Performance of Extended Local Clustering Organization (LCO) for Large Scale Job-Shop Scheduling Problem (JSP)

2009 ◽  
Vol 129 (7) ◽  
pp. 1363-1370 ◽  
Author(s):  
Yohko Konno ◽  
Keiji Suzuki
Keyword(s):  
Large Scale ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Local Clustering

Job-Shop Scheduling Approach to Order-Picking Problem

2012 ◽  
Author(s):  
Yukiyasu Iwasaki ◽  
Ikuo Suzuki ◽  
Masahito Yamamoto ◽  
Masashi Furukawa
Keyword(s):  
Numerical Experiments ◽  
Large Scale ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Order Picking ◽  
Scheduling Problem ◽  
Mail Order ◽  
Job Shop Scheduling Problem ◽  
Rule Based ◽  
Shop Scheduling

In recent years, a large-scale logistic center plays an important role in mail-order business with Internet. In the logistic center, the efficient managing is required to deliver products to customers as soon as possible. Researches to efficiently control the logistic center have been done in the various approaches. This study proposed a new method for the order-picking problem considering worker’s jamming at the same shelf in the logistic center. In the proposed method, we formulate worker’s scheduling in the logistic center as Job-shop Scheduling Problem and optimize this problem. Numerical experiments show the proposed method improve worker’s scheduling compared with rule-based scheduling.

A Job Shop Scheduling Heuristic Algorithm Based on Probabilistic Model of the Search Space

2006 ◽  
Vol 532-533 ◽  
pp. 1084-1087
Author(s):  
Hong An Yang ◽  
Ya Ping Xu ◽  
Shu Dong Sun ◽  
Jian Jun Yu
Keyword(s):  
Probabilistic Model ◽  
Large Scale ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Constraint Satisfaction Problem ◽  
Search Space ◽  
Time Interval ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

The job shop scheduling problem is an NP-hard problem and conveniently formulated as Constraint Satisfaction Problem (CSP). Research in CSP has produced variable and value ordering heuristics techniques that can help improve the efficiency of the basic backtrack search procedure. However, the popular variable and value ordering heuristics play poor in solving the large-scale job shop scheduling problem. In this paper, a new probabilistic model of the search space was introduced which allows to estimate the reliance of an operation on the availability of a reservation, and the degree of contention among unscheduled operations for the possession of a resource over some time interval. Based on this probabilistic model, new operation and reservation ordering heuristics were defined. new operation ordering heuristic selects the operation that relies most on the most contended resource/time interval, and new reservation ordering heuristic assigns to that operation the reservation which is expected to be compatible with the largest number of survivable job schedules. Computer simulations indicate that this new algorithm yields a optimal result of FT10 benchmark job shop scheduling problem under small time cost.

scholarly journals A Two-Stage Optimization Algorithm for Multi-objective Job-Shop Scheduling Problem Considering Job Transport

2020 ◽  
Vol 53 (6) ◽  
pp. 915-924
Author(s):  
Jianfeng Ren ◽  
Chunming Ye ◽  
Yan Li
Keyword(s):  
Large Scale ◽  
Ant Colony Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Polar Coordinates ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Neighborhood Structure ◽  
Two Stage ◽  
Shop Scheduling

This paper solves the job-shop scheduling problem (JSP) considering job transport, with the aim to minimize the maximum makespan, tardiness, and energy consumption. In the first stage, the improved fast elitist nondominated sorting genetic algorithm II (INSGA-II) was combined with N5 neighborhood structure and the local search strategy of nondominant relationship to generate new neighborhood solutions by exchanging the operations on the key paths. In the second stage, the ant colony algorithm based on reinforcement learning (RL-ACA) was designed to optimize the job transport task, abstract the task into polar coordinates, and further optimizes the task. The proposed two-stage algorithm was tested on small, medium, and large-scale examples. The results show that our algorithm is superior to other algorithms in solving similar problems.

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