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Sustainable supply chains are the developmental trend for economics. They require governments and enterprises to have strong abilities to face and deal with extensive, complicated and long-term problems to optimize multiple targets. This work enriches the content of product sustainability and constructs decision models of the focal company and supplier using four different strategies in the differential game with respect to the three pillars of sustainability. Then, it analyzes the effects of subsidies and the focal company's incentives. The results show that the profit sharing ratio is powerful, and it is positively related to the product sustainability and profits of each member. Subsidies have higher significant effects on the improvements in product sustainability, environmental performance, and the profits of each member and the holistic supply chain than incentives.

Selection of alternatives for sustainable infrastructure development is governed by many quantitative and qualitative factors that may be conflicting such as life cycle cost, environmental concerns, expansion feasibility, projected community growth, perception of the communities, lobbying efforts, and regulatory requirements. A framework to integrate these intrinsic factors is the basis of an optimal, sustainable, transparent, and practical decision. In this paper we present a decision model to reflect social and political dynamics in a multi-criteria decision making framework at the early stages of sustainable infrastructure development. The concepts of choosing by advantages (CBA), equilibria of social power between actors, and life cycle cost analysis are used to assess the decision in environmental/technical, social/political, and economic dimensions. Application of the model is discussed for the case of selecting urban/rural wastewater system alternatives. Theoretically, the proposed multi-dimensional decision model aims to integrate the political and social dynamics of the actors including their incentives into the structured decision space of sustainable infrastructure. The proposed model also further extends the existing literature through transparent account of trade-offs across technical, economic, and political dimensions of the selection of alternatives. Policy makers can specifically apply it in the feasibility stage of infrastructure development to facilitate integration of sustainability goals within a network of involved actors, observe the potential trade-offs between sustainability metrics, increase practicality and transparency of the public policy making, and explore the potential impact of nudges to instigate sustainability within infrastructure development.

This study shows how to optimize traditional and non-traditional concrete mixes from various perspectives, specifically technical performance (e.g. mechanical and durability characteristics), and economic (cost) and environmental (e.g. global warming and energy consumption) life cycle. Firstly, the weight to be considered for each of these perspectives (dimensions) of performance depends on the concrete application and on the user's requirements. In this study, concrete mixes containing various amounts of recycled concrete aggregates (RCA) and/or fly ash (FA) are optimized for different applications in the construction sector, namely high-rise building, sustainable residential housing, economical residential housing and residential housing close or far from the sea. For that purpose, the CONCRETop (a multi-criteria decision method for concrete optimization) methodology (developed by the authors of this study) was applied to these concrete mixes by considering the global requirement scenarios (e.g. cost, strength, green, service life and business as usual) that are normally demanded by users. This study is an example of CONCRETop's application and also contributes to its validation. The results show that, for all applications and scenarios that users may demand, the optimum concrete mixes in terms of concrete characteristics, cost and environmental impact are the ones produced with both FA and RCA incorporation, rather than their individual incorporation. This study shows that CONCRETop is straightforward in its application, i.e. it does not require excessive time and resources, and is focused on the final output, where the selection of the optimal concrete mixes can be directly used by the user, avoiding therefore lengthy inventory analysis and modifications.

Sustainable development and territorial cohesion are key objectives of spatial plans and policies. In order to assess the efficiency of planning measures to achieve sustainability, spatial plans are introducing evaluation and monitoring tools based on the use of indicators. This paper defines an assessment framework which permits quantifying the sustainability degree at both local and regional level in different planning scenarios. The framework consists in the aggregation of social, economic, environmental and spatial indicators into a single local index using a robust Principal Component Analysis and a Fuzzy-TOPSIS multi-criteria decision method which enables the ranking of municipalities and the estimation of a regional index depicting the degree of intraregional sustainability diversity. This methodology was applied to Cantabria, a northern Spanish region which is currently elaborating its first Regional Spatial Plan (PROT), for 3 scenarios: its current situation and two planning scenarios, a recessionary and a progressive one. The results show that both scenarios affect regional sustainability, the progressive one leading to a slightly more balanced region. However, this increase in sustainability achieved in the progressive scenario was quite moderate and, therefore, more ambitious measures should be taken to achieve a sustainable region. This methodology can be applied to diverse case studies, standing as a helpful decision-making tool in the field of spatial planning.

There have been various restoring technologies in remanufacturing industry for the recovery of designed dimension. The life cycle environmental performance, economic benefits, and quality reputation of remanufactured products are influenced by the restoration process. An appropriate selection of restoring technology would enhance the sustainability and assure the quality requirement of remanufactured products. The primary objective of the present study is to develop an effective and comprehensive multi-criteria decision-making approach for the application to the remanufacturing process considering the environmental impact, economic cost, and technical property. We applied the fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach to select a proper restoring technology for the crankshaft remanufacturing. The final results indicated that, based on the proposed criteria, the preferential ranking is brushing electroplating, plasma spray, plasma arc surfacing, and laser cladding. The present study would help facilitate and guide the selection of restoration technology in engine remanufacturing practice and benefit remanufacturers for the sustainability improvement.

To respond to the increasingly serious problems of a lack of workers, the need to control costs, and environmental issues such as carbon emissions, the footwear industry must change its production model to produce highly competitive products. This research aimed to solve the problem of the lack of workers and ameliorate environmental issues by using the activity-based costing (ABC) approach and the theory of constraint to effectively track related costs with the green production decision model (GPDM). This study used knitted footwear production as a case for effectively integrating the advantages of shoemaking and textiles with the ABC approach. These changes could help footwear companies to effectively control costs and increase productivity. From the literature and the sensitivity analysis of carbon tax rates, it could be reasoned that companies will have the motive to implement the low-carbon technologies with higher cost at the higher level of carbon tax rates.

As the world encounters insufficient fossil energy and worsening environmental pollution, the significant potential of water surface photovoltaic (WSPV) systems and the remarkable benefits are crucial to promoting cleaner production and sustainable development. This paper proposes installing overhead WSPVs along the open channels of long-distance water diversion projects (WDPs), creating new opportunities for the adaptive traceability and utilization of energy–water resources. A multi-objective decision model for the feasibility evaluation of WSPVs is presented and applied to typical WDPs in China to demonstrate the effects of this modeling framework and explore the co-benefits of WSPV systems. The results indicate that: (1) it is economically and technically feasible to install WSPVs over MLSNWDP with a suggested tilt angle range of 10°–12°; (2) the proposed WSPVs in the eight typical projects evaluated using the decision model with a capacity of 29,486 MW can convert 37,263 GWh of electricity at a 12° tilt angle while saving 3.19×108 m3 of water annually; (3) the adaptive utilization of the saved water and electricity can increase the ecological water level guarantee rate of Baiyang Lake by 12.64%, alleviating potential eco-environmental problems. Moreover, a recommended coverage rate of 50% is proposed to inhibit algae outbreaks. Overall, this research demonstrates the technical and economic feasibility of the construction of WSPVs along WDPs and the co-benefits to address challenging research questions on the sustainable development of energy–water resources.

The stone processing sector has begun to implement the Cleaner Production (CP) strategy to tackle with these burdens caused by the production process. Decision-makers need to select the most suitable cutting fluid for achieving the CP mode in their firm. However, the assessment methods of characteristics and performances of cutting fluid in granite processing field have rarely been examined in the literature. This paper proposed a multi-criteria evaluation system that enables decision-makers to quantitatively analyze the comprehensive effectiveness of application of cutting fluid in their granite production lines. A four-hierarchy indicator system was initially structured with three attributes, 13 characteristics and 30 indicators according to the context and literature of the stone processing field. The definitions of measurement method for all the indicators were closely related to the laws, rules, regulations and discharge standards. The decision algorithm was designed based on the integration of two distinctive Multiple Attribute Decision Making (MADM) techniques: Analytic Hierarchy Process (AHP) approach was used to determine the weights of each indicator, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique was applied to obtain the prioritization of alternative cutting fluids. This research introduced the statistical analysis principle to help the decision makers to further understand the nature of the final ranking: confidence interval (CI) provided the decision-makers an interval where the ranking may be valid instead of a single absolute rank; and Tukey's HSD tests were done to show if the final score of alternatives were statistically different from each other. A case of application demonstrated that the proposed decision method and procedures can become a scientific foundation for formulating guidelines to select the most suitable cutting fluid for implementing CP strategy in the granite processing organizations.

Selection of clean technology options requires systematic evaluation based on multiple criteria which are often conflicting. The optimal choice should consider not just technical performance but also the economic, environmental and social aspects of technologies. Furthermore, the interdependencies of these aspects should also be considered. The decision-maker often needs to make explicit trade-offs while ranking the alternatives. In addition, data gaps and imprecise information that are typical when dealing with emerging technologies make conventional methods ineffective. This work thus proposes a Stochastic Fuzzy Analytic Hierarchical Network Process decision model to address the complexity and uncertainty involved in the clean technology selection process. This method first decomposes the problem into a hierarchical network structure, and then derives the probability distribution of the priority weights needed for ranking. The capabilities of the methodology are demonstrated with three case studies, involving the comparison of different carbon nanotube synthesis methods, nutrient removal treatment technology options for municipal wastewater, and low-carbon electricity sources in the Philippines.

Involving stakeholders in the decision-making process can be very complex and time consuming. Decision theater (DT), which enables the combination of visualization and decision modeling capabilities together with human capacity of insight and interaction, is proposed for addressing this challenging problem in the forest sector. A generic framework for designing DTs to support participatory planning in the forest sector is proposed. To enable DT implementation and support decision-making in the DT in the province of Quebec, Canada, the conceptual design of a decision-support system called Forest Community-DSS (FC-DSS) has been developed. Implementing FC-DSS along with other technologies in a DT environment can contribute to engage the stakeholders in the decision-making process by increasing participation frequency, collecting more inputs from the stakeholders, supporting the development and evaluation of alternative options and the selection of preferred alternatives. A DT-based collaboration approach would contribute to address the multiple issues of the stakeholders involved in participatory planning in Quebec. Other Canadian provinces and other countries facing similar issues can benefit from the proposed approach.

Carbon emission is not as free as they used to be. Under the carbon emission tax policy, manufacturers face a choice of whether to reduce their carbon emission through remanufacturing or low-carbon technologies. In this paper, we characterize the optimal carbon emission tax policy through a two-period production decision model, in which new products and remanufactured products are clearly distinguishable. The effects of a carbon emission tax on the optimal production decision of manufacturing and remanufacturing is investigated. It is found that there are rational remanufacturing production strategies for different products based on different remanufacturing cost savings and carbon emission savings per remanufactured product under a carbon emission tax policy. Manufacturers can offset the cannibalisation and negative economic impact of the carbon emission tax policy by making low-carbon emission technology investment. Furthermore, the government can stimulate the manufacturer to invest in carbon emission reduction technologies and remanufacturing simultaneously by choosing a reasonable carbon emission tax.

Building the rail transit photovoltaic power station (RTPPS) to improve the utilization rate of renewable energy has become an effective way to reduce carbon emissions. Unlike the photovoltaic power stations built in the wide area, the urban rail transit in the big city must consider the demand of landscape, the nearby utilization of electric energy and the impact of light pollution on residents. Hence selecting a reasonable RTPPS plan is important, but it is still the research gap. There are still some problems encountered in the RTPPS plan selection. First, there is lack of an effective decision index system of RTPPS plan selection; second, the existing decision model cannot effectively balance three kind relationships: ①the expression convenience of decision preference and ability to deal with uncertainty; ② the subjectivity and objectivity of the criteria weights in the linguistic environment; ③ the overall effectiveness and local disadvantages of the RTPPS plan. Therefore, the proposed decision index system is established from five aspects: economic performance, rationality of photovoltaic integrated design, effectiveness of light pollution control, energy efficiency and support degree. Meanwhile, according to the principle of probabilistic linguistic term set, combination weighting method and VIKOR model, the corresponding model was proposed to balance tha above relationships. It has the following characteristics：① it improves the ability to deal with uncertain information without changing the expression habit of expert decision preference; ② The weight of decision criterion not only reflects the subjective intention of decision-makers, but also avoids too subjective decision-making; ③ The local disadvantages of the plan are considered in the final result, so as to ensure that the optimal alternative will not be abandoned because of the local disadvantages. The proposed decision index system and decision model jointly construct the optimal plan selection framework of RTPPS to improve the plan selection rationality. The effectiveness of this framework is proved by a China case study.

Sustainable supplier selection is critical to the operation of water environment treatment Public-Private Partnership (PPP) projects. This paper considered the sustainability of suppliers and established a sustainable supplier selection indicator system using five aspects. This paper introduces the Shapley value method to modify the index weights determined by the analytic hierarchy process (AHP) in order to overcome differences in expert cognition. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) decision model based on interval-valued intuitionistic fuzzy sets (IVIFSs) was constructed to select sustainable suppliers for water environmental treatment PPP projects. The decision-making model was used to select a supplier for a case, and the result of that selection was compared and verified with different decision-making methods. The use of IVIFSs in the decision-making process can reduce the ambiguity and uncertainty of expert judgment. The results show that the supplier's own sustainability has an important impact on the sustainable development of the project. Moreover, the supplier's sustainable financial capabilities and sustainable technology solutions are key factors that need to be considered when choosing a sustainable supplier. This paper conducts comparative verification and sensitivity analysis to verify the effectiveness of the method. This article considers the sustainability of suppliers, which provides a new research direction for improving the sustainability of PPP projects. The index system and decision-making methods in this article can help governments in developing countries select suitable water environment treatment suppliers and improve the sustainability of public services.

An increasing number of retailers in the U.S. have required labels indicating the use of controversial neonicotinoid insecticides in plant production. Recent research shows that banning neonicotinoids decreased yield, increased production cost and pest resistance. Using a nationwide survey, we analyze production practices in the ornamental horticulture industry with an emphasis on the use of neonicotinoid insecticides. We are interested in investigating how U.S. ornamental plant growers who use neonicotinoids in their production practices may be different from those who do not use them, and the factors that would affect growers’ adoption of non-neonicotinoid insecticides. A reduced-form decision model is empirically estimated using a sample of 166 growers. Results reveal discrepancies in firm and production characteristics between neonicotinoids users and non-users. Type of operation and anticipated cost changes are found to be strong indicators of pest control adoption decisions. Specifically, growers with open field are more likely to adopt non-neonicotinoids practices. Growers anticipating larger increases in labor factor input or labor cost are less likely to switch to non-neonicotinoids practices, indicating growers become reserved when environmentally friendly pest management practices have the potential to increase labor input factor and associated cost, and as a result, lead to lower revenues and profitability.

We develop a decision model to integrate three decisions pertaining to location, allocation, and routing of different varieties of recycled plastics. Our decision model allocates recycling collection points to the available centralized return center based on its capacity and ensures similar allocation to all facilities via optimal routing for trucks. The study addresses several pertinent questions such as how to deal with higher collection cost, how to develop a model that jointly considers operational cost reduction along with the achievement of higher environmental benefits, how to reduce sub optimization which is quite common when using standalone decision models, and what are the feasible ways to increase the utilization of collection facilities. We validate the model using data retrieved from a case study in India using a conventional decomposed modelling approach. Our findings demonstrate that the proposed integrated model reduces over ten percent of total recycling costs for both single and multiple products. The results also suggest managers to reduce variances in product quality levels in order to achieve substantial total costs reduction. In addition, to increase the plastic recycling returns and reduce the operational cost, managers need to cluster the customers based on the facility location and offer attractive incentives.

This research proposes a replacement purchase decision model in which trade-in and subsidy are provided for vehicle owners to replace their old conventional fuel-powered vehicles with hybrid electric vehicles (HEVs). The uncertainty in fuel prices has distinguished this model for its ability to illustrate a time-dependent cost trade-off between HEV-induced economic benefit and replacement cost. A cost-effective replacement time (CERT) is quantitatively determined using a dynamic programming approach. Afterwards, the theoretical model is illustrated with an empirical example, of which the result shows that the adoption of BYD Qin HEV in China is not optimal at current fuel price without subsidies. Moreover, sensitive analyses are presented, revealing significant effects of subsidy level, fuel price, and personal travel mileage on the cost-effective replacement time. Results suggest that the effect of subsidy is attenuated by an increase in fuel price, whereas CERT is shortened by a decrease in fuel price volatility. The implications and recommendations for policymakers are also provided.

The selection of an appropriate technology for waste management in a way that enhances sustainability is a complex problem requiring information from multiple disciplines. This decision is made more complex for large, diverse organizations for which individual sites or components have different requirements and capacities for waste management. For U.S. Army installations, sustainable waste management is a major obstacle for resource constrained and overburdened installation personnel. The Army has prioritized landfill diversion at installations; there is a significant push for each installation to develop and implement waste management plans that follow high-level goals set by U.S. Army Installation Management Command (IMCOM). The main objective of this study is to demonstrate an excel-based dashboard tool incorporating Multi-Criteria Decision Analysis (MCDA) for site-specific waste management technology selection. The dashboard uses the preferences of an installation and a series of weighted metrics to evaluate technologies. With this information the dashboard can rank the technologies to identify those that simultaneously meet IMCOM's objectives and are appropriate for specific sites' capabilities and resources. Fourteen different technology makes and models were compared in this version of the decision model including dehydrators, pulpers, garbage disposals, containerized in-vessel composting, windrow composting, forced-air static composting, and containerized anaerobic digestion. MCDA provides for transparent comparison of technologies for all sites, and the dashboard visualizes the level of appropriateness of each technology for each site. The approach developed here adds value in that it utilizes the priorities of the organization and identifies the best performing waste management technology given the specific capacity and capabilities of an individual site.

This paper proposes a two-stage location decision-making framework to study the site selection of distributed wind power coupled hydrogen storage (DWPCHS) project for the first time. In the first stage, the restriction criteria system that includes geographical constraints and networking requirements is established. Then the geographic information system is applied to evaluate the suitability of the study area and initially determine the feasible locations. In the second stage, the evaluation criteria system involved four aspects of resource, economy, environment and society is established. And the multi-criteria decision making (MCDM) methods are used for further evaluation. At this stage, the evaluation information of qualitative indicators is described by triangular intuitionistic fuzzy numbers. Afterwards, the fuzzy entropy method is used to calculate the weight of evaluation criteria. Finally, the fuzzy TODIM (an acronym in Portuguese of interactive and MCDM) method is used to rank the feasible locations and screen out the sites with excellent comprehensive performance. To verify the practicability, an empirical study in Hezhang County is conducted. Moreover, the sensitivity analysis and the comparative analysis are carried out to verify the reliability and stability of the decision model. This study can provide support for the site selection of DWPCHS project.

The freight transport industry of developing nations are trying very hard to achieve environmental sustainability in their logistics business. Meanwhile, it is essential to identify the transformation path towards sustainable freight transport for every nation. This paper has presented a framework based on transition management (TM) theory to identify, classify, and measure the important environmentally responsible transport practices (ERTPs). This work utilizes a two-step methodology comprised of a grey clustering (GC) algorithm and a compromise ranking method, i.e., VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) for prioritization of ERTPs performance. The GC algorithm is used to classify the ERTPs based on their impact on selected transport processes. Initially, this study identifies thirty-five ERTPs and four transport performance clusters. The proposed framework is validated with the Indian freight transport industry. The GC algorithm classifies twenty-one and fourteen practices as a high priority and moderate priority, respectively. The VIKOR method is used to compute the group utility, individual regret, and possibility value of high priority ERTPs. The ERTPs such as developing efficient freight terminal, compliance with government transport emission law and practice, promoting the use of the green container, environmental certification of freight carriers, and using alternative fuels are found as poor performing practices in the Indian freight transport industry. The proposed decision model robustness is tested with the sensitivity analysis, and a final ranking of the high priority ERTPs is presented. The outcomes of this research help logistics practitioners and policymakers in managing the ERTPs of freight logistics systems and develop a roadmap for improving freight transport sustainability.

In recent years, the concept of sustainability is emerging as one of the main concerns in modern society. In this context, industrial production processes are changing thanks to the development of green strategies. The selection of a new production solution is a crucial strategic decision and the introduction of new technologies could cause deep changes to the entire supply chain configuration. This paper introduces a new approach designed to understand the importance of supply chain considerations for a suitable technology assessment. It is aimed at proposing a quantitative model for the evaluation of different structure of supply chains based on different production technologies. In particular, by defining a set of Key Performance Indicators and applying a multi-criteria decision method, a final score is computed, giving important information about both environmental and economic aspects. A numerical example regarding the comparison between Additive and Traditional Manufacturing processes is presented in order to demonstrate the applicability and the effectiveness of the proposed method. Results show that additive solutions bring evident reduction in Carbon Emission and better supply chain and logistics performance. Anyway, production volume strongly influences the economic results and the competitiveness of additive manufacturing remains limited when compared to the traditional manufacturing.

Printed Circuit Board (PCB) manufacturing is a kind of energy-intensive and pollution-intensive industries. With the increment of individualized requirements, PCB manufacturers face massive customized orders with a variety of specifications. The individualized customization on orders results in large differentials of the profit, energy consumption, and environmental pollution. Making energy-efficient order acceptance decisions can reduce carbon consumption and improve material utilization during the whole manufacturing process. An order acceptance decision model is established based on a loosely-coupled integration of deep learning and reinforcement learning techniques. Firstly, different from a simple assumption of the linear cost function in a small-scale manufacturing system, the deep learning algorithm is presented for accurately predicting the production cost, makespan, and carbon consumption of incoming PCB orders in the large-scale manufacturing system. Secondly, these predicted cleaner production indicators are combined with original order features to perform a reinforcement learning-based order acceptance decision. The proposed loosely-coupled deep reinforcement learning approach is verified with a dataset built based on data collected from a PCB manufacturer in China. This research is expected to provide an environment-friendly order acceptance decision-making approach for sustainable manufacturing in the Industry 4.0 context.

This study aims to develop a comprehensive decision model for sustainable design of biomass based renewable energy supply chains and district heating systems (DHS) with thermal energy storages. The model integrates the strategic decisions such as location and capacity selection for energy plants, DHS, thermal storages and biomass storages with tactical decisions related to biomass production, supply and transportation planning, inventory management and energy production. The main purpose is to find the optimum configuration of the supply chain and DHS to meet the heat demand of a particular locality. The model combines cost and service level objectives and accounts for biomass supply, material flow, capacity, demand and technical constraints. The problem is formulated as a fuzzy Mixed Integer Linear Programming (MILP) model that comprises multiple biomass types and system uncertainties. To explore the viability of the proposed model, computational experiments are performed on a real-world case. Sensitivity analyses are conducted to examine the impacts of cost and capacity limit of thermal energy storage, as well as heat demand, on the objective functions and thermal storage capacity. The results reveal that the proposed model can effectively be used in practice to assist the decision makers in planning energy production systems in a sustainable and effective manner. (C) 2016 Elsevier Ltd. All rights reserved.

Sustainability indices are increasingly recognized as a powerful tool for assessing the performance of chemical process designs considering energy, environment, safety and technological improvement aspects and above all, economy. Sustainability indicators enable decision makers to simplify, quantify and analyze complex information. Generally, it is challenging to evaluate the performance of process designs on the basis of a large number of indicators. The integration of a set of key sustainability indicators in the form of a composite index is essential for simplifying the evaluation of sustainability performance. Currently available methodologies for sustainability assessment of process designs require detailed process data, which are typically unavailable at the conceptual design phase. This paper introduces a new composite sustainability index (CSI) that addresses the sustainability performance of chemical processes and which can be applied for early design phases where minimum amount of data are available. The three pillars of sustainability are considered in the development of the new composite index, which are energy, environmental and safety aspects. A conceptual decision model based on the analytical hierarchy process can be employed to compare and determine weights for the different sustainability indicators, which are then aggregated to obtain the CSI, when the indicators conflict. The capability of the proposed model is investigated by applying it to a hydrogenation case study to choose the more sustainable design among different alternatives.

Extant literature on sustainability in procurement and supplier selection suffers from a number of deficiencies. First, studies pertaining to the dynamic nature of stakeholders’ expectations of sustainability and its impact on determining a supply portfolio have not been studied before. Second, there is a genuine lack of study linking the stakeholders’ sustainability requirements, firm procurement strategies, and eventual supplier selection. Third, most of the existing studies address sustainability issues in procurement and supplier selection but fall short of determining an optimal portfolio of suppliers and corresponding optimal order quantities. This study addresses the above research gaps by developing a sustainability-focused multi-criteria decision model for supplier evaluation and determining optimal order allocation among the suppliers linking the stakeholders’ sustainability requirements and firm procurement strategies. Based on dynamic capability theory, we develop a decision support framework integrating multi-phased quality function deployment and dynamic optimization. We apply the decision support framework to a European apparel company which sources apparel from Bangladesh: a country that is a low cost sourcing destination. First, this study identifies the stakeholders’ sustainability requirements. It then explicates the company’s procurement strategies in terms of stakeholders’ requirements followed by translating the procurement strategies to relevant supplier assessment criteria. Finally, a linear optimization model is developed to maximize the suppliers’ sustainability performance in order to determine the optimal supply portfolio. The results identify two distinct groups of suppliers satisfying the overall sustainability performance. However the optimal order quantities among the suppliers vary randomly depending on the variations in demand and priority weights of the suppliers. The paper concludes with a detailed discussion of the results and implications.

The main objective of this paper is to propose a loan approval evaluation framework of public-private partnership project (PPP) of battery storage power station (BSPS) for the commercial bank to evaluate and select the optimal project to provide the loan and then promote the development of BSPS-PPP. In order to achieve this goal, this study proposed a loan approval evaluation index system which consist of 5 attributes, 14 criteria and 45 sub-criteria. The novelty of this loan evaluation index system is that it reflects the basic requirement of loan approval of the commercial bank based on the characteristics of BSPS and PPP project, the commercial banks can better prepare for possible risks of BSPS-PPP. The proposed decision model was established based on the interval-valued intuitionistic fuzzy value (IVIFV) and interval number. Its novelty is that ①The model not only satisfies the experts’ expression habit of uncertainty, but also can be handled the uncertainty in the performance evaluation value and the criteria’s weights properly; ②the proposed weight determining method based on the deviation maximization principle can not only maintain the will of decision makers as much as possible, but also decrease the uncertainty of weights. Finally, the effectiveness of loan approval evaluation framework was verified based on a case study of China. According to the application effect of the evaluation index system and the proposed model, 3 managerial implication are put forward.

The main objective of this paper is to propose a decision-making framework for FPHP to evaluate and select the optimal site to achieve the win-win situation. In order to achieve this goal, this study proposed a decision index system which consist of 5 attributes, 12 criteria and 29 sub-criteria. The novelty of this decision index system is that it not only considers the site selection requirements of SPPS, but also consider those of fish farm. It avoids the problem that too much attention is paid to SPPS, which makes the fish farm unsustainable. The proposed decision model was established based on the IVIFN and fuzzy measure. Its novelty is that it can handle with the problems, such as the diversity of data forms, the inability to reflect the professionalism in the fuzzy measure and the inability to satisfy the independence assumption in reality, which the FPHP must face in the process of site selection but the common used decision models cannot deal with. Then, the proposed site selection framework of FPHP was used in a case to show its applicability. The sensitivity analysis is performed to verify the robustness of the proposed site selection framework, meanwhile, the comparative analysis is performed to show the fact that if the site selection requirement of fish farm and the problems in the FPHP site selection are not considered, it will lead to decision-making mistakes.

Growing awareness about the environmental and social issues and pressure from all stakeholders has forced industries to adopt sustainability practices in their supply chains. Suppliers contribute significantly to establishing an effective sustainable supply chain. Hence industries need to evaluate its potential suppliers precisely on sustainability criteria. In this work, a two-phase decision model using Fuzzy Interference System (FIS) along with Fuzzy Kano philosophy for the sustainable environment has been proposed to select sustainable suppliers for a large scale industry. In the first phase, sustainability criteria clustering using fuzzy Kano model has been performed for the identification of ‘Must-be’ criteria in economic, environmental and social dimensions. Further, sub-criteria with quantified evaluation levels for Must-be criteria have been established. In the second phase, the three distinct Fuzzy Inference System (FIS) has been developed for evaluating the Sustainability Performance Index (SPI) values of each supplier in three sustainability dimensions. In FIS, the architecture of each model is modified in such a way that each FIS engine have to compute the minimum number of rules. Finally, weights are assigned to SPI in each dimension, and suppliers are ranked for final selection. Sensitivity analysis has been performed to demonstrate the robustness of the proposed decision framework. A sustainable supplier selection problem of the iron and steel industry of India has been investigated to show the effectiveness and applicability of the proposed decision framework.

Sourcing organic food products from global green supply chains (GGSCs) spreads across country borders and interacting among a wide spectrum of intermediary stakeholders, which adds complexity and incentivises profit-seeking stakeholders to engage in food adulteration. This phenomenon is frequently observed among green and organic products with credence attributes that are difficult to authenticate in both the prior- and post-purchase stages in both developed and developing countries. The case study that we investigate is a small-sized supermarket chain operator in Hong Kong, with an increasingly expanding range of organic food products. Whilst there is a proven set of hybrid multi-criterion decision making (MCDM) methods and model that have been implemented successfully in Australia, unfortunately, unable to assure total success in optimizing the selection of organic food suppliers when applying to this supermarket chain in Hong Kong. Even the obvious solution of certification and product labeling is not sufficient to solve this organic food credibility problem. Our study proposes a newly discovered criterion as postulated by the game theoretic framework, offering a more optimal and authentic solution to the organic food authentication problem. Apart from adopting the mixed strategy of random monitoring, fines should be imposed to penalise dishonest suppliers, instead of awarding bonuses to suppliers operating with integrity. An even better solution is to institutionalise these control mechanisms by the governing authorities with continuous monitoring and imposition of penalties.

This article conducts a literature review that finds three dominant topics; supplier selection, supplier development and supplier evaluation for sustainability-related supplier risk management (SSRM). It also brings together rationalist decision models with behavioral models. The main contributions and discoveries of our review are: (a) we propose a four stage typology combining a number of decision making frameworksapplied for the first time to understanding SSRM; (b) we found that multi-criteria decision models (MCDM) as the most prevalent, but with an increasing use of fuzzy heuristics; (c) a significant quantity of articles had an approach on sustainability risk centered on ethical business conduct issues, and corporate social responsibility (CSR) is employed can be considered an emerging topic. We therefore propose a future research agenda targeted at enriching a prescriptive decision analysis where both ramifications - rationalist and behavioral - are blended to encapsulate both psychological and political sources of behavioral bias and distortion, in order to better define the complex contexts of and ambiguities in problem formulation in rationalist models.

Achieving sustainability in product life cycle involves the complex decision making process such as the selection of conceptual schemes and manufacturing processes, and disposing method of end-of-life. Morphological matrix is simple and useful tool for mapping design requirements in conceptual design for new product design and redesign of out-of-date product. Although conceptual design using morphological matrix as an important step in the product development could achieve sustainability requirements through accurate choice, quantitative decision making method is seldom considered in selection of function solution principle for sustainability. Meanwhile, uncertainties of sustainability attributes in conceptual stage generally are not also considered in these morphological matrix. To solve these problems, this paper put forward an approach based on morphological matrix to satisfy sustainability requirements in product conceptual stage considering vagueness and uncertainty through intuitionistic fuzzy number. The aim of this paper is to quantify conceptual schemes considering sustainability attributes using group decision making and intuitionistic fuzzy preference relations. The decision makers’ weights are calculated based on theirs experiences and knowledge on the function, manufacturing, economic, environment, recycle and social properties of product. The morphological matrix is used to establish the relationship between function and solution principles based on sustainability information, then the preference degree are obtained from multiple experts employing intuitionistic fuzzy judgment matrix. The preference degree for each solution principle is quantified by intuitionistic fuzzy numbers through membership, non-membership and hesitancy degree to express the linguistic terminology. To ensure the consistency and consensus issues of preference relations, an additive consistency method is employed, and correction factor is used for achieving modification automatically of judgment matrix when the consistency requirement is not met. Finally, the integrated multi-criteria decision model for function solution principle is established to carry out the final sustainability decision problem in early design stage. The feasibility and validity of proposed method is proved through a practical example for spindle system design of CNC.

Remanufacturing can restore the function of end of life (EoL) products as that of new products to realize waste utilization, which saves resources and reduces environmental pollution. So, remanufacturing is generally considered a promising way to foster cleaner production. Before an EoL product is recycled, its remanufacturability is first estimated to determine remanufacturing feasibility. Most researches on remanufacturability assessment (RA) are concentrated here, that evaluation of the whole machine or a certain type of product. However, after entering the remanufacturing system, the EoL product is disassembled into parts with various failure types and failure degree, of which the remanufacturability still need to be evaluated to determine their remanufacturing value. Currently, the research in this area are relatively lacking. In this paper, we provided a novel perspective to evaluate remanufacturability of parts based on multi-process routes, and further explored the application of RA in production decision-making. First, a RA model was established, which involves four key indicators, i.e., economic remanufacturability indicator (ERI), quality remanufacturability indicator (QRI), resource consumption indicator (RCI), and environmental emission indicator (EEI). Second, an integrated decision model was proposed with consideration of RA, and an improved genetic algorithm with a two-layer structure was designed to solve the optimal process plan and scheduling results. In the further case study of commercial engine remanufacturing, we evaluated the remanufacturability of parts and discussed the application of RA-based strategy in four production scenarios. Moreover, we compared RA-based strategy and quality grading strategy in real-life remanufacturing production. Results showed that multi-process routes based RA of parts can lead to more efficient and cleaner remanufacturing.

Fast maturation and obsolescence of electronics have caused the accumulation of untreated Waste Electrical and Electronic Equipment (WEEE), which results in environmental issues around the globe. Accordingly, remanufacturing systems have been developed to recover or perform proper disposal of WEEE. Over the decades, remanufacturing has continuously grown and has attracted customers' interest in reprocessed products, hence, inducing the competition of the said market. For a remanufacturer to gain a competitive advantage in the market, it is necessary to optimize both operational and managerial aspects comprehensively. Therefore, this study aims to model the competition between independent remanufacturer player's (IR) supply chain systems that integrate these two aspects. First, IR's supply chain system operational decisions involving multi-period-horizon and multi-process production planning, product flow, and product purchasing and takeback will be formulated into a mixed integer linear model. Lastly, IRs' supply chain system compete with each other in a perfect-market-competition game model that considers technology licensing and product quality strategies. Results from numerical examples show that royalty licensing is the better technology licensing strategy compared to fixed-fee licensing strategy in terms of costs. In addition, selling remanufactured products, which are of higher quality, yield less profit than selling refurbished products. However, remanufactured products are less susceptible to product value depreciation and price changes over time, therefore, generating more stable income in different conditions. With the fast-paced technological development, the proposed model could address complex remanufacturing supply chain problems, offering decision-makers a tool in optimizing the remanufacturing system.

Conventional materials selection system was replaced with sophisticated software tools by rapid changing technology. The growing environmental concerns and regulations widely among the industry, especially in automobiles, force us to explore the natural fiber materials as a replacement for synthetic materials which is in common use. As a result of extensive research and development, new natural fiber reinforced composite materials are emerging and the database of materials growing exponentially. The decision of selecting optimized materials was complicated, as it involves diversified choice of materials, coupled with various influencing criteria for the selection process. To abstain from deciding inappropriate materials, the technology of expert system software tools can help us in the appropriate materials selection. The objective of this research was to explore the implementation of Analytical Hierarchy Process (AHP) using the expert choice software tool for deciding optimum natural fiber reinforced composite materials by considering main criteria and sub-criteria in the hierarchical model. The final judgement was performed with different scenarios of sensitivity analysis, giving priority to the environmental factors and sustainability. The result shows that the natural fiber composite material hemp and polypropylene gained the higher rank in the selection process and almost compliant with the requirements of industrial product design specification and can be recommended to automotive component manufacturers to enforce green technology.

There is a growing concern that business enterprises and their supply chains focus primarily on economic activities and ignore their impact on the environment and society. This paper aims to help decision makers, managers, and practitioners to achieve economic growth, societal development, and environmental protection by developing sustainable supply chain performance measures and proposes a partner selection and flow allocation decision-making model. Survey data from 278 business organizations from the Indian apparel industry supply chain network were used, and an integrated method of structural equation modeling, fuzzy analytical hierarchy process, and fuzzy multi-objective linear programming was applied to the proposed model. The results of the structural equation modeling analysis indicate that the survey respondents considered sustainable production performance to be of prime importance, which thus indicates its significance in developing a sustainable supply chain for the apparel industry. To illustrate the use of the proposed model for partner selection and flow allocation decision making, real-time data from an apparel manufacturer are presented. Optimal results were obtained for two strategies—sustainability and cost saving—to show a cost–benefit trade-off when developing a sustainable supply chain. Using comparative performance, a decision maker can choose an appropriate strategy based on cost–benefit analysis of the presented trade-offs.

The purpose of this study is to assess how the integration of activity-based costing (ABC) and the theory of constraints (TOC), as well as the application of a mixed-integer programming (MIP) model, can assist in making decisions about product-mix using green manufacturing technologies (GMTs). This study proposes a mathematical programming model to analyze the profitability of a product-mix decision based on the ABC and TOC, with the adoption of new GMTs. Using a numerical example from a metal component parts manufacturer in the automotive industry, the findings of this study provide insight into the value of mathematical programming approaches for GMTs investment and product-mix decision making based on ABC systems while simultaneously improving the value of green manufacturing technology investments.

This paper introduces a decision model and framework for subcontractor selection and team formation in the built environment based on the economic/business, social and environmental triple-bottom-line aspects of sustainability. The model and decision framework aid the decision maker in forming a construction project team that can most benefit the overall goal of sustainability and compatibility amongst the subcontractors (supply chain) from a number of trade industries. The model uses both the Analytic Hierarchy Process (AHP) and the Analytic Network Process (ANP) as its basis. Once the generic decision model is defined, an example application is described to show its use and feasibility. The robustness of the solution is demonstrated using sensitivity analysis, allowing the decision maker to appreciate the complexities in this decision environment. This work builds on the relatively sparse formal mathematical modeling research and applications to sustainability in the built environment, an industry that has significant economic, social, and environmental influences.

Remanufacturing, in contrast to material recycling and disposal, can reduce environmental impacts by retaining the geometrical form of the product, thereby regarded as a more eco-efficient approach. In this paper, an end-of-life (EOL) decision model for remanufacturing options is presented to facilitate remanufacturing. The proposed model, in order to maximize the economic value of remanufacturing options while meeting environmental regulations, takes an integrative approach to EOL-option decision-making. Also presented in this paper is a hierarchical approach that represents both the overall hierarchical structure of a product and the interconnections among components. Illustrative examples are provided to demonstrate the effectiveness of the model.

With increased outsourcing and environmental consciousness, this paper presents an analytic network process (ANP) approach to incorporate the issue of hazardous substance management (HSM) into supplier selection. In this study, identification of criteria of HSM competence is categorized into four dimensions, a multi-criteria decision model is proposed. ANP is then applied to supplier selection and is characterized by interdependencies among decision structure components. An illustrative example in an electronics company is presented to demonstrate how to select a most appropriate supplier in accordance with the requirements of hazardous substance for environmental regulations.

The need to include environmental criteria in the analysis of supply chains is increasingly recognized as a result both of limitations that are posed by legislation and regulations as well as of various motivations that a company may have. A decision model is proposed in this paper based on environmental performance indicators, which may support decision making in the case of supply chains in the presence of environmental considerations. The model uses a set of principles applicable to supply chains design and operation available from previous research work.

The number of organizations contemplating the integration of environmental practices into their strategic plans and daily operations is continuously increasing. Numerous initiatives have provided incentives for organizations to become more environmentally benign. Some of these regulation driven programs are mandatory, but increasingly numerous voluntary environmental programs are also introduced by organizations. Organizations view many of these environmental programs, which may include technological and organizational development projects, as possible alternatives for gaining or maintaining a competitive advantage. One environmental program area that continues to gain in importance is one that focuses on the external relationships among organizations. To help evaluate alternatives that will effect this relationship we present a strategic decision framework that will aid managerial decision-making. This decision framework is based on literature and practice in the area of environmentally conscious business practices. The focus of this paper will be on the components and elements of green supply chain management and how they serve as a foundation for the decision framework. We shall explore the applicability of a dynamic non-linear multiattribute decision model, defined as the analytical network process, for decision making within the green supply chain. Issues facing the modeling approach are also discussed.