International Journal of Research in Industrial Engineering
Volume:6 Issue: 2, Spring 2017

This paper presents a mathematical model for a facility location-allocation problem in order to design an integrated supply chain. We consider a supply chain including multiple suppliers, multiple products, multiple plants, multiple transportation alternatives and multiple customer zones. The problem is to determine a number and capacity level of plants, allocation of customers demand, and selection and order allocation of suppliers. A multi-objective mixed-integer linear programming (MOMILP) is presented with two conflicting objectives simultaneously. The first objective is to minimize the total costs of a supply chain including raw material costs, transportation costs and establishment costs of plants. The second objective function aims to minimize the total deterioration rate occurred by transportation alternatives. Finally, by applying the fuzzy goal programming, the model is solved as a single objective mixed-integer programming model. An experiment study shows that the proposed procedure can provide a promising result to design an efficient supply chain.

The organization can fully benefit from efforts to improve quality through programs such as total quality management. Just-in-time and total quality management combine to support the successful implementation of agile manufacturing programs which, in turn, result in the organization’s ability to respond rapidly and aggressively to changes in customer demand. We contend that adoption of a market orientation combined with just in time, total quality management, and agile manufacturing programs leads to organizational capabilities of relatively low cost operation, high quality product and service production, and rapid response to changes in customer needs and demand. While studies on total quality management implementation appear to focus on identifying the role of total quality management practices on organizational success, total quality management practices are still directed from within the organization. Market orientation, however, requires more external engagement and shares the same ultimate aim as total quality management implementations.

This paper formulates a competitive supply chain network throughout a mixed integer linear programming problem, considering demand uncertainty and retailers risk averseness. That is, makes the model more realistic in comparison with the others. Employed conditional value at risk method through the data-driven approach, makes the model to be convex and sensitive to the risk averseness level. Finally, the model outputs and its results are illustrated through a numerical example.

Recommender systems have become fundamental applications in overloaded information domains like e-commerce. These systems aim to provide users with suggestions about items that are likely to be of their interest. Collaborative Filtering (CF) is one of the most successful approaches in recommender systems. Regardless of its success in many application domains, CF has main limitations such as sparsity, cold start, gray sheep and scalability problems. In order to overcome these limitations, hybrid CF systems have been used which combine CF with other recommendation approaches. This paper provides a comprehensive survey of hybrid CF systems; it also provides a classification for these systems,  explains their strengths or weaknesses and compares their performance in dealing with the main limitations of CF.

This paper considers a dynamic pricing decision problem, in which two different manufacturers compete to distribute substitutable products through a single retailer under two presented scenarios. In the first scenario, the pricing policy is determined via a centralized decision-making, while the second scenario manages the policy in a decentralized one. Utilizing the game-theory-based modeling approaches, the pricing decision problem is achieved under with two different structures. Numerical experiments are also given to examine the effects of the presented scenarios and provide further managerial insights on the solutions.

In this paper we construct Non-Standard finite difference schemes (NSFD) for numerical solution of nonlinear Lane-Emden type equations which are nonlinear ordinary dierential equations on semi-infinite domain. They are categorized as singular initial value problems. This equation describes a variety of phenomena in theoretical physics and astrophysics.  The presented schemes are obtained by using the Non-Standard finite difference method. The use of NSFD method and its approximations play an important role for the formation of stable numerical methods. The main advantage of the schemes is that the algorithm is very simple and very easy to implement. Thus, this method may be applied as a simple and accurate solver for ODEs and PDEs and it can also be utilized as an accurate algorithm to solve linear and nonlinear equations arising in physics and other fields of applied mathematics. Illustrative examples have been discussed to demonstrate validity and applicability of the technique and the results have been compared with the exact solutions.

This paper attempts to solve a benchmark money in utility model by first order Taylor approximation to the policy function. After a brief summary of recent development in first order Taylor approximation in solving dynamic stochastic general equilibrium models, we choose Sidrauski’s Money in utility model as a standard model and follow the approach proposed by Uhlig [1] to solve for the recursive law of motion at first order.