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A New Vendor-Managed Inventory Model by Applying Blockchain Technology and Considering Environmental Problems

Process Integration and Optimization for Sustainability Aims and scope Submit manuscript

Abstract

One of the most common and successful approaches to integrated supply chain management (SCM) is vendor-managed inventory (VMI). One of the technologies that has been widely used recently to share information in the VMI is blockchain technology (BT). Given that many factors, such as scalability and adoption cost, play a role in obtaining the optimal number of transactions, it is of vital importance to account for them in the VMI. Since the VMI strategy aims for a long-term relationship between the vendor and the retailer and highly affects the supply chain’s total cost, the vendor must pay more attention in selecting retailers. Another contribution of this study is to consider the issue of environmental pollution generated by inventory holding, ordering, set-up, and transportation operations, which has not been thoroughly investigated in the existing literature. This study uses a green two-echelon multi-product, multiple-vendor, and multiple-retailer supply chain with a hybrid of multi-criteria decision-making (MCDM) methods, and a multi-objective programming under the VMI policy is developed. This paper examines BT for supply chain management by accounting for the most impactful criteria of BT implementation in retailer selection and optimization. For this purpose, this paper applies the Bayesian best–worst approach (BWM) as one of the MCDM techniques. The obtained weights are then plugged into the model as the inputs of the proposed model. Finally, the efficiency of the presented method is verified through a case study with actual data collected from the electronic supply chain.

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Data Availability

All data generated or analyzed during this study are included in this published article (and its Appendix information section).

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Correspondence to Nasser Motahari Farimani.

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Appendices

Appendix 1

Tables

Table 11 Data related to transactions, ordering costs, and demand

11 and

Table 12 Data related to holding cost, backorder cost, capacity, and production rate

12

Appendix 2. Tuning the GA and PSO Parameters

To run the GA and PSO, it is essential to effectively tune their parameter values, as the performance of these techniques is affected by modifying their parameters. To tune the parameters of the GA (i.e., the population size (Popsize), the crossover rate \(\left({p}_{c}\right)\), the percent of mutation \(\left({p}_{m}^{2}\right)\), and parameters of PSO: the inertia weights \(\left({\omega }_{i},{\omega }_{f}\right)\), the acceleration coefficients \(\left({c}_{1},{c}_{2}\right)\), and the Taguchi’s experiment design technique are leveraged. To this end, three different levels for each of the parameters are chosen. So, with three parameters in both algorithms, a design is employed with 9 experiments. The Taguchi’s orthogonal design L9 is selected to run the experiments. Table 7 presents various combinations of parameter levels in Taguchi’s experiment. The value S/N is obtained from Eq. (56), where n denotes the number of orthogonal arrays and \({y}_{i}\) denotes the response in the ith experiment, on which the efficiency of the experiments is based. The highest S/N ratio is specific to the optimal level for each factor.

$${~}^{S}\!\left/ \!{~}_{N}\right.=-10\mathrm{log}\left({~}^{1}\!\left/ \!{~}_{n}\right.\sum\limits_{i=1}^{n}{~}^{1}\!\left/ \!{~}_{{y}_{i}^{2}}\right.\right)$$
(56)

Tables

Table 13 Taguchi’s experimental results on test problem for the GA

13 and

Table 14 Taguchi’s experimental results on the test problem for the PSO

14 show Taguchi’s experimental results on test problems for the GA and PSO. OF in Table 4 represents the objective function.

Figure 

Fig. 7
figure 7

The relationship between the mean and S/N values for different contours of the objective function

7 shows the relationship between the mean and S/N values for different contours of the objective function. It is clear from Fig. 7 that the objective function increases with the increase of the mean and the decrease of S/N values.

As it is clear from Fig. 

Fig. 8
figure 8

The relationship between the mean and S/N values

8, according to the values of the objective functions obtained in different runs, the mean and S/N values are completely opposite to each other. That is, when S/N is at its maximum value, the mean is at its minimum value. Therefore, there is no need to repeat the experiments, so it can be said that the obtained results have sufficient validity.

Figure 

Fig. 9
figure 9

The relationship between the objective function and S/N values

9 shows the relationship between the values obtained from different runs of the objective and S/N values.

Figure 

Fig. 10
figure 10

Mean S/N ratios of the parameters of GA and PSO

10 shows the mean of S/N obtained by the Taguchi’s method on test problems for the GA and PSO, respectively. It is clear from Fig. 10 that the maximum points for factors 2 and 3 in GA are at level 1 and for factors 1 and 4 in GA are at level 3, so they are selected as optimal parameters of GA. Also, the maximum point for factors 1 and 3 in PSO are at level 3, and for factors 2 and 3 in PSO are at level 1, so they are selected as optimal parameters of PSO.

Table

Table 15 Controllable factors and their levels

15 shows the controllable factors and their levels.

Appendix 3. Convergence Diagram of the Best Implementation of the PSO and GA

Here, a convergence diagram of the best implementation of the PSO and GA is given (Fig. 

Fig. 11
figure 11

Convergence diagram of the best implementation of the PSO and GA

11).

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Modares, A., Farimani, N.M. & Dehghanian, F. A New Vendor-Managed Inventory Model by Applying Blockchain Technology and Considering Environmental Problems. Process Integr Optim Sustain 7, 1211–1239 (2023). https://doi.org/10.1007/s41660-023-00338-7

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