Ernesto Mastrocinque

Honey bees inspired optimization method: the Bees Algorithm

Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem.

A Multi-Objective Optimization for Supply Chain Network Using the Bees Algorithm

A supply chain is a complex network which involves the products, services and information flows between suppliers and customers. A typical supply chain is composed of different levels, hence, there is a need to optimize the supply chain by finding the optimum configuration of the network in order to get a good compromise between the multi-objectives such as cost minimization and lead-time minimization. There are several multi-objective optimization methods which have been applied to find the optimum solutions set based on the Pareto front line. In this study, a swarm-based optimization method, namely, the bees 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 and several experiments have been conducted in order to show the performance of the proposed model; in addition, the results have been compared to those achieved by the ant colony optimization method. The results show that the proposed bees algorithm is able to achieve better Pareto solutions for the supply chain problem.

A multi-objective supply chain optimisation using enhanced Bees Algorithm with adaptive neighbourhood search and site abandonment strategy

In this paper, an enhanced version of the Bees Algorithm is proposed in dealing with multi-objective supply chain model to find the optimum configuration of a given supply chain problem in order to minimise the total cost and the total lead-time. The new Bees Algorithm includes an adaptive neighbourhood size change and site abandonment (ANSSA) strategy which is an enhancement to the basic Bees Algorithm. The supply chain case study utilised in this work is taken from literature and several experiments have been conducted in order to show the performances, the strength, the weaknesses of the proposed method and the results have been compared to those achieved by the basic Bees Algorithm and Ant Colony optimisation. The results show that the proposed ANSSA-based Bees Algorithm is able to achieve better Pareto solutions for the supply chain problem.

Novel Genetic Bees Algorithm applied to single machine scheduling problem

The proposed novel Genetic Bees Algorithm (GBA) is an enhancement to the swarm-based Bees Algorithm (BA). It is called the Genetic Bees Algorithm because it has genetic operators. The structure of the GBA compared to the basic BA has two extra components namely, a Reinforced Global Search and a Jumping Function. The main advantage of adding the genetic operators to BA is that it will help the algorithm to avoid getting stuck in local optima. In this study the scheduling problem of a single machine was considered. When the basic BA was applied to solve this problem its performance was affected by its weakness in conducting global search to explore the search space. However, in most cases the proposed GBA overcame this issue due to the two new components which have been introduced.

Neural network design and feature selection using principal component analysis and Taguchi method for identifying wood veneer defects

Nowadays, ensuring high quality can be considered the main strength for a company’s success. Especially, in a period of economic recession, quality control is crucial from the operational and strategic point of view. There are different quality control methods and it has been proven that on the whole companies using a continuous improvement approach, eliminating waste and maximizing productive flow, are more efficient and produce more with lower costs. This paper presents a method to optimize the quality control stage for a wood manufacturing firm. The method is based on the employment of the principal component analysis in order to reduce the number of critical variables to be given as input for an artificial neural network (ANN) to identify wood veneer defects. The proposed method allows the ANN classifier to identify defects in real time and increase the response speed during the quality control stage so that veneers with defects do not pass through the whole production cycle but are rejected at the beginning.

Strategic Planning and Design of Supply Chains: a Literature Review

In this paper, a literature review of the mathematical models for supply chain design is proposed. The research is based on the study and analysis of publications of the last twelve years from the most widespread international journal about operations management and logistics. The aim of the work lies in identifying tendencies in the literature and related open issues about the strategic decisions, economic parameters, constraints and model features considered in the strategic planning and design of supply chains. After a description of the review methodology, comparison parameters and paper exhaustiveness, some guidelines are given in order to support future works in this field.

An enhancement to the Bees Algorithm with slope angle computation and Hill Climbing Algorithm and its applications on scheduling and continuous-type optimisation problem

This paper focuses on improvements to the Bees Algorithm (BA) with slope angle computation and Hill Climbing Algorithm (SACHCA) during the local search process. First, the SAC was employed to determine the inclination of the current sites. Second, according to the slope angle, the HCA was utilised to guide the algorithm to converge to the local optima. This enabled the global optimum of the given problem to be found faster and more precisely by focusing on finding the available local optima first before turning the attention on the global optimum. The proposed enhancements to the BA have been tested on continuous-type benchmark functions and compared with other optimisation techniques. The results show that the proposed algorithm performed better than other algorithms on most of the benchmark functions. The enhanced BA performs better than the basic BA, in particular on higher dimensional and complex optimisation problems. Finally, the proposed algorithm has been used to solve the single machine scheduling problem and the results show that the proposed SAC and HCA-BA outperformed the basic BA in almost all the considered instances, in particular when the complexity of the problem increases.

A multi-objective intelligent water drop algorithm to minimise cost Of goods sold and time to market in logistics networks

This is the first attempt to solve the SC design problem using IWD metaheuristic.We modify the single-objective IWD meta-heuristic to solve a bi-objective SC design problem.We compare our results to the ones computed by Ant Colony Optimisation (ACO).We solve several instances to show the performance of our hybrid algorithm.Our results outperform the ones computed by ACO. The Intelligent Water Drop (IWD) algorithm is inspired by the movement of natural water drops (WD) in a river. A stream can find an optimum path considering the conditions of its surroundings to reach its ultimate goal, which is often a sea. In the process of reaching such destination, the WD and the environment interact with each other as the WD moves through the river bed. Similarly, the supply chain problem can be modelled as a flow of stages that must be completed and optimised to obtain a finished product that is delivered to the end user. Every stage may have one or more options to be satisfied such as suppliers, manufacturing or delivery options. Each option is characterised by its time and cost. Within this context, multi-objective optimisation approaches are particularly well suited to provide optimal solutions. This problem has been classified as NP hard; thus, this paper proposes an approach aiming to solve the logistics network problem using a modified multi-objective extension of the IWD which returns a Pareto set.Artificial WD, flowing through the supply chain, will simultaneously minimise the cost of goods sold and the lead time of every product involved by using the concept of Pareto optimality. The proposed approach has been tested over instances widely used in literature yielding promising results which are supported by the performance measurements taken by comparison to the ant colony meta-heuristic as well as the true fronts obtained by exhaustive enumeration. The Pareto set returned by IWD is computed in 4źs and the generational distance, spacing, and hyper-area metrics are very close to those computed by exhaustive enumeration. Therefore, our main contribution is the design of a new algorithm that overcomes the algorithm proposed by Moncayo-Martinez and Zhang (2011).This paper contributes to enhance the current body of knowledge of expert and intelligent systems by providing a new, effective and efficient IWD-based optimisation method for the design and configuration of supply chain and logistics networks taking into account multiple objectives simultaneously.

A System of Systems Approach to Supply Chain Design

This paper proposes a novel System of Systems (SoSs) framework in order to design and optimise Supply Chains (SCs). In this paper the characteristics of System of Systems and Supply Chains have been discussed and a similarity match has been made between the two. It is interesting to note that although some of these SoSs characteristics are intrinsic in nature of the SCs others such as evolutionary behaviour and self-organization need to be modelled. In this paper, an adaptive supply chain multi-level multi-objective optimisation framework has been proposed in order to have both evolutionary and self-organized behaviour. This framework is capable of performing both local and global optimisation and adaptation to different scenarios.

Hybrid Genetic Bees Algorithm applied to single machine scheduling with earliness and tardiness penalties

Abstract This paper presents a hybrid Genetic-Bees Algorithm based optimised solution for the single machine scheduling problem. The enhancement of the Bees Algorithm (BA) is conducted using the Genetic Algorithm’s (GA’s) operators during the global search stage. The proposed enhancement aims to increase the global search capability of the BA gradually with new additions. Although the BA has very successful implementations on various type of optimisation problems, it has found that the algorithm suffers from weak global search ability which increases the computational complexities on NP-hard type optimisation problems e.g. combinatorial/permutational type optimisation problems. This weakness occurs due to using a simple global random search operation during the search process. To reinforce the global search process in the BA, the proposed enhancement is utilised to increase exploration capability by expanding the number of fittest solutions through the genetical variations of promising solutions. The hybridisation process is realised by including two strategies into the basic BA, named as “reinforced global search” and “jumping function” strategies. The reinforced global search strategy is the first stage of the hybridisation process and contains the mutation operator of the GA. The second strategy, jumping function strategy, consists of four GA operators as single point crossover, multipoint crossover, mutation and randomisation. To demonstrate the strength of the proposed solution, several experiments were carried out on 280 well-known single machine benchmark instances, and the results are presented by comparing to other well-known heuristic algorithms. According to the experiments, the proposed enhancements provides better capability to basic BA to jump from local minima, and GBA performed better compared to BA in terms of convergence and the quality of results. The convergence time reduced about 60% with about 30% better results for highly constrained jobs.