نتایج جستجو برای: due dates completion time genetic algorithm ga
تعداد نتایج: 3659140 فیلتر نتایج به سال:
In this paper, a novel mathematical model for a preemption multi-mode multi-objective resource-constrained project scheduling problem with distinct due dates and positive and negative cash flows is presented. Although optimization of bi-objective problems with due dates is an essential feature of real projects, little effort has been made in studying the P-MMRCPSP while due dates are included i...
this paper presents a new mathematical model for a hybrid flow shop scheduling problem with multiprocessor tasks in which sequence dependent set up times and preemption are considered. the objective is to minimize the weighted sum of makespan and maximum tardiness. three meta-heuristic methods based on genetic algorithm (ga), imperialist competitive algorithm (ica) and a hybrid approach of ga a...
abstract-due to the important role productivity plays in future decision making and programming, the productivity indexes should have accurate quantities. in this study, auto-regressive distributed lag (ardl) and genetic algorithm (ga) methods are applied to time series of 1978-2008 to accurately measure total factor productivity (tfp) in the agricultural sector of iran. the comparison of these...
Flow shop scheduling problem with missing operations is studied in this paper. Missing operations assumption refers to the fact that at least one job does not visit one machine in the production process. A mixed-binary integer programming model has been presented for this problem to minimize the makespan. The genetic algorithm (GA) and tabu search (TS) are used to deal with the optimization...
The problem of finding good solutions to scheduling problems is very important to real manufacturing systems, since the production rate and production costs are very dependent on the schedules used for controlling the work in the system. Most research in scheduling focuses on optimisation of static problems, where all problem data are known before scheduling starts. However many real world opti...
In this paper, Multi-Objective Flexible Job-Shop scheduling with Parallel Machines in Dynamic manufacturing environment (MO-FDJSPM) is investigated. Moreover considering dynamical job-shop environment (jobs arrived in non-zero time), It contains two kinds of flexibility which is effective for improving operational manufacturing systems. The non-flexibility leads to scheduling program which have...
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 combine...
Flow shop scheduling problem has a wide application in the manufacturing and has attracted much attention in academic fields. From other point, on time delivery of products and services is a major necessity of companies’ todays; early and tardy delivery times will result additional cost such as holding or penalty costs. In this paper, just-in-time (JIT) flow shop scheduling problem with preemp...
This study is focused on the development of a systematic computational approach which implements Genetic Algorithm (GA) to find the optimal rigorous kinetic models.A general Kinetic model for hydrogenolysis of dibenzothiophene (DBT) based on Langmuir-Hinshelwood type has been obtained from open literature. This model consists of eight continuous parameters(e.g., Arrhenus and Van't...
this paper presents a genetic algorithm (ga) for solving a generalized model of single-item resource-constrained aggregate production planning (app) with linear cost functions. app belongs to a class of pro-duction planning problems in which there is a single production variable representing the total production of all products. we linearize a linear mixed-integer model of app subject to hiring...
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