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Four relationships for the two intervals durationInv and duration P l
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We compare several techniques for scheduling shipment of customer orders for the Coors Brewing warehouse and production line. The goal is to minimize time at dock for trucks and railcars while also minimizing inventory. The techniques include a genetic algorithm, local search operators, heuristic rules, systematic search and hybrid approaches. Init...
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Citations
... Evolutionary algorithms for processor mapping have become popular through its robust nature of ensuring best result in every run. This thesis concerns the application of evolutionary algorithms such as genetic algorithms [3], memetic algorithm [4] and the optimisation of particulate swarms [5] in a systolic way. ...
Systolic processors offer a hardware design which can accommodate more functions in a small footprint. Hardware utilization efficiency can be enhanced by appropriately designating the intended hardware with a task in space and time through parallel computing platforms. Regular algorithms known for their computational complexity can be mapped to systolic array by dependence graphs, which allot hardware to the design data. Manual mapping techniques tend to be tedious with more inaccuracy and calls for efficient mapping techniques, automated through algorithmic procedures. Texture Analysis marks the preliminary progression of image analysis and interpretation. Automotive systems, Robotics, Industrial processing and similar automated applications can be simplified through texture analysis. This work deals with employing evolutionary algorithms for mapping texture analysis onto systolic architecture. Memetic Algorithms (MA) and Particle Swarm Optimization (PSO) algorithms were comparatively studied and the efficiency of designing a parallel architecture through systolic array is analyzed through cost function and processing time.
... Examples of the diverse underlying scheduling technique are local search [e.g., 11,13], simulated annealing [e.g., 17], constraint satisfaction [e.g., 3], and transformational [e.g., 13]. In [11,14] the authors have presented the mechanism of scheduling warehouses orders using standard GAs and a number of other search algorithms, they had proved the success of GAs in this area. The paper in [6] has proposed a GA for determining optimal replenishment cycles to minimize maximum warehouse space requirements. ...
Warehouses scheduling is the problem of sequencing requests of products to fulfill several customers' orders so as to minimize the average time and shipping costs. In this paper, a solution to the problem of multiple warehouses scheduling using the steady state genetic algorithm is presented. A mathematical model that organizes the relationships between customers and warehouses is also presented in this paper. Two scenarios of storage capacities (constants and varying capacities) and two strategies of search points (ideal point and random points) are compared. An analysis of the results indicates that multiple warehouses scheduling using the GENITOR approach with different warehouses capacities have better outcome than the usage of the traditional genetic algorithms).
In tier-to-tier four-way shuttle warehousing system (TFSWS), the idle of shuttles or lifts caused by unreasonable task assignment affects the system’s efficiency. To solve this problem, a task scheduling model is established, in which the job is abstracted into the coding of the task sequence and the third allocation of system resources. Then, an improved genetic algorithm (IGA) is proposed to solve the model. Finally, a practical case is adopted to validate the usability of the established model and the superiority of the proposed method.
With the increasing competition of market economies, many companies are pursuing higher levels of production automation in manufacturing industry. For example, the automated warehouses are employed in the field of manufacturing and processing field, in the process of which automated warehouses play a more and more significant role. Therefore, it is meaningful to have a research on the automated warehouses scheduling issue. The warehouse scheduling algorithm is studied combining with the project on the automatic production line of an enterprise in this paper, and a warehouse scheduling optimization algorithm is proposed based on IOQ(Index of Quality) parameters. Then the process of getting the value of IOQ is also simplified by applying the idea of sparse matrix. In addition, the algorithm uses the maximum of the IOQs to schedule warehouse on line, and is compared with other warehouse scheduling algorithms. The simulation results show that the warehouse scheduling algorithm can not only improve the quality of the product effectively, but also improve the efficiency of the scheduling largely. The desired result is achieved in the end.
Genetic algorithms represent a powerful global-optimisation tool applicable in solving tasks of high complexity in science, technology, medicine, communication, etc. The usual genetic-algorithm calculation scheme is extended here by introduction of a quadratic self-learning operator, which performs a partial local search for randomly selected representatives of the population. This operator is aimed as a minor deterministic contribution to the (stochastic) genetic search. The population representing the trial solutions is split into two equal subpopulations allowed to exhibit different mutation rates (so called asymmetric mutation). The convergence is studied in detail exploiting a crystallographic-test example of indexing of powder diffraction data of orthorhombic lithium copper oxide, varying such parameters as mutation rates and the learning rate. It is shown through the averaged (over the subpopulation) fitness behaviour, how the genetic diversity in the population depends on the mutation rate of the given subpopulation. Conditions and algorithm parameter values favourable for convergence in the framework of proposed approach are discussed using the results for the mentioned example. Further data are studied with a somewhat modified algorithm using periodically varying mutation rates and a problem-specific operator. The chance of finding the global optimum and the convergence speed are observed to be strongly influenced by the effective mutation level and on the self-learning level. The optimal values of these two parameters are about 6 and 5%, respectively. The periodic changes of mutation rate are found to improve the explorative abilities of the algorithm. The results of the study confirm that the applied methodology leads to improvement of the classical genetic algorithm and, therefore, it is expected to be helpful in constructing of algorithms permitting to solve similar tasks of higher complexity.
. The Commonality-Based Crossover Framework redefines crossover as a two step process: 1) preserve the maximal common schema of two parents, and 2) complete the solution with a construction heuristic. To demonstrate the utility of this design model, domainindependent operators, heuristic operators, and hybrid operators have been developed for benchmark and practical problems with standard and non-standard representations. The new commonality-based operators have performed consistently better than comparable operators which emphasize combination. In heuristic operators (which use problem specific heuristics during crossover), the effects of commonality-based selection have been isolated in GENIE (a genetic algorithm that eliminates fitness-based selection of parents). Since the effectiveness of construction heuristics can be amplified by using only commonality-based restarts, the preservation of common components has supplied selective pressure at the component (rather than individual
