<p style='text-indent:20px;'>One of the most common and successful approaches for the integrated supply chain management (SCM) is the vendor-managed inventory (VMI). In VMI, a vendor takes control of the inventory decisions for retailers. To establish a long-run relationship between the vendor and the retailer, it is necessary to consider two impactful factors: the vendors and retailers' reliability, and the optimal selection of retailers. For this purpose, the redundancy allocation problem (RAP), as an effective technique for increasing the reliability of vendors, is used in this paper. Also, the reliability of retailers as well as the reliability of the relationship between retailers and vendors are considered. In the retailer selection, process decisions on impactful criteria are simultaneously considered. For the retailers' selection, one analysis hierarchical process (AHP) is performed for each vendor, and the weights of retailers are obtained. Then, the obtained weights are plugged into the model as the inputs of the designed model. Since the developed model is a non-differentiable, non-convex, and mixed-integer function, genetic algorithm (GA) and particle swarm optimization (PSO) are leveraged to solve the formulated model. Finally, the efficiency of the presented method is verified through a case study with data collected from the electronic supply chain.</p>

<p style='text-indent:20px;'>Suppliers' selection problem has always been daunting challenges in the Newsvendor problem. Furthermore, since the failures in the supplier's products cause irreparable damage to the retailer, it is necessary to consider the reliability of products in ordering suppliers' products. This paper develops the Newsvendor model by considering the impactful criteria in supplier selection and product reliability so that the total cost of the chain is minimized in a multi-product and multi-period model with multiple suppliers. While multiple criteria decision making (MCDM) accounts for multiple criteria and their tradeoffs, its application in Newsvendor model is not considered. This paper applies the Bayesian best worst method (BWM), as one of the MCDM methods, for ranking criteria and the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) for prioritizing the suppliers. Then, the obtained weights are plugged into the model as the inputs of the designed model. A case study with real data in the electronic supply chain is considered. To validate the results obtained by the proposed method, genetic algorithm (GA) and particle swarm optimization (PSO) are leveraged to solve the proposed model. Finally, the efficiency of the designed model is verified through a case study.</p>

<p style='text-indent:20px;'>Redundancy allocation problem (RAP) is a common technique for increasing the reliability of systems. In this paper, a new model for the RAP is introduced that takes into account the warm standby and mixed strategy, the model dynamics, and the type of the strategy in redundancy allocation problems. A recursive formula is first obtained for the reliability function in the dynamic warm standby and mixed redundancy strategies that leverages the success mode analysis and works for any arbitrary failure-time distribution. Failure rates for warm standby units change before and after their replacement with a damaged unit, and, therefore, the reliability function in warm standby varies with time (i.e., the model is dynamic). Although dynamic models are commonplace in practice, they are more challenging to assess than static models, which have been mainly considered in the literature. An optimization problem is then formulated to select the best redundancy strategy and redundancy levels. Genetic algorithm and particle swarm optimization are leveraged to solve the problem. Finally, the efficiency of the presented method is verified through a numerical example. The experimental results verify that the proposed model for RAP significantly improves the system reliability, which can be of vital importance for system designers.</p>

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The growth of the rail transportation industry depends on providing adequate quality of service with reasonable pricing. Toward providing effective pricing strategies, one must find a measure to assess the effectiveness of ticket pricing. Despite the importance that pricing can have on the performance of businesses, it is mostly addressed based on managers’ own experiences. Moreover, data envelopment units employed for pricing are limited to showing the relationship between increasing service quality and increasing efficiency. Therefore, the main purpose of this study is to quantitatively evaluate ticket pricing from the perspective of passengers on one of the busiest rail routes, based on data envelopment analysis. First, train evaluation criteria are extracted by studying the literature. Then, the most influential criteria are identified based on the opinions of experts in rail transportation. The desirability and satisfaction level of each criterion are acquired for fifteen types of passenger trains. Finally, the ticket pricing efficiency is calculated based on the ratio between the train ticket prices and the quality of their services. The results show that out of fifteen examined trains, ten trains are inefficient in terms of pricing. To revise pricing, three decreasing, increasing and medial policies are introduced and illustrated in detail. All three policies are applied and the results are compared. Finally, it is investigated to verify in what environmental conditions each pricing policy can be more effective.