scholarly journals A Two-Level Metaheuristic Algorithm for the Job-Shop Scheduling Problem

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Pisut Pongchairerks
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimal Schedule ◽  
Solution Space ◽  
Scheduling Problem ◽  
Metaheuristic Algorithm ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Upper Level ◽  
Population Based Algorithm

This paper proposes a novel two-level metaheuristic algorithm, consisting of an upper-level algorithm and a lower-level algorithm, for the job-shop scheduling problem (JSP). The upper-level algorithm is a novel population-based algorithm developed to be a parameter controller for the lower-level algorithm, while the lower-level algorithm is a local search algorithm searching for an optimal schedule in the solution space of parameterized-active schedules. The lower-level algorithm’s parameters controlled by the upper-level algorithm consist of the maximum allowed length of idle time, the scheduling direction, the perturbation method to generate an initial solution, and the neighborhood structure. The proposed two-level metaheuristic algorithm, as the combination of the upper-level algorithm and the lower-level algorithm, thus can adapt itself for every single JSP instance.

Systematische Navigation für die Reihenfolgeplanung/Systematic solution space navigation for job shop scheduling problems

2020 ◽  
Vol 110 (07-08) ◽  
pp. 563-571
Author(s):  
Edzard Weber ◽  
Eduard Schenke ◽  
Luka Dorotic ◽  
Norbert Gronau
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Solution Space ◽  
Comparative Test ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Space Navigation ◽  
Systematic Solution

Dieser Beitrag stellt einen Algorithmus für das Job-shop-Scheduling-Problem vor, welcher den Lösungsraum indexiert und eine systematische Navigation zur Lösungssuche durchführt. Durch diese problemadäquate Aufbereitung wird der Lösungsraum nach bestimmten Kriterien vorzustrukturiert. Diese Problemrepräsentation wird formal beschrieben, sodass ihre Anwendung als Grundlage für ein navigationsorientiertes Suchverfahren dienen kann. Ein vergleichender Test mit anderen Optimierungsansätzen zeigt die Effizienz dieser Lösungsraumnavigation.   This paper presents an algorithm for the job-shop scheduling problem indexing the solution space and performing systematic navigation to find good solutions. By this problem-adequate preparation of the solution space, the solution space is pre-structured according to certain criteria. This problem representation is formally described so that its application can serve as a basis for a navigation-oriented search procedure. A comparative test with other optimization approaches shows the efficiency of this solution space navigation.

scholarly journals An Enhanced Two-Level Metaheuristic Algorithm with Adaptive Hybrid Neighborhood Structures for the Job-Shop Scheduling Problem

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Pisut Pongchairerks
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Metaheuristic Algorithm ◽  
Local Search Algorithm ◽  
Perturbation Operator ◽  
Job Shop Scheduling Problem ◽  
Neighborhood Structure ◽  
Shop Scheduling ◽  
Upper Level

For solving the job-shop scheduling problem (JSP), this paper proposes a novel two-level metaheuristic algorithm, where its upper-level algorithm controls the input parameters of its lower-level algorithm. The lower-level algorithm is a local search algorithm searching for an optimal JSP solution within a hybrid neighborhood structure. To generate each neighbor solution, the lower-level algorithm randomly uses one of two neighbor operators by a given probability. The upper-level algorithm is a population-based search algorithm developed for controlling the five input parameters of the lower-level algorithm, i.e., a perturbation operator, a scheduling direction, an ordered pair of two neighbor operators, a probability of selecting a neighbor operator, and a start solution-representing permutation. Many operators are proposed in this paper as options for the perturbation and neighbor operators. Under the control of the upper-level algorithm, the lower-level algorithm can be evolved in its input-parameter values and neighborhood structure. Moreover, with the perturbation operator and the start solution-representing permutation controlled, the two-level metaheuristic algorithm performs like a multistart iterated local search algorithm. The experiment’s results indicated that the two-level metaheuristic algorithm outperformed its previous variant and the two other high-performing algorithms in terms of solution quality.

scholarly journals IMPLEMENTASI ALGORITMA CAT SWARM OPTIMIZATION DALAM MENYELESAIKAN JOB SHOP SCHEDULING PROBLEM (JSSP)

2016 ◽  
Vol 5 (3) ◽  
pp. 90
Author(s):  
I WAYAN RADIKA APRIANA ◽  
NI KETUT TARI TASTRAWATI ◽  
KARTIKA SARI
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Scheduling Problem ◽  
Metaheuristic Algorithm ◽  
Job Shop Scheduling Problem ◽  
Optimal Result ◽  
Swarm Optimization ◽  
Shop Scheduling ◽  
Cat Swarm Optimization

Cat Swarm Optimization (CSO) algorithm is a metaheuristic algorithm which is based on two behaviors of cat, seeking and tracing. CSO algorithm is used in solving optimization problems. One of the optimization problems which can be seen in daily life is Job Shop Scheduling Problem (JSSP). This study aimed to observe the performance of CSO algorithm in solving JSSP. This study focused on 5 job-12 machine cases. According to this study, CSO algorithm was effective in solving real case of JSSP in 5 jobs – 12 machines scheduling at CV Mitra Niaga Indonesia agriculture tools industry. In implementing CSO algorithm in JSSP, a correct parameter choosing could lead to an optimal result. On other hand, the greater the number of jobs or machines the more complex and difficult the JSSP that needed to be solved.

scholarly journals Operating Room Surgery Scheduling with Fuzzy Surgery Durations Using a Metaheuristic Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Habibeh Nazif
Keyword(s):  
Operating Room ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Metaheuristic Algorithm ◽  
Surgery Scheduling ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

Operating room (OR) surgery scheduling is a challenging combinatorial optimization problem that determines the operation start time of every surgery to be performed in different surgical groups, as well as the resources assigned to each surgery over a schedule period. One of the main challenges in health care systems is to deliver the highest quality of care at the lowest cost. In real-life situations, there is significant uncertainty in several of the activities involved in the delivery of surgical care, including the duration of the surgical procedures. This paper tackles the operating room surgery scheduling problem with uncertain surgery durations, where uncertainty in surgery durations is represented by means of fuzzy numbers. The problem can be considered as a Fuzzy Flexible Job-shop Scheduling Problem (FFJSP) due to similarities between operating room surgery scheduling with uncertain surgery durations and a multi-resource constraint flexible job-shop scheduling problem with uncertain processing times. This research handles both the advanced and allocation scheduling problems simultaneously and provides an Ant Colony Optimization (ACO) metaheuristic algorithm which utilized a two-level ant graph to integrate sequencing jobs and allocating resources at the same time. To assess the performance of the proposed method, a computational study on five test surgery cases is presented, considering both deterministic and fuzzy surgery durations to enhance the significance of the study. The results of this experiment demonstrated the effectiveness of the proposed metaheuristic algorithm.

scholarly journals A DNA algorithm for the job shop scheduling problem based on the Adleman-Lipton model

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242083
Author(s):  
Xiang Tian ◽  
Xiyu Liu ◽  
Hongyan Zhang ◽  
Minghe Sun ◽  
Yuzhen Zhao
Keyword(s):  
Dna Computing ◽  
Deoxyribonucleic Acid ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimal Schedule ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Benchmark Instances ◽  
Dna Algorithm

A DNA (DeoxyriboNucleic Acid) algorithm is proposed to solve the job shop scheduling problem. An encoding scheme for the problem is developed and DNA computing operations are proposed for the algorithm. After an initial solution is constructed, all possible solutions are generated. DNA computing operations are then used to find an optimal schedule. The DNA algorithm is proved to have an O(n2) complexity and the length of the final strand of the optimal schedule is within appropriate range. Experiment with 58 benchmark instances show that the proposed DNA algorithm outperforms other comparative heuristics.

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