Sharif Digital Repository

In this paper we study a two-echelon decentralized supply chain consists of one retailer and several manufacturers in competitive environment. The manufacturers compete on their price and lead time. To model the interactions between the retailer and manufacturers, we use Stackelberg game in which the manufacturers decide on their wholesale price and lead time as leader and the retailer decides on retail price and its order quantity to each manufacturer as follower. Besides, we use the Nash Equilibrium analysis to investigate the horizontal competition between manufacturers to model their simultaneous decision making on their price and lead time. In this problem, the customers' demand is a... 

We consider a production system with a single product and two classes of customers. Customers are segmented into two classes: 1) loyal and 2) occasional. Each class has a different shortage cost for the system. The demand of the customer classes is assumed to be stochastic and price sensitive, and distributed as a Poisson process. The objective is to determine the inventory rationing as well as pricing policies in order to maximise the profit function of the system. We formulate the problem as a Markov decision problem and characterise the optimal policies for pricing and inventory rationing. The joint pricing and rationing threshold policy is shown to be optimal. The performance of the... 

In this article, we propose a bi-objective model for the pricing-queuing problem under a fuzzy environment. We consider two objectives: maximizing the profit function and minimizing the waiting time in queue. Imagine a firm which sells a product in a channel providing after sales services. The sales price and warranty length affect customer demand. We formulate the demand function as a fuzzy system considering the sales price and warranty length. The firm optimizes the sales price and warranty length, as well as waiting time, in the queue of after sales services, to maximize its revenues and minimize waiting time. To solve the derived model, we develop a hybrid solution method of a fuzzy... 

In this paper we develop a fuzzy system to obtain the demand based on input variables of price, warranty length and lead time of order delivery. We use linguistic terms to illustrate the situation of input and output variables and construct a fuzzy rule base. The fuzzy rule base is integrated with a genetic algorithm to construct a hybrid solution method. The best value of price, warranty length and lead time are obtained by maximizing the manufacturer profit. Finally a numerical example is presented to show the performance of proposed approach  

In this paper, we develop the multi-product, multi-constraint, Single Period Problem (SPP) with uncertain demands, considering an incremental discount situation. Three new models are presented for multi-product, multi-constraint SPP in fuzzy, stochastic and rough environments. We consider constraints, such as service rate, restriction on order quantity and restrictions on warehouse space and budget. We also consider that the order quantity is a multiplier of predefined batch size. Furthermore, three kinds of solution algorithm, (1) harmony search, (2) hybrid intelligent based on harmony search and fuzzy simulation and (3) hybrid intelligent based on harmony search and rough simulation, are... 

Success in forecasting and analyzing sales for given goods or services can mean the difference between profit and loss for an accounting period and, ultimately, the success or failure of the business itself. Therefore, reliable prediction of sales becomes a very important task. This article presents a novel sales forecasting approach by the integration of genetic fuzzy systems (GFS) and data clustering to construct a sales forecasting expert system. At first, all records of data are categorized into k clusters by using the K-means model. Then, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. In order to evaluate our K-means... 

This paper considers dynamic pricing in a production system with a single product which is demanded by several customer classes. We seek the structure of the optimal policy assuming m available prices and n demand classes that differ based on the lost sales cost they impose on the system. The assumption of different available prices leads to dynamic pricing structure and the assumption of several demand classes leads to rationing which is proposed in the literature of revenue management. We found that an optimal policy structure exists for this combined problem. The optimal policy has a threshold form which lower thresholds are related to the rationing decision and upper thresholds are... 

In this paper, we study a pricing-inventory model with multiple interdependent products and stochastic demand. In the relevant literature, there are many researches that try to optimize the inventory or price independently and also optimizing joint inventory and pricing problems for single product. But joint optimization of inventory and pricing for multiple interdependent products is relatively new and still there are many potential research gaps in this area. We show that the multiplicative demand form problem, that we develop, is a convex nonlinear programming (NLP) model or is convertible to it. We also provide upper and lower bounds for the additive demand form model  

Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all... 

Forecasting stock price time series is very important and challenging in the real world because they are affected by many highly interrelated economic, social, political and even psychological factors, and these factors interact with each other in a very complicated manner. This article presents an approach based on Genetic Fuzzy Systems (GFS) for constructing a stock price forecasting expert system. We use a GFS model with the ability of rule base extraction and data base tuning for next day stock price prediction to extract useful patterns of information with a descriptive rule induction approach. We evaluate capability of the proposed approach by applying it on stock price forecasting... 

This paper studies inventory and pricing policies in a non-cooperative supply chain with one supplier and several retailers who are involved in producing, delivering and selling a single product. We consider inventory policies in an information-asymmetric vendor managed inventory. The study consists of different scenarios where a supplier produces the product at the wholesale price to multiple retailers. The retailers also distribute the product in dispersed and independent markets at retail selling prices. The demand rate for each market is a non- decreasing concave function of the marketing expenditures of both local retailers and the manufacturer, but a non-increasing and convex function... 

Intelligent solutions, based on artificial intelligence (AI) technologies, to solve complicated practical problems in various sectors are becoming more and more widespread nowadays, because of their flexibility, symbolic reasoning, and explanation capabilities. Meanwhile, accurate forecasts on tourism demand and study on the pattern of the tourism demand from various origins is essential for the tourism-related industries to formulate efficient and effective strategies on maintaining and boosting tourism industry in a country. In this paper we develop a hybrid AI model to deal with tourist arrival forecasting problems. The hybrid model adopts Adaptive Neuro-Fuzzy Inference System (ANFIS) and... 

Cardiovascular diseases have been known as one of the main reasons of mortality all around the world. Nevertheless, this disease is preventable if it can be diagnosed in an early stage. Therefore, it is crucial to develop Clinical Decision Support Systems (CDSSs) that are able to help physicians diagnose the disease and its related risks. This study focuses on cardiovascular disease diagnosis in an Iranian community by developing a CDSS, based on Support Vector Machine (SVM) combined with Binary Particle Swarm Optimisation (BPSO). We used SVM as the classifier and benefited enormously from optimisation capabilities of BPSO in model development as well as feature selection. Finally,... 

In this paper, we develop the multi-products multi-constraints newsboyproblem with fuzzy demands considering the total discount situation. Since thedemands of products are often fuzzy in the real world, the profit of the newsboyis fuzzy too. A new model is presented for multi-products multi-constraintsnewsboy problem in fuzzy Environment (FM-PM-CNP). The objective function ofproposed model is to maximize the expected profit of newsboy. We consider theconstraints such as service rate, restriction on quantity order, and restrictionon warehouse space and budget. We also consider batch size for products order.Furthermore, a hybrid intelligent algorithm based on simulated annealing andfuzzy... 

In this paper we consider a multiple products inventory location problem with disruption. Disruptions may occur in any supply chain due to several reasons such as earthquakes, floods, fires, labor strikes, terrorist attacks, equipment breakdowns and economic crises. We develop a model by assuming that facilities may fail partially in case of disruption and considering substitutable products as a strategy to mitigate the risk of disruption. The developed model is a non-linear integer programming model and NP-Hard. To solve the model, a hybrid algorithm including Tabu Search (TS) and Variable Neighborhood Search (VNS) is proposed. Numerical results show reasonable performance of the algorithm... 

In this paper, we consider the multi-product multi-constraint newsboy problem with random fuzzy demands and total discount. The demand of the products is often stochastic in the real word but the estimation of the parameters of distribution function may be done by fuzzy manner. So an appropriate option to modeling the demand of products is using the random fuzzy variable. The objective function of proposed model is to maximize the expected profit of newsboy. We consider the constraints such as warehouse space and restriction on quantity order for products, and restriction on budget. We also consider the batch size for products order. Finally we introduce a random fuzzy multi-product... 

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... 

In this paper we consider the problem of joint location and pricing optimization for a firm in a heterogeneous market producing a single product. We assume that customers have a different willingness to pay for the product. We consider two classes of customers who are not uniformly distributed in the market and develop an analytical framework to determine the relationship between the optimal price and location of the firm. We demonstrate that the optimal price and location are closely related to each other, and thus there is a need for simultaneous optimization of the price and location. We provide both analytical and numerical results to illustrate the impact of transportation cost and the... 

In this paper, we have proposed a dual-channel supply chain model in uncertain environment to analyze the demand of manufacturer and retailer demand in which the profit being maximized. Linguistic terms are also utilized to establish two fuzzy systems for estimating the demand in direct and retail channels. In order to do that, a mathematical model is proposed based on decentralized situation of supply chain. To solve the model, we have developed a hybrid solution method of genetic algorithm, fuzzy system, and L-P metric. Finally, several test problems are first generated; then, the computational results are analyzed. © 2017 World Scientific Publishing Company