Performance Evaluation of Various Heuristic Algorithms to Solve Job Shop Scheduling Problem (JSSP)

Syarif, Admi; Pamungkas, Ade; Kumar, Renaldi; Gen, Mitsuo

doi:10.22266/ijies2021.0430.30

Cited by 4 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scheduling framework includes a succession of jobs and units while mediating an optimum solution job sequence on each machine under specified limitations. The shop scheduling becomes increasingly complex to solve when several performance measurements are taken into account (Admi Syarif et al 2021). There are several approaches for solving JSSP as described in the following section.…”

Section: Literature Reviewmentioning

confidence: 99%

Job shop scheduling using heuristics through Python programming and excel interface

Narayanan¹,

Kumar²,

Potluru³

et al. 2022

Decis. Mak. Appl. Manag. Eng.

View full text Add to dashboard Cite

Job shop scheduling problem (JSSP) has remained a challenge both for the practitioners and the researchers. A JSSP consists of multiple number of machines (m) and jobs (n).As the number of jobs increases, the complexity of the problem increases exponentially and it becomes difficult to schedule manually. Many papers in the literature discuss heuristic and metaheuristic solutions to solve Job shop scheduling problems. But there is no ease of use for practitioners who rely on their experience to schedule jobs in ad hoc sessions resulting in inefficient allocation of jobs and machines. In this paper, a job shop scheduling problem under static and dynamic conditions is solved using heuristic approaches using python programming with an MS Excel user interface. For a supplier of automotive parts with a set of jobs and machines, priority dispatching rules, viz., Shortest Processing Time (SPT), Earliest Due Date (EDD), First-In First-Out (FIFO), Critical Ratio (CR) and Slack Per Remaining Operation (S/RO) are evaluated. The obtained performance metrics such as makespan, and tardiness are compared between the heuristics to select an optimal schedule by the job shop. The user inputs the jobs, machines, start and due dates through the MS Excel interface and obtains faster, practically usable results. This reduces the time taken for job scheduling and helps in making faster productivity-based decisions to maximize resource utilization and the total time to produce the product. Integrating Python at the backend and Excel at the front end will encourage many MSMEs to perform optimized scheduling using heuristics thereby reducing the throughput time.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Job shop scheduling using heuristics through Python programming and excel interface

Narayanan¹,

Kumar²,

Potluru³

et al. 2022

Decis. Mak. Appl. Manag. Eng.

View full text Add to dashboard Cite

show abstract

“…In fact, by utilizing technological developments such as scheduling management applications, the process of mapping class schedules can be carried out effectively and efficiently. Many methods have been used in scheduling management applications for course scheduling [3][4][5][6][7][8][9][10][11][12][13] , job shop scheduling [14,15] , cloud environments scheduling [16,17] , and healthcare scheduling [18] . Scheduling management applications have become increasingly important in our fast-paced world.…”

Section: Introductionmentioning

confidence: 99%

User-Centered Design-Based Approach in Scheduling Management Application Design and Development

Herumurti

Bimantara

Supriana

2023

JTS

View full text Add to dashboard Cite

The process of manually making and setting course schedules using Microsoft Excel is ineffective, time-consuming, and still prone to errors. This research develops a website-based scheduling management application with a case study at SMK Pariwisata Margarana so that it can solve scheduling problems manually. The User-Centered Design (UCD) method is applied in the application prototype design stage.Open interviews, field observations, simulations, and questionnaires were used as research data collection methods. Three iterations were carried out at the prototype design stage to fulfill all user needs. The high-fidelity prototype in the last iteration is then implemented into an application. Application quality is measured using ISO/IEC 25010 with five characteristics. The test results on usability characteristics show that the scheduling management application obtains an average usability score of 91.2%. The appropriateness recognizability sub-characteristic obtained the highest usability score of 93.53%. UCD can help produce an application that can meet all the user's needs when implemented in the application design phase.

show abstract

“…Syarif et al [10] show various advances in the genetic algorithm (GA) to solve difficult optimization problems in the lot from inventory and production design issues. Kuczapski et al [11] proposed a method of enhancing genetic algorithms for the JSSP by generating near-optimal initial populations.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Binary Kidney-Inspired Algorithm Based on a Parallel Method for Solving the Job Shop Scheduling Problem

Abdel-Rehim

2023

Frontiers in Scientific Research and Technology

View full text Add to dashboard Cite

This paper proposes a binary kidney-inspired algorithm (KA) to tackle the job shop scheduling problem (JSSP), and its solution is essential in manufacturing, especially in real industrial engineering. The job shop scheduling problem is a computer science optimization problem, and it is among the most significant and challenging issues in the field of production scheduling. The proposed algorithm is based on a parallel method with many threads. This algorithm is checked using certain job shop benchmark problems. The findings are compared with those retrieved using two techniques: a genetic algorithm (GA) and a particle swarm optimization (PSO) method. These techniques indicate that applying the binary kidney-inspired in parallel is an efficient algorithm for solving the JSSP, shows remarkable competitiveness, and considerably accelerates speedups, especially in large-scale instances. The achieved results are based on four threads; the speedup is 3.13 for the FT06 instance, while the execution time is 2.24 seconds.

show abstract

Performance Evaluation of Various Heuristic Algorithms to Solve Job Shop Scheduling Problem (JSSP)

Cited by 4 publications

References 26 publications

Job shop scheduling using heuristics through Python programming and excel interface

Job shop scheduling using heuristics through Python programming and excel interface

User-Centered Design-Based Approach in Scheduling Management Application Design and Development

Implementation of a Binary Kidney-Inspired Algorithm Based on a Parallel Method for Solving the Job Shop Scheduling Problem

Contact Info

Product

Resources

About