scholarly journals A Bi-Objective Scheduling Problem in a Home Care Business

2021 ◽  
Vol 7 (1) ◽  
pp. 42
Author(s):  
Isabel Méndez-Fernández ◽  
Silvia Lorenzo-Freire ◽  
Ángel Manuel González-Rueda
Keyword(s):  
Home Care ◽  
Heuristic Algorithm ◽  
Pareto Frontier ◽  
Scheduling Problem ◽  
Routing And Scheduling ◽  
Preliminary Results ◽  
Conflicting Objectives ◽  
Good Behaviour

In this work we study a routing and scheduling problem for a home care business. The problem is composed of two conflicting objectives, therefore we study it as a bi-objective one. We obtain the Pareto frontier for small size instances using the AUGMECON2 method and, for bigger cases, we developed an heuristic algorithm. We also obtained some preliminary results that show the algorithm has good behaviour.

scholarly journals A two-phases matheuristic for the home care routing and scheduling problem

2016 ◽  
Vol 49 (12) ◽  
pp. 1484-1489 ◽  
Author(s):  
J. Decerle ◽  
O. Grunder ◽  
A. Hajjam El Hassani ◽  
O. Barakat
Keyword(s):  
Home Care ◽  
Scheduling Problem ◽  
Routing And Scheduling ◽  
Two Phases

scholarly journals Improved Hybrid Heuristic Algorithm Inspired by Tissue-Like Membrane System to Solve Job Shop Scheduling Problem

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 219
Author(s):  
Xiang Tian ◽  
Xiyu Liu
Keyword(s):  
Local Search ◽  
Heuristic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Membrane System ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Hybrid Heuristic ◽  
Shop Scheduling ◽  
Hybrid Heuristic Algorithm

In real industrial engineering, job shop scheduling problem (JSSP) is considered to be one of the most difficult and tricky non-deterministic polynomial-time (NP)-hard problems. This study proposes a new hybrid heuristic algorithm for solving JSSP inspired by the tissue-like membrane system. The framework of the proposed algorithm incorporates improved genetic algorithms (GA), modified rumor particle swarm optimization (PSO), and fine-grained local search methods (LSM). To effectively alleviate the premature convergence of GA, the improved GA uses adaptive crossover and mutation probabilities. Taking into account the improvement of the diversity of the population, the rumor PSO is discretized to interactively optimize the population. In addition, a local search operator incorporating critical path recognition is designed to enhance the local search ability of the population. Experiment with 24 benchmark instances show that the proposed algorithm outperforms other latest comparative algorithms, and hybrid optimization strategies that complement each other in performance can better break through the original limitations of the single meta-heuristic method.

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