Journal of Advances in Industrial Engineering
Volume:48 Issue: 1, 2014

Flexibility is one of the most important indicators of a supply chain. A flexible supply chain in the face of market changes and customer needs، must be responsive to the changes and needs، to be able to attract customers in the a competitive marketplace. Being aware of the flexibility of a supply chain helps managers to solve problems and overcome deficiencies and increase the competitive advantages. Supply chain flexibility has a hierarchical structure consisting of four dimensions: sourcing flexibility، operating system Flexibility، distribution flexibility and information system flexibility. The purpose of this paper is to provide measurement of supply chain flexibility، using a combination of two methods، fuzzy AHP and fuzzy TOPSIS، with a case study. Results show that the operating system flexibility is the most important dimension among the other dimensions. In addition، it is concluded that taking the information system flexibility into account will increase the overall flexibility.

In this paper، we study the multi-depot vehicle routing problem with inter-depot routes، in which the vehicles can replenish at intermediate depots. Vehicles leave the origin depot with load on-board and serve customers until out of load. They may visit an intermediate depot to replenish and finally return to the origin depot، completing their route. We initiate a mathematical mixed integer programming model for this problem. The objective of the problem is to find routes for vehicles at a minimal cost in terms of total travel cost and replenishments cost at intermediate depots، without violating the capacity constraints of the vehicles. The solution to the problem is obtained through CPLEX solver in commercial software GAMS 23. 5، Genetic Algorithm and Tabu Search algorithms. Computational results indicate the effectiveness of the proposed algorithms in terms of solution time and quality of results.

In this article، a game in which terrorists and the state government are the players is considered. On the one hand، terrorists try to attack major cities and impose damages (loss in the state’s point of view)، on the other hand، the state tries to reduce damages through establishing the optimal number of facilities that are also optimally located. Furthermore، allocating of fixed facilities in order to decrease the destructions caused by terrorist attacks is studied. Simultaneous attacks to a number of cities are also examined. To achieve the optimal number of facilities، prediction cost and budget are considered to be the constraints. Finally، according to the police of Iran، an example is designed and solved using the model.

Blood supply chain management is one of the main challenges in healthcare system. Optimal location of blood donation camps is one of the key factors in optimizing the blood supply chain. Disruption in location of blood donation camps because of disasters will have a negative impact on the entire supply chain. This paper develops a generalized network optimization model for the complex supply chain of human blood، that consists of blood donation camps، collection sites، disposal sites as well as points of demand and a stochastic programming formulation for the location of blood donation camps with unreliable facilities is presented. This model determines the location of blood donation camps and the number of transfused blood units taken under different scenarios between the components of supply chain. A sampling-based algorithm called sample average approximation algorithm is used to approximately solve this model and present the computational results.

The allocation of human resources to project activities in order to schedule them is one of the closest states to the actual conditions of scheduling problems. But regarding the fact that this problem belongs to the class of Np-hard problems; so far the exact methods have only been able to solve the small size problems. In this study، the renewable resources are humans in a way that every staff with multiple skills is only able to satisfy one of activities’ skill requirements at a specific time. In this paper، defining the efficiency degrees for the staff، a mathematical programming model is presented for simultaneous solving of the two problems. Since this problem belongs to Np-hard problems، a meta-heuristic differential evolution algorithm is developed for solving real-world cases. The results prove the efficiency of the algorithm in simultaneous solving of project scheduling and human resources allocation problems.

A simulation-optimization approach is proposed for replenishment planning and shelf space allocation. The customers’ demand is divided into fixed and variable demand. Each customer follows a given path while shopping in the store. By dividing the store space into several zones and calculating the related substantial and random utilities، an analytical equation is proposed for the probability of zone visits. Due to the complexity of the proposed equation، a simulation method is used to evaluate the customers’ movements and predict the variable demand. A profit-based integer-programming model is then formulated for the case problem that needs the output demand of the simulation phase. However، the optimal values of some decision variables should be available through simulation. Therefore، a simulation-optimization algorithm is iteratively run since a pre-determined small deviation from the expected profit goal is achieved. Finally، the numerical results are reported for a small-sized problem.

In this paper، drift change point estimation in the mean of polynomial profiles is considered. For this purpose، the proposed change point estimator is computed using maximum likelihood approach. Performance of the proposed estimator is evaluated using Monte Carlo simulations when T2 control chart issues an out-of-control signal. Simulation results show that the performance of the proposed estimator improves when the magnitude of shifts increases. Also، the desirable performance of the proposed estimator is clear in all range of shifts.

Identifications and analysis of time series are time consuming، based on trial and error and highly dependent on expert judgments. This is mainly due to the presence of various models for forecasting time series، as well as introducing new techniques for analysis and predictions. In this paper، expert system structure is used to replace traditional methods of model identifications for time series. Firstly، several search engines are defined and analytical methods are specified. Next، the knowledge base is developed such that a proper model can be assigned to each data set. The goodness of fit is then evaluated by mathematical indices. Repeating the process and modifying the responses to account for uncertain situations، will provide a set of models to make the final decision. Lastly، the performance of the proposed expert system is verified by a series of sample data as a case study and the efficiency of the system is approved.

The increasing consumption of electricity in Iran is one of the greatest concerns of the government. Using the subsidy-based pricing system is one of the main reasons of improper pattern of residential electricity consumption that has imposed great cost over the government due to the increased number of consumers and their improper way of consuming electricity. In this paper، we analyze the factors that affect residential electricity demand using artificial neural network (ANN) and predict the amount of electricity consumption in 2006 (the end of the year in which subsides are being removed) by definition of five different price scenarios. The per-capita residential electricity consumption is considered as a dependent variable of the model. Electricity price، GDP per capita، macroeconomic fluctuations and a variable representing weather temperatures are used as explanatory factors. The proposed model has a good explaining capability (R=0. 996) and with predicting independent variables up to 2016، the dependent variable were predicted using procedures like time series and ARIMA. The achieved results show that the price factors have limited role in defining the pattern of residential electricity consumption. So small changes in electricity price will not reduce the electricity consumption and committing scenarios with gradual changes in price will not lead to the reduction of electricity consumption. Therefore، it is necessary for the government to commit scenarios with significant increase of prices in order to correct the pattern of residential electricity consumption; otherwise، the electricity demand will increase uncontrollably due to the increasing population and consumption.

The redundancy allocation problem can be described as selecting the components and system configuration in order to optimize some objective function (s) without violating the system constraints. Operating conditions are not constant in many advanced engineering systems. Mostly، in the engineering applications selection، the number of redundancy components must be balanced among the constraints (cost، space، weight etc.) and the improve system reliability. In the presented model، Optimum number of redundancy components obtained by considering the working conditions and constraints in the series-parallel systems. In this model، the number of redundancy components establishing by maximizing the reliability of the system in all scenarios. Finally، for better understanding، a numerical example is presented.

In today’s turbulent world، the continuity of critical activities in the event of disruptions، and also fast recovery and return to business as usual، are vital for sustainable success of companies. Maintaining continuous delivery of critical business processes in the event of disruptions is a matter of business continuity. Business Continuity Management System (BCMS) is concerned with the development، maintaining and improving the continuity of critical business functions in the face of disruptive events. This paper investigates the business continuity management، its definitions and historical evolution and presents a summary of a survey results on Business Continuity Management trends in three distinct industry sections in Iran، namely، automotive، petroleum and chemical products and food products.

This article discusses the use of Value Stream Mapping (VSM) in a make-to-order production environment. According to this tool، a practical study carried out about the way of implementation of lean manufacturing in a commercial printing company in an action research basis. The aim of using VSM as a drawing tool of current value stream is to investigate the ways of process improvement and reducing waste. In this study، after mapping the current state value stream for making waste measurement، a detailed root-cause analysis was carried out and various improvement proposals has been identified based on the principles of lean manufacturing especially the continuous flow and low level of work in process and final goods inventory. Because of the importance of waste measurement in different phases of lean implementation in commercial printing companies as low volume high mix production environments، a dedicated deficiency measure was developed.

Making a rational decision about the free production capacity of a firm requires accurate and comprehensive information in different fields such as unscheduled time of the machines، materials or other required resources status in the firm and the related supply chain. In this study، we try to achieve the needed information through integrating advanced available-to-promise mechanism with scheduling in flow shop production systems. The aim is to determine the scheduling of the orders simultaneously with calculating the free capacity of the system to determine the ATP. Therefore، some different kinds of constraints including the availability of the machines and materials are considered. The obtained results from solving the proposed model lead to proper values for performance indexes.