A Very Fast Heuristic for Combinatorial Optimization With Specific Application to Priority Rule Sequencing in Operations Management

2018 ◽  
Vol 9 (3) ◽  
pp. 77-89 ◽  
Author(s):  
Kaveh Sheibani
Keyword(s):  
Combinatorial Optimization ◽  
Operations Management ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Solution Space ◽  
Combinatorial Optimization Problems ◽  
Generic Polynomial ◽  
Sequencing Problems ◽  
Greedy Methods

This article presents mathematics of a generic polynomial-time heuristic which can be integrated into approaches for hard combinatorial optimization problems. The proposed method evaluates objects in a way that combines fuzzy reasoning with a greedy mechanism, thereby exploiting a fuzzy solution space using greedy methods. The effectiveness and efficiency of the proposed method are demonstrated on job-shop scheduling as one of the most challenging classical sequencing problems in the area of combinatorial optimization.

Fuzzy Greedy Search

2017 ◽  
Vol 4 (2) ◽  
pp. 1-12
Author(s):  
Kaveh Sheibani
Keyword(s):  
Combinatorial Optimization ◽  
Flow Shop ◽  
Optimization Problems ◽  
Greedy Algorithms ◽  
Solution Space ◽  
Flow Shop Scheduling ◽  
Practical Significance ◽  
Combinatorial Optimization Problems ◽  
Permutation Flow Shop Scheduling ◽  
Greedy Methods

This paper presents mathematics of the so-called fuzzy greedy evaluation concept which can be integrated into approaches for hard combinatorial optimization problems. The proposed method evaluates objects in a way that combines fuzzy reasoning with a greedy mechanism, thereby exploiting a fuzzy solution space using greedy methods. Given that the greedy algorithms are computationally inexpensive compared to other more sophisticated methods for combinatorial optimization; this shows practical significance of using the proposed approach. The effectiveness and efficiency of the proposed method are demonstrated on permutation flow-shop scheduling as one of the most widely studied hard combinatorial optimization problems in the area of operational research and management science.

Space Contraction and Partition Algorithm for Combinatorial Optimization

2011 ◽  
Vol 421 ◽  
pp. 559-563
Author(s):  
Yong Chao Gao ◽  
Li Mei Liu ◽  
Heng Qian ◽  
Ding Wang
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Solution Space ◽  
Search Space ◽  
Small Scale ◽  
Optimal Solutions ◽  
Solution Quality ◽  
Intelligent Algorithms ◽  
Combinatorial Optimization Problems

The scale and complexity of search space are important factors deciding the solving difficulty of an optimization problem. The information of solution space may lead searching to optimal solutions. Based on this, an algorithm for combinatorial optimization is proposed. This algorithm makes use of the good solutions found by intelligent algorithms, contracts the search space and partitions it into one or several optimal regions by backbones of combinatorial optimization solutions. And optimization of small-scale problems is carried out in optimal regions. Statistical analysis is not necessary before or through the solving process in this algorithm, and solution information is used to estimate the landscape of search space, which enhances the speed of solving and solution quality. The algorithm breaks a new path for solving combinatorial optimization problems, and the results of experiments also testify its efficiency.

An Incorporation of the Fuzzy Greedy Search Heuristic With Evolutionary Approaches for Combinatorial Optimization in Operations Management

2017 ◽  
Vol 8 (2) ◽  
pp. 58-72
Author(s):  
Kaveh Sheibani
Keyword(s):  
Combinatorial Optimization ◽  
Operations Management ◽  
Flow Shop ◽  
Optimization Problems ◽  
Greedy Algorithms ◽  
Flow Shop Scheduling ◽  
Initial Population ◽  
Greedy Search ◽  
Combinatorial Optimization Problems ◽  
Starting Point

Although greedy algorithms are important, nowadays it is well assumed that the solutions they obtain can be used as a starting point for more sophisticated methods. This paper describes an evolutionary approach which is based on genetic algorithms (GA). A constructive heuristic, so-called fuzzy greedy search (FGS) is employed to generate an initial population for the proposed GA. The effectiveness and efficiency of the proposed hybrid method are demonstrated on permutation flow-shop scheduling as one of the most widely studied hard combinatorial optimization problems in the area of operational research.

Optimization of Total Holding Cost in Job Shop Scheduling by Using Hybrid Algorithm

2014 ◽  
Vol 591 ◽  
pp. 176-179
Author(s):  
S. Gobinath ◽  
C. Arumugam ◽  
G. Ramya ◽  
M. Chandrasekaran
Keyword(s):  
Hybrid Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Combinatorial Optimization Problems ◽  
Holding Cost ◽  
Hard Problems

The classical job-shop scheduling problem is one of the most difficult combinatorial optimization problems. Scheduling is defined as the art of assigning resources to tasks in order to insure the termination of these tasks in a reasonable amount of time. Job shop scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Mathematical and heuristic methods are the two major methods for resolving JSP. Job shop Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions. In this paper, a Hybrid algorithm combined artificial immune system and sheep flock heredity model algorithm is used for minimizing the total holding cost for different size benchmark problems. The results show that the proposed hybrid algorithm is an effective algorithm that gives better results than other hybrid algorithms compared in literature. The proposed hybrid algorithm is a good technique for scheduling problems.

The Simulation Optimization for Job-Shop Scheduling Based on Plant Simulation Using Genetic Algorithm

2012 ◽  
Vol 217-219 ◽  
pp. 1444-1448
Author(s):  
Xiang Ke Tian ◽  
Jian Wang
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Combinatorial Optimization Problems ◽  
Plant Simulation

The job-shop scheduling problem (JSP), which is one of the best-known machine scheduling problems, is among the hardest combinatorial optimization problems. In this paper, the key technology of building simulation model in Plant Simulation is researched and also the build-in genetic algorithm of optimizing module is used to optimize job-shop scheduling, which can assure the scientific decision. At last, an example is used to illustrate the optimization process of the Job-Shop scheduling problem with Plant Simulation genetic algorithm modules.

scholarly journals Selected Soft Computing Algorithms for Job Shop Problem (JSP)

2021 ◽  
Vol 10 (9) ◽  
pp. 125-131
Author(s):  
Adedeji Oluyinka Titilayo ◽  
Alade Oluwaseun Modupe ◽  
Makinde Bukola Oyeladun ◽  
OYELEYE Taye E
Keyword(s):  
Combinatorial Optimization ◽  
Computer Science ◽  
Soft Computing ◽  
Operations Research ◽  
Optimization Problem ◽  
Job Shop ◽  
Optimization Problems ◽  
Combinatorial Optimization Problems ◽  
Flower Pollination

Job Shop Problem (JSP) is an optimization problem in computer science and operations research in which jobs are assigned to resources at particular times. Each operation has a specific machine that it needs to be processed on and only one operation in a job can be processed at a given time. This problem is one of the best known combinatorial optimization problems. The aim of this project is to adapt Bat, Bee, Firefly, and Flower pollination algorithms, implement and evaluate the developed algorithms for solving Job Shop Problem.

A modified biogeography-based optimization algorithm with improved mutation operator for job shop scheduling problem with time lags

2020 ◽  
Author(s):  
Madiha Harrabi ◽  
Olfa Belkahla Driss ◽  
Khaled Ghedira
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Time Lag ◽  
Initial Population ◽  
Time Lags ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Combinatorial Optimization Problems

Abstract This paper addresses the job shop scheduling problem including time lag constraints. This is an extension of the job shop scheduling problem with many applications in real production environments, where extra (minimum and maximum) delays can be introduced between successive operations of the same job. It belongs to a category of problems known as NP-hard problem due to large solution space. Biogeography-based optimization is an evolutionary algorithm which is inspired by the migration of species between habitats, recently proposed by Simon in 2008 to optimize hard combinatorial optimization problems. We propose a hybrid biogeography-based optimization (HBBO) algorithm for solving the job shop scheduling problem with additional time lag constraints with minimization of total completion time. In the proposed HBBO, the effective greedy constructive heuristic is adapted to generate the initial population of habitat. Moreover, a local search metaheuristic is investigated in the mutation step in order to ameliorate the solution quality and enhance the diversity of the population. To assess the performance of HBBO, a series of experiments on well-known benchmark instances for job shop scheduling problem with time lag constraints is performed.

scholarly journals Applying the big bang-big crunch metaheuristic to large-sized operational problems

2020 ◽  
Vol 10 (3) ◽  
pp. 2484 ◽  
Author(s):  
Yousef K. Qawqzeh ◽  
Ghaith Jaradat ◽  
Ali Al-Yousef ◽  
Anmar Abu-Hamdah ◽  
Ibrahim Almarashdeh ◽  
...  
Keyword(s):  
Combinatorial Optimization ◽  
Job Shop ◽  
Optimization Problems ◽  
Big Bang ◽  
Quadratic Assignment ◽  
Scheduling Problems ◽  
High Quality ◽  
Combinatorial Optimization Problems ◽  
Big Crunch ◽  
The Big Bang

In this study, we present an investigation of comparing the capability of a big bang-big crunch metaheuristic (BBBC) for managing operational problems including combinatorial optimization problems. The BBBC is a product of the evolution theory of the universe in physics and astronomy. Two main phases of BBBC are the big bang and the big crunch. The big bang phase involves the creation of a population of random initial solutions, while in the big crunch phase these solutions are shrunk into one elite solution exhibited by a mass center. This study looks into the BBBC’s effectiveness in assignment and scheduling problems. Where it was enhanced by incorporating an elite pool of diverse and high quality solutions; a simple descent heuristic as a local search method; implicit recombination; Euclidean distance; dynamic population size; and elitism strategies. Those strategies provide a balanced search of diverse and good quality population. The investigation is conducted by comparing the proposed BBBC with similar metaheuristics. The BBBC is tested on three different classes of combinatorial optimization problems; namely, quadratic assignment, bin packing, and job shop scheduling problems. Where the incorporated strategies have a greater impact on the BBBC's performance. Experiments showed that the BBBC maintains a good balance between diversity and quality which produces high-quality solutions, and outperforms other identical metaheuristics (e.g. swarm intelligence and evolutionary algorithms) reported in the literature.

A Survey of Quantum Genetic Algorithm for Combinatorial Optimization Problems

2014 ◽  
Vol 568-570 ◽  
pp. 822-826 ◽  
Author(s):  
Feng Mei Wei ◽  
Jian Pei Zhang ◽  
Bing Li ◽  
Jing Yang
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Job Shop ◽  
Optimization Problems ◽  
The Other ◽  
Quantum Genetic Algorithm ◽  
Combinatorial Optimization Problems ◽  
Combinational Optimization ◽  
Rotation Strategy

Combined with quantum computing and genetic algorithm, quantum genetic algorithm (QGA) shows considerable ability of parallelism. Experiments have shown that QGA performs quite well on TSP, job shop problem and some other typical combinatorial optimization problems. The other problems like nutritional diet which can be transformed into specific combinational optimization problem also can be solved through QGA smoothly. This paper sums up the main points of QGA for general combinatorial optimization problems. These points such as modeling of the problem, qubit decoding and rotation strategy are useful to enhance the convergence speed of QGA and avoid premature at the same time.

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