Scheduling
Vahid Bahmani; Mohammad Amin Adibi; Esmaeil Mehdizadeh
Abstract
This paper provides an integrated model for a two-stage assembly flow shop scheduling problem and distribution through vehicle routing in a soft time window. So, a mixed-integer linear programming (MILP) model has been proposed with the objective of minimizing the total cost of distribution, holding ...
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This paper provides an integrated model for a two-stage assembly flow shop scheduling problem and distribution through vehicle routing in a soft time window. So, a mixed-integer linear programming (MILP) model has been proposed with the objective of minimizing the total cost of distribution, holding of products, and penalties of violating delivery time windows. To solve this problem, an improved meta-heuristic algorithm based on whale optimization algorithm (WOA) has been developed. A comparison of the integrated and non-integrated model in a case study of industrial gearboxes production shows that the integrated model compared to the non-integrated model has saved 15.6% and 13.6% in terms of delay time and total costs, respectively. Computational experiments also indicate the efficiency of improved WOA in converging to optimal solution and achieve better solution in comparison to the genetic algorithm (GA).
Scheduling
Bahareh Vaisi; Hiwa Farughi; Sadigh Raissi; Heibatolah Sadeghi
Abstract
In this study, we model a stochastic scheduling problem for a robotic cell with two unreliable machines susceptible to breakdowns and subject to the probability of machine failure and machine repair. A single gripper robot facilitates the loading/unloading of parts and cell-internal movement. Since it ...
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In this study, we model a stochastic scheduling problem for a robotic cell with two unreliable machines susceptible to breakdowns and subject to the probability of machine failure and machine repair. A single gripper robot facilitates the loading/unloading of parts and cell-internal movement. Since it is more complicated than the other cycles, the focus has been on the S_2 cycle as the most frequently employed robot movement cycle. Therefore, a multi-objective mathematical formulation is proposed to minimize cycle time and operational costs. The -constraint method is used to solve small-sized problems. Non-dominated sorting genetic algorithm II (NSGA-II), is used to solve large-sized instances based on a set of randomly generated test problems. The results of several Test problems were compared with those of the GAMS software to evaluate the algorithm's performance. The computational results indicate that the proposed algorithm performs well. Compared to GAMS software, the average results for maximum spread (D) and non-dominated solutions (NDS) are 0.02 and 0.04, respectively.
Scheduling
Manizheh Teimoori; Houshang Taghizadeh; Jafar Pourmahmoud; Morteza Honarmand Azimi
Abstract
Air traffic management is an important job and often faces various problems. One of the most common problems in this area is the issue of aircraft sequencing, which is a multi-dimensional problem due to the large number of flights and their different positional conditions. Previously proposed models ...
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Air traffic management is an important job and often faces various problems. One of the most common problems in this area is the issue of aircraft sequencing, which is a multi-dimensional problem due to the large number of flights and their different positional conditions. Previously proposed models were based on First Come, First Service (FCFS) have not considered the time factor, resulting in increased delay penalties. In this regard, this article proposes a model in which the time factor is one of the factors that is managed and additional costs due to delay will be eliminated. This paper proposed the Multi-Objective Grey Wolf Optimization (MOGWO) algorithm to evaluate three objective functions such as the airport runway efficiency, the apron and parking costs, and the fuel consumption costs. The proposed algorithm compared with well- known NSGA-II (non–dominated Sorting Genetic Algorithm). The obtain results represented that in the case of using all the data for the first, second and third-objective function, MOGWO performs better than NSGA-II. The brilliant results demonstrated the superiority of the proposed model. In this study, using the proposed model, the data set of Shahid Hasheminejad International Airport in Mashhad was analyzed.
Scheduling
Behnaz Zanjani; Maghsoud Amiri; Payam Hanafizadeh; Maziar Salahi
Abstract
Scheduling is an important decision-making process that aims to allocate limited resources to the jobs in a production process. Among scheduling problems, Hybrid Flow Shop (HFS) scheduling has good adaptability with most real world applications including innumerable cases of uncertainty of parameters ...
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Scheduling is an important decision-making process that aims to allocate limited resources to the jobs in a production process. Among scheduling problems, Hybrid Flow Shop (HFS) scheduling has good adaptability with most real world applications including innumerable cases of uncertainty of parameters that would influence jobs processing when the schedule is executed. Thus a suitable scheduling model should take parameters uncertainty into account. The present study develops a multi-objective Robust Mixed-Integer Linear Programming (RMILP) model to accommodate the problem with the real-world conditions in which due date and processing time are assumed uncertain. The developed model is able to assign a set of jobs to available machines in order to obtain the best trade-off between two objectives including total tardiness and makespan under uncertain parameters. Fuzzy Goal Programming (FGP) is applied to solve this multi objective problem. Finally, to study and validate the efficiency of the developed RMILP model, some instances of different size are generated and solved using CPLEX solver of GAMS software under different uncertainty levels. Experimental results show that the developed model can find a solution to show the least modifications against uncertainty in processing time and due date in an HFS problem.
Scheduling
Hassan Rashidi; Maryam Hassanpour
Abstract
The scheduling of academic courses is a problem in which a weekly schedule is produced for educational purposes. Many different types of scheduling problems exist at various universities in accordance with their laws, needs, and constraints. These problems fall into the category of NP-hard problems and ...
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The scheduling of academic courses is a problem in which a weekly schedule is produced for educational purposes. Many different types of scheduling problems exist at various universities in accordance with their laws, needs, and constraints. These problems fall into the category of NP-hard problems and are incredibly complex. In this paper, an intelligent system for scheduling courses using the deep-belief network is proposed. The reason why the proposed system is intelligent is that it can learn the constraints, inputs, and other necessary parameters in one step by receiving the inputs as well as the training needed by the deep-belief network. The deep-belief network used has one output layer, four hidden layers, and four input layers. The experimental results of this research show that the deep-belief network proposed for the scheduling of academic courses provides a better score, less error, and execution time compared with Sequence-Based Selection Hyper-Heuristic (SSHH) approach.
Scheduling
Furkan Uysal; Selçuk Kürşat Işleyen; Cihan Cetinkaya
Abstract
Despite the dynamic nature of real life scheduling problems, few studies focus on stochastic resource constrained project scheduling problem and its variants. In this study, we consider stochastic resource possibilities and propose a new chance constraint, piecewise-linear and mixed integer programming ...
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Despite the dynamic nature of real life scheduling problems, few studies focus on stochastic resource constrained project scheduling problem and its variants. In this study, we consider stochastic resource possibilities and propose a new chance constraint, piecewise-linear and mixed integer programming model. Model is tested and verified with known project instances. One of the main strengths of the proposed model is it can be used to construct baseline schedules with a predetermined confidence interval. This gives scheduler an opportunity to construct proactive actions in order to minimize disruptions.
