Hybrid Metaheuristic Algorithm Based on Iterative Optimization for the Job Shop Scheduling Problem

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.

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

E-Jurnal Matematika ◽

10.24843/mtk.2016.v05.i03.p126 ◽

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.

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

Advances in Operations Research ◽

10.1155/2018/8637598 ◽

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.

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