275 research outputs found

    Serial-batch scheduling – the special case of laser-cutting machines

    Get PDF
    The dissertation deals with a problem in the field of short-term production planning, namely the scheduling of laser-cutting machines. The object of decision is the grouping of production orders (batching) and the sequencing of these order groups on one or more machines (scheduling). This problem is also known in the literature as "batch scheduling problem" and belongs to the class of combinatorial optimization problems due to the interdependencies between the batching and the scheduling decisions. The concepts and methods used are mainly from production planning, operations research and machine learning

    Energy-aware coordination of machine scheduling and support device recharging in production systems

    Get PDF
    Electricity generation from renewable energy sources is crucial for achieving climate targets, including greenhouse gas neutrality. Germany has made significant progress in increasing renewable energy generation. However, feed-in management actions have led to losses of renewable electricity in the past years, primarily from wind energy. These actions aim to maintain grid stability but result in excess renewable energy that goes unused. The lost electricity could have powered a multitude of households and saved CO2 emissions. Moreover, feed-in management actions incurred compensation claims of around 807 million Euros in 2021. Wind-abundant regions like Schleswig-Holstein are particularly affected by these actions, resulting in substantial losses of renewable electricity production. Expanding the power grid infrastructure is a costly and time-consuming solution to avoid feed-in management actions. An alternative approach is to increase local electricity consumption during peak renewable generation periods, which can help balance electricity supply and demand and reduce feed-in management actions. The dissertation focuses on energy-aware manufacturing decision-making, exploring ways to counteract feed-in management actions by increasing local industrial consumption during renewable generation peaks. The research proposes to guide production management decisions, synchronizing a company's energy consumption profile with renewable energy availability for more environmentally friendly production and improved grid stability

    Evolutionary Algorithms for Resource Constrained Project Scheduling Problems

    Full text link
    The resource constrained project scheduling problems (RCPSPs) are well-known challenging research problems that require efficient solutions to meet the planning need of many practical high-value projects. RCPSPs are usually solved using optimization problem-solving approaches. In recent years, evolutionary algorithms (EAs) have been extensively employed to solve optimization problems, including RCPSPs. Despite that numerous EAs have been developed for solving various RCPSPs, there is no single algorithm that is consistently effective across a wide range of problems. In this context, this thesis aims to propose a few new algorithms for solving different RCPSPs that include singular-resource and multiple-resource problems with single and multiple objectives. In general, RCPSPs are solved with an assumption that its activities are homogeneous, where all activities require all resource types. However, many activities are often singular, requiring only a single resource to complete an activity. Even though the existing algorithms that were developed for multi-resource problems, can solve this RCPSP variant with minor modifications, they are computationally expensive because they include some unnecessary resource constraints in the optimization process. In this thesis, at first, a problem with singular resource and single objective is considered. A heuristic-embedded genetic algorithm (GA) has been proposed for solving this problem, and it's effectiveness has been investigated. To enhance the performance of this algorithm, three heuristics are proposed and integrated with it. As there are no test problems available for singular resource problems, new benchmark problems are generated by modifying the existing multi-resource RCPSPs test set. As compared with experimental results of one of the modified algorithms and an exact solver, it was shown that the proposed algorithm achieved a better quality of solution while requiring a significantly smaller computational budget. The proposed algorithm is then extended to make it suitable for solving multi-resource cases with a single objective, which are known as traditional RCPSPs. A self-adaptive GA is developed for this problem. The proposed self-adaptive component of the algorithm selects an appropriate genetic operator based on their performance as the evolution progresses and increases. To judge the performance of this algorithm, small to large-scale problem instances have been solved from the PSP Library and the results are compared with state-of-the-art algorithms. Based on the experimental results, it was found that the proposed algorithm was able to obtain much better solutions than the non-self-adaptive GA. Furthermore, the proposed approach outperformed the state-of-the-art algorithms. In practice, cost of some resources varies with the day of the week or specific days in the month or year. To consider these day dependent costs, a new cost function is developed that is integrated with the usual cost fitness function in a multi-objective version of RCPSPs. Completion time is considered as the second objective. A heuristic-embedded self-adaptive multi-objective GA is proposed for both singular and multi-resource problems. In this algorithm, the selection mechanism is based on crowding distance and a reference point. A customized mutation operator is also introduced. The experimental results show that the proposed variant, with reference points-based selection, outperformed the variant, with crowding distance-based selection. In many situations, resource availability varies with time, such as time of the day and in some particular days. A dynamic multi-operators-based GA is proposed to deal with this variant. Along with the genetic operators, two local search methods are also included in the self-adaptive mechanism. The proposed approach has been validated using both large-scale singular and multi-resource problem instances with a single objective. Its experimental results demonstrate the efficiency of the proposed dynamic multi-operator-based approach. In summary, the proposed algorithms can solve different variants of RCPSPs that cover a broad spectrum of project scheduling problems, with significantly less computational tim

    Operational Research: methods and applications

    Get PDF
    This is the final version. Available on open access from Taylor & Francis via the DOI in this recordThroughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

    Two is better than one? Order aggregation in a meal delivery scheduling problem

    Get PDF
    We address a single-machine scheduling problem motivated by a last-mile-delivery setting for a food company. Customers place orders, each characterized by a delivery point (customer location) and an ideal delivery time. An order is considered on time if it is delivered to the customer within a time window given by the ideal delivery time , where is the same for all orders. A single courier (machine) is in charge of delivery to all customers. Orders are either delivered individually, or two orders can be aggregated in a single courier trip. All trips start and end at the restaurant, so no routing decisions are needed. The problem is to schedule courier trips so that the number of late orders is minimum. We show that the problem with order aggregation is -hard and propose a combinatorial branch and bound algorithm for its solution. The algorithm performance is assessed through a computational study on instances derived by a real-life application and on randomly generated instances. The behavior of the combinatorial algorithm is compared with that of the best ILP formulation known for the problem. Through another set of computational experiments, we also show that an appropriate choice of design parameters allows to apply the algorithm to a dynamic context, with orders arriving over time

    Operational research:methods and applications

    Get PDF
    Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

    Modelling and Optimizing Supply Chain Integrated Production Scheduling Problems

    Full text link
    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
    • …
    corecore