An Improvement of Instance Generator Featuring Assembly Operations with Parallel Machines for Multi-Level and Multi-Operation Scheduling Problems

This paper considers two stage hybrid flow shop (HFS) with identical parallel machine. The objectives is to determine makespan have been minimized. This paper presented memetic algorithm procedure to solve two stage HFS problems. To evaluated performance of propose method, the results have been compared with two meta-heuristic, genetic algorithm, simulated annealing. The experimental results show that propose method is more effective and efficient than genetic algorithm and simulated annealing to solve two stage HFS scheduling problems.

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Semi-Online Scheduling on Two Machines withGoSLevels and Partial Information of Processing Time

The Scientific World JOURNAL ◽

10.1155/2014/576234 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 2

Author(s):

Taibo Luo ◽

Yinfeng Xu

Keyword(s):

Processing Time ◽

Parallel Machines ◽

Partial Information ◽

Online Scheduling ◽

Scheduling Problems ◽

Total Processing Time ◽

Grade Of Service ◽

Two Machines

This paper investigates semi-online scheduling problems on two parallel machines under a grade of service (GoS) provision subject to minimize the makespan. We consider three different semi-online versions with knowing the total processing time of the jobs with higherGoSlevel, knowing the total processing time of the jobs with lowerGoSlevel, or knowing both in advance. Respectively, for the three semi-online versions, we develop algorithms with competitive ratios of3/2,20/13, and4/3which are shown to be optimal.

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Utilizing Model Knowledge for Design Developed Genetic Algorithm to Solving Problem

International Journal of Professional Business Review ◽

10.26668/businessreview/2018.v3i2.49 ◽

2018 ◽

Vol 3 (2) ◽

pp. 172

Author(s):

Hamidreza Salmani mojaveri

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Parallel Machines ◽

Job Shop ◽

Optimization Problems ◽

Premature Convergence ◽

Local Optimum ◽

Scheduling Problems ◽

Algorithm Implementation

One of the discussed topics in scheduling problems is Dynamic Flexible Job Shop with Parallel Machines (FDJSPM). Surveys show that this problem because of its concave and nonlinear nature usually has several local optimums. Some of the scheduling problems researchers think that genetic algorithms (GA) are appropriate approach to solve optimization problems of this kind. But researches show that one of the disadvantages of classical genetic algorithms is premature convergence and the probability of trap into the local optimum. Considering these facts, in present research, represented a developed genetic algorithm that its controlling parameters change during algorithm implementation and optimization process. This approach decreases the probability of premature convergence and trap into the local optimum. The several experiments were done show that the priority of proposed procedure of solving in field of the quality of obtained solution and convergence speed toward other present procedure.

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Scheduling on a Single Machine and Parallel Machines with Batch Deliveries and Potential Disruption

Complexity ◽

10.1155/2020/6840471 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Hua Gong ◽

Yuyan Zhang ◽

Puyu Yuan

Keyword(s):

Polynomial Time ◽

Single Machine ◽

Manufacturing Industry ◽

Parallel Machines ◽

Parallel Machine Scheduling ◽

Parallel Machine ◽

Approximation Schemes ◽

Scheduling Problems ◽

Delivery Scheduling ◽

Production Part

In this paper, we study several coordinated production-delivery scheduling problems with potential disruption motivated by a supply chain in the manufacturing industry. Both single-machine environment and identical parallel-machine environment are considered in the production part. The jobs finished on the machines are delivered to the same customer in batches. Each delivery batch has a capacity and incurs a delivery cost. There is a situation that a possible disruption in the production part may occur at some particular time and will last for a period of time with a probability. We consider both resumable case and nonresumable case where a job does not need (needs) to restart if it is disrupted for a resumable (nonresumable) case. The objective is to find a coordinated schedule of production and delivery that minimizes the expected total flow times plus the delivery costs. We first present some properties and analyze the NP-hard complexity for four various problems. For the corresponding single-machine and parallel-machine scheduling problems, pseudo-polynomial-time algorithms and fully polynomial-time approximation schemes (FPTASs) are presented in this paper, respectively.

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A New Meta-Heuristic Algorithm for Solving the Flexible Dynamic Job-Shop Problem with Parallel Machines

Symmetry ◽

10.3390/sym11020165 ◽

2019 ◽

Vol 11 (2) ◽

pp. 165 ◽

Cited By ~ 9

Author(s):

Arun Sangaiah ◽

Mohsen Suraki ◽

Mehdi Sadeghilalimi ◽

Seyed Bozorgi ◽

Ali Hosseinabadi ◽

...

Keyword(s):

Manufacturing Systems ◽

Parallel Machines ◽

Job Shop ◽

Two Dimensional ◽

Scheduling Problems ◽

Operational Flexibility ◽

Machine Failure ◽

Different Dimensions ◽

First Time ◽

Dynamic Job Shop

In a real manufacturing environment, the set of tasks that should be scheduled is changing over the time, which means that scheduling problems are dynamic. Also, in order to adapt the manufacturing systems with fluctuations, such as machine failure and create bottleneck machines, various flexibilities are considered in this system. For the first time, in this research, we consider the operational flexibility and flexibility due to Parallel Machines (PM) with non-uniform speed in Dynamic Job Shop (DJS) and in the field of Flexible Dynamic Job-Shop with Parallel Machines (FDJSPM) model. After modeling the problem, an algorithm based on the principles of Genetic Algorithm (GA) with dynamic two-dimensional chromosomes is proposed. The results of proposed algorithm and comparison with meta-heuristic data in the literature indicate the improvement of solutions by 1.34 percent for different dimensions of the problem.

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Preemptive Scheduling with Controllable Processing Times on Parallel Machines

Journal of Systems Science and Information ◽

10.1515/jssi-2015-0068 ◽

2015 ◽

Vol 3 (1) ◽

pp. 68-76

Author(s):

Guiqing Liu ◽

Kai Li ◽

Bayi Cheng

Keyword(s):

Polynomial Time ◽

Resource Use ◽

Parallel Machines ◽

Optimal Algorithm ◽

Parallel Machine Scheduling ◽

Controllable Processing Times ◽

Scheduling Problems ◽

Processing Times ◽

Online Problems ◽

Machine Scheduling Problems

AbstractThis paper considers several parallel machine scheduling problems with controllable processing times, in which the goal is to minimize the makespan. Preemption is allowed. The processing times of the jobs can be compressed by some extra resources. Three resource use models are considered. If the jobs are released at the same time, the problems under all the three models can be solved in a polynomial time. The authors give the polynomial algorithm. When the jobs are not released at the same time, if all the resources are given at time zero, or the remaining resources in the front stages can be used to the next stages, the offline problems can be solved in a polynomial time, but the online problems have no optimal algorithm. If the jobs have different release dates, and the remaining resources in the front stages can not be used in the next stages, both the offline and online problems can be solved in a polynomial time.

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