Journal Of Industrial Engineering International
Volume:13 Issue: 1, Winter 2017

This work considers cooperative advertising in a manufacturer–retailer supply chain. While the manufacturer is the Stackelberg leader, the retailer is the follower. Using Sethi model it models the dynamic effect of the manufacturer and retailer’s advertising efforts on sale. It uses optimal control technique and stochastic differential game theory to obtain the players’ advertising strategies and the long-run value of the awareness share. Further, it models the relationship between the payoffs of both players and the awareness share. The work shows that with the provision of subsidy the retail advertising effort increases while the manufacturer’s advertising effort reduces. It further shows that the total channel payoff is higher for subsidised retail advertising. However, the subsidy can only be possible if the rate of growth of the manufacturer’s payoff is twice higher than that of the retailer.

It is observed that the separated design of location for depots and routing for servicing customers often reach a suboptimal solution. So, solving location and routing problem simultaneously could achieve better results. In this paper, waste collection problem is considered with regard to economic and societal objective functions. A non-dominated sorting genetic algorithm (NSGA-II) is used to locate depots and treatment facilities and design the routes starting from depots to serve customers. A new mathematical model is proposed and two objective functions including economic objective (opening cost of depots and treatment facility and transportation cost) and societal objective; that is, negative impact of treatment facilities which are close to towns are addressed in this study. A straightforward order based solution representation is applied for coding solutions of the problem and clustering approach is used to generate appropriate initial solutions. Moreover, three multi-objective decomposition methods including weighted sum, goal programming, and goal attainment are applied to validate the performance of the proposed algorithm. Number of test problems are conducted and the results obtained by algorithms are compared with respect to some comparison metrics. Finally, the experimental results show that the proposed hybrid NSGA-II outperforms all decomposition methods, but the computational times for decomposition methods are less than NSGA-II.

The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a “data mining-based evolutionary fuzzy expert system” (DEFES) approach to estimate the behavior of stock price. This tool is developed in seven-stage architecture. Data mining is used in three stages to reduce the complexity of the whole data space. The first stage, noise filtering, is used to make our raw data clean and smooth. Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model. In the third stage, K-means is used to divide the data into sub-populations to decrease the effects of noise and rebate complexity of the patterns. At next stage, extraction of Mamdani type fuzzy rule-based system will be carried out for each cluster by means of genetic algorithm and evolutionary strategy. In the fifth stage, we use binary genetic algorithm to rule filtering to remove the redundant rules in order to solve over learning phenomenon. In the sixth stage, we utilize the genetic tuning process to slightly adjust the shape of the membership functions. Last stage is the testing performance of tool and adjusts parameters. This is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting the whole knowledge base of fuzzy expert system for stock price forecasting problems. The superiority and applicability of DEFES are shown for International Business Machines Corporation and compared the outcome with the results of the other methods. Results with MAPE metric and Wilcoxon signed ranks test indicate that DEFES provides more accuracy and outperforms all previous methods, so it can be considered as a superior tool for stock price forecasting problems.

The binary states, i.e., success or failed stateassumptions used in conventional reliability are inappropriatefor reliability analysis of complex industrial systemsdue to lack of sufficient probabilistic information. For largecomplex systems, the uncertainty of each individualparameter enhances the uncertainty of the system reliability.In this paper, the concept of fuzzy reliability has beenused for reliability analysis of the system, and the effect ofcoverage factor, failure and repair rates of subsystems onfuzzy availability for fault-tolerant crystallization systemof sugar plant is analyzed. Mathematical modeling of thesystem is carried out using the mnemonic rule to deriveChapman–Kolmogorov differential equations. These governingdifferential equations are solved with Runge–Kuttafourth-order method.

Tolerancing conducted by design engineers to meet customers’ needs is a prerequisite for producing high-quality products. Engineers use handbooks to conduct tolerancing. While use of statistical methods for tolerancing is not something new, engineers often use known distributions, including the normal distribution. Yet, if the statistical distribution of the given variable is unknown, a new statistical method will be employed to design tolerance. In this paper, we use generalized lambda distribution for design and analyses component tolerance. We use percentile method (PM) to estimate the distribution parameters. The findings indicated that, when the distribution of the component data is unknown, the proposed method can be used to expedite the design of component tolerance. Moreover, in the case of assembled sets, more extensive tolerance for each component with the same target performance can be utilized.

Improving the product reliability is the main concern in both manufacturing and service processes which is obtained by monitoring the reliability-related quality characteristics. Nowadays, products or services are the result of processes with dependent stages referred to as multistage processes. In these processes, the quality characteristic in each stage is affected by the quality characteristic in the previous stages known as cascade property. Two regression-adjusted control schemes are applied to monitor the output quality variables of interest. Moreover, censoring is among the main limitations while monitoring the reliability-related quality characteristics, causing not to record the real values of some observations. Hence, the right censored observations are used to extend monitoring schemes under both the fixed- and variable-competing risks. In this paper, the accelerated failure time (AFT) is used to relate the reliability-related quality characteristic with lognormal distribution to the incoming variables. Then, two cause-selecting control charts are developed to monitor outgoing quality variables when censoring happens in each reliability-related stage. The performance of the control charts is evaluated and compared through extensive simulation studies under the censored and non-censored scenarios.

To increase the reliability of a specific system, using redundant components is a common method which is called redundancy allocation problem (RAP). Some of the RAP studies have focused on k-out-of-n systems. However, all of these studies assumed predetermined active or standby strategies for each subsystem. In this paper, for the first time, we propose a k-out-of-n system with a choice of redundancy strategies. Therefore, a k-out-of-n series–parallel system is considered when the redundancy strategy can be chosen for each subsystem. In other words, in the proposed model, the redundancy strategy is considered as an additional decision variable and an exact method based on integer programming is used to obtain the optimal solution of the problem. As the optimization of RAP belongs to the NP-hard class of problems, a modified version of genetic algorithm (GA) is also developed. The exact method and the proposed GA are implemented on a well-known test problem and the results demonstrate the efficiency of the new approach compared with the previous studies.

In this paper, a hybrid meta-heuristic algorithm, based on imperialistic competition algorithm (ICA), harmony search (HS), and simulated annealing (SA) is presented. The body of the proposed hybrid algorithm is based on ICA. The proposed hybrid algorithm inherits the advantages of the process of harmony creation in HS algorithm to improve the exploitation phase of the ICA algorithm. In addition, the proposed hybrid algorithm uses SA to make a balance between exploration and exploitation phases. The proposed hybrid algorithm is compared with several meta-heuristic methods, including genetic algorithm (GA), HS, and ICA on several well-known benchmark instances. The comprehensive experiments and statistical analysis on standard benchmark functions certify the superiority of the proposed method over the other algorithms. The efficacy of the proposed hybrid algorithm is promising and can be used in several real-life engineering and management problems.

Multiple attribute decision making (MADM) methods are very much essential in all fields of engineering, management and other areas where limited alternatives exist and the decision maker has to select the best alternative. Different methods are available in the literature to tackle the MADM problems. The MADM problems are classified as scoring methods, compromising methods and concordance methods. The concordance methods are difficult to understand compared to scoring and compromising methods. Present work introduces a simple-to-understand and easy-to-convince method for multiple attribute decision making problems. This method is based on the philosophy of both scoring and comprising methods and relies on the loss for not choosing the ideal best alternative. Different loss functions such as linear, quadratic and cubic functions have been proposed to calculate the loss. Example problems have been taken from the literature and the proposed method is implemented. Besides the simplicity of the proposed method, the results obtained are found to be in close agreement with rather difficult methods.

Natural disasters, such as earthquakes, affect thousands of people and can cause enormous financial loss. Therefore, an efficient response immediately following a natural disaster is vital to minimize the aforementioned negative effects. This research paper presents a network design model for humanitarian logistics which will assist in location and allocation decisions for multiple disaster periods. At first, a single-objective optimization model is presented that addresses the response phase of disaster management. This model will help the decision makers to make the most optimal choices in regard to location, allocation, and evacuation simultaneously. The proposed model also considers emergency tents as temporary medical centers. To cope with the uncertainty and dynamic nature of disasters, and their consequences, our multi-period robust model considers the values of critical input data in a set of various scenarios. Second, because of probable disruption in the distribution infrastructure (such as bridges), the Monte Carlo simulation is used for generating related random numbers and different scenarios; the p-robust approach is utilized to formulate the new network. The p-robust approach can predict possible damages along pathways and among relief bases. We render a case study of our robust optimization approach for Tehran’s plausible earthquake in region 1. Sensitivity analysis’ experiments are proposed to explore the effects of various problem parameters. These experiments will give managerial insights and can guide DMs under a variety of conditions. Then, the performances of the “robust optimization” approach and the “p-robust optimization” approach are evaluated. Intriguing results and practical insights are demonstrated by our analysis on this comparison.