The impact of two-product joint lifecycles on capacity planning of remanufacturing networks

Georgiadis, Patroklos; Athanasiou, Efstratios

doi:10.1016/j.ejor.2009.05.022

Cited by 68 publications

(38 citation statements)

References 33 publications

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“…Flapper (2003) claims that the latter are strongly influenced by the factors that originally motivated the company to define them, while Fleischmann (2001) states that in order to successfully exploit the opportunities of recovering value from used products, companies need to design a logistics structure that facilitates the optimal management of goods' flows. To this end, decisions need to be taken on how to collect recoverable products from the former user, where to grade collected products in order to separate recoverable resources from scrap, where to reprocess collected products to make them fit for reuse, how to plan capacity and production process (see, for example, Georgiadis and Athanasiou, 2010) and how to distribute recovered products to future customers. More specifically, some indicative strategies suggested by researchers (Pokharel andMutha, 2009, Guide, 2000 etc.…”

Section: Environmental Consciousness and Performance Measuresmentioning

confidence: 99%

A multi-period model for managing used product returns

Nenes

Nikolaidis

2012

International Journal of Production Research

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During the last decades there has been a consistent need for companies to manufacture "green" products in order to contribute to environmental protection. The utilization of used products (literally, the extension of their useful life cycle) is an excellent, indirect way for companies to conform to this requirement and, at the same time, increase their profit. In this paper a Mixed Integer Linear Programming mathematical model is proposed, which can be used for the optimization of procurement, remanufacturing, stocking and salvaging decisions. The model is flexible enough to incorporate multiple suppliers, several quality levels of returned products and multiple periods of time.The applicability of the model is demonstrated through the optimization of alternative scenarios, the optimal solutions of which reveal the potential profitability of used products exploitation.

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Section: Environmental Consciousness and Performance Measuresmentioning

confidence: 99%

A multi-period model for managing used product returns

Nenes

Nikolaidis

2012

International Journal of Production Research

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“…Such studies consider stochastic parameters; however, we try to develop a new solution methodology for a deterministic model. Georgiadis and Athanasiou [20] extends the system dynamic-based simulation model by Georgiadis et al [21] by considering two sequential product lifecycles of two product types under two market scenarios with regard to customer preferences over the product types. Recently, they have dealt with long-term demand-driven capacity planning policies in the reverse supply chains with remanufacturing under high capacity acquisition cost coupled with uncertainty in actual demand, sales patterns, and quality, and timing of end-of-use product returns [22].…”

Section: Literature Reviewmentioning

confidence: 98%

A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks

Soleimani

Govindan

2015

Applied Mathematical Modelling

239

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Today, tracking the growing interest in closed-loop supply chain shown by both practitioners and academia is easily possible. There are many factors, which transform closed-loop supply chain issues into a unique and vital subject in supply chain management, such as environmental legislation, customer awareness, and the economical motivations of the organizations. However, designing and planning a closed-loop supply chain is an NP-hard problem, which makes it difficult to achieve acceptable results in a reasonable time. In this paper, we try to cope with this problem by proposing a new and effective solution methodology. On the other hand, this research considers improving closed-loop supply chain network optimization processes through dealing with mathematical programming tools; developing a deterministic multi-product, multi-echelon, multi-period model; and finally presenting an appropriate methodology to solve various sizes of instances. Both design and planning decision variables (location and allocation) are considered in the proposed network. Besides, in order to have a reliable performance evaluation process, large-scale instances are regarded in computational analysis. Two popular metaheuristic algorithms are considered to develop a new elevated hybrid algorithm: The Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Analyzing the above-mentioned algorithms' strengths and weaknesses leads us to attempt to improve the GA using some aspects of PSO. Therefore, a new hybrid algorithm is proposed and a complete validation process is undertaken using CPLEX and MATLAB software. In small instances, the global optimum points of CPLEX for the proposed hybrid algorithm are compared to genetic algorithm, and particle swarm optimization. Then, in small, mid, and large-size instances, performances of the proposed meta-heuristics are analyzed and evaluated. Finally, a case study involving an Iranian hospital furniture manufacturer is used to evaluate the proposed solution approach. The results reveal the superiority of the proposed hybrid algorithm when compared to the GA and PSO.

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“…Morgan and Gagnon [12] organized and reviewed journals up to 2011 with the intention of gathering a progression timeline of remanufacturing. The impact of two-product joint life cycles on the capacity planning of remanufacturing networks was presented by Georgiadis and Athanasiou [13]. The authors put emphasis on the inherent uncertainties of remanufacturing systems and proposed a system dynamics model design representing capacity-planning experiments.…”

Section: Environmentally Conscious Manufacturing and End-of-life Prodmentioning

confidence: 99%

One-Dimensional Renewable Warranty Management within Sustainable Supply Chain

Alqahtani

Gupta

2017

Resources

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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.

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The impact of two-product joint lifecycles on capacity planning of remanufacturing networks

Cited by 68 publications

References 33 publications

A multi-period model for managing used product returns

A multi-period model for managing used product returns

A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks

One-Dimensional Renewable Warranty Management within Sustainable Supply Chain

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