Sharif Digital Repository

In hierarchical service networks, facilities at different levels provide different types of service. For example, in health care systems, general centers provide low-level services, such as primary health care, while specialized hospitals provide high-level services. Because of the demand congestion at service networks, the location of servers and their allocation of demand nodes can have a strong impact on the length of the queue at each server, as well as on the response time to service calls. This study attempts to develop hierarchical location-allocation models for congested systems by employing a queueing theory in a fuzzy framework. The parameters of each model are approximately... 

This article presents a fuzzy location-allocation model for congested systems. In service networks, such as health and emergency services, public safety, fire fighting and so on, the location of servers and allocation of demand nodes to them have a strong impact on the congestion at each server and as such, on the quality of service. The previous efforts in this area have concentrated on enhancing the reliability and quality of service with a probabilistic orientation. In this paper we utilize fuzzy theory to develop a queuing maximal covering location-allocation model which we call the fuzzy queuing maximal covering location-allocation model. We consider fuzzified queuing parameters as well... 

Since in the emergency service networks the length of time to attend a call is of utmost importance, it is imperative to observe guidelines in locating the service centers within a service network and allocating the demand nodes to them in a manner that service can be provided within a reasonably short period of time. So far both qualitative and quantitative models have been developed to tackle these problems. The latest models which assume a probabilistic orientation, use queuing theory notions to achieve a more suitable solution. This article attempts to apply both queuing theory and fuzzy theory to the location-allocation problem in hope of analyzing the problem in a more realistic... 

The aim of this article is presenting a new constrained multi-product pricing and inventory model in which perishable products are put into three categories: substitute, complementary and independent. The general demand is found by considering the relations of different products and the impact of their prices. The goal of the model is deciding on the prices as well as the inventory and production decisions in order to maximize the total profit. The derived model is a non-linear programming model and a genetic algorithm is developed for its solution. To enhance the performance of our genetic algorithm, we apply the Taguchi experimental design method to tune the parameters of the algorithm.... 

There exist various service systems that have hierarchical structure. In hierarchical service networks, facilities at different levels provide different types of services. For example, in health care systems, general centers provide low-level services such as primary health care services, while the specialized hospitals provide high-level services. Because of demand congestion in service networks, location of servers and allocation of demand nodes have a strong impact on the length of queues at servers as well as on the response times to service calls. The thrust of this article is the development of hierarchical location-allocation models for congested systems by employing queueing theory... 

Objectives. Stressors have a serious role in precipitating mental and somatic disorders and are an interesting subject for many clinical and community-based studies. Hence, the proper and accurate measurement of them is very important. We revised the stressful life event (SLE) questionnaire by adding weights to the events in order to measure and determine a cut point. Methods. A total of 4569 adults aged between 18 and 85 years completed the SLE questionnaire and the general health questionnaire-12 (GHQ-12). A hybrid model of genetic algorithm (GA) and artificial neural networks (ANNs) was applied to extract the relation between the stressful life events (evaluated by a 6-point Likert scale)... 

In this paper, we consider the problem of making simultaneous decisions on the location, service rate (capacity) and the price of providing service for facilities on a network. We assume that the demand for service from each node of the network follows a Poisson process. The demand is assumed to depend on both price and distance. All facilities are assumed to charge the same price and customers wishing to obtain service choose a facility according to a Multinomial Logit function. Upon arrival to a facility, customers may join the system after observing the number of people in the queue. Service time at each facility is assumed to be exponentially distributed. We first present several... 

This paper studying the impact of strategic customer behavior on decentralized supply chain gains and decisions, which includes a supplier, and a monopoly firm as a retailer who sells a single product over a finite two periods of selling season. We consider three types of customers: myopic, strategic and low-value customers. The problem is formulated as a bi-level game where at the second level (e.g. horizontal game), the retailer determines his/her equilibrium pricing strategy in a non-cooperative simultaneous general game with strategic customers who choose equilibrium purchasing strategy to maximize their expected surplus. At the first level (e.g. vertical game), the supplier competes... 

We consider a two-echelon inventory system with a manufacturer operating from a warehouse supplying multiple distribution centers (DCs) that satisfy the demand originating from multiple sources. The manufacturer has a finite production capacity and production times are stochastic. Demand from each source follows an independent Poisson process. We assume that the transportation times between the warehouse and DCs may be positive which may require keeping inventory at both the warehouse and DCs. Inventory in both echelons is managed using the base-stock policy. Each demand source can procure the product from one or more DCs, each incurring a different fulfilment cost. The objective is to... 

Treated waste water (TWW) quality varies due to the occurrence of polycyclic aromatic hydrocarbons (PAHs) up to low μg L−1. In this study, a non-targeted metabolomic analysis was performed on lettuce (Lactuca sativa L) exposed to 4 PAHs by irrigation. The plants were watered with different concentrations of contaminants (0–100 μg L−1) for 39 days under controlled conditions and then harvested, extracted and analyzed by nuclear magnetic resonance (NMR). Different chemometric tools based on principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are proposed for the analysis of the complex data sets generated in the different exposure experiments.... 

Nowadays, location of distribution centers integrated with inventory or transportation decision play an important role in optimizing supply chain management. Location-inventory models analyze the location and inventory policies in distribution network, simultaneously. Developing location-inventory models under fuzzy environment can enrich the model, and this is our approach in this article. We consider the demand as a fuzzy variable and formulate the problem using credibility theory in order to locate distribution centers (DCs) as well as determining inventory levels in DCs. The derived model belongs to nonlinear mixed integer programming problems, and we presented a genetic algorithm to... 

In this paper we develop a hybrid forecasting model which combines artificial intelligence and technical analysis to predict short-term stock price index. The results show that using technical indices as neural network's inputs yields good performance in forecasting short-term prices, but this model cannot predict long-term prices well. To overcome this shortcoming we have exploited a fuzzy inference system based on analyzing the historical effects of macro economic variables on the stock markets' indices. Our forecasting models differ from the other ones in two main aspects: the first one is analyzing previous macroeconomics trends in order to build a Mamdani FIS and the second one is... 

Algorithmic trading based on machine learning is a developing and promising field of research. Financial markets have a complex, uncertain, and dynamic nature, making them challenging for trading. Some financial theories, such as the fractal market hypothesis, believe that the markets behave based on the collective psychology of investors who trade with different investment horizons and interpretations of information. Accordingly, a multi-agent deep reinforcement learning framework is proposed in this paper to trade on the collective intelligence of multiple agents, each of which is an expert trader on a specific timeframe. The proposed framework works in a hierarchical structure in which...