Scientia Iranica
Volume:26 Issue: 2, Mar-Apr 2019

This study provides a review of hypercube queuing models (HQMs) in emergency service systems (ESSs). This survey presents a comprehensive review and taxonomy of models, solutions and applications related to the HQM after Larson [12]. In addition, the structural aspects of HQMs are examined. Important contributions of the existing research are addressed by taking into account multiple factors. In particular, the integration of location decisions with HQMs for designing an ESS is discussed. Finally, a list of issues for future studies are presented.

In recent decades, there are intensive researches on deteriorating inventory. However, only a few researchers focus on the inventory routing problem for deteriorating item. There are many items such as foods, electronic products that deteriorate with time, and many other products in the market also have perishable characteristic. The items not only decay during the stockpiling period but they also deteriorate throughout transportation time. Since deteriorated rate and time is necessary, in this paper, an inventory routing problem with time windows for deteriorating items is developed. Particle Swarm Optimization (PSO) is used to solve the problem since PSO can solve problems in a reasonable period with near optimal solutions. We use two examples to illustrate the model. In a sensitivity analysis, way parameters that impact costs are demonstrated. Our results show that the deteriorating rate in inventory has bigger effects than deteriorating rate in the vehicle, so this research has a significant contribution and managers can give more effort to reduce deteriorating in inventory than the deteriorating rate in vehicles.

Time series prediction is a remarkable research interest, which is widely followed by scientists / researchers. Because many fields include analyzing processes over such time series, different kinds of approaches, methods, and techniques are often employed in order to achieve alternative prediction ways. It seems that Artificial Intelligence oriented solutions have strong potential on providing effective and accurate prediction approaches in even most complicated time series structures. In the sense of the explanations, this study aims to introduce an alternative, Artificial Intelligence based approach of Artificial Neural Networks, and Cognitive Development Optimization Algorithm, a recent intelligent optimization technique introduced by the authors. Here, it has been aimed to predict different kinds of time series, by using the introduced system / approach. In this way it has been possible to discuss about application potential of the hybrid system and report findings related to its success on prediction. The authors believe that the study has been a good chance to support the literature with an alternative solution approach and see potential of a newly developed, Artificial Intelligence oriented optimization algorithm on different applications.

This paper presents a new multiple disinfection operation problem (MDOP) in which several buildings have to be sprayed with various disinfectants. The MDOP seeks to minimize the total cost of disinfection operations for all buildings. The problem is different from the typical vehicle routing problem since: (a) each building has to receive multiple spray applications of disinfectants; (b) the final spray application of disinfectant in each building is fixed; and (c) for safety, the time interval between two consecutive spray applications of disinfectants for each building must meet or exceed a specified minimum. The MDOP problem is NP-hard and difficult to solve directly. In this paper, we firstly develop an efficient encoding of spray operations to simultaneously determine the optimal sequence of buildings and their respective treatments with spray disinfectants. Secondly, we adopt immune algorithm to solve the presented MDOP. Finally, as a demonstration of our method, we solve the problem for a campus case to determine the optimal disinfection strategy and routes assuming both single and multiple vehicle scenarios. Numerical results of immune algorithm are discussed and compared with those of genetic algorithm and PSO to show the effectiveness of the adopted algorithm.

The Bonferroni mean (BM) has the advantages that it can capture the interrelationship among the input arguments, and the Harmonic mean is a conservative average lying between the max and min operators. The 2-dimension uncertain linguistic variables add a subjective evaluation on the reliability of the evaluation results given by decision makers, so they can better express fuzzy information. In this paper, in order to combine the advantages of them, we first propose the 2-dimensional uncertain linguistic weighted Bonferroni mean (2DULWBM) operator. However, it cannot consider the case when the given arguments are too high or too low. So we further proposed the 2-dimensional uncertain linguistic improved weighted Bonferroni harmonic mean (2DULIWBHM) operator, which combine the 2DULWBM with Harmonic Mean. Furthermore, we study some desirable properties and some special cases of them. Further, we develop a new method to deal with some multi-attribute group decision making (MAGDM) problems under 2-dimension uncertain linguistic environment based on the proposed operators. Finally, an illustrative example is given to testify the validity of the developed method by comparing with the other existing methods.

There are many sources of risk affecting the network elements may lead to network failure, so planners need to consider them in the network design. One of the most important strategies for disruption risk management is the static resilience. In this strategy, the network functionality is maintained after the disruption event by the prevention and hardening actions. In this paper, a resilient capacitated fixed-charge location-allocation model is proposed. Both facility hardening and equipping of the network to backup facilities for disrupted elements are considered together to avoid supply network failure due to random disruption. Facilities are decided to be hardened in multiple levels before disruption events. The problem is formulated as a non-linear integer programming model, then its equivalent linear form is presented. A Lagrangian decomposition algorithm (LDA) is developed to solve large-scale instances. Computational results confirm the efficacy of the proposed solution approach comparing to classical solution approaches in large-scale problems. Moreover, the superiority of the proposed model is confirmed by comparing to the classical models.

Randomized response is an efficacious and effective survey method to collect subtle information. It entitles respondents to respond to over-sensitive issues and defensive questions (such as criminal behavior, gambling habits, addiction to drugs, abortions, etc) while maintaining confidentiality. In this paper, we conducted a Bayesian analysis of a general class of randomized response models by using different prior distributions, such as Beta, Uniform, Jeffreys and Haldane, under squared error, precautionary and Degroot loss functions. We have also expanded our proposal for the case of mixture of Beta priors under squared error loss function. The performance of the Bayes and maximum likelihood estimators is evaluated in terms of mean squared errors. Moreover, an application with real data set is also provided to explain the proposal for practical considerations.

Reliability improvement for electronics and mechanical systems is vital for engineers in order to design of these systems. For this reason, there are many researches in this scope to help engineers in real world applications. One of the useful methods in reliability optimization is redundancy allocation problem (RAP). In the most previous works, the failure rates of system components are considered to be constant based on negative exponential distribution; whereas, nearly all systems in real world have components with time-dependent failure rates; i.e., the failure rates of system components will be changed time by time. In this paper, we have worked on a RAP for a system under k-out-of-n subsystems with time-dependent components failure rates based on Weibull distribution. Also, the redundancy policy of the proposed system is considered as mixed strategy and the optimization method was based on the simulation technique to obtain reliability function as implicit function. Finally, a branch and bound algorithm has been used to solve the model, exactly.

Green supply chain management is a crucial challenge for the sustainable development of the enterprises. In this paper, we study the problem of supplier selection for the multi-attribute and multi-source green procurement of electric coal under fuzzy information environment. Concretely, we establish a new index system of supplier selection by considering both the economic factors and environmental factors, and then present a multi-attribute decision making method based on 2-tuple deviation degree to rank all alternative suppliers in the green procurement of electric coal. We also highlight the implementation, availability, and feasibility of the green procurement decision method of electric coal by using an example of the multi-source procurement of electricity coal. We try to provide theoretical basis and decision-making reference for the thermal power enterprise to implement scientific green procurement management of electric coal.

Interval-valued Pythagorean fuzzy set (IVPFS), originally proposed by Peng and Yang, is a novel tool to deal with vagueness and incertitude. As a generalized set, IVPFS has close relationship with interval-valued intuitionistic fuzzy set (IVIFS). IVPFS can be reduced to IVIFS satisfying the condition $mu^++nu^+ leq 1$. However, the related operations of IVPFS do not take different conditions into consideration. In this paper, we initiate some new interval-valued Pythagorean fuzzy operators ($diamondsuit, Box, spadesuit, clubsuit, maltese, rightarrow, $ $) and discuss their properties in relation with some existing operators $(cup, cap, oplus, otimes)$ in detail. It will promote the development of interval-valued Pythagorean fuzzy operators. Later, we propose an algorithm to deal with multi-attribute decision making (MADM) problem based on proposed $spadesuit$ operator. Finally, the effectiveness and feasibility of approach is demonstrated by mine emergency decision making example.