Four decades of research on the open-shop scheduling problem to minimize the makespan

One of the basic scheduling problems, the open-shop scheduling problem has a broad range of applications across different sectors. The problem concerns scheduling a set of jobs, each of which has a set of operations, on a set of different machines. Each machine can process at most one operation at a time and the job processing order on the machines is immaterial, i.e., it has no implication for the scheduling outcome. The aim is to determine a schedule, i.e., the completion times of the operations processed on the machines, such that a performance criterion is optimized. While research on the problem dates back to the 1970s, there have been reviving interests in the computational complexity of variants of the problem and solution methodologies in the past few years. Aiming to provide a complete road map for future research on the open-shop scheduling problem, we present an up-to-date and comprehensive review of studies on the problem that focuses on minimizing the makespan, and discuss potential research opportunities

Energy Efficient Policies, Scheduling, and Design for Sustainable Manufacturing Systems

Climate mitigation, more stringent regulations, rising energy costs, and sustainable manufacturing are pushing researchers to focus on energy efficiency, energy flexibility, and implementation of renewable energy sources in manufacturing systems. This thesis aims to analyze the main works proposed regarding these hot topics, and to fill the gaps in the literature. First, a detailed literature review is proposed. Works regarding energy efficiency in different manufacturing levels, in the assembly line, energy saving policies, and the implementation of renewable energy sources are analyzed. Then, trying to fill the gaps in the literature, different topics are analyzed more in depth. In the single machine context, a mathematical model aiming to align the manufacturing power required to a renewable energy supply in order to obtain the maximum profit is developed. The model is applied to a single work center powered by the electric grid and by a photovoltaic system; afterwards, energy storage is also added to the power system. Analyzing the job shop context, switch off policies implementing workload approach and scheduling considering variable speed of the machines and power constraints are proposed. The direct and indirect workloads of the machines are considered to support the switch on/off decisions. A simulation model is developed to test the proposed policies compared to others presented in the literature. Regarding the job shop scheduling, a fixed and variable power constraints are considered, assuming the minimization of the makespan as the objective function. Studying the factory level, a mathematical model to design a flow line considering the possibility of using switch-off policies is developed. The design model for production lines includes a targeted imbalance among the workstations to allow for defined idle time. Finally, the main findings, results, and the future directions and challenges are presented

Advances and Novel Approaches in Discrete Optimization

Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled ‘Advances and Novel Approaches in Discrete Optimization’. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Ensuring Service Level Agreements for Composite Services by Means of Request Scheduling

Building distributed systems according to the Service-Oriented Architecture (SOA) allows simplifying the integration process, reducing development costs and increasing scalability, interoperability and openness. SOA endorses the reusability of existing services and aggregating them into new service layers for future recycling. At the same time, the complexity of large service-oriented systems negatively reflects on their behavior in terms of the exhibited Quality of Service. To address this problem this thesis focuses on using request scheduling for meeting Service Level Agreements (SLAs). The special focus is given to composite services specified by means of workflow languages. The proposed solution suggests using two level scheduling: global and local. The global policies assign the response time requirements for component service invocations. The local scheduling policies are responsible for performing request scheduling in order to meet these requirements. The proposed scheduling approach can be deployed without altering the code of the scheduled services, does not require a central point of control and is platform independent. The experiments, conducted using a simulation, were used to study the effectiveness and the feasibility of the proposed scheduling schemes in respect to various deployment requirements. The validity of the simulation was confirmed by comparing its results to the results obtained in experiments with a real-world service. The proposed approach was shown to work well under different traffic conditions and with different types of SLAs

Conception d'un modèle architectural collaboratif pour l'informatique omniprésente à la périphérie des réseaux mobiles

Le progrès des technologies de communication pair-à-pair et sans fil a de plus en plus permis l’intégration de dispositifs portables et omniprésents dans des systèmes distribués et des architectures informatiques de calcul dans le paradigme de l’internet des objets. De même, ces dispositifs font l'objet d'un développement technologique continu. Ainsi, ils ont toujours tendance à se miniaturiser, génération après génération durant lesquelles ils sont considérés comme des dispositifs de facto. Le fruit de ces progrès est l'émergence de l'informatique mobile collaborative et omniprésente, notamment intégrée dans les modèles architecturaux de l'Internet des Objets. L’avantage le plus important de cette évolution de l'informatique est la facilité de connecter un grand nombre d'appareils omniprésents et portables lorsqu'ils sont en déplacement avec différents réseaux disponibles. Malgré les progrès continuels, les systèmes intelligents mobiles et omniprésents (réseaux, dispositifs, logiciels et technologies de connexion) souffrent encore de diverses limitations à plusieurs niveaux tels que le maintien de la connectivité, la puissance de calcul, la capacité de stockage de données, le débit de communications, la durée de vie des sources d’énergie, l'efficacité du traitement de grosses tâches en termes de partitionnement, d'ordonnancement et de répartition de charge. Le développement technologique accéléré des équipements et dispositifs de ces modèles mobiles s'accompagne toujours de leur utilisation intensive. Compte tenu de cette réalité, plus d'efforts sont nécessaires à la fois dans la conception structurelle tant au matériel et logiciel que dans la manière dont il est géré. Il s'agit d'améliorer, d'une part, l'architecture de ces modèles et leurs technologies de communication et, d'autre part, les algorithmes d'ordonnancement et d'équilibrage de charges pour effectuer leurs travaux efficacement sur leurs dispositifs. Notre objectif est de rendre ces modèles omniprésents plus autonomes, intelligents et collaboratifs pour renforcer les capacités de leurs dispositifs, leurs technologies de connectivité et les applications qui effectuent leurs tâches. Ainsi, nous avons établi un modèle architectural autonome, omniprésent et collaboratif pour la périphérie des réseaux. Ce modèle s'appuie sur diverses technologies de connexion modernes telles que le sans-fil, la radiocommunication pair-à-pair, et les technologies offertes par LoPy4 de Pycom telles que LoRa, BLE, Wi-Fi, Radio Wi-Fi et Bluetooth. L'intégration de ces technologies permet de maintenir la continuité de la communication dans les divers environnements, même les plus sévères. De plus, ce modèle conçoit et évalue un algorithme d'équilibrage de charge et d'ordonnancement permettant ainsi de renforcer et améliorer son efficacité et sa qualité de service (QoS) dans différents environnements. L’évaluation de ce modèle architectural montre des avantages tels que l’amélioration de la connectivité et l’efficacité d’exécution des tâches. Advances in peer-to-peer and wireless communication technologies have increasingly enabled the integration of mobile and pervasive devices into distributed systems and computing architectures in the Internet of Things paradigm. Likewise, these devices are subject to continuous technological development. Thus, they always tend to be miniaturized, generation after generation during which they are considered as de facto devices. The success of this progress is the emergence of collaborative mobiles and pervasive computing, particularly integrated into the architectural models of the Internet of Things. The most important benefit of this form of computing is the ease of connecting a large number of pervasive and portable devices when they are on the move with different networks available. Despite the continual advancements that support this field, mobile and pervasive intelligent systems (networks, devices, software and connection technologies) still suffer from various limitations at several levels such as maintaining connectivity, computing power, ability to data storage, communication speeds, the lifetime of power sources, the efficiency of processing large tasks in terms of partitioning, scheduling and load balancing. The accelerated technological development of the equipment and devices of these mobile models is always accompanied by their intensive use. Given this reality, it requires more efforts both in their structural design and management. This involves improving on the one hand, the architecture of these models and their communication technologies, and, on the other hand, the scheduling and load balancing algorithms for the work efficiency. The goal is to make these models more autonomous, intelligent, and collaborative by strengthening the different capabilities of their devices, their connectivity technologies and the applications that perform their tasks. Thus, we have established a collaborative autonomous and pervasive architectural model deployed at the periphery of networks. This model is based on various modern connection technologies such as wireless, peer-to-peer radio communication, and technologies offered by Pycom's LoPy4 such as LoRa, BLE, Wi-Fi, Radio Wi-Fi and Bluetooth. The integration of these technologies makes it possible to maintain the continuity of communication in the various environments, even the most severe ones. Within this model, we designed and evaluated a load balancing and scheduling algorithm to strengthen and improve its efficiency and quality of service (QoS) in different environments. The evaluation of this architectural model shows payoffs such as improvement of connectivity and efficiency of task executions

Modelling and Optimizing Supply Chain Integrated Production Scheduling Problems

Globalization and advanced information technologies (e.g., Internet of Things) have considerably impacted supply chains (SCs) by persistently forcing original equipment manufacturers (OEMs) to switch production strategies from make-to-stock (MTS) to make-to-order (MTO) to survive in competition. Generally, an OEM follows the MTS strategy for products with steady demand. In contrast, the MTO strategy exists under a pull system with irregular demand in which the received customer orders are scheduled and launched into production. In comparison to MTS, MTO has the primary challenges of ensuring timely delivery at the lowest possible cost, satisfying the demands of high customization and guaranteeing the accessibility of raw materials throughout the production process. These challenges are increasing substantially since industrial productions are becoming more flexible, diversified, and customized. Besides, independently making the production scheduling decisions from other stages of these SCs often find sub-optimal results, creating substantial challenges to fulfilling demands timely and cost-effectively. Since adequately managing these challenges asynchronously are difficult, constructing optimization models by integrating SC decisions, such as customer requirements, supply portfolio (supplier selection and order allocation), delivery batching decisions, and inventory portfolio (inventory replenishment, consumption, and availability), with shop floor scheduling under a deterministic and dynamic environment is essential to fulfilling customer expectations at the least possible cost. These optimization models are computationally intractable. Consequently, designing algorithms to schedule or reschedule promptly is also highly challenging for these time-sensitive, operationally integrated optimization models. Thus, this thesis focuses on modelling and optimizing SC-integrated production scheduling problems, named SC scheduling problems (SCSPs). The objective of optimizing job shop scheduling problems (JSSPs) is to ensure that the requisite resources are accessible when required and that their utilization is maximally efficient. Although numerous algorithms have been devised, they can sometimes become computationally exorbitant and yield sub-optimal outcomes, rendering production systems inefficient. These could be due to a variety of causes, such as an imbalance in population quality over generations, recurrent generation and evaluation of identical schedules, and permitting an under-performing method to conduct the evolutionary process. Consequently, this study designs two methods, a sequential approach (Chapter 2) and a multi-method approach (Chapter 3), to address the aforementioned issues and to acquire competitive results in finding optimal or near-optimal solutions for JSSPs in a single objective setting. The devised algorithms for JSSPs optimize workflows for each job by accurate mapping between/among related resources, generating more optimal results than existing algorithms. Production scheduling can not be accomplished precisely without considering supply and delivery decisions and customer requirements simultaneously. Thus, a few recent studies have operationally integrated SCs to accurately predict process insights for executing, monitoring, and controlling the planned production. However, these studies are limited to simple shop-floor configurations and can provide the least flexibility to address the MTO-based SC challenges. Thus, this study formulates a bi-objective optimization model that integrates the supply portfolio into a flexible job shop scheduling environment with a customer-imposed delivery window to cost-effectively meet customized and on-time delivery requirements (Chapter 4). Compared to the job shop that is limited to sequence flexibility only, the flexible job shop has been deemed advantageous due to its capacity to provide increased scheduling flexibility (both process and sequence flexibility). To optimize the model, the performance of the multi-objective particle swarm optimization algorithm has been enhanced, with the results providing decision-makers with an increased degree of flexibility, offering a larger number of Pareto solutions, more varied and consistent frontiers, and a reasonable time for MTO-based SCs. Environmental sustainability is spotlighted for increasing environmental awareness and follow-up regulations. Consequently, the related factors strongly regulate the supply portfolio for sustainable development, which remained unexplored in the SCSP as those criteria are primarily qualitative (e.g., green production, green product design, corporate social responsibility, and waste disposal system). These absences may lead to an unacceptable supply portfolio. Thus, this study overcomes the problem by integrating VIKORSORT into the proposed solution methodology of the extended SCSP. In addition, forming delivery batches of heterogeneous customer orders is challenging, as one order can lead to another being delayed. Therefore, the previous optimization model is extended by integrating supply, manufacturing, and delivery batching decisions and concurrently optimizing them in response to heterogeneous customer requirements with time window constraints, considering both economic and environmental sustainability for the supply portfolio (Chapter 5). Since the proposed optimization model is an extension of the flexible job shop, it can be classified as a non-deterministic polynomial-time (NP)-hard problem, which cannot be solved by conventional optimization techniques, particularly in the case of larger instances. Therefore, a reinforcement learning-based hyper-heuristic (HH) has been designed, where four solution-updating heuristics are intelligently guided to deliver the best possible results compared to existing algorithms. The optimization model furnishes a set of comprehensive schedules that integrate the supply portfolio, production portfolio (work-center/machine assignment and customer orders sequencing), and batching decisions. This provides numerous meaningful managerial insights and operational flexibility prior to the execution phase. Recently, SCs have been experiencing unprecedented and massive disruptions caused by an abrupt outbreak, resulting in difficulties for OEMs to recover from disruptive demand-supply equilibrium. Hence, this study proposes a multi-portfolio (supply, production, and inventory portfolios) approach for a proactive-reactive scheme, which concerns the SCSP with complex multi-level products, simultaneously including unpredictably dynamic supply, demand, and shop floor disruptions (Chapter 6). This study considers fabrication and assembly in a multi-level product structure. To effectively address this time-sensitive model based on real-time data, a Q-learning-based multi-operator differential evolution algorithm in a HH has been designed to address disruptive events and generate a timely rescheduling plan. The numerical results and analyses demonstrate the proposed model's capability to effectively address single and multiple disruptions, thus providing significant managerial insights and ensuring SC resilience