JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL
Year:2012 | Volume:8 | Issue:8|Papers Count:5

We consider an open shop scheduling problem with setup and processing times separately such that not only the setup times are dependent on the machines, but also they are dependent on the sequence of jobs that should be processed on a machine. A novel bi-objective mathematical programming is designed in order to minimize the total tardiness and the makespan. Among several multi-objective decision making (MODM) methods, an interactive one, called the TH method is applied for solving small-sized instances optimally and obtaining Pareto-optimal solutions by the Lingo software. To achieve Pareto-optimal sets for medium to large-sized problems, an improved non-dominated sorting genetic algorithm II (NSGA-II) is presented that consists of a heuristic method for obtaining a good initial population. In addition, by using the design of experiments (DOE), the efficiency of the proposed improved NSGA-II is compared with the efficiency of a well-known multi-objective genetic algorithm, namely SPEAII.Finally, the performance of the improved NSGA-II is examined in a comparison with the performance of the traditional NSGA-II.

To enter new markets and remain competitive in the existing markets, companies need to shift their focus from traditional means and ways to some innovative approaches. Though the paper industry in India has improved remarkably on its technological and environmental issues, yet it shows a low rate of innovation. The present paper attempts to review the industry in the perspective of technological innovations and investigates empirically the role of innovations in performance improvement and pollution control. Multivariate analysis of variance and discriminant function analysis are applied for data processing. The findings reveal that the mean scores on the factors, such as sales, quality, and flexibility, are higher for the good innovators than those for the poor innovators. Conversely, the factors which are likely to be reduced as a result of innovations, such as time, cost, emissions, and disposal of waste, have shown higher means for the poor innovators.

A cutting stock problem is one of the main and classical problems in operations research that is modeled as LP problem. Because of its NP-hard nature, finding an optimal solution in reasonable time is extremely difficult and at least non-economical. In this paper, two meta-heuristic algorithms, namely simulated annealing (SA) and tabu search (TS), are proposed and developed for this type of the complex and large-sized problem. To evaluate the efficiency of these proposed approaches, several problems are solved using SA and TS, and then the related results are compared. The results show that the proposed SA gives good results in terms of objective function values rather than TS.

In this paper, using supply chain operational reference, the reliability evaluation of available relationships in supply chain is investigated. For this purpose, in the first step, the chain under investigation is divided into several stages including first and second suppliers, initial and final customers, and the producing company. Based on the formed relationships between these stages, the supply chain system is then broken down into different subsystem parts.The formed relationships between the stages are based on the transportation of the orders between stages. Paying attention to the system elements' location, which can be in one of the five forms of series namely parallel, series/ parallel, parallel/series, or their combinations, we determine the structure of relationships in the divided subsystems.According to reliability evaluation scales on the three levels of supply chain, the reliability of each chain is then calculated. Finally, using the formulas of calculating the reliability in combined systems, the reliability of each system and ultimately the whole system is investigated.

This paper considers a three-stage assembly flowshop scheduling problem with sequence-dependent setup times at the first stage and blocking times between each stage in such a way that the weighted mean completion time and makespan are minimized. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches or optimization tools is extremely difficult. Thus, this paper proposes a meta-heuristic method based on simulated annealing (SA) in order to solve the given problem. Finally, the computational results are shown and compared in order to show the efficiency of our proposed SA.