Approximation algorithms for two-stage flexible flow shop scheduling

2019 ◽  
Vol 39 (1) ◽  
pp. 1-14
Author(s):  
Minghui Zhang ◽  
Yan Lan ◽  
Xin Han
Keyword(s):  
Approximation Algorithms ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Two Stage ◽  
Flexible Flow Shop ◽  
Shop Scheduling ◽  
Flexible Flow Shop Scheduling

A Heuristics Based on RA for Two-Stage Flexible Flow Shop Scheduling with Head Group Constraint

2011 ◽  
Vol 101-102 ◽  
pp. 783-789
Author(s):  
Zhan Tao Li ◽  
Qing Xin Chen ◽  
Ning Mao
Keyword(s):  
Flow Shop ◽  
Heuristic Method ◽  
Flow Shop Scheduling ◽  
Head Group ◽  
Start Time ◽  
Two Stage ◽  
Flexible Flow Shop ◽  
Shop Scheduling ◽  
Flow Shop Problem ◽  
Flexible Flow Shop Scheduling

Rapid access (RA) is an effective heuristic method for solving the permutation flow shop problem with the objective of makespan. This paper proposes a heuristic IRA (i.e., improved RA) for the two-stage flexible flow shop scheduling problem with head group constraint. In the heuristic IRA, two dispatch rules, i.e., earliest completion time first (ECT)) and earliest start time first (EST), are proposed for allocating devices for tasks. Benchmark testing results show that using ECT dispatch rule can significantly improve the performance of heuristic IRA. As comparison, the heuristic CDS and heuristic Palmer have also simulated under the same conditions, and the results show that the IRA is able to provide much better performance.

An Improved Palmer-Based Heuristic for Two-Stage Flexible Flow Shop with Group Constraint

2011 ◽  
Vol 314-316 ◽  
pp. 2076-2081
Author(s):  
Zhan Tao Li ◽  
Qing Xin Chen ◽  
Ning Mao
Keyword(s):  
Job Shop ◽  
Flow Shop ◽  
Mathematic Model ◽  
Flow Shop Scheduling ◽  
Release Dates ◽  
Two Stage ◽  
Flexible Flow Shop ◽  
Unrelated Machines ◽  
Shop Scheduling ◽  
Flexible Flow Shop Scheduling

Based on a background to the mould job shop, this paper considers a two-stage flexible flow shop scheduling problem subject to release dates, where the first stage is made up of unrelated machines and tasks have group constraint. The objective is to find a schedule that minimizes makespan in that flexible flow shop environment. For this problem, a mathematic model is formulated. Because this problem is NP-hard, an improved Palmer-based heuristic (denoted by MPL) is proposed. Based on MPL, a new heuristic (denoted by IMPL) is developed. In order to test the efficiency of the two heuristics, sets of examples are designed. Compared to the MPL, the performance of IMPL is more superior.

Scheduling rules for two-stage flexible flow shop scheduling problem subject to tail group constraint

2013 ◽  
Vol 146 (2) ◽  
pp. 667-678 ◽  
Author(s):  
Zhan-tao Li ◽  
Qing-xin Chen ◽  
Ning Mao ◽  
Xiaoming Wang ◽  
Jianjun Liu
Keyword(s):  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Two Stage ◽  
Flexible Flow Shop ◽  
Shop Scheduling ◽  
Flexible Flow Shop Scheduling

scholarly journals Clarifying Cutting and Sewing Processes with Due Windows Using an Effective Ant Colony Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Rong-Hwa Huang ◽  
Shun-Chi Yu
Keyword(s):  
Ant Colony Optimization ◽  
Flow Shop ◽  
Pso Algorithm ◽  
Ant Colony ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Two Stage ◽  
Flexible Flow Shop ◽  
Shop Scheduling ◽  
Flexible Flow Shop Scheduling

The cutting and sewing process is a traditional flow shop scheduling problem in the real world. This two-stage flexible flow shop is often commonly associated with manufacturing in the fashion and textiles industry. Many investigations have demonstrated that the ant colony optimization (ACO) algorithm is effective and efficient for solving scheduling problems. This work applies a novel effective ant colony optimization (EACO) algorithm to solve two-stage flexible flow shop scheduling problems and thereby minimize earliness, tardiness, and makespan. Computational results reveal that for both small and large problems, EACO is more effective and robust than both the particle swarm optimization (PSO) algorithm and the ACO algorithm. Importantly, this work demonstrates that EACO can solve complex scheduling problems in an acceptable period of time.

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