An efficient job-shop scheduling algorithm based on particle swarm optimization☆

Lin, Chung Yi; Horng, Shi–Jinn; Kao, Tzong-Wann; Chen, Yuan‐Hsin; Run, Ray-Shine; Chen, Rong-Jian; Lai, Jui-Lin; Kuo, I-Hong

doi:10.1016/j.eswa.2009.08.015

Cited by 185 publications

(68 citation statements)

References 25 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerical experiments are performed in C++ language on a PC with Pentium IV 2.93 GHz processor and 2GB memory. The numerical results are compared with those reported in some existing literatures using other approaches [30,[38][39][40][41][42], including some heuristic and meta-heuristic algorithms. The selection of parameter settings in CIA is listed in Table 2.…”

Section: Coevolutionary Intelligence Algorithm Based On the Proposed mentioning

confidence: 98%

A Coevolutionary Bacterial Foraging Model Using PSO in Job-Shop Scheduling Environments

Sun¹,

Wang²,

Ge³

2016

IJGDC

View full text Add to dashboard Cite

show abstract

Section: Coevolutionary Intelligence Algorithm Based On the Proposed mentioning

confidence: 98%

A Coevolutionary Bacterial Foraging Model Using PSO in Job-Shop Scheduling Environments

Sun¹,

Wang²,

Ge³

2016

IJGDC

View full text Add to dashboard Cite

show abstract

“…The schedules for each problem are generated using ISFHA. The results obtained by ISFHA are compared with the Best Known Solution, GA (Goncalves et al, 2005), Modified PSO (Lin et al, 2010) and CSFHA (Vikram & Chandramouli, 2011). The coding for the optimization of scheduling has been developed using MATLAB.…”

Section: Mutation Probabilitiesmentioning

confidence: 99%

“…Here, the immune procedure was combined with a simulated annealing algorithm with the objective of minimizing total weighted tardiness. A new hybrid swarm intelligence algorithm was presented (Lin et al, 2010). The algorithm consists of particle swarm optimization (PSO), simulated annealing (SA) technique and multi-type individual enhancement scheme.…”

Section: Introductionmentioning

confidence: 99%

An improved sheep flock heredity algorithm for job shop scheduling and flow shop scheduling problems

Anandaraman¹

2011

IJIEC

View full text Add to dashboard Cite

Job Shop Scheduling Problem (JSSP) and Flow Shop Scheduling Problem (FSSP) are strong NP-complete combinatorial optimization problems among class of typical production scheduling problems. An improved Sheep Flock Heredity Algorithm (ISFHA) is proposed in this paper to find a schedule of operations that can minimize makespan. In ISFHA, the pairwise mutation operation is replaced by a single point mutation process with a probabilistic property which guarantees the feasibility of the solutions in the local search domain. A Robust-Replace (R-R) heuristic is introduced in place of chromosomal crossover to enhance the global search and to improve the convergence. The R-R heuristic is found to enhance the exploring potential of the algorithm and enrich the diversity of neighborhoods. Experimental results reveal the effectiveness of the proposed algorithm, whose optimization performance is markedly superior to that of genetic algorithms and is comparable to the best results reported in the literature.

show abstract

“…Since its invention, PSO has been applied with success on various COPs such as, the unit commitment problem 31 , the traveling salesman problem 32 , the task assignment problem 33 , an optimal operational path finding for automated drilling operations 34 , a multi-objective order planning production problem in steel sheets manufacturing 35 , scheduling problems involving duedates 29 , the shortest path problem 36 , etc. Recently, its application has been extended on scheduling problems such as, flow-shop scheduling problems [37][38][39][40][41] , the singlemachine total weighting tardiness problem 27,42 , the single machine scheduling problem with periodic maintenance 28 , the two-stage assembly-scheduling problem 43 , and job-shop scheduling problems 44,45 . Assuming the problem of minimizing a real-valued function ƒ(x), x∈Ω⊂ℜ D (Ω is assumed to be the feasible search space of the problem), PSO utilizes a set (called swarm) of Ns particles as a population to search Ω toward the global optimal solution.…”

Section: The Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

A Particle Swarm Optimization Algorithm for Scheduling Against Restrictive Common Due Dates

Nearchou¹,

Omirou²

2013

IJCIS

View full text Add to dashboard Cite

Abstract:Focusing on the just-in-time (JIT) operations management, earliness as well as, tardiness of jobs' production and delivery should be discouraged. In accordance to this philosophy, scheduling problems involving earliness and tardiness penalties are very critical for the operations manager. In this paper, a new population heuristic based on the particle swarm optimization (PSO) technique is presented to solve the single machine early/tardy scheduling problem against a restrictive common due date. This type of scheduling sets costs depending on whether a job finished before (earliness), or after (tardiness) the specified due date. The objective is to minimize a summation of earliness and tardiness penalty costs, thus pushing the completion time of each job as close as possible to the due date. The problem is known to be NP-hard, and therefore large size instances cannot be addressed by traditional mathematical programming techniques. The performance of the proposed PSO heuristic is measured over benchmarks problems with up to 1000 jobs taken from the open literature, and found quite high and promising in respect to the quality of the solutions obtained. Particularly, PSO was found able to improve the 82% of the existing best known solutions of the examined benchmarks test problems.

show abstract

An efficient job-shop scheduling algorithm based on particle swarm optimization☆

Cited by 185 publications

References 25 publications

A Coevolutionary Bacterial Foraging Model Using PSO in Job-Shop Scheduling Environments

A Coevolutionary Bacterial Foraging Model Using PSO in Job-Shop Scheduling Environments

An improved sheep flock heredity algorithm for job shop scheduling and flow shop scheduling problems

A Particle Swarm Optimization Algorithm for Scheduling Against Restrictive Common Due Dates

Contact Info

Product

Resources

About