Scheduling Algorithms: Challenges Towards Smart Manufacturing

Collecting, processing, analyzing, and driving knowledge from large-scale real-time data is now realized with the emergence of Artificial Intelligence (AI) and Deep Learning (DL). The breakthrough of Industry 4.0 lays a foundation for intelligent manufacturing. However, implementation challenges of scheduling algorithms in the context of smart manufacturing are not yet comprehensively studied. The purpose of this study is to show the scheduling No.s that need to be considered in the smart manufacturing paradigm. To attain this objective, the literature review is conducted in five stages using publish or perish tools from different sources such as Scopus, Pubmed, Crossref, and Google Scholar. As a result, the first contribution of this study is a critical analysis of existing production scheduling algorithms\u27 characteristics and limitations from the viewpoint of smart manufacturing. The other contribution is to suggest the best strategies for selecting scheduling algorithms in a real-world scenario

An MINLP model to support the movement and storage decisions of the Indian food grain supply chain

This paper addresses the novel three stage food grain distribution problem of Public Distribution System (PDS) in India which comprises of farmers, procurement centers, base silos and field silos. The Indian food grain supply chain consists of various activities such as procurement, storage, transportation and distribution of food grain. In order to curb transportation and storage losses of food grain, the Food Corporation of India (FCI) is moving towards the modernized bulk food grain supply chain system. This paper develops a Mixed Integer Non-Linear Programming (MINLP) model for planning the movement and storage of food grain from surplus states to deficit states considering the seasonal procurement, silo capacity, demand satisfaction and vehicle capacity constraints. The objective function of the model seeks to minimize the bulk food grain transportation, inventory holding, and operational cost. Therein, shipment cost contains the fixed and variable cost, inventory holding and operational cost considered at the procurement centers and base silos. The developed mathematical model is computationally complex in nature due to nonlinearity, the presence of numerous binary and integer variables along with a huge number of constraints, thus, it is very difficult to solve it using exact methods. Therefore, recently developed, Hybrid Particle-Chemical Reaction Optimization (HP-CRO) algorithm has been employed to solve the MINLP model. Different problem instances with growing complexities are solved using HP-CRO and the results are compared with basic Chemical Reaction Optimization (CRO) and Particle Swarm Optimization (PSO) algorithms. The results of computational experiments illustrate that the HP-CRO algorithm is competent enough to obtain the better quality solutions within reasonable computational time

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

Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

VERTEX COVER BASED LINK MONITORING TECHNIQUES FOR WIRELESS SENSOR NETWORKS

VERTEX COVER BASED LINK MONITORING TECHNIQUES FOR WIRELESS SENSOR NETWORKSAbstractWireless sensor networks (WSNs) are generally composed of numerous battery-powered tiny nodes that can sense from the environment and send this data through wireless communication. WSNs have wide range of application areas such as military surveillance, healthcare, miner safety, and outer space exploration. Inherent security weaknesses of wireless communication may prone WSNs to various attacks such as eavesdropping, jamming and spoofing. This situation attracts researchers to study countermeasures for detection and prevention of these attacks. Graph theory provides a very useful theoretical basis for solving WSN problems related to communication and security issues. One of the important graph theoretic structures is vertex cover (VC) in which a set of nodes are selected to cover the edges of the graph where each edge is incident to at least one node in VC set. Finding VC set having the minimum cardinality for a given graph is an NP-hard problem. In this paper, we describe VC algorithms aiming link monitoring where nodes in VC are configured as secure points. We investigate variants of VC problems such as weight and capacity constrained versions on different graph types to meet the energy-efficiency and load-balancing requirements of WSNs. Moreover, we present clustering and backbone formation operations as alternative applications of different VC infrastructures. For each VC sub-problem, we propose greedy heuristic based algorithms.Keywords: Wireless Sensor Networks, Link Monitoring, Graph Theory, Vertex Cover, NP-Hard Problem.KABLOSUZ SENSÖR AĞLARI İÇİN KÖŞE ÖRTME TABANLI BAĞLANTI İZLEME TEKNİKLERİÖzetKablosuz sensor ağlar (KSAlar) genellikle ortamdan algılayabilen ve bu verileri kablosuz iletişim yoluyla gönderebilen pille çalışan çok sayıda küçük düğümden oluşur. KSAlar askeri gözetim, sağlık hizmetleri, madenci güvenliği ve uzay keşfi gibi çok çeşitli uygulama alanlarına sahiptir. Kablosuz iletişimin doğasında var olan güvenlik zayıflıkları, KSAları gizli dinleme, sinyal bozma ve sahtekarlık gibi çeşitli saldırılara eğilimli hale getirebilmektedir. Bu durum, araştırmacıları bu saldırıların tespiti ve önlenmesine yönelik karşı önlemleri incelemeye yöneltmektedir. Çizge teorisi, iletişim ve güvenlik sorunları ile ilgili KSA sorunlarını çözmek için çok yararlı bir teorik temel sağlar. Önemli çizge teorik yapılardan biri köşe örtmedir (KÖ), bu yapıda her bir kenarın KÖ kümesindeki en az bir düğüme bitişik olacak şekilde çizgenin tüm kenarlarını kapsayacak bir dizi düğüm seçilmektedir. Verilen bir çizge için en az elemana sahip KÖ kümesini bulmak NP-zor bir problemdir. Bu makalede, KÖdeki düğümlerin güvenli noktalar olarak yapılandırıldığı bağlantı izlemeyi amaçlayan KÖ algoritmaları açıklanmaktadır. KSAların enerji verimliliği ve yük dengeleme gereksinimlerini karşılamak için, farklı çizge yapılarında KÖ problemlerinin ağırlık ve kapasite kısıtlı versiyonları gibi çeşitli türleri çalışılmaktadır. Ayrıca kümeleme ve omurga oluşturma işlemlerini farklı KÖ altyapılarının alternatif uygulamaları olarak sunulmaktadır. Her KÖ alt problemi için, açgözlü sezgisel tabanlı algoritmalar önerilmektedir.Anahtar Kelimeler: Kablosuz Sensör Ağları, Bağlantı İzleme, Çizge Teorisi, Kenar Örtme, NP-Zor Problem.

Integrated feedstock optimisation for multi-product polymer production

Thesis (PhD)--Stellenbosch University, 2022.ENGLISH ABSTRACT: A chemical complex can have multiple value chains, some of which may span across geographical locations. Decisions regarding the distribution of feedstock and intermediate feedstock to different production units can occur at different time intervals. This is highlighted as two problems, a feedstock distribution problem and an intermediate feedstock distribution problem. Unexpected events can cause an imbalanced value chain which requires timely decision-making to mitigate further adverse consequences. Scheduling methods can provide decision support during such events. The purpose of this research study is to develop an integrated decision support system which handles the two problems as a single problem and maximises profit in the value chain for hourly and daily decision-making. A high-level DSS architecture is presented that incorporates metaheuristic algorithms to generate production schedules for distribution of feedstock through the value chain. The solution evaluation process contains a balancing period to enable the application of metaheuristics to this type of problem and a novel encoding scheme is proposed for the hourly interval problem. It was found that metaheuristics algorithms can be used for this problem and integrated into the proposed decision support system.AFRIKAANSE OPSOMMING: ’n Chemiese kompleks kan verskeie waardekettings hê, waarvan sommige oor geografiese gebiede strek. Besluite rakende die verspreiding van grondstowwe en intermediêre grondstowwe na verskillende produksie-eenhede kan op verskillende tydsintervalle plaasvind. Dit word uitgelig as twee probleme: ’n probleem met die verspreiding van grondstowwe en ’n intermediêre grondstowwe verspreidingsprobleem. Onverwagte gebeure kan ’n ongebalanseerde waardeketting veroorsaak wat tydige besluitneming benodig om verdere gevolge te versag. Beplanningsmetodes kan ondersteuning bied tydens sulke geleenthede. Die doel van hierdie navorsingstudie was om ’n geïntegreerde stelsel vir besluitnemingsondersteuning oor die twee probleme as een probleem te ontwikkel, wat wins in die waardeketting vir uurlikse en daaglikse besluitneming maksimeer. ’n Hoëvlak DSS-argitektuur word aangebied met metaheuristieke om produksieskedules vir verspreidingstowwe deur die waardeketting te genereer. Die oplossingsevalueringsproses bevat ’n balanseerperiode om die metaheuristiek op hierdie tipe probleme toe te pas, en ’n nuwe koderingskema word voorgestel vir die uurlikse intervalprobleem. Die gevolgtrekking word gemaak dat metaheuristieke vir hierdie probleem gebruik kan word en ge¨ıntegreer kan word in die voorgestelde ondersteuningsstelsel vir besluitneming.Doctora