Extending evolutionary multi-objective optimization of business process designs

Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2019.Optimizing a problem to produce a set of improved solutions is not a new concept. Many scientific areas have been benefited by the application of optimizations techniques and so have business processes. The competitive business environments have led organizations into examining and re-designing their core business processes, aiming for improving their performance and market responsiveness. The optimization and the continuous improvement of business processes within a company, can give the advantage to the company to be more competitive by reducing its costs, improving the delivery quality and efficiency, and enabling adaptation to changing environments. This thesis focuses on business process multi-objective optimization with evolutionary algorithms. There have already been optimization approaches with evolutionary algorithms for business process optimization problems that demonstrated rather satisfactory results. This thesis aims to improve and extent those approaches by providing a revised and refined version of an existing business process optimization framework by Vergidis (2008), that incorporates a pre-processing technique for enhancing the efficiency of the employed Evolutionary Multi-objective Optimization Algorithms (EMOAs), a new process composition algorithm that make the new framework capable of fulfilling more real-life constraints and handling more complex problems and many other features such as ease of use, more efficient I/O, better interactivity and easy maintenance. The proposed pre-processing technique was tested as a standalone procedure and demonstrated satisfactory results, managing to reduce drastically the problem dataset of all scenarios examined. The results of the whole optimization framework for the real-life scenarios examined, were very promising and indicated that the framework work as expected. It can automate the process composition and identify alternative business process designs with optimized attribute values

Integrating continuous differential evolution with discrete local search for meander line RFID antenna design

The automated design of meander line RFID antennas is a discrete self-avoiding walk(SAW) problem for which efficiency is to be maximized while resonant frequency is to beminimized. This work presents a novel exploration of how discrete local search may beincorporated into a continuous solver such as differential evolution (DE). A prior DE algorithmfor this problem that incorporates an adaptive solution encoding and a bias favoringantennas with low resonant frequency is extended by the addition of the backbite localsearch operator and a variety of schemes for reintroducing modified designs into the DEpopulation. The algorithm is extremely competitive with an existing ACO approach and thetechnique is transferable to other SAW problems and other continuous solvers. The findingsindicate that careful reintegration of discrete local search results into the continuous populationis necessary for effective performance

Finding High-Dimensional D-OptimalDesigns for Logistic Models via Differential Evolution

D-optimal designs are frequently used in controlled experiments to obtain the most accurateestimate of model parameters at minimal cost. Finding them can be a challenging task, especially whenthere are many factors in a nonlinear model. As the number of factors becomes large and interact withone another, there are many more variables to optimize and the D-optimal design problem becomes highdimensionaland non-separable. Consequently, premature convergence issues arise. Candidate solutions gettrapped in local optima and the classical gradient-based optimization approaches to search for the D-optimaldesigns rarely succeed. We propose a specially designed version of differential evolution (DE) which is arepresentative gradient-free optimization approach to solve such high-dimensional optimization problems.The proposed specially designed DE uses a new novelty-based mutation strategy to explore the variousregions in the search space. The exploration of the regions will be carried out differently from the previouslyexplored regions and the diversity of the population can be preserved. The proposed novelty-based mutationstrategy is collaborated with two common DE mutation strategies to balance exploration and exploitationat the early or medium stage of the evolution. Additionally, we adapt the control parameters of DE as theevolution proceeds. Using logistic models with several factors on various design spaces as examples, oursimulation results show our algorithm can find D-optimal designs efficiently and the algorithm outperformsits competitors. As an application, we apply our algorithm and re-design a 10-factor car refueling experimentwith discrete and continuous factors and selected pairwise interactions. Our proposed algorithm was able toconsistently outperform the other algorithms and find a more efficient D-optimal design for the problem

Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution

An ESPC algorithm based approach to solve inventory deployment problem

Global competitiveness has enforced the hefty industries to become more customized. To compete in the market they are targeting the customers who want exotic products, and faster and reliable deliveries. Industries are exploring the option of satisfying a portion of their demand by converting strategically placed products, this helps in increasing the variability of product produced by them in short lead time. In this paper, authors have proposed a new hybrid evolutionary algorithm named Endosymbiotic-Psychoclonal (ESPC) algorithm to determine the amount and type of product to stock as a semi product in inventory. In the proposed work the ability of previously proposed Psychoclonal algorithm to exploit the search space has been increased by making antibodies and antigen more cooperative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results obtained, are compared with other evolutionary algorithms such as Genetic Algorithm (GA) and Simulated Annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained, and convergence time required to reach the optimal /near optimal value of the solution