Joint Economic Lot-sizing in Multi-product Multi-level Integrated Supply Chains: Generalized Benders Decomposition

Gharaei, Abolfazl; Karimi, Mostafa; Shekarabi, Seyed Ashkan Hoseini

doi:10.1080/23302674.2019.1585595

Cited by 47 publications

(17 citation statements)

References 61 publications

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“…When these delayed payments constitute a large proportion of total annual turnover, a company's cash flow may be at risk (Coface 2020). In addition, shortage of working capital among certain SMEs will further negatively affect its upstream and downstream business partners (Gharaei et al 2020;Rabbani et al 2020), causing widespread funding problems. To solve this problem, it is, therefore, necessary to accelerate cash flow along the pipeline and promote financing for SMEs through innovative approaches.…”

Section: Background and Motivationmentioning

confidence: 99%

Dynamic discounting program of supply chain finance based on a financial information matching platform

2022

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The dynamic discounting (DD) program based on the financial information matching platform (FIMP) is an emerging but underexplored supply chain finance (SCF) model. This study explores the configuration and operation of the DD program, compares and analyzes the DD decision between the traditional model and the FIMP-based DD model, and reveals how the latter model improves the cash flow of participants. Compared with the traditional model, the FIMP-based DD model can improve the accuracy of the daily discount rate for small and medium-sized enterprises; it offers enterprises more capital sources and helps participants determine an optimal payment period. There are three major contributions of this study. First, it explores the mechanism of the platform in promoting the DD model. The DD program is not participated in by a single buyer and supplier, but by one buyer with multiple (N ) suppliers based on an FIMP. Once the fundamental decision parameters can be met, several suppliers can enjoy early payment from a specific buyer, thereby improving liquidity of the benefitting participants. Second, the study offers important managerial insights. Suppliers can calculate the discount rate to motivate buyers' early payment choices. Besides, early payments could be constituted from a mixed portfolio with buyer's own capital and that from financial institutions. Third, the study explores key parameters. The discount rate is not only related to the cost of working capital but also to the financing costs. Based on the varying relationships between discount rate and capital costs, the present study makes a reasonable argument for the use of mixed funds and then calculates the optimal early payment period. Finally, this study conducts a sensitivity analysis with numerical examples, which finds that, by adopting the FIMP-based DD program, both buyers and suppliers enjoy a fairer procurement environment and more profitable multi-lateral relationships.

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Section: Background and Motivationmentioning

confidence: 99%

Dynamic discounting program of supply chain finance based on a financial information matching platform

2022

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“…According to optimality criteria such as the number of iterations, optimality error, infeasibility, and complementarity, the suggested technique performed exceptionally well. There are extensive reviews of alternative sensitivity analysis solution approaches that use a similar optimality criterion ( Awasthi and Omrani, 2019 , Gharaei, Hoseini Shekarabi et al, 2020 , Gharaei, Karimi et al, 2020 , Gharaei, Karimi et al, 2019 , Giri and Masanta, 2020 , Rabbani et al, 2020 , Tsao, 2015 ). The proposed algorithm is used to select the optimal error based on the number of iterations in the proposed architectures, the optimizer for loss calculation, the number of epoch sizes, learning rates, and batch sizes.…”

Section: Managerial Implicationsmentioning

confidence: 99%

Wavelet and deep learning-based detection of SARS-nCoV from thoracic X-ray images for rapid and efficient testing

Verma

Inturi

Saurabh

et al. 2021

Expert Systems with Applications

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This paper proposes a wavelet and artificial intelligence-enabled rapid and efficient testing procedure for patients with Severe Acute Respiratory Coronavirus Syndrome (SARS-nCoV) through a deep learning approach from thoracic X-ray images. Presently, the virus infection is diagnosed primarily by a process called the real-time Reverse Transcriptase- Polymerase Chain Reaction (rRT-PCR) based on its genetic prints. This whole procedure takes a substantial amount of time to identify and diagnose the patients infected by the virus. The proposed research uses a wavelet-based convolution neural network architectures to detect SARS-nCoV. CNN is pre-trained on the ImageNet and trained end-to-end using thoracic X-ray images. To execute Discrete Wavelet Transforms (DWT), the available mother wavelet functions from different families, namely Haar, Daubechies, Symlet, Biorthogonal, Coiflet, and Discrete Meyer, were considered. Two-level decomposition via DWT is adopted to extract prominent features peripheral and subpleural ground-glass opacities, often in the lower lobes explicitly from thoracic X-ray images to suppress noise effect, further enhancing the signal to noise ratio. The proposed wavelet-based deep learning models of both, two-class instances (COVID vs. Normal) and four-class instances (COVID-19 vs. PNA bacterial vs. PNA viral vs. Normal) were validated from publicly available databases using k-Fold Cross Validation (k-Fold CV) technique. In addition to these X-ray images, images of recent COVID-19 patients were further used to examine the model’s practicality and real-time feasibility in combating the current pandemic situation. It was observed that the Symlet 7 approximation component with two-level manifested the highest test accuracy of 98.87%, followed by Biorthogonal 2.6 with an efficiency of 98.73%. While the test accuracy for Symlet 7 and Biorthogonal 2.6 is high, Haar and Daubechies with two levels have demonstrated excellent validation accuracy on unseen data. It was also observed that the precision, the recall rate, and the dice similarity coefficient for four-class instances were 98%, 98%, and 99%, respectively, using the proposed algorithm.

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“…However, in addition to the references that can be found therein, some interesting recent papers deserve to be cited, since they consider aspects of coordination and cooperation that are directly related with our model. Related with the concept of coordination, Gharaei et al [11] develop an integrated lot-sizing policy in a multi-level multi-product supply chain under stochastic conditions with limitations. They use generalised Benders decomposition to obtain a satisfactory performance in optimal solution, the number of iterations, dual infeasibility, constraint violation, and complementarity.…”

Section: Literature Reviewmentioning

confidence: 99%

Unitary Owen Points in Cooperative Lot-Sizing Models with Backlogging

2021

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This paper analyzes cost sharing in uncapacitated lot-sizing models with backlogging and heterogeneous costs. It is assumed that several firms participate in a consortium aiming at satisfying their demand over the planning horizon with minimal operating cost. Each individual firm has its own ordering channel and holding technology, but cooperation with other firms consists in sharing that information. Therefore, the firms that cooperate can use the best ordering channels and holding technology among members of the consortium. This mode of cooperation is stable. in that allocations of the overall operating cost exist, so that no group of agents benefit from leaving the consortium. Our contribution in the current paper is to present a new family of cost sharing allocations with good properties for enforcing cooperation: the unitary Owen points. Necessary and sufficient conditions are provided for the unitary Owen points to belong to the core of the cooperative game. In addition, we provide empirical evidence, through simulation, showing that, in randomly-generated situations, the above condition is fulfilled in 99% of the cases. Additionally, a relationship between lot-sizing games and a certain family of production-inventory games, through Owen’s points of the latter, is described. This interesting relationship enables easily constructing a variety of coalitionally stable allocations for cooperative lot-sizing models.

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Joint Economic Lot-sizing in Multi-product Multi-level Integrated Supply Chains: Generalized Benders Decomposition

Cited by 47 publications

References 61 publications

Dynamic discounting program of supply chain finance based on a financial information matching platform

Dynamic discounting program of supply chain finance based on a financial information matching platform

Wavelet and deep learning-based detection of SARS-nCoV from thoracic X-ray images for rapid and efficient testing

Unitary Owen Points in Cooperative Lot-Sizing Models with Backlogging

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