Enhanced Differential Evolution Based on Adaptive Mutation and Wrapper Local Search Strategies for Global Optimization Problems

AbstractDifferential evolution (DE) is a simple, powerful optimization algorithm, which has been widely used in many areas. However, the choices of the best mutation and search strategies are difficult for the specific issues. To alleviate these drawbacks and enhance the performance of DE, in this paper, the hybrid framework based on the adaptive mutation and Wrapper Local Search (WLS) schemes, is proposed to improve searching ability to efficiently guide the evolution of the population toward the global optimum. Furthermore, the effective particle encoding representation named Particle Segment Operation-Machine Assignment (PSOMA) that we previously published is applied to always produce feasible candidate solutions for solving the Flexible Job-shop Scheduling Problem (FJSP). Experiments were conducted on comprehensive set of complex benchmarks including the unimodal, multimodal and hybrid composition function, to validate performance of the proposed method and to compare with other state-of-the art DE variants such as jDE, JADE, MDE_pBX etc. Meanwhile, the hybrid DE model incorporating PSOMA is used to solve different representative instances based on practical data for multi-objective FJSP verifications. Simulation results indicate that the proposed method performs better for the majority of the single-objective scalable benchmark functions in terms of the solution accuracy and convergence rate. In addition, the wide range of Pareto-optimal solutions and more Gantt chart decision-makings can be provided for the multi-objective FJSP combinatorial optimizations

Hybrid Monte Carlo tree search based multi-objective scheduling

As markets demand targeted products for highly differentiated use cases, the number of variants in production increases, whilst the volume per variant decreases. Different product variants result in differences in work content on workstation level which cause takt time losses and result in a poor utilization. In this context, matrix-structured production systems with neither temporal nor spacial linkage emerged to reduce the effects of different work content on the entire production system. However, matrix-structured production systems require far more complex production control. To that end, this paper presents a scheduling approach. The proposed scheduling system considers variable process sequences and their allocation to different workstations in order to optimize scheduling objectives. This contribution presents a Monte Carlo tree search based optimizer combined with local search as post optimizer to derive schedules in a short time span to enabling reactive scheduling. The application of the scheduler to a benchmark problem and an industrial scheduling problem demonstrates the quality of the results and illustrates how the scheduler reassigns the work content dynamically

A Hybrid Artificial Bee Colony Algorithm for Flexible Job Shop Scheduling Problems

In this paper, we propose a hybrid Pareto-based artificial bee colony (HABC) algorithm for solving the multi-objective flexible job shop scheduling problem. In the hybrid algorithm, each food sources is represented by two vectors, i.e., the machine assignment vector and the operation scheduling vector. The artificial bee is divided into three groups, namely, employed bees, onlookers, and scouts bees. Furthermore, an external Pareto archive set is introduced to record non-dominated solutions found so far. To balance the exploration and exploitation capability of the algorithm, the scout bees in the hybrid algorithm are divided into two parts. The scout bees in one part perform randomly search in the predefined region while each scout bee in another part randomly select one non-dominated solution from the Pareto archive set. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm

Solving the Multi Objective Flexible Job Shop Problem Using Combinational Meta Heuristic Algorithm Based on Genetic Algorithm and Tabu-Search 1

ABSTRACT Flexible Job Shop Scheduling Problem (FJSSP) has an especial place in industry environments; due to this issue and also due to its mathematical characteristics large number of managers and researchers are considered this problem. Flexible Job Shop Scheduling Problem is more complexthan JSSP and classified as Np-Hard problems. So in this paper a combinational optimization Meta heuristic based on Genetic algorithm is proposed for solving this problem and Tabu search method as a local search algorithm is used to increasing the quality of solutions. In current paper the FJSSP is studied in Multi objective mode and during the solving the problem three objective functions are considered as Makespan, Total workload of machines and Maximum workload of machines and all of them should be minimized. This problem is coded by VBA Software and finally the solutions of proposed algorithm are compared with other papers and the efficiency of solution will be examined

A research survey: review of flexible job shop scheduling techniques

In the last 25 years, extensive research has been carried out addressing the flexible job shop scheduling (JSS) problem. A variety of techniques ranging from exact methods to hybrid techniques have been used in this research. The paper aims at presenting the development of flexible JSS and a consolidated survey of various techniques that have been employed since 1990 for problem resolution. The paper comprises evaluation of publications and research methods used in various research papers. Finally, conclusions are drawn based on performed survey results. A total of 404 distinct publications were found addressing the FJSSP. Some of the research papers presented more than one technique/algorithm to solve the problem that is categorized into 410 different applications. Selected time period of these research papers is between 1990 and February 2014. Articles were searched mainly on major databases such as SpringerLink, Science Direct, IEEE Xplore, Scopus, EBSCO, etc. and other web sources. All databases were searched for “flexible job shop” and “scheduling” in the title an

Industrial engineering applications in metrology: Job scheduling, calibration interval and average outgoing quality

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityThis research deals with the optimization of metrology and calibration problems. The optimization involved here is the application scientifically sound operations research techniques to help in solving the problem intended optimally or semi-optimally with a practical time frame. The research starts by exploring the subject of measurement science known as metrology. This involves defining all the constituents of metrology facilities along with their various components. The definitions include the SI units’ history and structure as well as their characteristics. After that, a comprehensive description of most of the operations and parameters encountered in metrology is presented. This involves all sources of uncertainties in most of the parameters that affect the measurements. From the background presented and using all the information within it; an identification of the most important and critical general problems is attempted. In this treatment a number of potential optimization problems are identified along with their description, problem statement definition, impact on the system and possible treatment method. After that, a detailed treatment of the scheduling problem, the calibration interval determination problem and the average outgoing quality problem is presented. The scheduling problem is formulated and modelled as a mixed integer program then solved using LINGO program. A heuristic algorithm is then developed to solve the problem near optimally but in much quicker time, and solution is packaged in a computer program. The calibration interval problem treatment deals with the determination of the optimal CI. Four methods are developed to deal with different cases. The cases considered are the reliability target case, the CI with call cost and failure cost of both first failure and all failures and the case of large number of similar TMDEs. The average out going quality (AOQ) treatment involves the development two methods to assess the AOQ of a calibration facility that uses a certain multistage inspection policy. The two methods are mathematically derived and verified using a simulation model that compares them with an actual failure rate of a virtual calibration facility

Ferramenta de apoio ao escalonamento da produção

A imprevisibilidade e instabilidade dos mercados obrigam cada vez mais resposta rápida por parte das empresas. Todas as indústrias têm necessidade de investimento no sentido aprimorar as suas respostas, que deriva do crescimento do nível de competitividade dos mercados em que estão inseridas. A eficiência da sua produção é um dos fatores que mais condiciona a rentabilidade de uma organização. Tendo isto em conta, há cada vez maior necessidade na eficácia do planeamento e programação da produção que permita maximizar os recursos instalados. O escalonamento da produção consiste na alocação e sequenciação das tarefas nas respetivas máquinas com o desígnio de encontrar a melhor sequência para o processamento das mesmas. A presente dissertação tem por objetivo a criação de uma ferramenta de apoio à decisão, com intuito de colmatar as dificuldades no escalonamento da produção. Determinando uma afetação e sequenciação das tarefas às máquinas, minimizando uma determinada medida de desempenho. Procedeu-se à descrição do problema de escalonamento, identificando formas de resolução e otimização dos diferentes tipos de problemas. Centrou-se os esforços na resolução de todos os tipos de problema de escalonamento, incluindo os problemas em Job-Shop, que consistem num conjunto de operações que têm de ser executadas numa máquina pré-determinada, obedecendo a um determinado sequenciamento com tempos pré-definidos. São caracterizados por uma complexidade NP-hard e sendo o protótipo capaz de resolver este tipo de problema fica automaticamente habilitado a resolver qualquer tipo de problema do escalonamento da produção. A meta-heurística escolhida que permitiu a resolução dos problemas foi o Simulated Annealing, sendo escolhida por apresentar a vantagem de convergir para uma solução ótima, quando existe uma minuciosa escolha dos seus parâmetros combinado um arrefecimento muito lento. A finalidade desta meta-heurística foi construída para a minimização do makespan, ou seja, na minimização do tempo de fluxo total. O framework desenvolvido utiliza o SA, mas podem ser implementadas outras meta-heurísticas sem alterações significativas do modelo. Como forma de demonstrar as potencialidades do framework foram analisadas instâncias de Flow- -Shop e Job-Shop, quem têm por base conjuntos de problemas padronizados, previamente definidos por autores da área científica em questão. Conclui-se a viabilidade da ferramenta criada e, em articulação com os procedimentos de otimização escolhidos, constituindo assim um fator diferenciador para as organizações e permitindo uma melhoria no desempenho dos recursos disponíveis.The increasing unpredictability and instability of the markets requires a quick response from companies. All industries have a need for investment in order to improve their responses, which stems from the continuous growth in the level of competitiveness of the markets in which they operate. One of the factors that most affects the profitability of an organization is the efficiency of its production. Bearing this in mind, there is an increasing need for effective production planning and scheduling to maximize the installed resources. The scheduling of production consists of the allocation and sequencing of tasks on the respective machines, with the aim of finding the best sequence for processing them. This dissertation aims to create a decision support tool, in order to overcome the difficulties in scheduling production. Determining the assignment and sequencing of tasks to the machines, minimizing a certain measure of performance. The scheduling problem was described, identifying ways to solve and optimize the different types of problems. Efforts were focused on solving all types of scheduling problems, including job shop problems, which consist of a set of operations that have to be performed on a pre-determined machine, following a specific sequence with pre-defined times. They are characterized by an NPhard complexity and being the prototype capable of solving this type of problem, it is automatically enabled to solve any type of production scheduling problem. The chosen meta-heuristic that allowed the resolution of the problems was Simulated Annealing, being chosen because it has the advantage of converging to an optimal solution, when there is a meticulous choice of parameters combined with very slow cooling. The purpose of this metaheuristic was built to minimize the makespan, that is, to minimize the total flow time. The developed framework uses SA, but other meta-heuristics can be implemented without significant changes to the model. As a way to demonstrate the framework’s potential, instances of flow shop and job shop were analyzed, which are based on sets of standardized problems, previously defined by authors of the scientific area in question. The viability of the created tool is concluded and, in articulation with the chosen optimization procedures, constituting a differentiating factor for the organizations and allowing an improvement in the performance of the available resources

Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the classical Job Shop Scheduling Problem (JSSP). The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA) have gained the most popularity. Keeping in view this aspect, this article presents a comprehensive literature review of the FJSSPs solved using the GA. The survey is further extended by the inclusion of the hybrid GA (hGA) techniques used in the solution of the problem. This review will give readers an insight into use of certain parameters in their future research along with future research directions

A study on flexible flow shop and job shop scheduling using meta-heuristic approaches

Scheduling aims at allocation of resources to perform a group of tasks over a period of time in such a manner that some performance goals such as flow time, tardiness, lateness, and makespan can be minimized. Today, manufacturers face the challenges in terms of shorter product life cycles, customized products and changing demand pattern of customers. Due to intense competition in the market place, effective scheduling has now become an important issue for the growth and survival of manufacturing firms. To sustain in the current competitive environment, it is essential for the manufacturing firms to improve the schedule based on simultaneous optimization of performance measures such as makespan, flow time and tardiness. Since all the scheduling criteria are important from business operation point of view, it is vital to optimize all the objectives simultaneously instead of a single objective. It is also essentially important for the manufacturing firms to improve the performance of production scheduling systems that can address internal uncertainties such as machine breakdown, tool failure and change in processing times. The schedules must meet the deadline committed to customers because failure to do so may result in a significant loss of goodwill. Often, it is necessary to reschedule an existing plan due to uncertainty event like machine breakdowns. The problem of finding robust schedules (schedule performance does not deteriorate in disruption situation) or flexible schedules (schedules expected to perform well after some degree of modification when uncertain condition is encountered) is of utmost importance for real world applications as they operate in dynamic environments

Vorausschauende und reaktive Mehrzieloptimierung für die Produktionssteuerung einer Matrixproduktion

Ein immer vielfältigeres Produktionsprogramm mit unsicheren Stückzahlen macht es schwierig, Produktionssysteme wirtschaftlich zu betreiben. Verursacht die Produktindividualisierung unterschiedliche Bearbeitungszeiten an den Produktionsstationen, entstehen Taktzeitverluste. Schwankungen in den Anteilen der Produktvarianten können zudem zu dynamischen Engpässen führen. Das Konzept der Matrixproduktion verfolgt eine Flexibilisierung der Produktionsstruktur durch Auflösung der starren Verkettung, der Taktzeitbindung sowie durch den Einsatz redundanter Mehrzweckstationen. Diese Maßnahmen erlauben es der Produktionssteuerung, die Reihenfolge der Arbeitsvorgänge innerhalb der Grenzen des Vorranggraphs zu variieren und die Route jedes Auftrags anzupassen. Eine reaktive Mehrzielsteuerung ist erforderlich, um diese Freiheitsgrade zu nutzen und die unterschiedlichen Zielgrößen der Produktionssysteme zu erfüllen. Durch die Verwendung von Domänenwissen bei der Optimierung kann die Effizienz für spezifische Problem gesteigert werden. Aufgrund der Vielfalt der Produktionssysteme und Zielgrößen sollte sich die Produktionssteuerung jedoch selbstständig an den jeweiligen Anwendungsfall und die Zielgrößen anpassen können. Da die Dauern für Bearbeitungs-, Transport- und Rüstzeiten wichtige Eingangsgrößen für die Produktionssteuerung sind, wird eine Methode zur Ermittlung realistischer Werte benötigt. Aufgrund der Komplexität der Steuerungsentscheidung sind Heuristiken am besten geeignet. Insbesondere die Monte Carlo Tree Search (MCTS) als iteratives Suchbaumverfahren hat gute Eigenschaften für den Einsatz als reaktive Produktionssteuerung. Bisher fehlten jedoch Ansätze, die den Anforderungen an die Steuerung einer Matrixproduktion gerecht werden. In dieser Arbeit wird eine reaktive Mehrzielsteuerung auf Basis von MCTS für die Produktionssteuerung einer Matrixproduktion unter Berücksichtigung von Rüst- und Transportvorgängen entwickelt. Zusätzlich wird eine auf lokaler Suche basierende Post-Optimierung in den MCTS Ablauf integriert. Um schnell eine hohe Lösungsqualität für unterschiedliche Zielsetzungen und Produktionssysteme zu erreichen, werden zwei Methoden zur selbstständigen Anpassung der Produktionssteuerung entwickelt. Um die Genauigkeit der in der Produktionssteuerung verwendeten Dauern zu gewährleisten, wird eine Methode zur Ableitung und Aktualisierung der zugrunde liegenden Verteilungen vorgestellt. Die detaillierten Auswertungen anhand verschiedener Anwendungsfälle zeigen, dass die Produktionssteuerung in der Lage ist, verschiedene Ziele erfolgreich zu optimieren. Die Methoden zur selbstständigen Anpassung führen zudem zu einem schnelleren Anstieg der Lösungsgüte. Der Vergleich mit optimalen Referenzlösungen und mit Benchmark-Problemen aus der Literatur belegt ebenfalls die hohe Lösungsgüte. Die Anwendung auf ein reales Praxisbeispiel demonstriert das Verhalten der Produktionssteuerung bei Ausfällen und Abweichungen. Diese Arbeit untersucht detailliert das Verhalten der Produktionssteuerung und den Einfluss der entwickelten Methoden auf die Erreichbarkeit der unterschiedlichen Zielgrößen, den Anstieg der Lösungsgüte und die erreichte absolute Lösungsgüte