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The e-waste from high-technology products is at the forefront of many studies that focus on remanufacturing and selling end-of-use electronics. For the market of high-technology products, new commodities belonging to the early generation depreciate faster due to the rapid technology development and the challenge faced from remanufactured products belonging to the latest generation. The aim of this research is to develop pricing strategies for these items and understand how customer’s acceptance towards remanufactured products and the technology obsolescence of new products influence the pricing decisions. This study considers a pricing model in a system with a manufacturer, a remanufacturer, and a retailer. The manufacturer sells the new products belonging to the early generation to the retailer, while the remanufacturer sells the remanufactured product belonging to the latest generation. The customers, categorized into quality-conscious and technology-savvies, select one of the items based on the price and perceived value. The results of five game theory models (viz., Nash Equilibrium, Retailer-Stackelberg balancing power, Retailer-Stackelberg manufacturer lead, Manufacturer-Stackelberg balancing power, and Manufacturer-Stackelberg manufacturer lead) are compared. The impact of different value perceptions between quality-conscious customers and technology-savvies and each customer segment’s relative size are discussed in the five game theory models. The result shows that acting as a follower is a wise decision and suggests that the retailer, manufacturer, and remanufacturer coordinate by balancing their power.
Liangchuan Zhou; Surendra M. Gupta. Pricing strategy and competition for new and remanufactured products across generations. Journal of Remanufacturing 2021, 1 -42.
AMA StyleLiangchuan Zhou, Surendra M. Gupta. Pricing strategy and competition for new and remanufactured products across generations. Journal of Remanufacturing. 2021; ():1-42.
Chicago/Turabian StyleLiangchuan Zhou; Surendra M. Gupta. 2021. "Pricing strategy and competition for new and remanufactured products across generations." Journal of Remanufacturing , no. : 1-42.
The timely recovery and disassembly of waste electrical and electronic equipment (WEEE) can not only obtain a higher economic benefit but also can reduce the impact of hazardous substances on the environment. The parallel disassembly line can disassemble different kinds of WEEE synchronously and improve disassembly efficiency. Therefore, a parallel partial disassembly line balancing model with stochastic disassembly time is established in this paper. The evaluation indexes of the disassembly line include the number of workstations, workload smoothness, and disassembly profits. A new genetic simulated annealing algorithm is proposed to optimize the model. The encoding and decoding strategies are constructed according to the characteristics of partial disassembly and parallel layout. Two-point mapping crossover and single-point insertion mutation operations are designed to ensure that the disassembly sequence meets the precedence constraints and disassembly constraints. The simulated annealing operation is applied to the results of the genetic operation. The proposed algorithm obtains better solutions than the tabu search algorithm in stochastic parallel assembly line balancing problems, and the proposed algorithm has better performance than the CPLEX solver, genetic algorithm, and simulated annealing in parallel disassembly line balancing problems. Finally, a parallel partial disassembly line for waste televisions and refrigerators is constructed, and the performance of the proposed multi-objective algorithm is superior to those of five classical multi-objective algorithms. The results show that the proposed model has a better practical application ability and that the proposed algorithm can improve the performance of disassembly lines.
Kaipu Wang; Xinyu Li; Liang Gao; Peigen Li; Surendra M. Gupta. A genetic simulated annealing algorithm for parallel partial disassembly line balancing problem. Applied Soft Computing 2021, 107, 107404 .
AMA StyleKaipu Wang, Xinyu Li, Liang Gao, Peigen Li, Surendra M. Gupta. A genetic simulated annealing algorithm for parallel partial disassembly line balancing problem. Applied Soft Computing. 2021; 107 ():107404.
Chicago/Turabian StyleKaipu Wang; Xinyu Li; Liang Gao; Peigen Li; Surendra M. Gupta. 2021. "A genetic simulated annealing algorithm for parallel partial disassembly line balancing problem." Applied Soft Computing 107, no. : 107404.
The ever-increasing concerns of the growth in the volume of waste tires and new strict government legislations to reduce the environmental impact of the end-of-life (EOL) tires have increased interest among companies to design a sustainable and efficient closed-loop supply-chain (CLSC) network. In the real world, the CLSC network design is subject to a variety of uncertainties, such as random and fuzzy (epistemic) uncertainties. Designing a reliable and environmentally cautious CLSC with consideration of risks and the uncertainty of the parameters in the network is necessary for a successful supply-chain network. This study proposes a sustainable and environmentally cautious closed-loop supply-chain network for the tire industry, by considering several recovery options, including retreading, recycling, and energy recovery. This study aims to design and develop a robust multi-objective, multi-product, multi-echelon, multi-cycle, multi-capacity, green closed-loop supply-chain network under hybrid uncertainty. There are two types of uncertainties associated with the parameters in the network. There is an uncertainty associated with the demand, which is expressed in some future scenarios according to the probability of their occurrences, as well as fuzzy-based uncertainty associated with return rates, retreading rates, recycling rates, procurement, and production costs, which are expressed with possibilistic distributions. In order to deal with this hybrid uncertainty, a robust fuzzy stochastic programming approach has been proposed, and the proposed mixed integer programming model is applied to a case study in the tire industry to validate the model. The result indicates the applicability of the proposed model and its efficiency to control the hybrid uncertainties and the risk level in the network.
Mohsen Tehrani; Surendra Gupta. Designing a Sustainable Green Closed-Loop Supply Chain under Uncertainty and Various Capacity Levels. Logistics 2021, 5, 20 .
AMA StyleMohsen Tehrani, Surendra Gupta. Designing a Sustainable Green Closed-Loop Supply Chain under Uncertainty and Various Capacity Levels. Logistics. 2021; 5 (2):20.
Chicago/Turabian StyleMohsen Tehrani; Surendra Gupta. 2021. "Designing a Sustainable Green Closed-Loop Supply Chain under Uncertainty and Various Capacity Levels." Logistics 5, no. 2: 20.
Manufacturing and supply chain operations are on the cusp of an era with the emergence of groundbreaking technologies. Among these, the digital twin technology is characterized as a paradigm shift in managing production and supply networks since it facilitates a high degree of surveillance and a communication platform between humans, machines, and parts. Digital twins can play a critical role in facilitating faster decision making in product trade-ins by nearly eliminating the uncertainty in the conditions of returned end-of-life products. This paper demonstrates the potential effects of digital twins in trade-in policymaking through a simulated product-recovery system through blockchain technology. A discrete event simulation model is developed from the manufacturer’s viewpoint to obtain a data-driven trade-in pricing policy in a fully transparent platform. The model maps and mimics the behavior of the product-recovery activities based on predictive indicators. Following this, Taguchi’s Orthogonal Array design is implemented as a design-of-experiment study to test the system’s behavior under varying experimental conditions. A logistics regression model is applied to the simulated data to acquire optimal trade-in acquisition prices for returned end-of-life products based on the insights gained from the system.
Özden Tozanlı; Elif Kongar; Surendra Gupta. Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain. Sustainability 2020, 12, 5416 .
AMA StyleÖzden Tozanlı, Elif Kongar, Surendra Gupta. Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain. Sustainability. 2020; 12 (13):5416.
Chicago/Turabian StyleÖzden Tozanlı; Elif Kongar; Surendra Gupta. 2020. "Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain." Sustainability 12, no. 13: 5416.
Growing rates of innovation and consumer demand resulted in rapid accumulation of waste of electrical and electronic equipment or electronic waste (e-waste). In order to build and sustain green cities, efficient management of e-waste rises as a viable response to this accumulation. Accurate e-waste predictions that municipalities can utilize to build appropriate reverse logistics infrastructures gain significance as collecting, recycling and disposing the e-waste become more complex and unpredictable. In line with its significance, the related literature presents several methodologies focusing on e-waste generation forecasting. Among these methodologies, grey modeling approach has aroused interest due to its ability to present meaningful results with small-sized or limited data. In order to improve the overall success rate of the approach, several grey modeling-based forecasting techniques have been proposed throughout the past years. The performance of these models, however, profoundly leans on the parameters used with no established consensus regarding the suitable criteria for better accuracy. To address this issue and to provide a guideline for academicians and practitioners, this paper presents a comparative analysis of most utilized grey modeling methods in the literature improved by particle swarm optimization. A case study employing e-waste data from Washington State is provided to demonstrate the comparative analysis proposed in the study.
Gazi Murat Duman; Elif Kongar; Surendra M. Gupta. Predictive analysis of electronic waste for reverse logistics operations: a comparison of improved univariate grey models. Soft Computing 2020, 24, 15747 -15762.
AMA StyleGazi Murat Duman, Elif Kongar, Surendra M. Gupta. Predictive analysis of electronic waste for reverse logistics operations: a comparison of improved univariate grey models. Soft Computing. 2020; 24 (20):15747-15762.
Chicago/Turabian StyleGazi Murat Duman; Elif Kongar; Surendra M. Gupta. 2020. "Predictive analysis of electronic waste for reverse logistics operations: a comparison of improved univariate grey models." Soft Computing 24, no. 20: 15747-15762.
Governmental regulations for expansion of e-waste and customers’ awareness of green goods drive remanufacturing practices in high-technology electronic products. Selling remanufactured high-technology products is a challenge because of the shorter residual life of usage and potential cannibalisation of new items. Smartphones and tablets, as hi-tech products, are replaced by new generation models quickly due to rapid development. Products belonging to earlier generations become less demanding. Customer’s acceptance for outmoded remanufactured items is even less. Learning how value depreciated is vital for the sellers to make a wise price decision for these new and remanufactured products belonging to various generations. This study leverages transaction data of iPhones and iPads from eBay, uses partial least square method to explore the factors that affect value depreciation rate and price differentiation between new and remanufactured products. Variables which are categorised as marketing elements, technology features, appearance design, and customer attention are hypothesised to be critical to value depreciation. The result differs in iPhones and iPads but shows some consistency that time since release, product thickness, camera resolution, and fans/hits ratio are highly correlated to the value depreciation rate. ApplePay slows down the rate for iPhones while cellular data function accelerates the rate for iPads.
Liangchuan Zhou; Surendra M. Gupta. Value depreciation factors for new and remanufactured high-technology products: a case study on iPhones and iPads. International Journal of Production Research 2020, 58, 7218 -7249.
AMA StyleLiangchuan Zhou, Surendra M. Gupta. Value depreciation factors for new and remanufactured high-technology products: a case study on iPhones and iPads. International Journal of Production Research. 2020; 58 (23):7218-7249.
Chicago/Turabian StyleLiangchuan Zhou; Surendra M. Gupta. 2020. "Value depreciation factors for new and remanufactured high-technology products: a case study on iPhones and iPads." International Journal of Production Research 58, no. 23: 7218-7249.
Growing environmental awareness and widening extended producer responsibility have heightened the need for economically, environmentally, and socially sustainable business strategies levered by digital technologies. As an extension, various take-back policies focusing on product waste and recovery are put in place by the high-tech manufacturing industry. With an attempt to increase sales while ensuring the environmental sustainability of products, trade-in programmes that incentivize consumers to exchange used goods for new and most recent technology products became a value-adding strategy for businesses. Due to the high unpredictability in the quality of returned devices however, determining trade-in margins is a challenging task for original equipment manufacturers (OEMs). This inevitably reveals the need for incorporating intelligent technologies into the formation of manufacturing and logistics architectures to simultaneously preserve OEMs profitability and ensure the sustainable development of the closed-loop supply chain activities. With this motivation, this study presents the use of IoT-embedded products in a blockchain-enabled disassembly-to-order system to determine the optimal trade-in-to-upgrade policy. A discrete-event simulation model is developed to obtain the expected cost of the disassembly-to-order system. Optimal incentives for varying product qualities are then computed by utilising this cost in the trade-in policy model.
Özden Tozanlı; Elif Kongar; Surendra M. Gupta. Trade-in-to-upgrade as a marketing strategy in disassembly-to-order systems at the edge of blockchain technology. International Journal of Production Research 2020, 58, 7183 -7200.
AMA StyleÖzden Tozanlı, Elif Kongar, Surendra M. Gupta. Trade-in-to-upgrade as a marketing strategy in disassembly-to-order systems at the edge of blockchain technology. International Journal of Production Research. 2020; 58 (23):7183-7200.
Chicago/Turabian StyleÖzden Tozanlı; Elif Kongar; Surendra M. Gupta. 2020. "Trade-in-to-upgrade as a marketing strategy in disassembly-to-order systems at the edge of blockchain technology." International Journal of Production Research 58, no. 23: 7183-7200.
Rapid and revolutionary changes in technology and rising demand for consumer electronics have led to staggering rates of accumulation of electrical and electronic equipment waste, viz., WEEE or e-waste. Consequently, e-waste has become one of the fastest growing municipal solid waste streams in the United States making its efficient management crucial in supporting the efforts to create and sustain green cities. Accurate estimations on the amount of e-waste might help in increasing the efficiency of waste collection, recycling and disposal operations that have become more complicated and unpredictable. Early work focusing on prediction of e-waste generation includes a wide range of methodologies. Among these, grey forecasting models have drawn attention due to their capability to provide meaningful results with relatively small-sized or limited data. The performance of grey models heavily rely on their parameters. The purpose of this study is to present a novel forecasting technique for e-waste predictions with multiple inputs in presence of limited historical data. The proposed nonlinear grey Bernoulli model with convolution integral NBGMC(1,n) improved by Particle Swarm Optimization (PSO) demonstrates superior accuracy over alternative forecasting models. The proposed model and its findings are delineated with the help of a case study utilizing Washington State e-waste data. The results indicate that population density has a major impact on the generated e-waste followed by household income level. The findings also show that the e-waste generation forms a saturated distribution in Washington State. These results can help decision makers plan for more effective reverse logistics infrastructures that would ensure proper collection, recycling and disposal of e-waste.
Gazi Murat Duman; Elif Kongar; Surendra M. Gupta. Estimation of electronic waste using optimized multivariate grey models. Waste Management 2019, 95, 241 -249.
AMA StyleGazi Murat Duman, Elif Kongar, Surendra M. Gupta. Estimation of electronic waste using optimized multivariate grey models. Waste Management. 2019; 95 ():241-249.
Chicago/Turabian StyleGazi Murat Duman; Elif Kongar; Surendra M. Gupta. 2019. "Estimation of electronic waste using optimized multivariate grey models." Waste Management 95, no. : 241-249.
The concept of efficiency has always been and will continue to be important for competitive business environments where limited resources exist. Owing to the growing complexity of organizations and economy in general, this trend is expected to continue to remain a high priority for organizations. Continuous performance evaluations that utilize both qualitative and quantitative information play a significant role in sustaining efficient and effective business processes. Therefore, the literature offers a wide range of performance evaluation methodologies to assess the operational efficiency in various industries. Majority of these models, however, focus solely on quantitative criteria, avoiding the interrelations and dependencies between qualitative and quantitative measurements. Furthermore, these methodologies tend to utilize discrete and contemporary information eliminating historical performance data. With these motivations, this paper proposes an integrated approach combining fuzzy decision-making trial and evaluation laboratory (DEMATEL), analytic network process (ANP), and artificial neural network (ANN) methodologies for performance evaluation. In the proposed model, DEMATEL and ANP methodologies are utilized in a group decision-making concept to obtain priorities of the evaluation criteria. Following this, an ANN model is designed and trained with historical performance data collected from the organization and the results of the fuzzy DEMATEL-ANP model. The outcomes include the relational data among the criteria and alternatives used in the model in addition to their relative rankings. A food industry case study is presented to demonstrate the steps of the proposed model.
Gazi Murat Duman; Ahmed ElSayed; Elif Kongar; Surendra M. Gupta. An Intelligent Multiattribute Group Decision-Making Approach With Preference Elicitation for Performance Evaluation. IEEE Transactions on Engineering Management 2019, 67, 885 -901.
AMA StyleGazi Murat Duman, Ahmed ElSayed, Elif Kongar, Surendra M. Gupta. An Intelligent Multiattribute Group Decision-Making Approach With Preference Elicitation for Performance Evaluation. IEEE Transactions on Engineering Management. 2019; 67 (3):885-901.
Chicago/Turabian StyleGazi Murat Duman; Ahmed ElSayed; Elif Kongar; Surendra M. Gupta. 2019. "An Intelligent Multiattribute Group Decision-Making Approach With Preference Elicitation for Performance Evaluation." IEEE Transactions on Engineering Management 67, no. 3: 885-901.
Disassembly sequence planning (DSP) is a nondeterministic polynomial time (NP) complete problem, making the utilization of metaheuristic approaches a viable alternative. DSP aims at creating efficient algorithms for deriving the optimum or near-optimum disassembly sequence for a given product or a product family. The problem-specific nature of such algorithms, however, requires these solutions to be validated, proving their versatility in accommodating substantial variations in the problem environment. To achieve this goal, this paper utilizes Taguchi’s orthogonal arrays to test the robustness of a previously-proposed Simulated Annealing (SA) algorithm. A comparison with an exhaustive search is also conducted to verify the efficiency of the algorithm in generating an optimum or near-optimum disassembly sequence for a given product. In order to further improve the solution, a distributed task allocation technique is also introduced into the model environment to accommodate multiple robot arms.
Mohammad Alshibli; Ahmed ElSayed; Elif Kongar; Tarek Sobh; Surendra M. Gupta. A Robust Robotic Disassembly Sequence Design Using Orthogonal Arrays and Task Allocation. Robotics 2019, 8, 20 .
AMA StyleMohammad Alshibli, Ahmed ElSayed, Elif Kongar, Tarek Sobh, Surendra M. Gupta. A Robust Robotic Disassembly Sequence Design Using Orthogonal Arrays and Task Allocation. Robotics. 2019; 8 (1):20.
Chicago/Turabian StyleMohammad Alshibli; Ahmed ElSayed; Elif Kongar; Tarek Sobh; Surendra M. Gupta. 2019. "A Robust Robotic Disassembly Sequence Design Using Orthogonal Arrays and Task Allocation." Robotics 8, no. 1: 20.
New generations of high-technology products are frequently launched before the previous model is sold out. Customers have an incentive to end the use of their old product and purchase a new one with the latest technological innovations. The unsold old models become less attractive, while the supply of remanufactured products from end-of-use products is uncertain in time, quantity, and quality. Other than adjusting the price, upgrading the returning unsold new products may be a source of remedy. This study provides profit maximization models associated with customer choice demand functions based on manufacturer, retailer, and joint supply chain scenarios. Two acquisition strategies are compared: acquire end-of-use products only and collect both end-of-use products and unsold old-style new products. The results reveal that returning the optimal quantity of overstocked new products brings about a greater benefit in all scenarios. Compared to the remanufacturer, the retailer is the optimal undertaker for collecting used products. In addition to this, slow technological development of the new-generation model causes a decrease in profit for the manufacturer. The optimal quantity of new products to be bought back decreases, because both the manufacturer and the retailer prefer to promote unsold outmoded products rather than upgrade the used products.
Liangchuan Zhou; Surendra M. Gupta. A Pricing and Acquisition Strategy for New and Remanufactured High-Technology Products. Logistics 2019, 3, 8 .
AMA StyleLiangchuan Zhou, Surendra M. Gupta. A Pricing and Acquisition Strategy for New and Remanufactured High-Technology Products. Logistics. 2019; 3 (1):8.
Chicago/Turabian StyleLiangchuan Zhou; Surendra M. Gupta. 2019. "A Pricing and Acquisition Strategy for New and Remanufactured High-Technology Products." Logistics 3, no. 1: 8.
Internet of Things (IoT) can play a crucial role in End-of-Life (EOL) product recovery. It can help in determining conditions of returned EOL products with the help of sensors and RFID tags, which then can be used to decide a feasible recovery process for the EOL product amongst disassembly, remanufacturing, recycling or disposal. Product design is a key criterion which affects the choice of recovery process. Complex product designs will increase the cost of disassembly which will lead to higher recovery costs. Therefore, considering the recovery operations during a product's design phase can lead to effective recovery process after its EOL. In order to see the effect of product design on product recovery using IoT, this paper proposes an Advanced-Remanufacturing-To-Order-Disassembly-To-Order (ARTODTO) system which receives sensors and Radio Frequency Identification (RFID) tags embedded End-Of-Life (EOL) products to satisfy various products, components and materials demands. The received EOL products can be recovered via disassembly to meet the components demands, remanufactured to meet the products demands or recycled to meet the materials demands. The remaining EOL products can be disposed of. The model evaluates different designs of a product for the ease of disassembly and remanufacturing based on three criteria viz., total profit, quality level and the number of disposed items. To solve the proposed multi-criteria decision-making model, linear physical programming is used. An example of laptops is considered for illustration of the proposed methodology.
Aditi D. Joshi; Surendra M. Gupta. Evaluation of design alternatives of End-Of-Life products using internet of things. International Journal of Production Economics 2018, 208, 281 -293.
AMA StyleAditi D. Joshi, Surendra M. Gupta. Evaluation of design alternatives of End-Of-Life products using internet of things. International Journal of Production Economics. 2018; 208 ():281-293.
Chicago/Turabian StyleAditi D. Joshi; Surendra M. Gupta. 2018. "Evaluation of design alternatives of End-Of-Life products using internet of things." International Journal of Production Economics 208, no. : 281-293.
Unlike consumer durable goods, high technology products have shorter life cycles. Many companies launch new items at a fast rate of innovation. As a result, the latest products often compete in the same market with earlier-generation products. In addition to brand-new products, used products returned by early-adopting customers still have significant value. Returned products can be restored using the remanufacturing process and offered to other customers. Therefore, multiple generations of new products and remanufactured products can be available side-by-side in the marketplace. New products are affected by technology obsolescence, while the remanufactured products are affected by quality and technology obsolescence. The prices of each product should be adjusted according to the product value and the customer perceived value over time. This research paper discusses pricing considerations for new and remanufactured products across multiple generations. The iPhone is used as an example because it is a high-tech electronic product with a short life cycle. In this study, pricing data of the new and remanufactured products of different generations is extracted from eBay. Factors that influence pricing and interaction among these factors are identified. In addition, price trend functions with the average quality and the technology depreciation rates are proposed.
Liangchuan Zhou; Surendra M. Gupta. Marketing research and life cycle pricing strategies for new and remanufactured products. Journal of Remanufacturing 2018, 9, 29 -50.
AMA StyleLiangchuan Zhou, Surendra M. Gupta. Marketing research and life cycle pricing strategies for new and remanufactured products. Journal of Remanufacturing. 2018; 9 (1):29-50.
Chicago/Turabian StyleLiangchuan Zhou; Surendra M. Gupta. 2018. "Marketing research and life cycle pricing strategies for new and remanufactured products." Journal of Remanufacturing 9, no. 1: 29-50.
The costs associated with inspecting wind turbines are high due to their size and complexity. One potential method by which such costs can be reduced, is through the development of robust systems that can monitor the conditions of wind turbines remotely. This study proposes embedding sensors into wind turbines to monitor the conditions of the wind turbines throughout their life cycles. The information retrieved from these sensors could be helpful in two ways: It could facilitate the provision of predictive maintenance for the turbines and enhance the performance of end-of-life (EOL) processing operations. During the maintenance phase, sensors can help to predict failures before they occur because they provide condition information about the products. During the EOL processing phase, they help to improve disassembly and inspection operations. Therefore, the use of embedded sensors in wind turbines could potentially reduce maintenance costs and increase EOL profit. This study compares regular and sensor-embedded wind turbine systems, which are modeled using discrete event simulation. A design of experiments study was carried out on the models. During the experimental stage, while conducting experiments, key variables, such as maintenance cost, disassembly cost, inspection cost, and EOL profit, were monitored. At the analysis stage, pairwise t-tests were performed to determine the statistical significance of the results. The results stage revealed that sensors can provide significant benefits to closed-loop supply chain systems when they are embedded into wind turbines.
Mehmet Talha Dulman; Surendra M. Gupta. Maintenance and remanufacturing strategy: using sensors to predict the status of wind turbines. Journal of Remanufacturing 2018, 8, 131 -152.
AMA StyleMehmet Talha Dulman, Surendra M. Gupta. Maintenance and remanufacturing strategy: using sensors to predict the status of wind turbines. Journal of Remanufacturing. 2018; 8 (3):131-152.
Chicago/Turabian StyleMehmet Talha Dulman; Surendra M. Gupta. 2018. "Maintenance and remanufacturing strategy: using sensors to predict the status of wind turbines." Journal of Remanufacturing 8, no. 3: 131-152.
Sensors are commonly employed to monitor products during their life cycles and to remotely and continuously track their usage patterns. Installing sensors into products can help generate useful data related to the conditions of products and their components, and this information can subsequently be used to inform EOL decision-making. As such, embedded sensors can enhance the performance of EOL product processing operations. The information collected by the sensors can also be used to estimate and predict product failures, thereby helping to improve maintenance operations. This paper describes a study in which system maintenance and EOL processes were combined and closed-loop supply chain systems were constructed to analyze the financial contribution that sensors can make to these procedures by using discrete event simulation to model and compare regular systems and sensor-embedded systems. The factors that had an impact on the performance measures, such as disassembly cost, maintenance cost, inspection cost, sales revenues, and profitability, were determined and a design of experiments study was carried out. The experiment results were compared, and pairwise t-tests were executed. The results reveal that sensor-embedded systems are significantly superior to regular systems in terms of the identified performance measures.
Mehmet Dulman; Surendra Gupta. Evaluation of Maintenance and EOL Operation Performance of Sensor-Embedded Laptops. Logistics 2018, 2, 3 .
AMA StyleMehmet Dulman, Surendra Gupta. Evaluation of Maintenance and EOL Operation Performance of Sensor-Embedded Laptops. Logistics. 2018; 2 (1):3.
Chicago/Turabian StyleMehmet Dulman; Surendra Gupta. 2018. "Evaluation of Maintenance and EOL Operation Performance of Sensor-Embedded Laptops." Logistics 2, no. 1: 3.
Growing environmental awareness coupled with stricter governmental regulations has fueled the need for integrating sustainability into supply chain and logistics activities. Accordingly, recent studies in the literature have emphasized the significance of environmentally concerned logistics operations (ECLO). Research in the broad area of ECLO encompasses a wide range of topics including sustainable supply chain, green supply chain, closed-loop supply chain, low-carbon logistics, and waste management. In this paper, a comprehensive content analysis and area review is presented. Over 800 papers published between 1994 and 2017 in peer-reviewed journals, proceedings, and book chapters are utilized. These papers are analyzed in consecutive stages after being reviewed under a structural dimension process that addresses the fields of environmentally concerned logistics operations. Following the state-of-the-art review, a detailed analysis of ECLO research with a special emphasis on fuzzy applications is provided. The findings clearly indicate that the fuzzy multi-criteria decision making technique is a frequently used hybrid method, whereas fuzzy sets theory and other fuzzy hybrid techniques identify a gap in the related literature. This paper provides further critical analysis and other research suggestions in order to clarify these gaps and offer additional research perspectives. This information may provide extensive data that will enable future researchers to fill these gaps within this field.
Ozden Tozanli; Gazi Duman; Elif Kongar; Surendra Gupta. Environmentally Concerned Logistics Operations in Fuzzy Environment: A Literature Survey. Logistics 2017, 1, 4 .
AMA StyleOzden Tozanli, Gazi Duman, Elif Kongar, Surendra Gupta. Environmentally Concerned Logistics Operations in Fuzzy Environment: A Literature Survey. Logistics. 2017; 1 (1):4.
Chicago/Turabian StyleOzden Tozanli; Gazi Duman; Elif Kongar; Surendra Gupta. 2017. "Environmentally Concerned Logistics Operations in Fuzzy Environment: A Literature Survey." Logistics 1, no. 1: 4.
Purpose: Remanufactured products, in addition to being environment friendly, are popular with consumers because they can offer the latest technology with lower prices in comparison to brand new products. However, some consumers are hesitant to buy remanufactured products because they are skeptical about the quality of the remanufactured product and thus are unsure of the extent to which the product will render services when compared to a new product. A strategy that remanufacturers may employ to entice customers is to offer warranties on remanufactured products. To that end, this paper studies and scrutinizes the impact of offering renewing warranties on remanufactured products. Specifically, the paper suggests a methodology which simultaneously minimizes the cost incurred by the remanufacturers and maximizes the confidence of the consumers towards buying remanufacturing products.Design/methodology/approach: This study uses discrete-event simulation to optimize the implementation of a two-dimensional renewing warranty policy for remanufactured products. The implementation is illustrated using a specific product recovery system called the Advanced Remanufacturing-To-Order (ARTO) system. The experiments used in the study were designed using Taguchi’s Orthogonal Arrays to represent the entire domain of the recovery system so as to observe the system behavior under various experimental conditions. In order to determine the optimum strategy offered by the remanufacturer, various warranty and preventive maintenance scenarios were analyzed using pairwise t-tests along with one-way analysis of variance (ANOVA) and Tukey pairwise comparisons tests for every scenario.Findings: The proposed methodology is able to simultaneously minimize the cost incurred by the remanufacturer, optimize the warranty price and period, and optimize the preventive maintenance strategy resulting in increased consumer confidence.Originality/value: This is the first study that evaluates in a quantitative and comprehensive manner the potential benefits of offering warranties with preventive maintenance on remanufactured products.
Ammar Alqahtani; Surendra M. Gupta. Optimizing two-dimensional renewable warranty policies for sensor embedded remanufactured products. Journal of Industrial Engineering and Management 2017, 10, 145 -187.
AMA StyleAmmar Alqahtani, Surendra M. Gupta. Optimizing two-dimensional renewable warranty policies for sensor embedded remanufactured products. Journal of Industrial Engineering and Management. 2017; 10 (2):145-187.
Chicago/Turabian StyleAmmar Alqahtani; Surendra M. Gupta. 2017. "Optimizing two-dimensional renewable warranty policies for sensor embedded remanufactured products." Journal of Industrial Engineering and Management 10, no. 2: 145-187.
Sensor embedded products utilize sensors implanted into products during their production process. Sensors are useful in predicting the best warranty policy and warranty period to offer a customer for remanufactured components and products. The conditions and remaining lives of components and products can be estimated prior to offering a warranty based on the data provided by the sensors. This helps reduce the number of claims during warranty periods, determines the right preventive maintenance (PM) policy, and eliminates unnecessary costs inflicted on the remanufacturer. The renewing, one-dimensional Free Replacement Warranty (FRW), Pro-Rata Warranty (PRW), and combination FRW/PRW policies’ costs for remanufactured products and components were evaluated with/without offering PM for different periods in this paper. To that end, the effect of offering renewable, one-dimensional, Free Replacement Warranty (FRW), or Pro-Rata Warranty (PRW), or combination FRW/PRW warranty policies for each disassembled component and sensor embedded remanufactured product was examined, and the impact of sensor embedded products on warranty costs was assessed. A case study and varying simulation scenarios is examined and presented to illustrate the model’s applicability.
Ammar Y. Alqahtani; Surendra M. Gupta. One-Dimensional Renewable Warranty Management within Sustainable Supply Chain. Resources 2017, 6, 16 .
AMA StyleAmmar Y. Alqahtani, Surendra M. Gupta. One-Dimensional Renewable Warranty Management within Sustainable Supply Chain. Resources. 2017; 6 (2):16.
Chicago/Turabian StyleAmmar Y. Alqahtani; Surendra M. Gupta. 2017. "One-Dimensional Renewable Warranty Management within Sustainable Supply Chain." Resources 6, no. 2: 16.
Electronic products enter the waste stream rapidly due to technological enhancements. Their parts and material recovery involve significant economic and environmental gain. To regain the value added to such products a certain level of disassembly may be required. Disassembly operations are often expensive and the complexity of determining the best disassembly sequence increases as the number of parts in a product grows. Therefore, it is necessary to develop methodologies for obtaining optimal or near optimal disassembly sequences to ensure efficient recovery process. To that end, this chapter introduces a Genetic Algorithm based methodology to develop disassembly sequencing for end-of-life products. A numerical example is presented to provide and demonstrate better understating and functionality of the algorithm.
Ahmed Elsayed; Elif A. Kongar; Surendra M. Gupta. A Heuristic Approach for Disassembly Sequencing Problem for Robotic Disassembly Operations. Genetic Algorithms and Applications for Stock Trading Optimization 2012, 438 -447.
AMA StyleAhmed Elsayed, Elif A. Kongar, Surendra M. Gupta. A Heuristic Approach for Disassembly Sequencing Problem for Robotic Disassembly Operations. Genetic Algorithms and Applications for Stock Trading Optimization. 2012; ():438-447.
Chicago/Turabian StyleAhmed Elsayed; Elif A. Kongar; Surendra M. Gupta. 2012. "A Heuristic Approach for Disassembly Sequencing Problem for Robotic Disassembly Operations." Genetic Algorithms and Applications for Stock Trading Optimization , no. : 438-447.
Rapid technological developments are leading to a significant decrease in the demand for old technology products. As a result, old technology products are rushed to their end-of-lives (EOLs) even though they still function properly and have the ability to satisfy stated needs. It is therefore important to find environmentally and economically benign ways to handle this accumulating waste to regain the value added to such products and to reduce the environmental damage. However, EOL recovery options are not always economically justifiable due to the complexity and uncertainty involved in the process. To reduce these setbacks, it is crucial to perform an analysis prior to taking any action and rank the products according to the importance of their EOL processing outcomes. To this end, this chapter proposes a data envelopment analysis (DEA) algorithm to determine the technical efficiency of end-of-life processing of household appliances and automobiles depending on various tangible and intangible performance criteria.
Elif Kongar; Surendra M. Gupta. A Data Envelopment Analysis Approach for Household Appliances and Automobile Recycling. Green Technologies 2011, 378 -387.
AMA StyleElif Kongar, Surendra M. Gupta. A Data Envelopment Analysis Approach for Household Appliances and Automobile Recycling. Green Technologies. 2011; ():378-387.
Chicago/Turabian StyleElif Kongar; Surendra M. Gupta. 2011. "A Data Envelopment Analysis Approach for Household Appliances and Automobile Recycling." Green Technologies , no. : 378-387.