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In this paper, a mass spring system is developed to simulate the draping behavior of knitted fabric. A new type of mesh based on loop structure, which is called Loop mesh, is introduced. The effective properties on fabric drape are measured using the Fast system, and the experimental data are incorporated into the mass spring model to simulate the draping behavior of knitted fabrics. 3D shape of real drape behavior of fabric samples is achieved using depth camera. Then in order to reduce error value between simulated and real results, distances between mass points in the Loop mesh are optimized using the Imperialistic Competitive Algorithm (ICA). In order to investigate validity and accuracy of the Loop mesh, drape behavior of knitted fabrics falling over a square table are also simulated using common meshes. It can be found that the Loop mesh produced a closest drape shape to the real fabric drape than other mesh models. Finally, fabrics drape is simulated in other situations and compared with it...

The pollution produced by power purchased from electrical network and the extensive effort of Iranian government toward the elimination of energy subsides are the encouraging factors in Iran for em...

In traditional methods for modelling the behaviour of manufacturing processes by using artificial neural networks, design of optimal architecture of these networks needs many trials and errors. Sometimes it may not achieve the best architecture of networks because of the time-consuming and tedious procedure of these methods. In this paper, the failure behaviour of magnesium alloy sheet forming at warm temperature is modelled by using the adaptive neuro-fuzzy inference systems (ANFIS) network. In order to reduce the time of finding the best architecture of ANFIS network, an optimisation algorithm, imperialistic competitive algorithm, was used. By combining this optimisation algorithm with the ANFIS, the best architecture of ANFIS network can be proposed to model the accurate behaviour of the manufacturing process in the least time possible.

The resource-constrained project scheduling problem (RCPSP) has received wide attention. In this paper, an activity-list-based nested partitions algorithm (ALNP) is developed for solving the RCPSP and a P-ALNP is proposed to improve ALNP with local adjustment. In the algorithms, to improve the search efficiency, a partial double justification is employed as local search mechanism. The computational experiments on the PSPLIB and analysis on robustness of the algorithms show that ALNP outperforms the traditional serial scheduling scheme for solving the large-scale, complex RCPSPs, and P-ALNP can improve ALNP and obtain better results. P-ALNP is a competitive algorithm for solving the RCPSP.

—The generation scheduling of different units in the power system is one of the procedures that affects the cascading blackout. Generators are usually scheduled based on the solution of the unit commitment problem, which should minimize the running cost of the power system. In this article, a new objective function is presented to consider the load-shedding effect on the power generation scheduling. The simulations are carried out in MATLAB software (The MathWorks, Natick, Massachusetts, USA), showing that it may lead to high risk of blackout in some conditions if the generation scheduling is done only by considering the cost of generation.

We consider the problem of utilizing a pack of m batteries serving n current demands in electric vehicles. When serving a demand, the current allocation might be split among the batteries in the pack. A battery’s life depends on the discharge current used for supplying the requests. Any deviation from the optimal discharge-current is associated with a penalty. Thus, the problem is to serve an online sequence of current requests in a way that minimizes the total penalty associated with the service.We show that the offline problem, for which the sequence of current demands is known in advance, is strongly NP-hard and hard to approximate within an additive gap of Ω(m) from the optimum. For the online problem, we present a competitive algorithm associated with the redundant penalty at most m. Finally, we provide a lower bound of 1.5 for the multiplicative competitive ratio of any online algorithm.

Implementation of mixed-model U-shaped assembly lines (MMUL) is emerging and thriving in modern manufacturing systems owing to adaptation to changes in market demand and application of just-in-time production principles. In this study, the line balancing and model sequencing (MS) problems in MMUL are considered simultaneously, which results in the NP-hard mixed-model U-line balancing and sequencing (MMUL/BS) problem. A colonial competitive algorithm (CCA) is developed and modified to solve the MMUL/BS problem. The modified CCA (MCCA) improves performance of original CCA by introducing a third type of country, independent country, to the population of countries maintained by CCA. Implementation details of the proposed CCA and MCCA are elaborated using an illustrative example. Performance of the proposed algorithms is tested on a set of test-bed problems and compared with that of existing algorithms such as co-evolutionary algorithm, endosymbiotic evolutionary algorithm, simulated annealing, and genetic alg...