scholarly journals Presentation of Multi-Skill Workforce Scheduling Model and Solving the Model Using Meta-Heuristic Algorithms

2016 ◽  
Vol 10 (2) ◽  
pp. 194
Author(s):  
Iman Fozveh ◽  
Hooman Salehi ◽  
Kamran Mogharehabed
Keyword(s):  
Heuristic Algorithms ◽  
Night Shift ◽  
Workforce Scheduling ◽  
Multi Objective ◽  
Bee Colony ◽  
De Algorithm ◽  
Scheduling Model ◽  
Proposed Model ◽  
Bee Colony Optimization ◽  
Quality Dispersion

<span lang="EN-US">In the present article, a multi-objective mathematical model for scheduling multi-skilled multi-objective workforce has been proposed with the aims of minimizing the number of night-shift engineers, minimizing the total cost of workforce and maximizing the number of engaged workforce. To solve the proposed model for scheduling workforce, bee colony optimization algorithm and DE algorithm have been employed, and in order to investigate the efficiency of these two algorithms, the results have been compared with each other in terms of quality, dispersion and uniformity factors. In order to solve the model three sample problems (40, 70 and 280 workforce) were designed and then solved by the two mentioned algorithms. Bee algorithm is able to find higher-quality answers. Also the results of the comparison of dispersion and uniformity index indicate that bee colony algorithm is able to find answers with more dispersion and more homogeneous than DE algorithm. The comparison of solution time of both algorithms indicate that bee colony algorithm is faster than DE algorithm and needs less time to reach quality, dispersed and homogenous answers.</span>

scholarly journals Cuckoo search algorithm for multi-objective supply chain problem

2018 ◽  
Vol 189 ◽  
pp. 06001 ◽  
Author(s):  
Fathy Elkazzaz ◽  
Abdelmageed Mahmoud ◽  
Ali Maher
Keyword(s):  
Supply Chain ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Chain Model ◽  
Multi Objective ◽  
Optimum Configuration ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Chain Problem

A meta-heuristic algorithm called, the cuckoo search algorithm is proposed in dealing with the multi-objective supply chain model to find the optimum configuration of a given supply chain problem which minimizes the total cost and the total lead-time. The supply chain problem utilized in this study is taken from literature to show the performance of the proposed model; in addition, the results have been compared to those achieved by the bee colony optimization algorithm and genetic algorithm. Those obtained results indicate that the proposed cuckoo search algorithm is able to get better Pareto solutions (non-dominated set) for the supply chain problem.

scholarly journals An Artificial Bee Colony Algorithm Based on a Multi-Objective Framework for Supplier Integration

2019 ◽  
Vol 9 (3) ◽  
pp. 588 ◽  
Author(s):  
Muhammad Farooq ◽  
Qazi Salman ◽  
Muhammad Arshad ◽  
Imran Khan ◽  
Rehman Akhtar ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Relevant Literature ◽  
Factors Affecting ◽  
Multi Objective ◽  
Supplier Integration ◽  
Expert Opinions ◽  
Bee Colony ◽  
Swarm Algorithms ◽  
Real World Problem ◽  
Bee Colony Optimization

Modern day industries strive to obtain long-term supplier integrations (SI) with potentially stronger supplier groups, to achieve fast and reliable production. This paper studies the process of selecting vendors, while simultaneously considering the aspects of random factors, multiple criteria, and efficiently reaching optimal solutions to improve the SI. A framework was developed that consists of three layers of expert opinions, supplier requirements, and multi-objective bee colony optimization. The model factors affecting the SI decision were explored from the comprehensive relevant literature, and these factors were shortlisted and prioritized. Routines for the modeled framework were coded by using the proposed algorithms which were implemented for a real-world problem from a manufacturing small and medium enterprise (SME) in Pakistan. Optimization of SI was carried out on an archived artificial bee colony (AABC). Its effectiveness was also evaluated by comparison with simple artificial bee colony (ABC) and particle swarm algorithms. The methodologically calculated results, obtained from simulation of a mathematically reinforced optimization framework, are highly beneficial for the industry, as well as local and international suppliers. A detailed and in-depth evaluation of suppliers was provided by the sensitivity analysis, which presented a more rigorous authentication and elaboration of the results. The presented framework is the first of its kind for the SMEs of Pakistan and can be applied with little modification to other industries.

scholarly journals Autonomous Bee Colony Optimization for multi-objective function

2010 ◽  
Author(s):  
Fanchao Zeng ◽  
James Decraene ◽  
Malcolm Yoke Hean Low ◽  
Philip Hingston ◽  
Cai Wentong ◽  
...  
Keyword(s):  
Objective Function ◽  
Multi Objective ◽  
Bee Colony ◽  
Bee Colony Optimization
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