Upgrading evolutionary algorithms through multiplicity for multiobjective optimization in job shop scheduling problems

In previous works the ability of CPS-MCPC (an evolutionary, co-operative, population search method with multiple crossovers per couple) to build well delineated Pareto fronts in diverse multiobjective optimization problems (MOOPs) was demonstrated. To test the potential of the novel method when dealing with the Job Shop Scheduling Problem (JSSP), regular and non-regular objectives functions were chosen. They were the makespan and the mean absolute deviation (of job completion times from a common due date, an earliness/tardiness related problem). Diverse representations such as priority list representation (PLR), job-based representation (JBR) and operation-based representation (OBR) among others were implemented and tested. The latter showed to be the best one. As a good parameter setting can enhance the behaviour of an evolutionary algorithm distinct parameters combinations were implemented and their influence studied. Multiple crossovers on multiple parents (MCMP), a powerful multirecombination method showed some enhancement in single objective optimization when compared with MCPC. This paper shows the influence of different recombination schemes when building the Pareto front under OBR and using the best parameter settings determined in previous works on a set of demonstrative Lawrence´s instances. Details of implementation and results are discussed.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

ADAPTIVE, MULTI-OBJECTIVE JOB SHOP SCHEDULING USING GENETIC ALGORITHMS

This research proposes a method to solve the adaptive, multi-objective job shop scheduling problem. Adaptive scheduling is necessary to deal with internal and external disruptions faced in real life manufacturing environments. Minimizing the mean tardiness for jobs to effectively meet customer due date requirements and minimizing mean flow time to reduce the lead time jobs spend in the system are optimized simultaneously. An asexual reproduction genetic algorithm with multiple mutation strategies is developed to solve the multi-objective optimization problem. The model is tested for single day and multi-day adaptive scheduling. Results are compared with those available in the literature for standard problems and using priority dispatching rules. The findings indicate that the genetic algorithm model can find good solutions within short computational time

Teaching modern heuristics in combinatorial optimization : The example of a demonstration and research tool employing metaheuristics in scheduling

The article describes the proposition and implementation of a demonstration, learning and decision support system for the resolution of combinatorial optimization problems under multiple objectives. The system brings together two key aspects of higher education: research and teaching. It allows the user to define modern metaheuristics and test their resolution behavior on machine scheduling problems. The software may be used by students and researcher with even little knowledge in the mentioned field of research, as the interaction of the user with the system is supported by an extensive graphical user interface. All functions can be easily parameterized, and expensive software licenses are not required. In order to address a large number of users, the system is localizable with little effort. So far, the user interface is available in seven languages. The software has been honored in Ronneby (Sweden) with the European Academic Software Award 2002, a prize for learning and research software awarded biannually by EKMA, the European Knowledge Media Association (http://www.easa-award.net/, http://www.bth.se/llab/easa_2002.nsf).Education for the 21 st century - impact of ICT and Digital Resources ConferenceRed de Universidades con Carreras en Informática (RedUNCI

Weapon Release Scheduling from Multiple-Bay Aircraft using Multi-Objective Evolutionary Algorithms

The United States Air Force has put an increased emphasis on the timely delivery of precision weapons. Part of this effort has been to us multiple bay aircraft such the B-1B Lancer and B-52 Stratofortress to provide Close Air Support and responsive strikes using 1760 weapons. In order to provide greater flexibility, the aircraft carry heterogeneous payloads which can require deconfliction in order to drop multiple different types of weapons. Current methods of deconfliction and weapon selection are highly crew dependent and work intensive. This research effort investigates the optimization of an algorithm for weapon release which allows the aircraft to perform deconfliction automatically. This reduces crew load and response time in order to deal with time-sensitive targets. The overall problem maps to the Job-Shop Scheduling problem. Optimization of the algorithm is done through the General Multiobjective Parallel Genetic Algorithm (GENMOP). We examine the results from pedagogical experiments and real-world test scenarios in the light of improving decision making. The results are encouraging in that the program proves capable of finding acceptable release schedules, however the solution space is such that applying the program to real world situations is unnecessary. We present visualizations of the schedules which demonstrate these conclusions

MOEA/D with Tabu Search for multiobjective permutation flow shop scheduling problems

Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) decomposes a multiobjective optimisation problem into a number of single-objective problems and optimises them in a collaborative manner. This paper investigates how to use Tabu Search (TS), a well-studied single objective heuristic to enhance MOEA/D performance. In our proposed approach, the TS is applied to these subproblems with the aim to escape from local optimal solutions. The experimental studies have shown that MOEA/D with TS outperforms the classical MOEA/D on multiobjective permutation flow shop scheduling problems. It also have demonstrated that use of problem specific knowledge can significantly improve the algorithm performance

Particle swarm optimization and differential evolution for multi-objective multiple machine scheduling

Production scheduling is one of the most important issues in the planning and operation of manufacturing systems. Customers increasingly expect to receive the right product at the right price at the right time. Various problems experienced in manufacturing, for example low machine utilization and excessive work-in-process, can be attributed directly to inadequate scheduling. In this dissertation a production scheduling algorithm is developed for Optimatix, a South African-based company specializing in supply chain optimization. To address the complex requirements of the customer, the problem was modeled as a flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and production down time. The algorithm development process focused on investigating the application of both particle swarm optimization (PSO) and differential evolution (DE) to production scheduling environments characterized by multiple machines and multiple objectives. Alternative problem representations, algorithm variations and multi-objective optimization strategies were evaluated to obtain an algorithm which performs well against both existing rule-based algorithms and an existing complex flexible job shop scheduling solution strategy. Finally, the generality of the priority-based algorithm was evaluated by applying it to the scheduling of production and maintenance activities at Centurion Ice Cream and Sweets. The production environment was modeled as a multi-objective uniform parallel machine shop problem with sequence-dependent set-up times and unavailability intervals. A self-adaptive modified vector evaluated DE algorithm was developed and compared to classical PSO and DE vector evaluated algorithms. Promising results were obtained with respect to the suitability of the algorithms for solving a range of multi-objective multiple machine scheduling problems. CopyrightDissertation (MEng)--University of Pretoria, 2009.Industrial and Systems Engineeringunrestricte

Evolutionary computing for routing and scheduling applications

Ph.DDOCTOR OF PHILOSOPH

Adaptive Order Dispatching based on Reinforcement Learning: Application in a Complex Job Shop in the Semiconductor Industry

Heutige Produktionssysteme tendieren durch die Marktanforderungen getrieben zu immer kleineren Losgrößen, höherer Produktvielfalt und größerer Komplexität der Materialflusssysteme. Diese Entwicklungen stellen bestehende Produktionssteuerungsmethoden in Frage. Im Zuge der Digitalisierung bieten datenbasierte Algorithmen des maschinellen Lernens einen alternativen Ansatz zur Optimierung von Produktionsabläufen. Aktuelle Forschungsergebnisse zeigen eine hohe Leistungsfähigkeit von Verfahren des Reinforcement Learning (RL) in einem breiten Anwendungsspektrum. Im Bereich der Produktionssteuerung haben sich jedoch bisher nur wenige Autoren damit befasst. Eine umfassende Untersuchung verschiedener RL-Ansätze sowie eine Anwendung in der Praxis wurden noch nicht durchgeführt. Unter den Aufgaben der Produktionsplanung und -steuerung gewährleistet die Auftragssteuerung (order dispatching) eine hohe Leistungsfähigkeit und Flexibilität der Produktionsabläufe, um eine hohe Kapazitätsauslastung und kurze Durchlaufzeiten zu erreichen. Motiviert durch komplexe Werkstattfertigungssysteme, wie sie in der Halbleiterindustrie zu finden sind, schließt diese Arbeit die Forschungslücke und befasst sich mit der Anwendung von RL für eine adaptive Auftragssteuerung. Die Einbeziehung realer Systemdaten ermöglicht eine genauere Erfassung des Systemverhaltens als statische Heuristiken oder mathematische Optimierungsverfahren. Zusätzlich wird der manuelle Aufwand reduziert, indem auf die Inferenzfähigkeiten des RL zurückgegriffen wird. Die vorgestellte Methodik fokussiert die Modellierung und Implementierung von RL-Agenten als Dispatching-Entscheidungseinheit. Bekannte Herausforderungen der RL-Modellierung in Bezug auf Zustand, Aktion und Belohnungsfunktion werden untersucht. Die Modellierungsalternativen werden auf der Grundlage von zwei realen Produktionsszenarien eines Halbleiterherstellers analysiert. Die Ergebnisse zeigen, dass RL-Agenten adaptive Steuerungsstrategien erlernen können und bestehende regelbasierte Benchmarkheuristiken übertreffen. Die Erweiterung der Zustandsrepräsentation verbessert die Leistung deutlich, wenn ein Zusammenhang mit den Belohnungszielen besteht. Die Belohnung kann so gestaltet werden, dass sie die Optimierung mehrerer Zielgrößen ermöglicht. Schließlich erreichen spezifische RL-Agenten-Konfigurationen nicht nur eine hohe Leistung in einem Szenario, sondern weisen eine Robustheit bei sich ändernden Systemeigenschaften auf. Damit stellt die Forschungsarbeit einen wesentlichen Beitrag in Richtung selbstoptimierender und autonomer Produktionssysteme dar. Produktionsingenieure müssen das Potenzial datenbasierter, lernender Verfahren bewerten, um in Bezug auf Flexibilität wettbewerbsfähig zu bleiben und gleichzeitig den Aufwand für den Entwurf, den Betrieb und die Überwachung von Produktionssteuerungssystemen in einem vernünftigen Gleichgewicht zu halten

Holistic, data-driven, service and supply chain optimisation: linked optimisation.

The intensity of competition and technological advancements in the business environment has made companies collaborate and cooperate together as a means of survival. This creates a chain of companies and business components with unified business objectives. However, managing the decision-making process (like scheduling, ordering, delivering and allocating) at the various business components and maintaining a holistic objective is a huge business challenge, as these operations are complex and dynamic. This is because the overall chain of business processes is widely distributed across all the supply chain participants; therefore, no individual collaborator has a complete overview of the processes. Increasingly, such decisions are automated and are strongly supported by optimisation algorithms - manufacturing optimisation, B2B ordering, financial trading, transportation scheduling and allocation. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems like supply chains. It is well-known that decisions made at one point in supply chains can have significant consequences that ripple through linked production and transportation systems. Recently, global shocks to supply chains (COVID-19, climate change, blockage of the Suez Canal) have demonstrated the importance of these interdependencies, and the need to create supply chains that are more resilient and have significantly reduced impact on the environment. Such interacting decision-making systems need to be considered through an optimisation process. However, the interactions between such decision-making systems are not modelled. We therefore believe that modelling such interactions is an opportunity to provide computational extensions to current optimisation paradigms. This research study aims to develop a general framework for formulating and solving holistic, data-driven optimisation problems in service and supply chains. This research achieved this aim and contributes to scholarship by firstly considering the complexities of supply chain problems from a linked problem perspective. This leads to developing a formalism for characterising linked optimisation problems as a model for supply chains. Secondly, the research adopts a method for creating a linked optimisation problem benchmark by linking existing classical benchmark sets. This involves using a mix of classical optimisation problems, typically relating to supply chain decision problems, to describe different modes of linkages in linked optimisation problems. Thirdly, several techniques for linking supply chain fragmented data have been proposed in the literature to identify data relationships. Therefore, this thesis explores some of these techniques and combines them in specific ways to improve the data discovery process. Lastly, many state-of-the-art algorithms have been explored in the literature and these algorithms have been used to tackle problems relating to supply chain problems. This research therefore investigates the resilient state-of-the-art optimisation algorithms presented in the literature, and then designs suitable algorithmic approaches inspired by the existing algorithms and the nature of problem linkages to address different problem linkages in supply chains. Considering research findings and future perspectives, the study demonstrates the suitability of algorithms to different linked structures involving two sub-problems, which suggests further investigations on issues like the suitability of algorithms on more complex structures, benchmark methodologies, holistic goals and evaluation, processmining, game theory and dependency analysis