Scientia Iranica
Volume:29 Issue: 5, Sep-Oct 2022

The congestion problems with processor vacations have confronted with increasing intricacy, and their explicit transient solutions are exceptionally hard to compute. The transient solution is more significant for studying the dynamical behavior of computing systems over a finite period and predominantly utilizes within the state-of-the-art design architect for a real-time I/O system. Motivated from this, we adopt the mathematical concepts, namely continued fractions and generating function, to derive explicit expressions for transient-state probabilities. Transient-state probabilities of the processing delay problem with a single processor which adopts the multiple vacations policy to save power consumption and thermal trip error with discouragement and feedback are obtained in terms of modified Bessel functions using the properties of the confluent hypergeometric function. Due to the inaccessibility of processor, discouragement behaviors balking and reneging of the job requests are prone to exhibit. Routing back for the service feedback for the processed job request is also critical to maintaining the quality of service $(QoS)$. For the glance of the I/O system performance, the expected value of the state of the computing system using stationary queue-size distribution is also derived.

Changing the structure of supply chains to move towards less polluting industries and better performance has attracted many researchers in recent studies. Design of such networks is a process associated with uncertainties and control of the uncertainties during decision-making is of particular importance. In this paper, a two-stage stochastic programming model was presented for the design of a green closed-loop supply chain network. In order to reach the environmental goals, an upper bound of emission capability that helps governments and industries to control greenhouse gas emissions was considered. During the reverse logistics of this supply chain, waste materials are returned to the forward flow by the disassembly centers. To control the uncertainty of strategic decisions, demand and the upper bound of emission capacity with three possible scenarios is considered. To solve the model, a new accelerated Benders decomposition algorithm along with Pareto-Optimal-Cut was used. The efficiency of the proposed algorithm was compared with the regular Benders algorithm. The effect of different numerical values of parameters and probabilities of scenarios on the total cost was also examined.

Wheat is a staple food in many countries, and as a result, the first most cultivated crop worldwide. Since wheat is a vital product in terms of food security, and its supply chain needs to be studied and planned carefully. This paper proposes a mixed-integer linear programming model for integrated planning of imported and domestically-produced wheat that addresses supplier selection, order planning, transportation, storage, and distribution problems at the same time. Specifically, this model focuses on the wheat quality and wheat sleep period. Moreover, differentiation of long-term and short-term storage facilities and consideration of intra-layer flows between storage facilities are other characteristics of this model. A fuzzy chance-constrained programming approach is employed to cope with the uncertainties associated with domestic supply, demand, and global wheat prices. Applicability and advantages of the developed model are demonstrated using real data from the wheat supply chain of Iran. Results show that the current status of the wheat supply chain of Iran is far from being optimal, and there are many opportunities for improvement.

This paper develops a three-layer supply chain for defective and non-defective types of produced items by supplier and manufacturer. The condition of the chain that without any financial liability on the manufacturer, defective items will be sent back to the supplier after screening. In the subsequent stage, the retailer accepts the non-defective items produced by the manufacturer after the screening and sends back the defective items to the manufacturer. Hence the retailer receives the perfect quality items for selling to the customers, but the retailer considers the effect of the deterioration of items. This model also considers the impact of several business strategies such as; optimal order size of raw materials, production rate, unit production cost, idle time costs of the supplier, and manufacturer in a collaborative marketing system, etc. to determine the optimum average profit of the integrated model. This study discusses the selling price of the retailer, demand rate of the customer, purchase cost of supplier and holding cost, which can be a significant breakthrough in expanding the profit of the business in real terms. Numerical example and sensitivity analysis are presented to illustrate the phenomenon of theoretical study and demonstrate the managerial implication of the model.

Supplier selection is vital in the supply chain, with significant effects on the chain structure. Three important factors contribute to this process, namely, product/technology selection, selection of the technology/product transfer method, and supplier selection. In this study, after defining the influential criteria for these factors, the best-worst method (BWM) was employed for measuring the weights. Next, the three factors were incorporated into goal programming (GP) to minimize the cost and failure and maximize the level of service and environmental compliance. The results of the GP indicated the level of demand allocation to the supplier(s). Overall, the gray analytical network process (GANP) is used as the best decision-making method, and over the past four years, BWM has been applied in decision-making processes. Therefore, the GANP method was used to measure the weights of criteria. These weights were also incorporated into GP for comparison with the proposed combination. The results showed the superiority of BWM-GP over GANP-GP, given the reduced cost and failure, besides the increased level of service and environmental compliance.

Home Health Care (HHC) is characterized as preparing medical and paramedical services for patients at their place of residence. In the HHC industry, it is imperative for decision-makers to appoint nurses to patients and plan visiting patterns to confront with conflicting objectives and boost service quality. This study offers important insights into Home Health Care Routing and Scheduling Problem (HHCRSP) by dealing with three patient-oriented objectives. Moreover, the proposed model accounts for real-life constraints such as emergency patients, nurses’ proficiency and patients’ preferences. Owing to the multi-objective nature of the model, the Augmented Epsilon Constraint approach and Fuzzy Goal Programming are used for trading off the objectives. Further, getting as close as possible to the real-world problems, some parameters are considered uncertain and consequently a robust approach along with three dissimilar uncertainty sets are used to control uncertainty. Numerical results demonstrate that, regardless of the type of the uncertainty set, increasing control parameters make objective values farther than ideal ones, and the comparison performed among the sets also highlights the stringency of the Box space. A unique indicator, presented to validate the robust approaches, features all sets are almost the same in terms of equal optimality and feasibility criteria.

This paper presents a robust location-allocation planning problem for emergency relief in a disaster situation, which is formulated as a robust optimization model. It is a multi-objective, multi-commodity, multi-vehicle and multi-level logistics model considering injury variety through service prioritizing for more injuries and considering unmet demand of particular item type in various damaged areas, public donation of different relief goods, using capacitated medical centers and emergency centers regarding damage type and capacitated relief distribution centers and disaster management centers. This a non-linear mixed-integer programming model that simultaneously optimizes three objectives; i.e., maximizing service fairness to damaged areas, maximizing fair commodity disaster management, and minimizing the total logistics cost. To solve such a hard problem, an NSGA-II is developed and the Taguchi method is applied to adjust its parameters. The ε-constraint method is used for the evaluation of the proposed algorithm performance. Three comparison metrics, including diversification, spacing and mean ideal distance, are used. The results verify the algorithm’s effectiveness in a reasonable computational time. Eventually, to examine the applicability of the presented model and the proposed algorithm, a case study is analyzed in the area located in the north of Iran, known with historical earthquake records and aggregated active faults.

Researchers have recently devoted a lot of attention to the development of control charts for monitoring healthcare systems. Accordingly, the purpose of this paper is to design a risk-adjusted cumulative sum (CUSUM) control chart to detect decreasing shifts. The proposed chart is used to monitor the survival times of patients who may be subject to an assignable cause such as human mistakes during a surgery. To this end, risk adjustment is performed to consider the impact of each patient's preoperative risks on survival times using survival analysis regression models. However, using the risk-adjusted CUSUM requires that the control chart parameters are determined. Hence, a multi-objective economic-statistical model is proposed and a two-stage solution method including non-dominated sorting genetic algorithm (NSGA-II) and Data Envelopment Analysis (DEA) is implemented to solve the model and obtain the optimal design parameters. The performance of the proposed approach is also studied in a real cardiac surgery center. Finally, to confirm the effectiveness of the proposed multi-objective design, two comparisons with the bi-objective and pure economic designs are made. The results show that the performance of the risk-adjusted CUSUM obtained from the proposed model is better than the two other designs considering statistical and economic properties.

In this paper, the carrier selection problem is addressed with the purpose of avoiding shortage regarding vehicle necessity and creating a vehicle rental framework agreement. Achieving in-time delivery of relief supplies to the disaster inflicted areas is of utmost significance in terms of humanitarian relief. We propose a new two-stage stochastic model for determining the pre-disaster and post-disaster decisions in order to delivering relief items to the injured survivors. The pre-disaster phase focuses on determining amount of vehicle in the framework of contracts with suppliers, and deciding an appropriate coverage distance with regards to time and cost. The post-disaster phase aims to respond to the requests made by disaster inflicted areas swiftly and cost-effectively. The proposed MILP model considers a scenario-based approach to handle the uncertainty of demand. The L-shaped algorithm is used to solve this model. A real case study is presented with the aim of demonstrating the efficiency of the model. Moreover, numerical analyses are practiced to illustrate the importance and impact of the cost and the number of vehicle rental contracts in the studied problem. Finally, managerial insights have been presented to assist the relief organization management in making appropriate and efficient decisions.

This paper presents an incentive contract model for allocating the income of venture projects. Venture Capital (VC), as one of the main sources of financing innovative projects, faces challenges like moral hazards, information asymmetry and interest conflicts (three agency problems). In addition to identifying the items that may affect the income of venture projects and the introduction of cost functions, we present an optimal incentive contract model from the perspective of both venture capitalists and entrepreneurs. In this model, a venture capitalist, as an active investor, provides managerial and training assistance to the entrepreneur. The results showed that the higher the initial ability of the entrepreneur, the less money the venture capitalist pays for training. Furthermore, the wealth that the contract parties can obtain if the venture contract is not accepted, is an influential factor in the contract payment function. This model has also been studied with bounded rationality hypothesis and has been implemented using the Q-learning algorithm. In addition, the results obtained from the Q-learning approach, are reasonably convergent with the Nash equilibrium.

Refunding and bundling reservation are known as two popular methods to increase profit where in recent years have gained attention of researchers. One main application of refunding policy emerges for online product sale methods, where consumers can be refunded by returning goods which are not favorite according to their interest. Examining three scenarios including refunding, bundle reservation and refunding along with bundle reservation policies, we will investigate a model for each corresponding scenario. We try to compare two refund and bundle reservation pricing policies in a two-level supply chain including one manufacturer and one wholesaler, and we provide a combined model including two products. The demand is constant and also the population-related information about the division of the population into two types of consumers, strategic consumers (consumers who can predict the second stage discount) and myopic consumers (consumers who can not predict the second stage discount) are available. In addition, the percentage of consumers who refund the product due to regret, the inability to install the product or other reasons, is constant and is independent of the amount of refund. We show that the combined model is optimal and has a higher profit margin than any other policy alone.

The mixed EWMA-CUSUM and CUSUM-EWMA control charts are among the control charts provided in recent years by combining two exponentially weighted moving average and cumulative sum charts for efficient monitoring of the process mean. In this paper, we extended these mixed control charts by using new median ranked set sampling and double ranked set sampling procedures. The performance of the proposed mixed control charts is evaluated through extensive Monte Carlo simulations in terms of average run length criterion and the results show the proposed charts outperform the similar charts for detecting different shifts in process mean. Furthermore, a real data-set is also presented for explaining the implementation of the proposed control charts.