Sustainable Reverse Logistics Research Articles

Theory Building of Quattro Bottom Line Approach for Sustainable Reverse Logistics from Government Perspective: The Indonesia Evidence

Theory Building of Quattro Bottom Line Approach for Sustainable Reverse Logistics from Government Perspective: The Indonesia Evidence

Selection of a sustainable third-party reverse logistics provider based on the robustness analysis of an outranking graph kernel conducted with ELECTRE I and SMAA

Pressure from legislation and customers has motivated companies to consider reverse logistics (RL) in their operations. Since it is a complex procedure that requires an adequate system, the recent trend consists in outsourcing RL to third-party reverse logistics providers (3PRLPs). This paper provides the background of sustainable triple bottom line theory with focus on economic, environmental, and social aspects under 3PRL concerns. The relevant sustainability criteria are used in a case study conducted in cooperation with an Indian automotive remanufacturing company. To select the most preferred service provider, we use a hybrid method combining a variant of ELECTRE I accounting for the effect of reinforced preference, the revised Simos procedure, and Stochastic Multi-criteria Acceptability Analysis. The incorporated approach exploits all parameters of an outranking model compatible with the incomplete preference information of the Decision Maker. In particular, it derives the newly defined kernel acceptability and membership indices that can be interpreted as a support given to the selection of either a particular subset of alternatives or a single option. The proposed ELECTRE-based method enriches the spectrum of multiple criteria decision analysis approaches that can be used to effectively approach the problem of the 3PRLP selection. As indicated by the extensive review presented in the paper, this application field was so far dominated by Analytic Hierarchy Process and TOPSIS, whose weaknesses can be overcome by applying the outranking methods.

Integrating and extending data and decision tools for sustainable third-party reverse logistics provider selection

Third-party reverse logistics provider (3PRLP) selection plays an important role in the operation and implemention of reverse logistics or reverse supply chains. Economic and cost based partner relationships are no longer acceptable for 3PRLPs or for organizations that seek to introduce sustainable supply chain management. Recent emphasis on sustainability has made 3PRLP evaluation and selection more complex. In order to advance this area of research and to help further incorporate sustainability into 3PRLPs selection modeling, a novel multi-stage, multi-method, multi-criteria approach is developed. Methodologically, this is the first time that neighborhood rough set (NRS) theory is integrated with TOPSIS and VIKOR techniques. Neighborhood rough set, as a data management and soft computation tool, can help reduce the number of 3PRLP to be evaluated and ranked using TOPSIS combined with VIKOR's compromise conception decision tools. A conceptual application is developed using business, environmental, and social factors within the context of reverse logistics management decisions. A sensitivity analysis evaluating various neighborhood rough set parameters is also introduced to investigate robustness of solutions using the multi-stage methodology. Methodological implications and future research and application directions conclude the paper.

Resource commitment and sustainability: a reverse logistics performance process model

PurposeThe purpose of this paper is to extend existing and motivate future sustainable supply chain management (SCM) and logistics research by examining a structure-conduct-performance framework linking resource commitment to sustainable SCM, reverse logistics, and operational performance. A sustainable reverse logistics capability is investigated as mediating the performance benefits associated with resource commitments to sustainable SCM.Design/methodology/approachSurvey methods and structural equation modeling were used to collect and analyze data from 180 supply chain professionals.FindingsThe results of a mediated model suggest that resource commitments may be used to develop a sustainable reverse logistics capability, reducing the environmental impact of reverse logistics activities. A strong sustainable reverse logistics capability results from resources committed specifically to sustainable reverse logistics and a commitment to the sustainability of the supply chain.Research limitations/implicationsThis study applied a purposefully general sampling procedure. Specific industries may have additional constraints (e.g. risk, transparency, governance factors) that directly impact reverse logistics. These constraints are limitations of the study as well as opportunities for future research. Resource commitment is critical to the success of an overall firm strategy to build a sustainable supply chain, especially when considering reverse logistics.Practical implicationsAs managers examine the benefits of sustainable SCM, they must consider the resources required. For firms engaging in sustainable SCM, developing a sustainable reverse logistics capability is a key success factor for improved performance.Originality/valueGiven the growing acceptance and importance of sustainable SCM, this research provides insights to managers and academics regarding the key mediating role of a sustainable reverse logistics capability when integrated into existing and future supply chain research frameworks and processes.

Sustainable third-party reverse logistics provider evaluation and selection using fuzzy SWARA and developed fuzzy COPRAS in the presence of risk criteria

Reverse logistics is the backward process of collecting and redistributing products at the end-of-life from customers to producers and manufacturers for reuse, remanufacturing and disposal purposes. While reverse logistics brings several economic benefits, it seems to become a necessity for businesses to remain competitive in a world that environmental and social aspects of business activities are key to sustainable development. The operations and management of reverse logistics systems is a complex task that requires substantial level of infrastructure, technology, expertise and experience. Therefore, increasingly many business organizations tend to outsource their reverse logistics activities to third-party reverse logistics providers (3PRLPs). In this paper, we propose a multiple attribute decision making (MADM) model to rank and select 3PRLPs, using fuzzy step-wise weight assessment ratio analysis (SWARA) to weight the evaluation criteria. Accordingly, a developed fuzzy complex proportional assessment of alternatives (COPRAS) was proposed to rank and select the sustainable 3PRLPs in the presence risk factors. The suggested model was applied to a case study from automotive industry. Eventually, COPRAS and COPRAS-G methods were considered for the purpose of comparison and validation. As a result, the most sustainable 3PRLP was selected. While incorporating risk factors into our analysis, our study shows that environmental and social drivers are increasingly becoming dominant when selecting 3PRLPs.

IoT-enabled Dynamic Optimisation for Sustainable Reverse Logistics

Currently, typical challenges that logistics industry faces include the exploding logistics (including reverse logistics) tasks, the lack of real-time and accurate logistics information, and demands towards sustainable logistics. Therefore, it is difficult for logistic companies to achieve highly-efficient and sustainable reverse logistics. This paper adopts a bottom-up logistics strategy that aims to achieve the real-time information-driven dynamic optimisation distribution for logistics tasks. Under this strategic framework, an IoT-enabled real-time information sensing model is designed to sense and capture the real-time data of logistics resources, which are shared among companies after the value-added processes. Real-time information-driven dynamic optimisation for logistics tasks is proposed to optimise the configuration of logistics resources, reduce logistics cost, energy consumption and the distribution distance, and alleviate the environmental pollution. The objective of this research is to develop an innovative logistics distribution model for sustainable logistics.

An integrated decision making model for the selection of sustainable forward and reverse logistic providers

Due to rising concerns for environmental sustainability, the Indian electronic industry faces immense pressure to incorporate effective sustainable practices into the supply chain (SC) planning. Consequently, manufacturing enterprises (ME) are exploring the option of re-examining their SC strategies and taking a formalized approach towards a sustainable partnership with logistics providers. To begin with, it is imperative to associate with sustainable forward and reverse logistics providers to manage effectively the upward and downstream flows simultaneously. In this context, this paper proposes an integrated SC network for the evaluation and selection of forward distribution partners (FDP) and third party reverse logistic providers (3PRLP) from a sustainable perspective of an Indian electronic ME. The sustainable evaluation of the logistic partners is performed using fuzzy analytic hierarchy process and the technique for order performance by similarity to ideal solution. The integrated logistics network is modeled as a bi-objective mixed-integer programming problem with the objective of maximizing the profit of the manufacturer and maximizing the sustainable score of the selected forward and reverse logistics providers. The novelty of the study is its ranking of the FDPs and 3PRLPs on the economic, environmental, and social dimensions of sustainability and the simultaneous integration of logistics outsourcing decisions for the forward and reverse flow of products. Goal programming approach is utilized to capture the trade-off between the conflicting objectives and to attain a satisfying solution to the bi-objective problem. The results indicate that integrating the strategic decisions of selection of logistics partners with the operational flow planning decisions can immensely improve the sustainable performance value of the SC network and secure reasonable profits. The managerial implications drawn from the result analysis provide a sustainable framework to the ME for enhancing its corporate image.

Sustainable multi-period reverse logistics network design and planning under uncertainty utilizing conditional value at risk (CVaR) for recycling construction and demolition waste

Decreasing the environmental effect and increasing the social effect as well as the profit of organizations has gained a considerable attention in supply chain network problems. Construction and demolition wastes (C&D wastes) management poses serious challenges to the government and private sector in terms of the environmental damages and potential gains due to recycling these types of wastes. On the other hand, considering the triple bottom line in reverse logistics network design problems simultaneously is an issue that has gained little attention in previous studies. Therefore in this research we propose a multi-period multi-objective mixed integer linear programming to design and plan a network of reverse logistics under uncertainty for recycling C&D wastes in which the objectives are represented as profit and social impact maximization and environmental effect minimization. In this paper the network design problem with stochastic demand of recycled products and rate on investment is regarded. In order to cope with the uncertainty in the model, we consider a risk averse two-stage stochastic programming, where we specify the conditional value at risk (CVaR) as the risk measure. We also apply off-site and on-site separation as two common method for segregating C&D wastes. Appropriate numerical is conducted to learn the effects of crucial parameters on the model and gain managerial insights as well.

Evaluating efforts to build sustainable WEEE reverse logistics network design: comparison of regulatory and non-regulatory approaches

Many developing countries such as Turkey are still making an effort on building an infrastructure for waste of electrical and electronic equipment (WEEE) reverse logistic network design (RLND) processes. It is obvious that policies/laws/regulations related to WEEE management provide a sustainable framework for implementation in the RLND. The question is here: Does the implementation of WEEE directives make sense in terms of reducing the total cost of the network in the long term? This study aims to compare regulatory and non-regulatory situations of WEEE RLND in developing countries by formulating two models named as ‘regulatory’ and ‘non-regulatory’. Model 1 is considered as sustainable with economic, environmental and social goals, and the quotas imposed by the environmental directive are taken into consideration as the data of product return amount. In Model 2, only economic goal is considered, and product return amount is forecasted using Artificial Neural Network (ANN). A case study is conducted in a recycling company in order to evaluate performance of the proposed models. This study contributes to the relevant literature by (1) comparing the regulatory and non-regulatory situations RL models explicitly and (2) proposing ANN model to forecast EEE product return or WEEE quantity for non-regulatory situation.

A carbon-constrained stochastic optimization model with augmented multi-criteria scenario-based risk-averse solution for reverse logistics network design under uncertainty

With the increase of the concern from the public for environmental pollution and waste of resources, the value recovery through reuse, repair, remanufacturing and recycling from the end-of-use (EOU) and end-of-life (EOL) products have become increasingly important. Reverse logistics is the process for capturing the remaining value from the EOU and EOL products and also for the proper disposal of the non-reusable and non-recyclable parts. A well-designed reverse logistics system will yield both economic and environmental benefits, so the development of an advanced decision-making tool for reverse logistics system design is of significant importance. The paper presents a novel multi-product multi-echelon stochastic programming model with carbon constraint for sustainable reverse logistics design under uncertainty. Compared with the previous stochastic optimization models in reverse logistics system design, which mainly focuses on the expectation of the optimal value, this paper, however, emphasizes on both optimal value expectation and its reliability in decision-making. Due to this reason, a multi-criteria scenario-based risk-averse solution method is developed based on a latest research in order to obtain the optimal solution with high level of confidence. Later in this paper, the application of the model and the augmented solution method is illustrated and the managerial implications are discussed through the numerical experiment and sensitivity analysis. The result of the study shows that the model can be used for providing decision-makers with a deep insight into the relationship between profit and carbon emission requirement, understanding and resolution of the infeasibility caused by capacity limitation, the use of flexible manufacturing system in reverse logistics, and proper use of the government subsidy as a leverage in reverse logistics design.

A multi-objective fuzzy mathematical approach for sustainable reverse supply chain configuration

Background: Designing and implementation of reverse logistics (RL) network which meets the sustainability targets have been a matter of emerging concern for the electronics companies in India.Objectives: The present study developed a two-phase model for configuration of sustainable RL network design for an Indian manufacturing company to manage its end-of-life and endof-use electronic products. The notable feature of the model was the evaluation of facilities under financial, environmental and social considerations and integration of the facility selection decisions with the network design.Method: In the first phase, an integrated Analytical Hierarchical Process Complex Proportional Assessment methodology was used for the evaluation of the alternative locations in terms of their degree of utility, which in turn was based on the three dimensions of sustainability. In the second phase, the RL network was configured as a bi-objective programming problem, and fuzzy optimisation approach was utilised for obtaining a properly efficient solution to the problem.Results: The compromised solution attained by the proposed fuzzy model demonstrated that the cost differential for choosing recovery facilities with better environmental and social performance was not significant; therefore, Indian manufacturers must not compromise on the sustainability aspects for facility location decisions.Conclusion: The results reaffirmed that the bi-objective fuzzy decision-making model can serve as a decision tool for the Indian manufacturers in designing a sustainable RL network. The multi-objective optimisation model captured a reasonable trade-off between the fuzzy goals of minimising the cost of the RL network and maximising the sustainable performance of the facilities chosen.

A Stochastic Programming Approach with Improved Multi-Criteria Scenario-Based Solution Method for Sustainable Reverse Logistics Design of Waste Electrical and Electronic Equipment (WEEE)

Today, the increased public concern about sustainable development and more stringent environmental regulations have become important driving forces for value recovery from end-of-life and end-of use products through reverse logistics. Waste electrical and electronic equipment (WEEE) contains both valuable components that need to be recycled and hazardous substances that have to be properly treated or disposed of, so the design of a reverse logistics system for sustainable treatment of WEEE is of paramount importance. This paper presents a stochastic mixed integer programming model for designing and planning a generic multi-source, multi-echelon, capacitated, and sustainable reverse logistics network for WEEE management under uncertainty. The model takes into account both economic efficiency and environmental impacts in decision-making, and the environmental impacts are evaluated in terms of carbon emissions. A multi-criteria two-stage scenario-based solution method is employed and further developed in this study for generating the optimal solution for the stochastic optimization problem. The proposed model and solution method are validated through a numerical experiment and sensitivity analyses presented later in this paper, and an analysis of the results is also given to provide a deep managerial insight into the application of the proposed stochastic optimization model.

A fuzzy multi-objective optimization model for sustainable reverse logistics network design

Decreasing the environmental impact, increasing the degree of social responsibility, and considering the economic motivations of organizations are three significant features in designing a reverse logistics network under sustainability respects. Developing a model, which can simultaneously consider these environmental, social, and economic aspects and their indicators, is an important problem for both researchers and practitioners. In this paper, we try to address this comprehensive approach by using indicators for measurement of aforementioned aspects and by applying fuzzy mathematical programming to design a multi-echelon multi-period multi-objective model for a sustainable reverse logistics network. To reflect all aspects of sustainability, we try to minimize the present value of costs, as well as environmental impacts, and optimize the social responsibility as objective functions of the model. In order to deal with uncertain parameters, fuzzy mathematical programming is used, and to obtain solutions on Pareto front, a customized multi-objective particle swarm optimization (MOPSO) algorithm is applied. The validity of the proposed solution procedure has been analyzed in small and large size test problems based on four comparison metrics and computational time using analysis of variance. Finally, in order to indicate the applicability of the suggested model and the practicality of the proposed solution procedure, the model has been implemented in a medical syringe recycling system. The results reveal that the suggested MOPSO algorithm overtakes epsilon-constraint method from the aspects of quality of the solutions as well as computational time. Proper use of the proposed process could help managers efficiently manage the flow of recycled products with regard to environmental and social considerations, and the process offers a sustainable competitive advantage to corporations.

A Multi-objective Mathematical Programming For Sustainable Reverse Logistics Network Design. Part I: Model Formulation

Green OA (SR). Author can archive publisher's version/PDF. Link to publisher's version: http://doi.org/10.2495/IWAMA150331

Identification of Key Facilitators for Adoption of Socially Sustainable Reverse Logistics System

Identification of Key Facilitators for Adoption of Socially Sustainable Reverse Logistics System

Reprint of “Green decision-making model in reverse logistics using FUZZY-VIKOR method”

Due to the increasing global environmental consciousness and sustainability concerns, different industries that deal with Reverse Logistics (RL) are seeking methods to measure and analyze the impacts of their RL activities on the environment. However, making the greenest decision in the vague and complex area of RL makes this process somewhat difficult. Hence, to address these issues and provide a more meticulous and closer approach to the real world situations, a FUZZY-VIKOR method using interval-valued trapezoidal fuzzy numbers is proposed in the current research. As the further discussions explain, first, the significant factors in environmental sound practices together with the main processes and recovery options in RL are identified. Second, the influences of each green environmental factor on each RL recovery option are analyzed and ranked. To obtain concise results, the elicitation of experts is sought from among academicians and industry. The final results illustrate that, intriguingly, disposing of the returns has the lowest negative impact on the environment; thereby the best recovery option, while reselling of the returns was perceived as the worst recovery option. This research also suggests new directions for future research. From the managerial point of view, it would be interesting to study a sustainable reverse logistics through the proposed model. We only considered the impacts of environmental factors on RL, while the final decision will be more exact if the economic and societal aspects of RL are also analyzed. In addition, to reflect the technical views of all the stakeholders in RL towards the best recovery option and also take into account their concerns, we suggest designing a group decision making model across the RL using the Fuzzy-MADM methods.

Green decision-making model in reverse logistics using FUZZY-VIKOR method

Due to the increasing global environmental consciousness and sustainability concerns, different industries that deal with Reverse Logistics (RL) are seeking methods to measure and analyze the impacts of their RL activities on the environment. However, making the greenest decision in the vague and complex area of RL makes this process somewhat difficult. Hence, to address these issues and provide a more meticulous and closer approach to the real world situations, a FUZZY-VIKOR method using interval-valued trapezoidal fuzzy numbers is proposed in the current research. As the further discussions explain, first, the significant factors in environmental sound practices together with the main processes and recovery options in RL are identified. Second, the influences of each green environmental factor on each RL recovery option are analyzed and ranked. To obtain concise results, the elicitation of experts is sought from among academicians and industry. The final results illustrate that, intriguingly, disposing of the returns has the lowest negative impact on the environment; thereby the best recovery option, while reselling of the returns was perceived as the worst recovery option. This research also suggests new directions for future research. From the managerial point of view, it would be interesting to study a sustainable reverse logistics through the proposed model. We only considered the impacts of environmental factors on RL, while the final decision will be more exact if the economic and societal aspects of RL are also analyzed. In addition, to reflect the technical views of all the stakeholders in RL towards the best recovery option and also take into account their concerns, we suggest designing a group decision making model across the RL using the Fuzzy-MADM methods.

An analysis of keywords used in the literature on green supply chain management

Purpose The purpose of this paper is to analyze the keywords used in peer-reviewed literature on green supply chain management. Design/methodology/approach To determine the keywords that were used in this area, an analysis of 629 papers was conducted. The papers were identified through searches of 13 keywords on green supply chains. Trends in keyword usage were analyzed in detail focusing on examining variables such as the most frequently used journals/keywords, their frequencies, citation frequency and research contribution from different disciplines/countries. Findings A number of different terms have been used for research focused on the environmental impacts of supply chains, including green supply chains, sustainable supply chains, reverse logistics and closed-loop supply chains, among others. The analysis revealed that the intensity of research in this area has more than tripled in the past six years and that the most used keyword was “reverse logistics”. The use of the terms “green supply chains” and “sustainable supply chains” is increasing, and the use of “reverse logistics” is decreasing. Research limitations/implications The analysis is limited to 629 papers from the Scopus database during the period of 2007 and 2012. Originality/value The paper presents the first systematic analysis of keywords used in the literature on green supply chains. Given the broad array of terms used to refer to research in this area, this is a needed contribution. This work will help researchers in choosing keywords with high frequency and targeting journals for publishing their future work. The paper may also provide a basis for further work on developing consolidated definitions of terms focused on green supply chain management.

Performance measurement of sustainable third party reverse logistics provider by data envelopment analysis: a case study of an Indian apparel manufacturing group

In the current trend, research on reverse logistics domain is gaining its importance and has its own complexity in terms of design and operations, due to several factors. There are several stake holders participating in managing the reverse supply chains. The role of third party service provider in reverse supply chains is imperative and their performance vary according to the various dimensions. Previous studies support and evaluate the relative performance using various multi criteria decision making (MCDM) techniques. The current paper selects one such tool, data envelopment analysis (DEA) using the automated platform STATA to analyse the performance of third party logistics service provider. The study is restricted to the apparel industry domain, a least explored and a dynamic field. The factors to be in this current study are selected through a structured process. The work measures current level of performance in an observed firm which is employing the third party logistics service provider.

Planning a sustainable reverse logistics system: Balancing costs with environmental and social concerns

The present work aims to support tactical and operational planning decisions of reverse logistics systems while considering economic, environmental and social objectives. In the literature, when addressing such systems economic aspects have been often used, while environmental concerns have emerged only recently. The social component is the one less studied and rarely the combination of the three concerns has been analyzed. This work considers the three objectives and was motivated by the challenge of supporting decision makers when managing a real case study of a recyclable waste collection system, where strategic decisions on the number and location of depots, vehicles and containers were taken beforehand. Tactical and operational decisions are studied involving the establishment of service areas for each depot and the definition and scheduling of collection routes for each vehicle. Such decisions should represent a compromise solution between the three objectives translating a sustainable reverse logistics plan. The problem is modeled as a multi-objective, multi-depot periodic vehicle routing problem with inter-depot routes. A mathematical formulation and a solution approach are proposed. An approximation to the Pareto front is obtained for the case study and the trade-offs between the objectives are discussed. A balanced solution is proposed.