Multi-processor system real-time scheduling algorithm for flight assignment problem was proposed. By constructing the flight queue model, the real-time scheduling algorithm was applied to a example and achieved satisfactory results. The time cost was less than 3 ms. The experiment results showed that the algorithm is faster than the genetic algorithm 2 orders of magnitude, faster than branch and bound almost an order of magnitude.

This paper proposes an order-searching genetic algorithm to solve multi-dimensional assignment problem by fusing the order-searching algorithm and the genetic algorithm. The new algorithm searches the optimal solution in the whole solution space firstly, then memorizes and improves the better solution by using cross and mutation operations, so that the convergence speed is accelerated and the accuracy of the data association is improved. Simulation results show that the new algorithm is feasible and effective.

Cross-docking is widely adopted as an alternative to traditional warehousing in many industries. It consolidates different deliveries from suppliers into specified shipments catered for respective customers, thus reducing transportation and inventory holding costs. This book chapter addresses the scheduling problem of delivery where the products are expected to ship from suppliers to cross-docking faculties to customers within time window. For generating online delivery scheduling for the distribution network, the problem, which is formulated with the objective of minimising the inventory, transportation and penalty cost, is solved by genetic algorithm. Experiments were conducted to study the robustness of the model and the performance of the important parameters. From the results, it was also found that as the number of deliveries, pickups, cross-docks, time horizon and product type increase, the number of variables involved increases which in turn increase the complexity of the model. With higher number of variables, the computational time elapsed increase tremendously and total cost increases with the number of product types.

Enterprise applications are increasingly becoming capable of sharing information and coordinating decisions autonomously without any human intervention. This open business environment gives rise to several new supply chain planning problems that have to be solved by individual entities of a supply chain. This paper first mathematically formulates the supply chain planning problems emerging in the open business environment and second proposes a heuristic solution procedure based on the framework of genetic algorithm applicable to large-scale problems. The procedure has two stages as the original problem is decomposed into two sub-problems in an effort to reduce the overall problem complexity. The performance of the proposed algorithm is empirically proven to be effective in both solution quality and search time in various problem sizes.

This paper focuses on the development of an effective methodology to determine the optimum welding conditions that maximize the strength of joints produced by ultrasonic welding using response surface methodology (RSM) coupled with genetic algorithm (GA). RSM is utilized to create an efficient analytical model for welding strength in terms of welding parameters namely pressure, weld time, and amplitude. Experiments were conducted as per central composite design of experiments for spot and seam welding of 0.3- and 0.4-mm-thick Al specimens. An effective second-order response surface model is developed utilizing experimental measurements. Response surface model is further interfaced with GA to optimize the welding conditions for desired weld strength. Optimum welding conditions produced from GA are verified with experimental results and are found to be in good agreement.

This paper studies a job shop scheduling problem with due dates and deadlines in the presence of tardiness and earliness penalties. Due dates are desired completion dates of jobs given by the customer, while deadlines are determined by the manufacturer based on customer due dates. Due dates can be violated at the cost of tardiness, whereas deadlines must be met and cannot be violated. The aforementioned scheduling problem, which is NP-hard, can be formulated with the objective function of minimizing the sum of weighted earliness and weighted tardiness of jobs subject to due dates and deadlines. In order to solve this problem, an enhanced genetic algorithm (EGA) is introduced in this paper. EGA utilizes an operation-based scheme to represent schedules as chromosomes. After the initial population of chromosomes is randomly generated, each chromosome is processed through a three-stage decoder, which first reduces tardiness based on due dates, second ensures deadlines are not violated, and finally reduces earliness based on due dates. After the population size is reached, EGA continues with selection, crossover, and mutation. The proposed algorithm is tested on 180 job shop scheduling problems of varying sizes and its performance is discussed.

This paper analysed the traditional back-propagation (TBP) algorithm, studied the CRI-fuzzy adaptive back-propagation model (CFABP), and proposed a new method to get the fuzzy rules based on genetic algorithm. Finally, Two practical examples were used for comparing the performance of TBP algorithm with improved BP algorithm. The simulation results indicated that the new algorithm had improved TBP algorithm effectively.

This paper considers the problem of no-wait flow shop scheduling, in which a number of jobs are available for processing on a number of machines in a flow shop context with the added constraint that there should be no waiting time between consecutive operations of a job. Each operation has a separable setup time, meaning that the setup time of an operation is independent on the previous operations; and the machine can be prepared for a specific operation and remain idle before the operation actually starts. The considered objective function in this paper is the makespan. The problem is proven to be NP-hard. In this paper, two frameworks based on genetic algorithm and particle swarm optimization are developed to deal with the problem. For the case of no-wait flow shop problem without setup times, the developed algorithms are applied to a large number of benchmark problems from the literature. Computational results confirm that the proposed algorithms outperform other methods by improving many of the best-known solutions for the test problems. For the problems with setup time, the algorithms are compared against the famous 2-Opt algorithm. Such comparison reveals the efficiency of the proposed method in solving the problem when separable setup times are considered.

This paper deals with the performance enhancement for crystallization unit of a sugar plant using genetic algorithm. The crystallization unit of a sugar industry has three main subsystems arranged in series. Considering exponential distribution for the probable failures and repairs, the mathematical formulation of the problem is done using probabilistic approach, and differential equations are developed on the basis of Markov birth-death process. These equations are then solved using normalizing conditions so as to determine the steady-state availability of the crystallization unit. The performance of each subsystem of crystallization unit in a sugar plant has also been optimized using genetic algorithm. Thus, the findings of the present paper will be highly useful to the plant management for the timely execution of proper maintenance decisions and, hence, to enhance the system performance.

Many algorithms for classification need to discretize the continuous attributes for their development. Therefore the discretization of continuous attributes is a very important part in data mining. In this paper, we propose a technique for discretizing continuous attributes by means of a series of fuzzy sets which constitute a fuzzy partition of the domain of these attributes. The definition of these sets is very important as it affects the results obtained in the classification algorithms. Throughout this document we present a strategy to construct fuzzy sets in order to improve classification results. Additionally, we give some ideas about how to improve this strategy in order to work with another kinds of data. Also, we show various experimental results which evaluate our proposal in comparison with previously existing ones and where the results have been statistically validated.