scholarly journals Hybrid Flow Shop with Limited Transportation Scheduling Problem: A Comparison Between Genetics Algorithm, and a Novel Recursive Local Search Heuristic

2021 ◽  
Author(s):  
Arash Amirteimoori ◽  
Reza Kia ◽  
Reza Tavakkoli-Moghaddam
Keyword(s):  
Local Search ◽  
Flow Shop ◽  
Statistical Tests ◽  
Mixed Integer ◽  
Hybrid Flow Shop ◽  
Search Heuristic ◽  
Local Search Heuristic ◽  
Concurrent Scheduling ◽  
Objective Value ◽  
Technical Features

Abstract In this paper, concurrent scheduling of jobs and transportation in a hybrid flow shop system is studied, where multiple jobs, transporters, and stages with parallel unrelated machines are considered. In addition to the mentioned technical features, jobs are able to omit one or more stages, and may not be executable by all the machines, and similarly, transportable by all the transporters. Unlike most studies in the literature, the transport resource is finite and needs to be simultaneously scheduled with the jobs. Initially, a new mixed integer linear programming (MILP) model is proposed to minimize the makespan. Then, a novel Recursive Local Search Heuristic (RLSH) is proposed to tackle the large-sized instances, which otherwise could not be solved via MILP solver (Gurobi) in reasonable time. RLSH is also compared against Genetics Algorithm (GA) on a set of numerical examples generated from the uniform distribution. As the computational results demonstrate, it is concluded that RLSH is extremely efficacious dealing with the problem and outperforms GA in the objective value quality. Finally, using two well-known statistical tests: Wald and analysis of variance(ANOVA), we assess the performance of the suggested approaches.

scholarly journals Deanonymizing Social Networks Using Structural Information

2019 ◽  
Author(s):  
Ioannis Caragiannis ◽  
Evanthia Tsitsoka
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Local Search ◽  
Structural Information ◽  
Initial Solution ◽  
Bipartite Matching ◽  
Search Heuristic ◽  
Local Search Heuristic ◽  
Anonymous Network ◽  
Random Models

We study the following fundamental graph problem that models the important task of deanonymizing social networks. We are given a graph representing an eponymous social network and another graph, representing an anonymous social network, which has been produced by the original one after removing some of its nodes and adding some noise on the links. Our objective is to correctly associate as many nodes of the anonymous network as possible to their corresponding node in the eponymous network. We present two algorithms that attack the problem by exploiting only the structure of the two graphs. The first one exploits bipartite matching computations and is relatively fast. The second one is a local search heuristic which can use the outcome of our first algorithm as an initial solution and further improve it. We have applied our algorithms on inputs that have been produced by well-known random models for the generation of social networks as well as on inputs that use real social networks. Our algorithms can tolerate noise at the level of up to 10%. Interestingly, our results provide further evidence to which graph generation models are most suitable for modeling social networks and distinguish them from unrealistic ones.

Energy-efficient multi-objective scheduling algorithm for hybrid flow shop with fuzzy processing time

2019 ◽  
Vol 233 (10) ◽  
pp. 1282-1297 ◽  
Author(s):  
Binghai Zhou ◽  
Wenlong Liu
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Processing Time ◽  
Flow Shop ◽  
Scheduling Algorithm ◽  
Mixed Integer ◽  
Computational Time ◽  
Hybrid Flow Shop ◽  
Multi Objective ◽  
Fuzzy Processing Time

Increasing costs of energy and environmental pollution is prompting scholars to pay close attention to energy-efficient scheduling. This study constructs a multi-objective model for the hybrid flow shop scheduling problem with fuzzy processing time to minimize total weighted delivery penalty and total energy consumption simultaneously. Setup times are considered as sequence-dependent, and in-stage parallel machines are unrelated in this model, meticulously reflecting the actual energy consumption of the system. First, an energy-efficient bi-objective differential evolution algorithm is developed to solve this mixed integer programming model effectively. Then, we utilize an Nawaz-Enscore-Ham-based hybrid method to generate high-quality initial solutions. Neighborhoods are thoroughly exploited with a leader solution challenge mechanism, and global exploration is highly improved with opposition-based learning and a chaotic search strategy. Finally, problems in various scales evaluate the performance of this green scheduling algorithm. Computational experiments illustrate the effectiveness of the algorithm for the proposed model within acceptable computational time.

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