Industrial Management Perspective
Year:2020 | Volume:10 | Issue:2 (38)|Papers Count:7

A flowshop problem with objective functions of minimizing makespan and energy cost has been investigated. Reducing production costs is one of the goals that industries always have in mind. Increasing public awareness about the energy issues creates a new attitude toward minimizing energy costs. In order to make the problem more compatible with the real-world conditions, the problem is considered under uncertainty. An existing research gap inspired this study. It is assumed that machines can use the three slow, normal and fast speeds to process jobs. At high speeds, consumption rate increases and completion time decreases, and vice versa. The difference in machine processing speeds yields different and contradictory values in the objective functions. Therefore, a method should be proposed in which, in addition to the order of jobs, the speed of machines could be determined. A mathematical model is presented, and then a simulation-based genetic algorithm is used to solve the problem on a large scale. Simulation is used for each evaluation of the objective function in the genetic algorithm to consider the uncertainty of processing times. Due to the stochastic processing time, the expected value model is used to deal with uncertainty. The computational results indicate that the algorithm and approach show a good performance.

One of the main components of competition in the current competitive environment is supply chain; therefore, organizations need to have a reliable supply chain to increase efficiency and effectiveness. Moreover, due to the increase in environmental pollution and the requirements imposed by the governments to harness polluting activities, organizations are obliged to follow green supply chain practices that account for environmental considerations along with economic aspects. hence, in this study, a bi-objective model for a green, closed-loop supply chain under demand uncertainty is proposed which takes into account environmental consideration and economic aspects. Another important aspect of the supply chain network design is the concept of uncertainty. Due to societal and political evolutions and the scarcity of raw materials in the decision-making horizon, uncertainty is a significant measure in the models of supply chain. Indeed, in this study, the model was developed for a supply chain under uncertainty so that more compatibility with real-world conditions would be achieved. The results show that considering uncertainties makes the model more flexible. The advancement of technology and unpredictable behaviors of customers in markets have created a very complex competitive atmosphere. To evaluate the performance of the developed model, the case study of the Iran Transfo Company is considered.

The aim of purchasing portfolio is commodity classification to determine the suitable purchasing strategy. In this research, we propose a new method for the classification of commodities with the extension of purchasing portfolio approaches. Indeed, the purpose of this research is to determine the appropriate purchasing strategy for commodities with the consideration of purchasing portfolio. Furthermore, first, by reviewing the literature and surveying the specialists and databases, the dimensions of profit impact, market complexity, and the balance of supplier and buyer power were considered and appropriate criteria for classifying commodities and services were determined. After determining the number of dimensions, the weight of the criteria in each dimension was calculated using the best-worst method. Then, using the TOPSIS method, the score of commodities was obtained in each dimension and the position of the commodities and services was determined on a matrix. To determine the most suitable strategy by considering commodity position based on three defined dimensions, at first, the appropriate strategies were collected by reviewing the research literature, and then, the most appropriate strategy for each category of commodities was determined. Finally, the proposed method was implemented for the commodities of a company, and the results were presented.

In this research, a novel model is presented to optimize the periodic inspection for a complicated two-component system with dependent failures. In this model, the failures of the first and the second component are soft and hard, respectively. A soft failure of the first component does not have any impact on the second component, but a hard failure of the second component shocks the first component and increases its failure rate. A soft failure cannot be recognized before preventive maintenance. This component is inspected in specific periods and if it has a problem, it is repaired to become similar to a new one. Since a soft failure in the first component will increase the operational costs, in this study, in addition to the periodic inspections, the first component inspection is also carried out during the hard failure of the second component. A novel model is developed here to find the optimum inspection periods in order to minimize the costs of inspection, repair and penalty for delay in identifying the soft failures. A numerical experiment is used and the sensitivity analysis is performed to show the performance and efficiency of the developed model.

This paper presents an integrated model as a combination of fuzzy analytical hierarchy process (FAHP), scenario-based two-stage stochastic programming and robust optimization approaches for the problem of supplier selection and order allocation under supply risk conditions. Uncertainties in the supply of materials are considered under three scenarios: increased sanctions, stability of sanctions, and sanctions removal. In the first step, the main qualitative factors for the selection of suppliers are identified and a specific weight is assigned to each supplier through FAHP. In the second step, these weights are introduced as inputs to a two-stage stochastic programming model and affect the second-stage variables. In the third step, we use the Mulvey formulation and then linearize the resulted robust two-stage stochastic model. The model is a integer linear programming model solved by CPLEX for a case study and the results are discussed. Finally, a sensitivity analysis is performed on the parameters of the robust model and the balance between the total cost and the unfulfilled demand is shown.

Analysis of the right response to risk is one of the important processes in project management. The purpose of this research is to categorize the risks of the urban projects. To this end, after identifying the risks of the urban project, the most important indicators are developed in line with experts’ opinions to evaluate risks. These include impact on time, cost, quality, probability of occurrence, environmental impact, safety effects, importance of risk, risk manageability and risk response strategy. Then, the risk assessment is performed using the desired indicators. All steps are implemented according to CRISP-DM standard methodology and the importance of risk, risk manageability, and risk response strategy are predicted by data mining algorithms. The results show that classification algorithms performed in risk management successfully. Importance of risk and risk manageability are predicted by logistic regression whose accuracy rates are respectively equal 0. 88 and 0. 9. For risk response strategy, the Naï ve Bayes algorithm performed better than other algorithms with an accuracy rate of 0. 84. For further investigation of the used algorithms, the results are compared with one of the MCDM methods, the TOPSIS method. Data mining algorithms performed better than the TOPSIS method.

The recent increase in the number of natural disasters, earthquake in particular, underlines the need to investigate the problem of emergency location. In this study, a new hybrid approach is presented for emergency location-allocation problem which incorporates GIS, system dynamics, Coburn and Spence model, and stochastic programming. In the proposed approach, first, the candidate places are identified based on a number of indices using GIS. Since the emergency location demand is considered as an uncertain parameter depending on different scenarios of the earthquake, in the next step, a combination of system dynamics and the casualty estimation model proposed by Coburn and Spence is used to estimate the demand. Then, proposing a stochastic location-allocation model, the demand is assigned to the candidate places determined by GIS. Finally, the minimax regret model is used to identify the final locations.