTHOR]

In this paper, we develop a decisional framework and a mathematical model for the sustainable design of biodiesel supply chain networks (BSCND). We consider a broad group of sustainable aims, i.e. standard economic goals (revenue and logistics costs of BSCND), environmental issues based on a biodiesel production life cycle assessment (LCA), and the social incidental aspects (e.g. crime control, political stability, and community attitude, among others). The framework begins with a simulation of the biodiesel production system, feeding later, with suitable input parameters and suitable solutions, and a macro-location model designed to establish the regional positioning of biofuel conversion plants. Optimal regions are used as initial feasible solution space for the proposed micro-location model. In this part of the framework, we introduce an adaptation of Extended Goal Programming to rank the best municipalities (definitive locations) emphasizing the social aspects under consideration. The framework application is set up in the following Colombian context: first generation biodiesel production from palm oil feedstock. Our computational results indicate that the Colombian city of Rionegro (in the Santader region) is the most sustainable location for a new biodiesel plant. [ABSTRACT FROM AUTHOR]

Environmental Sustainability is one of the ‘Grand Challenges’ for manufacturing success in the 21st century. The present article mainly focuses on energy use during the manufacturing steps used for assembling products. In particular, industry still needs systematic and reliable ways to capture information for ultimately accounting for energy efficiency in manufacturing processes. For that purpose, an information model in the form of a formal ontology was created using information elements as building blocks, which emerge from manufacturing process models, supply chain operations, manufacturing process parameters, 3D form features and exergy-based thermodynamic analysis. These information elements were integrated by means of semantic relationships in an ontology. Once this information model was constructed, an application example of a welded hull panel assembly was used for verification of the model's representativeness and completeness. Results show that it is feasible to correlate all required data by means of semantic relationships for estimating energy efficiency indicators prior to physical prototyping, or ultimately comparing different alternatives for manufacturing processes regarding energy efficiency. However, manufacturing processes must be described in detail in order to allow energy efficiency analyses. In addition, geometric models need to be able to represent form features that can best relate to specific manufacturing process parameters. And finally, energy efficiency indicators, such as the degree-of-perfection can be derived from a comprehensive thermodynamic analysis, to be performed in each individual process step. Results indicate it is possible to provide an information structure for the calculation of the degree-of- perfection associated with an exergy-based thermodynamic analysis in assembly processes. Therefore, it has the potential to serve as a basis for a unifying framework for developing software applications that could allow the calculation of the degree of perfection in manufacturing operations. In addition, the resulting information model may facilitate data integration in the extended enterprise environment as well as the development of standards that can lead to seamless interoperability of manufacturing information systems. This paper presents the means by which energy efficiency can be accessed directly from an integrated semantic model, and opens the possibility to be extended for many different sorts of manufacturing processes. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid BSC-DEA framework for performance evaluation in sustainable supply chains. The proposed DEA model is capable of dealing with both qualitative and quantitative indicators while accounting for desirable and undesirable indicators. A tailored network DEA model involving a set of comprehensive sustainability indicators is applied to rank different supply chains from sustainability viewpoint to find the efficient and benchmarked units at each echelon. Then, sustainability indicators are classified into four groups according to BSC perspectives to help policy makers and top managers to have a more comprehensive and thorough understanding of the sustainability with respect to the long- and short term strategies. Finally, a number of sensitivity analyses are performed to identify the effective factors and strengths and weaknesses of each supply chain are identified based on BSC perspectives. To demonstrate the capabilities of the proposed approach, this framework is implemented for performance evaluation of plastic recycling companies in Mazandaran and Golestan provinces of Iran and some helpful managerial insights are derived from the numerical results. [ABSTRACT FROM AUTHOR]

Electricity generated from forest-based biomass is an attractive source of renewable energy. However, the cost of generating heat and/or electricity from it is relatively high due to the low energy density of wood, high moisture content and variations in its quality and availability. Models have been developed to optimize the supply chain and reduce the cost per kilowatt hour generated. This paper focuses on incorporating uncertainty in the supply chain of such a model. The model considers the tactical supply chain planning of a power plant over a one-year time horizon with monthly time steps. Uncertain parameters which impact the net profit of the power plant include ‘biomass quality,’ namely moisture content and higher heating value, and ‘monthly available biomass’ from different suppliers. Robust optimization is used to model uncertainty in the quality of biomass. Then a hybrid, multi-stage, ‘stochastic programming-robust optimization’ model is presented in order to simultaneously include uncertainty in biomass quality and biomass availability. It is demonstrated that the hybrid model takes advantage of both modelling approaches to balance the profit estimates and the tractability to various circumstances. The model provides solution considering all instances of the uncertain parameters within the defined sets and scenario tree. The results revealed a major trade-off between profit and range of biomass quality. Profit decreased by up to 23% when there was ±13% variation in moisture content and ±5% change in higher heating value. The model achieved a biomass purchase cost that was lower than the current commercial costs at the power plant. Implementing the model could prevent production curtailment and undesirable fluctuation in storage levels which occurred in the past due to variations in biomass availability and quality. [ABSTRACT FROM AUTHOR]

With the growing level of public environmental consciousness, sustainability has become an important issue in the area of supply chain management. This paper focuses on a channel coordination problem in a two-echelon sustainable supply chain involving one socially responsible manufacturer and one fair-minded retailer. Constructing mathematical models to characterize the problem, four particular contracts are developed to investigate the environmental performance and channel profit under the framework of two non-cooperative contracts: wholesale price and two-part tariff, and two cooperative contracts: Nash bargaining and Rubinstein bargaining. Theoretical analysis verified by numerical experiments shows that the wholesale price contract performs worse than the other three in terms of the carbon emission per unit product and channel profit. In addition, the two-part tariff contract can partially coordinate the supply chain, whereas both bargaining contracts can achieve perfect coordination to provide outcomes of the centralized decision. Comparing with the fair-neutral scenarios, we find that the fairness behavior promotes the retailer's profit, but reduces the manufacturer's profit under different contracts. Sensitivity analysis reveals that with intensified fairness concerns, the sustainability level and channel profit diminish under non-cooperative contracts, but remain the same under cooperative contracts. The Nash bargaining and Rubinstein bargaining contracts are robust to the degree of the retailer's fairness behavior and could be the best choice for the socially responsible manufacturer. • A channel coordination issue in a two-echelon sustainable supply chain is studied. • Four contracts are developed to examine the impact of retailer's fairness behavior. • Fairness behavior worsens wholesale price contract compared to two-part tariff one. • Nash and Rubinstein bargaining contracts are robust to levels of fairness concerns. • Both bargaining contracts achieve perfect coordination in reducing carbon emission. [ABSTRACT FROM AUTHOR]

We consider a supply chain supernetwork constrained by stringent carbon policy of mandatory cap, the purpose of which is to examine how stringent carbon regulations and operational decision modes jointly influnce the profitability and emission control of the system. We show that two key aspects of caps sharing schemes and decision modes eventually constitute four basic configurations of operations environment. Drawing on these configurations, this paper formulates novel carbon-capped mathematical gaming models to portray the supply chain supernetwork operations, which is then transformed into equivalent ones based on variational inequality approach and solved by devising augmented algorithms. The algorithms empower us to conduct typical computational experiments to compare supply chain performances resulting from diverse scenarios. The results reveal that only when the mandatory cap is set in intermediate level rather than excessively mild or tight can it be effective to balance system profitability and emission control well. Furthermore, regardless of decision modes, caps sharing scheme can always incur more system profit than the nonsharing scheme, while this rule does not apply to total carbon emission. Specifically, it is illustrated that properly changing the combination of decision mode and cap scheme can achieve Pareto improvement on low-carbon supernetwork performances (i.e. increasing profit and reducing emission), which is however dependent on the emission intensities contrast of all emitting firms. This study provides the advanced analytical framework and new insights for better understanding the joint impact of stringent (mandatory) cap policies and decision modes on supply chain performances. We shed light on that properly adjusting the cap tightness and the combination of decision mode and cap-sharing scheme may achieve Paretoly improvement for individual profits and particularly attain win-win result for system profit and emission abatement. [ABSTRACT FROM AUTHOR]