Iranian Journal Of Operations Research
Volume:14 Issue: 2, Summer and Autumn 2023

With this research, author presents an understanding of business value that would be enhanced by adopting a new technology into the system. A healthcare center is the case here and the technology considered is radio frequency identification (RFID). To present such framework for evaluation purposes, a two phase analysis is introduced. In the first phase and with the help of a multi attribute decision making in the context of hierarchical fuzzy TOPSIS, an RFID-based system among a set of proposed RFID based-systems are selected. In the second phase, with the help of system dynamics approach, the behaviors of system for goal variables are determined. To fully understand this approach, a sample case is provided and analyzed. This type of integrated decision-making approach can provide a deep understanding of the system because of providing one or more trends on key system variables based upon the optimal decision made at the present time using an MADM tool.  Due to the fact that this research combines four fields of knowledge into an interesting research problem, of highly concerned to the users, it makes a true contribution to health, system dynamics, RFID and MADM. Integration of MADM and SD approaches in healthcare system has some very important benefits for healthcare managers. It allows managers in seeing the system behaviour now under the decision made at the present time using multi attribute decision making approach.

In the evaluation of decision making units with classical models of data envelopment analysis, it is assumed that the factors are deterministic. In some decision-making problems, the amount of inputs or outputs of the units is not exactly known and it is a three-parameter interval in grey form. In this case, it is recommended to choose the factors from their center of gravity. In the classic models of data envelopment analysis, all factors are also considered desirable, but in real problems there are undesirable factors too which cannot be used to evaluate problems with undesirable inputs and undesirable outputs. In this paper, a model is presented for calculating the efficiency of decision making units in the presence of the center of gravity of undesirable three-parameter interval grey undesirable factors based on the combination of strong and weak disposability principles. To this end, the proposed method is discussed with a practical example.

This paper considers the progressively Type-II censoring and determines the optimal sample size using a Bayesian prediction approach. To this end, two criteria, namely the Bayes risk function of the point predictor for a future progressively censored order statistic and the designing cost of the experiment are considered. In the Bayesian prediction, the general entropy loss function is applied. We find the optimal sample size such that the Bayes risk function and the cost of the experiment do not exceed two pre-fixed values. To show the usefulness of the results, some numerical computations are presented.

The biggest issue facing both industrialized and emerging nations these days is traffic congestion, which has changed people's perspectives on public transportation systems and accelerated efforts to make them more efficient. Urban traffic issues are a result of various factors, including population growth and the rise in private vehicle ownership. Urban public transportation is currently one of the primary strategies for reducing urban traffic congestion. Building simulation models allows for a more precise analysis of the basic capacity of buses at transfer stations on a given route, trip frequency, passenger behavior, and waiting times. This study suggests using a digital twin design to plan bus routes, reduce wait times for passengers, maximize bus frequency, and investigate the relationship between overall traffic flow and passengers. The Anylogic package program was utilized, which is a helpful tool for digital twin modeling and multi-method simulation. The usefulness of the digital twin concept—which links the physical and virtual worlds—was highlighted in determining the ideal number of trips and trip intervals as a result of the examinations made with the model's outputs. This allows for the instantaneous monitoring and storing of data in its physical conditions.

In this paper, a closed-loop supply chain (CLSC) is modeled to obtain the best location of retailers and allocate them to other utilities. The structure of CLSC includes production centers, retailers’ centers, probabilistic customers, collection, and disposal centers. In this research, two strategies are considered to find the best location for retailers by focusing on 1) the type of expected movement and 2) expected coverage. To this end, a bi-objective nonlinear programming model is proposed. This model concurrently compares Strategies 1 and 2 to select the best competitor. Based on the chosen strategy, the best allocation is determined by employing two heuristic algorithms, and the locations of the best retailers are determined. As the proposed model is NP-hard, a meta-heuristics (non-dominated sorting genetic) algorithm is employed for the solution process. Afterward, the effectiveness of the proposed model is validated and confirmed, and the obtained results are analyzed. For this purpose, a numerical example is given and solved through the optimization software.

The purpose of this article is to investigate the modes of vehicles based on the type and number of urban travel facilities for passengers. As you know, to divide transportation models based on  goal programming, is to divide all transportation modes for urban station routes by type and region.The main objective of this is to present the best mode (vehicle) of transportation based on travel modeling in transportation areas of urban trips for multi-objective transportation goal programming. In this case, the type of transportation solution is determined in the desired area on the way to the stations, according to which the pollution reduction, travel time reduction, cost reduction, availability, maximum safety and comfort of the means of transportation are reduced, increased or liminated.

In this paper the pricing of reverse products in a two-level closed-loop supply chain is considered and a game theory approach is used to solve it. Pricing is a sensitive and vital issue for businesses. In the market of reverse products, this issue will be much more difficult and complex due to difficulties associated with collecting and re-manufacturing processes. On the other hand, the use of the internet and direct channels for collecting products from customers alongside traditional retailers is an important issue that requires management and coordination. The proposed price for buying second-hand and defective products from customers should be high enough to convince them that returning the products has more benefits than discarding or keeping them at home. At the same time, the price should be low enough to make it economically viable for producers to carry out the repair and re-manufacturing operations and resell them in the direct supply chain for the producer. The use of game theory, where the decisions of one player affect the decisions and outcomes of other players as well as their outcomes, is a suitable method for solving the problem of pricing reverse products in a two-level closed-loop supply chain

Supply chains often have different technologies. Additionally, organizations with multiple stages can evaluate their operational efficiency by analyzing scale elasticity, which helps determine if they are functioning optimally or if there is room for improvement. This evaluation allows for the identification of potential inefficiencies and opportunities for enhancement. Consequently, this research introduces a two-stage DEA-based approach with undesirable outputs to examine the scale elasticity of supply chains within meta and group frontiers. The measurement of group and meta performance of general systems and stages is conducted for this purpose. Moreover, the study addresses the scale elasticity of supply chains with undesirable outputs by considering the heterogeneity of technologies. To achieve this, the study focuses on the right and left scale elasticity of efficient general systems and each stage. A real-world application from the soft drink industry is provided to illustrate the proposed model. The results show the applicability of the introduced methodology.