A Comparison of Local Search Metaheuristics for a Hierarchical Flow Shop Optimization Problem with Time Lags

2013 ◽  
pp. 69-96 ◽  
Author(s):  
Emna Dhouib ◽  
Jacques Teghem ◽  
Daniel Tuyttens ◽  
Taïcir Loukil
Keyword(s):  
Local Search ◽  
Optimization Problem ◽  
Flow Shop ◽  
Time Lags

Local Search Optimization Immune Algorithm Based on Job Insert Method for HFSS

2011 ◽  
Vol 267 ◽  
pp. 947-952
Author(s):  
Zhi Feng Liu ◽  
Peng Qiao ◽  
Wen Tong Yang ◽  
Jian Hua Wang
Keyword(s):  
Local Search ◽  
Optimization Problem ◽  
Flow Shop ◽  
Low Cost ◽  
Scheduling Algorithm ◽  
Hybrid Flow Shop ◽  
Organization Mode ◽  
Search Optimization ◽  
High Flexibility ◽  
Production Organization

Hybrid flow shop (HFS), as a production organization mode with characteristics of high flexibility, low cost and quick replacement job, has attracted extensive attention of research units and enterprises. In order to resolve the complex HFS scheduling optimization problem, an improved immune scheduling algorithm (ISA) model is proposed in this paper. A local search optimization algorithm，which can optimize the scheduling instance, accelerate the convergence of ISA, and improve scheduling results eventually, is adopted in the model on the basis of the job insert method. The results which simulate the manufacturing model of a camshaft enterprise show that the improved algorithm model is effective.

scholarly journals A Local Search-Based Generalized Normal Distribution Algorithm for Permutation Flow Shop Scheduling

2021 ◽  
Vol 11 (11) ◽  
pp. 4837
Author(s):  
Mohamed Abdel-Basset ◽  
Reda Mohamed ◽  
Mohamed Abouhawwash ◽  
Victor Chang ◽  
S. S. Askar
Keyword(s):  
Local Search ◽  
Search Strategy ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Discrete Problem ◽  
Mutation Operator ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Local Search Strategy ◽  
Generalized Normal Distribution

This paper studies the generalized normal distribution algorithm (GNDO) performance for tackling the permutation flow shop scheduling problem (PFSSP). Because PFSSP is a discrete problem and GNDO generates continuous values, the largest ranked value rule is used to convert those continuous values into discrete ones to make GNDO applicable for solving this discrete problem. Additionally, the discrete GNDO is effectively integrated with a local search strategy to improve the quality of the best-so-far solution in an abbreviated version of HGNDO. More than that, a new improvement using the swap mutation operator applied on the best-so-far solution to avoid being stuck into local optima by accelerating the convergence speed is effectively applied to HGNDO to propose a new version, namely a hybrid-improved GNDO (HIGNDO). Last but not least, the local search strategy is improved using the scramble mutation operator to utilize each trial as ideally as possible for reaching better outcomes. This improved local search strategy is integrated with IGNDO to produce a new strong algorithm abbreviated as IHGNDO. Those proposed algorithms are extensively compared with a number of well-established optimization algorithms using various statistical analyses to estimate the optimal makespan for 41 well-known instances in a reasonable time. The findings show the benefits and speedup of both IHGNDO and HIGNDO over all the compared algorithms, in addition to HGNDO.

A Parallel Iterated Local Search for Two-Agent Flow Shop Scheduling

2014 ◽  
Vol 926-930 ◽  
pp. 3476-3484 ◽  
Author(s):  
Xiao Qiang Xu ◽  
De Ming Lei
Keyword(s):  
Local Search ◽  
Flow Shop ◽  
Search Algorithm ◽  
Iterated Local Search ◽  
Flow Shop Scheduling ◽  
Local Search Algorithm ◽  
Shop Scheduling ◽  
Transition Mechanism ◽  
Neighborhood Structures ◽  
Agent Flow

In this paper a two-agent flow shop scheduling problem is studied and a simple parallel iterated local search algorithm is proposed to minimize the makespan of jobs from the first agent and the total tardiness of jobs from the second agent simultaneously. Parallelization is implemented by applying multiple independent searches, each of which uses three neighborhood structures with dynamical transition mechanism. The current solution of each independent search is replaced with a solution, which is randomly chosen from the non-dominated set and perturbed. The computational experiments show the promising advantage of the proposed method when compared to other algorithms of the problem.

Iterated Local Search and Other Algorithms for Buffered Two-Machine Permutation Flow Shops with Constant Processing Times on One Machine

2020 ◽  
pp. 1-25
Author(s):  
Hoang Thanh Le ◽  
Philine Geser ◽  
Martin Middendorf
Keyword(s):  
Local Search ◽  
Flow Shop ◽  
Iterated Local Search ◽  
Flow Shop Scheduling ◽  
Np Hard ◽  
Ant Colony Optimization Algorithm ◽  
Solution Quality ◽  
Processing Times ◽  
One Machine ◽  
Special Case

The two-machine permutation flow shop scheduling problem with buffer is studied for the special case that all processing times on one of the two machines are equal to a constant c. This case is interesting because it occurs in various applications, e.g., when one machine is a packing machine or when materials have to be transported. Different types of buffers and buffer usage are considered. It is shown that all considered buffer flow shop problems remain NP-hard for the makespan criterion even with the restriction to equal processing times on one machine. However, the special case where the constant c is larger or smaller than all processing times on the other machine is shown to be polynomially solvable by presenting an algorithm (2BF-OPT) that calculates optimal schedules in [Formula: see text] steps. Two heuristics for solving the NP-hard flow shop problems are proposed: i) a modification of the commonly used NEH heuristic (mNEH) and ii) an Iterated Local Search heuristic (2BF-ILS) that uses the mNEH heuristic for computing its initial solution. It is shown experimentally that the proposed 2BF-ILS heuristic obtains better results than two state-of-the-art algorithms for buffered flow shop problems from the literature and an Ant Colony Optimization algorithm. In addition, it is shown experimentally that 2BF-ILS obtains the same solution quality as the standard NEH heuristic, however, with a smaller number of function evaluations.

An Efficient and Accurate Solution Methodology for Bilevel Multi-Objective Programming Problems Using a Hybrid Evolutionary-Local-Search Algorithm

2010 ◽  
Vol 18 (3) ◽  
pp. 403-449 ◽  
Author(s):  
Kalyanmoy Deb ◽  
Ankur Sinha
Keyword(s):  
Problem Solving ◽  
Local Search ◽  
Bilevel Programming ◽  
Optimization Problem ◽  
Practical Importance ◽  
Test Problems ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Bilevel Programming Problems ◽  
Solution Methodology

Bilevel optimization problems involve two optimization tasks (upper and lower level), in which every feasible upper level solution must correspond to an optimal solution to a lower level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy developments, transportation problems, and others. However, they are commonly converted into a single level optimization problem by using an approximate solution procedure to replace the lower level optimization task. Although there exist a number of theoretical, numerical, and evolutionary optimization studies involving single-objective bilevel programming problems, not many studies look at the context of multiple conflicting objectives in each level of a bilevel programming problem. In this paper, we address certain intricate issues related to solving multi-objective bilevel programming problems, present challenging test problems, and propose a viable and hybrid evolutionary-cum-local-search based algorithm as a solution methodology. The hybrid approach performs better than a number of existing methodologies and scales well up to 40-variable difficult test problems used in this study. The population sizing and termination criteria are made self-adaptive, so that no additional parameters need to be supplied by the user. The study indicates a clear niche of evolutionary algorithms in solving such difficult problems of practical importance compared to their usual solution by a computationally expensive nested procedure. The study opens up many issues related to multi-objective bilevel programming and hopefully this study will motivate EMO and other researchers to pay more attention to this important and difficult problem solving activity.

Sign in / Sign up

Export Citation Format

Share Document