scholarly journals Randomized Problem-Relaxation Solving for Over-Constrained Schedules

2021 ◽  
Author(s):  
Patrick Rodler ◽  
Erich Teppan ◽  
Dietmar Jannach
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Production Capacity ◽  
Main Idea ◽  
Planning Horizon ◽  
Benchmark Problems ◽  
Scheduling Problems ◽  
Constraint Solver ◽  
Set Optimization Problem ◽  
Constraint Model

Optimal production planning in the form of job shop scheduling problems (JSSP) is a vital problem in many industries. In practice, however, it can happen that the volume of jobs (orders) exceeds the production capacity for a given planning horizon. A reasonable aim in such situations is the completion of as many jobs as possible in time (while postponing the rest). We call this the Job Set Optimization Problem (JOP). Technically, when constraint programming is used for solving JSSPs, the formulated objective in the constraint model can be adapted so that the constraint solver addresses JOP, i.e., searches for schedules that maximize the number of timely finished jobs. However, also highly specialized solvers which proved very powerful for JSSPs may struggle with the increased complexity of the reformulated problem and may fail to generate a JOP solution given practical computation timeouts. As a remedy, we suggest a framework for solving multiple randomly modified instances of a relaxation of the JOP, which allows to gradually approach a JOP solution. The main idea is to have one module compute subset-minimal job sets to be postponed, and another one effectuating that random job sets are found. Different algorithms from literature can be used to realize these modules. Using IBM’s cutting-edge CP Optimizer suite, experiments on well-known JSSP benchmark problems show that using the proposed framework consistently leads to more scheduled jobs for various computation timeouts than a standalone constraint solver approach.

A PARTICLE SWARM OPTIMIZATION-BASED ALGORITHM FOR JOB-SHOP SCHEDULING PROBLEMS

2005 ◽  
Vol 02 (03) ◽  
pp. 419-430 ◽  
Author(s):  
H. W. GE ◽  
Y. C. LIANG ◽  
Y. ZHOU ◽  
X. C. GUO
Keyword(s):  
Particle Swarm Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Particle Swarm ◽  
Traditional Scheduling ◽  
Benchmark Problems ◽  
Scheduling Problems ◽  
Swarm Optimization ◽  
Shop Scheduling ◽  
Novel Concept

A novel particle swarm optimization (PSO)-based algorithm is developed for job-shop scheduling problems (JSSP), which are the most general and difficult issues in traditional scheduling problems. Our goal is to develop an efficient algorithm based on swarm intelligence for the JSSP. Thereafter a novel concept for the distance and velocity of particles in the PSO is proposed and introduced to pave the way for the JSSP. The proposed algorithm effectively exploits the capabilities of distributed and parallel computing systems, with simulation results showing the possibilities of high quality solutions for typical benchmark problems.

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.

scholarly journals Hybrid genetic algorithms: solutions in realistic dynamic and setup dependent job-shop scheduling problems

2016 ◽  
Vol 4 (2) ◽  
pp. 75 ◽  
Author(s):  
Rogério M. Branco ◽  
Antônio S. Coelho ◽  
Sérgio F. Mayerle
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Fitness Function ◽  
Evolutionary Process ◽  
Planning Horizon ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Alternate Routes ◽  
Dependent Setup Times

<p>This paper discusses the application of heuristic-based evolutionary technique in search for solutions concerning the dynamic job-shop scheduling problems with dependent setup times and alternate routes. With a combinatorial nature, these problems belong to an NP-hard class, with an aggravated condition when in realistic, dynamic and therefore, more complex cases than the traditional static ones. The proposed genetic algorithm executes two important functions: choose the routes using dispatching rules when forming each individual from a defined set of available machines and, also make the scheduling for each of these individuals created. The chromosome codifies a route, or the selected machines, and also an order to process the operations. In essence , each individual needs to be decoded by the scheduler to evaluate its time of completion, so the fitness function of the genetic algorithm, applying the modified Giffler and Thomson’s algorithm, obtains a scheduling of the selected routes in a given planning horizon. The scheduler considers the preparation time between operations on the machines and can manage operations exchange respecting the route and the order given by the chromosome. The best results in the evolutionary process are individuals with routes and processing orders optimized for this type of problema.</p>

scholarly journals Improved Differential Evolution Algorithm for Flexible Job Shop Scheduling Problems

2019 ◽  
Vol 24 (3) ◽  
pp. 80 ◽  
Author(s):  
Prasert Sriboonchandr ◽  
Nuchsara Kriengkorakot ◽  
Preecha Kriengkorakot
Keyword(s):  
Differential Evolution ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Differential Evolution Algorithm ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Relative Errors ◽  
Improved Differential Evolution Algorithm

This research project aims to study and develop the differential evolution (DE) for use in solving the flexible job shop scheduling problem (FJSP). The development of algorithms were evaluated to find the solution and the best answer, and this was subsequently compared to the meta-heuristics from the literature review. For FJSP, by comparing the problem group with the makespan and the mean relative errors (MREs), it was found that for small-sized Kacem problems, value adjusting with “DE/rand/1” and exponential crossover at position 2. Moreover, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 3.25. For medium-sized Brandimarte problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave a mean relative error of 7.11. For large-sized Dauzere-Peres and Paulli problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 4.20. From the comparison of the DE results with other methods, it was found that the MRE was lower than that found by Girish and Jawahar with the particle swarm optimization (PSO) method (7.75), which the improved DE was 7.11. For large-sized problems, it was found that the MRE was lower than that found by Warisa (1ST-DE) method (5.08), for which the improved DE was 4.20. The results further showed that basic DE and improved DE with jump search are effective methods compared to the other meta-heuristic methods. Hence, they can be used to solve the FJSP.

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