Journal of Industrial and Systems Engineering
Volume:8 Issue: 2, Spring 2015

Among the various existing models for the warehousing management, the simultaneous use of private and public warehouses is as the most well-known one. The purpose of this article is to develop a queuing theory-based model for determining the optimal capacity of private warehouse in order to minimize the total corresponding costs. In the proposed model, the available space and budget to create a private warehouse are limited. Due to the ambiguity, some parameters are naturally simulated by expert-based triangular fuzzy numbers and two well-known methods are applied to solve the queuing-based fuzzy programming model and optimize the private warehouse capacity. The numerical results for three cases confirm that unlike the previous approaches, the proposed one may easily and efficiently be matched with various lines of manufacturing environments and conditions.

Cancer is one of the major causes of death all over the globe and radiotherapy is considered one of its most effective treatment methods. Designing a radiotherapy treatment plan was done entirely manually in the past. RecentlyIntensity Modulated Radiation Therapy (IMRT) was introduced as a new technology with advanced medical equipmentin the recent years. IMRT provides the opportunity to deliver complex dose distributions to cancer cells while sparing the vital tissues and cells from the harmful effects of the radiations. Designing an IMRT treatment plan is a very complex matter due to the numerous calculations and parameters which must be decided for. Such treatment plan is designed in three separate phases: 1) selecting the number and the angle of the beams 2) extracting the intensity matrix or the corresponding dose map of each beam and 3) realizing each intensity matrix. The third phase has been studied in this research and a nonlinear mathematical model has been proposed for multileaf collimators. The proposed model has been linearized through two methods and an algorithm has been developed on its basis in order to solve the model with cardinality objective function. Obtained results are then compared with similar studies in the literature which reveals the capability of proposed method.

Cellular manufacturing system, an application of group technology, has been considered as an effective method to obtain productivity in a factory. For design of manufacturing cells, several mathematical models and various algorithms have been proposed in literature. In the present research, we propose an improved version of discrete particle swarm optimization (PSO) to solve manufacturing cell formation problem effectively. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optimum becomes difficult. To avoid premature convergence of PSO, we present a new hybrid evolutionary algorithm, called discrete particle swarm optimization-simulated annealing (DPSO-SA), based on the idea that PSO ensures fast convergence, while SA brings search out of local optimum. To illustrate the behavior of the proposed model and verify the performance of the algorithm, we introduce a number of numerical examples. The performance evaluation shows the effectiveness of the DPSO-SA.

Utilizing renewable energies is identified as one of significant issues for economical and social significance in future human life. Thus, choosing the best renewable energy among renewable energy candidates is more important. To address the issue, multi-criteria group decision making (MCGDM) methods with imprecise information could be employed to solve these problems. The aim of this paper is to propose a new compromise solution approach based on technique for order preference by similarity to ideal solution (TOPSIS) method under evaluations of a group of experts with hesitant fuzzy information. The hesitant fuzzy set (HFS) is a modern fuzzy set which could help the experts by providing some membership degrees for renewable energy candidates under the evaluation criteria to margin of errors. Also, weights of each expert and criterion are determined by proposing extended hesitant fuzzy entropy and maximizing deviation methods based on hesitant fuzzy Euclidean-Hausdorff distance measure. In addition, the judgments (preferences) of experts are aggregated in the final step to prevent the loss of data. Finally, an illustrative example about the energy policy selection is presented to demonstrate the procedure of the proposed decision approach. Also, a comparative analysis is provided with the recent decision method of the literature to show the capability of the proposed approach.

In this paper, the flow-shop scheduling problem with unrelated parallel machines at each stage as well as sequence-dependent setup times under minimization of the sum of earliness and tardiness are studied. The processing times, setup times and due-dates are known in advance. To solve the problem, we introduce a hybrid memetic algorithm as well as a particle swarm optimization algorithm combined with genetic operators. Also, an application of simulated annealing is presented for the evaluation of the algorithms. A Taguchi design is conducted to set their parameters. Finally, a comparison is made via 16 small size and 24 large size test problems and each problem is run 10times. The results of one-way ANOVA demonstrate that the proposed memetic algorithm performs as efficient as the HSA qualitatively and with 63.77% decline in elapsed time.

Today, “Sustainable production” has attracted a great deal of interest by academic researchers and practitioners due to the raising environmental and social concerns. Sustainable EPQ model has been developed as a result of this interest and also necessity. This paper develops a novel sustainable EPQ (SEPQ) model under partial backordering consideration. The model converts all emission variations of inventory production lifecycle into economic tangible factors. A solution procedure to determine the optimal solutions of the problem is developed for this SEPQ-PBO model. In order to demonstrate validity of the proposed model and applicability of the developed solution procedure, numerical examples accompanied by comprehensive sensitivity analysis of key parameters of the model are provided.