Journal of Advances in Industrial Engineering
Volume:52 Issue: 3, 2018

Selective maintenance is of crucial importance in the multi-state systems because of the necessity of running consecutive missions with a limited break. This paper presents a novel approach based on Game Theory for the problem of selective maintenance in the case of multi-state systems under the influence of stochastic dependency. Because of the priority of customer’s opinion in today’s world, the Stackelberg model is used in order to show the interaction between the customer and workshop. In this model, first, the customer, as the leader, introduce the suggested contract containing the level of system reliability for maximizing desirability. Consequently, the workshop, as the follower, determines costs based on details of the proposed contract. Numerical examples are studied the model performance, and the sensitivity analysis of results about the key parameters of the model is probed.

There is usually uncertainty in the information during a disaster. These uncertainties are revealed in different stages during the time, but they still exist. Therefore, when information is appeared over the time, it is necessary to model and solve the problem in a multi-stage stochastic programming to make more real decisions. In this paper, a multi-stage relief routing model is presented for a disaster problem. It is assumed that the routing plan can be rerouted in each stage according to new received information. Also, an approximation algorithm is presented based on the two-stage stochastic programming. It is shown that the proposed algorithm is an appropriate approximation of the multi-stage model. Comparison of results with the deterministic model indicates that more survivors will be achieved by the proposed model comparing to the deterministic one and it shows effectiveness of the proposed approach.

Exchange of goods with goods or supplies Although the long-standing method is in trade, today, new and emerging networks of national and international networks have found a special place. Aiding economic boom, confronting monetary sanctions, maintaining foreign exchange reserves, maintaining labor force, etc. is one of the benefits of modern markets. Given the widespread presence of these markets, proper management to balance the market, reduce costs, reduce the risk of entry into a network, etc., requires a thorough and math-molded modeling. In this paper, a multipurpose mathematical optimization problem has been designed and implemented with the aim of reducing the costs and liabilities of countries by considering a government-run network of several countries as members of the network to supply the goods needed by each country. The results of solving the proposed model show that the proposed model can provide a suitable model for the production and exchange of goods between the countries forming a public government network.

In this research, a three objective (lean, agile, pivotal values) nonlinear integer programming mathematical model is proposed to optimize general commodities supply chain in this model, all parameters for objective functions and restrictions are assumed to have fixed and known values. Proposed model have three objective functions including: minimizing transportation costs, delivery tardiness and making contracts. Maximizing agility and pivotal values. Intended model designs a lean, agile and value supply chain network. This network consists of Producers, Distributers and Customers that which indexes and criteria values and agility is assumed for producers and distributers. To solve the model, first suitable weights for agility and observance the value for producers and distributers are examined using multi-criteria decision making. These weights and other problem parameters are input data for proposed mathematical model. Proposed mathematical model is solved in a case-study form using GAMS program. Then two multi objective meta heuristic algorithms including genetic algorithm and Frog leaping algorithm using non dominated sorting is proposed and Meta heuristic algorithms and GAMS`s results are Compared to Validate proposed algorithms. Results of two algorithms NSGA-II and NSMOSFLA for comparing criteria`s of multi objective algorithms are Analyzed.

Scheduling is one of the most applicable problems in industry that is considerably studied by researchers in the recent years. It is necessary to extend the models that can be applied in real situations. To this end researchers have tried to consider assembly and processing stages simultaneously. In this research according to the importance of different production stages in industry, and also to consider problem in real situation, job shop scheduling problem by considering a parallel assembly stage is studied to minimize completion time for all products. At first, this problem is reduced to graph coloring. Because this problem and graph coloring problem are NP-hard, a hybrid Genetic-Particle swarm optimization algorithm for medium and large size problems used. So in this research a lower bound for this problem based on graph coloring problem is proposed to evaluate the efficiency and effectiveness of the proposed algorithm. Keywords: Scheduling, Job shop, Parallel Assembly, Graph Coloring

Today, fiber-optic due to its higher bandwidth and cost effective in compare with other similar technologies is one of the most important tools of data transfer. In this paper, an integrated mathematical model is presented for the three-level network of fiber-optic by considering the backbone network and local access networks simultaneously. Used topologies in its levels are respectively ring, star, and star. The purpose of this model is determine the location of the central hubs and concentrators, communication of central hubs and also assign each user to one of the concentrators such that the costs of the fiber link and installation the concentrators get minimize as well as providing the bandwidth required per user. Due to NP-hardness of this problem, small size of proposed model validated by GAMS software, then the model is solved by two meta-heuristic methods of DE and GA in large-sizes and the results of the two algorithms have been compared in terms of time and objective function value. The result shows the required time to achieve optimum solution in the DE is less than GA and also DE has better performance in small and medium-sized of problem but for the large size of problem the GA algorithm is better than DE

In this paper , a new mathematical model for the electricity generation expansion planning problem are presented. The model, in terms of planning, is among the long-term planning in their literature. However, the steps of model are on a monthly basis. Considering the soft and hard constraints together and fits with other features is the model concept in the real world. Classifying plants according to construction areas should be added to other model features. Then the exact solution to small - scale of the model is presented by the software (GAMS). Then, due to the complexity of the problem, two meta-heuristic algorithms, genetic and traininig – learninig based optimization, was presented. Genetic algorithm parameters are set using RSM. The 22 series of numerical examples by each of these two algorithms are solved and then the results were compared with each other. The results show that the genetic algorithm has the advantage.

Accurate monthly power demand network forecasting can help to plan the energy and it can handle the correct management of the power consumption. It has been found that the monthly electricity consumption demonstrates a complex nonlinear characteristic and has an obvious seasonal tendency. One of the models that is widely used to predict the nonlinear time series is the support vector regression model (SVR) in which the selection of key parameters and the effect of seasonal changes could be considered. The important issues in this research are to determine the parameters of the support vector regression model optimally, as well as the adjustment of the nonlinear and seasonal trends of the electricity data. The method that is proposed by this study is to hybrid the support vector regression model (SVR) with Fruit fly optimization Algorithm (FOA) and the seasonal index adjustment to forecast the monthly power demand. In addition, in order to evaluate the performance of the hybrid predictive model a small sample of the monthly power demand from Iran and a large sample of Iran monthly electricity production has been used to demonstrate the predictive model performance. This study also evaluates the superiority of the SFOASVR model to the other known predictive methods. In terms of the prediction accuracy, we used the evaluation criteria such as Root Mean Square Error (RMSE) and mean absolute percentage error (MAPE) as well as Wilcoxon's nonparametric statistical test. The results show that the SFOASVR model has less error than the other forecasting models and is superior to the most other models in terms of Wilcoxon test. Therefore, SFOASVR method is an appropriate option for prediction of the power demand.

In this paper, a two-stage supply-chain scheduling problem including manufacturers and distributors will be investigated and modeled. The objective function is to minimize the makespan, which is equivalent to the completion time of the last job to leave the system. A minimum makespan usually implies a good utilization of the machine(s). In this research, serial batching machines do jobs processing and then the jobs will deliver to customers (in the next stage) for further processing. The capacity of each batch is limited. Delivery unit cost of each batch is fixed and independent of the number of jobs in the batch. Processing and setup time of jobs are varying according to jobs types. The setup time is determined according to jobs type within each batch. The problem has been formulated as a mixed integer-programming model. Finally, a lower bound will be provided. Computational experiments demonstrate the performance of new lower band.

Correct selection of projects The first step is project-based organizations in the targeted management of project portfolios. This is a complex process selection that includes many factors and considerations. Market conditions, global rapid changes in various dimensions and other related issues in the real environment have increased the uncertainty and ignorance of these issues. It is therefore necessary to provide models for showing the real status of the organization and its goals and preferences. In this paper, the goal is to provide a fuzzy fuzzy multi-objective model for the portfolio of rail transport projects considering the uncertainties in variables; budget, time needed to complete the project, environmental pollution, risk, and quality. In this model, minimizing environmental pollution, maximizing quality, minimizing the risk and cost of projects under inflation is considered in the objectives of the problem. Due to the fact that the model was presented, a particle swarm algorithm was used to solve the problem, and finally, the results were compared with the genetic algorithm in order to measure the efficiency.

One of the major factors of supply chain is distribution network and the problem of locating distribution centers is considered as one of the important decisions in supply chain. Also, one of the most important goals of the supply chain is customer satisfaction. So, Reliability can also be effective in delivering adequate productions to customers. A new multi-item multi-period multi-objective nonlinear mixed integer programming model is developed which aims minimizing total cost, warehouse space of distribution centers, tardiness and earliness times and maximizing distribution center’s reliability. The model is developed for a four-echelon supply chain. E-constraint and BOM methods are used to solve the model. Finally, some numerical examples are generated in different dimensions and solved to evaluate the performance of proposed model and solution methods and the results are compared together.

In the present competitive world, the necessity of minimizing costs and production time and increasing the productivity in manufacturing systems are more and more felt. Because when production costs are reduced, the final price of product is reduced too and when the production time is reduced, afterward the response time to customers order is reduced too. This paper presents a bi-objective mathematical model of multi period cell formation problem base on grouping efficacy in dynamic environment with the flexibility in operator assignment. The advantages of the proposed model are as follows: considering multi period planning horizon, dynamic system reconfiguration, duplicate machine, machine capacity, available time of operators and operator assignment. The aims of the proposed model are to maximize the total value of grouping efficacy (TVGE) and minimize the total costs (TC) include purchasing new machines cost, machine overhead cost, machine processing and reconfiguration costs, hiring, firing and salary costs. Computational results are presented by solving some numerical examples with improved e-constraint method to validate and verify the proposed model.