Near-optimal Schedules Research Articles

Near-Optimal Scheduling for IC Packaging Operations Considering Processing-Time Variations and Factory Practices

Due to the short life cycles of electronic products, trial run lots of new products are crucial in IC packaging for production verification and engineering adjustments. The processing time of trial run lots may differ significantly from production lots due to engineering adjustments and is difficult to predict without sufficient historical data. The purpose of this paper is to develop a framework for near-optimal scheduling of IC packaging considering processing time variations and factory practices. Using an orthogonal greedy algorithm and recurrent neural network, this framework extracts key features and predicts the processing times of trial run lots and production lots for each operation using these algorithms. After formulating the scheduling problem in an integer linear programming form, an ordinal optimization method is embedded within the decomposition and coordination framework to obtain dynamic and near-optimal schedules in a computationally efficient manner. This approach can improve the efficiency of IC packaging and potentially other operations by optimizing lot scheduling, reducing production tardiness, and increasing productivity in the factory, paving the way for self-optimizing factories in the future.

A genetic scheduling strategy with spatial reuse for dense wireless networks

Novel networking technologies such as massive Internet-of-Things and 6G-and-beyond cellular networks are based on ultra-dense wireless communications. A wireless communication channel is a shared medium that demands access control, such as proper transmission scheduling. The SINR model can improve the performance of ultra-dense wireless networks by taking into consideration the effects of interference to allow multiple simultaneous transmissions in the same coverage area and using the same frequency band. However, scheduling in wireless networks under the SINR model is an NP-hard problem. This work presents a bioinspired solution based on a genetic heuristic to solve that problem. The proposed solution, called Genetic-based Transmission Scheduler (GeTS) produces a complete transmission schedule optimizing size, increasing the number of simultaneous transmissions (i.e., spatial reuse) thus allowing devices to communicate as soon as possible. Simulation results are presented for GeTS, including a convergence test and comparisons with other alternatives. Results confirm the ability of the solution to produce near-optimal schedules.

HP3O algorithm-based all electric ship energy management strategy integrating demand-side adjustment

To tackle the energy management challenge that integrates power generation scheduling and demand-side adjustment for all-electric ship in uncertain marine environment, a hybrid penalized proximal policy optimization algorithm (HP3O)-based energy management strategy is proposed. First, demand-side adjustment, which involves adjusting the power of the ship's electric propulsion motors and flexible service loads, is integrated into the energy management problem. Second, HP3O algorithm is employed to obtain both continuous and discrete variables simultaneously. It utilizes a continuous actor network to obtain continuous variables, such as the generator's power and ship cruising speed, while employing a discrete actor network to determine discrete variables, i.e., the on/off status of the generators. Third, to handle complex constraints reasonably, the energy management problem is formulated as a constrained Markov decision process (CMDP), and an action mask mechanism is also integrated into the energy management framework to make agent's actions more reliable. The simulation results of an all-electric cruise ship validate the effectiveness and superiority of the proposed strategy in achieving near-optimal scheduling while satisfying operation constraints. Furthermore, a case study on a hybrid diesel-electric ferry confirms its generalization performance.

Energy Efficient Task Scheduling Strategy using Modified Coot Optimization Algorithm for Cloud Computing

Cloud computing (CC) refers to a current computing method that provides the virtualization of computing services as a utility to Cloud service users. Problems based on ineffective task mapping to cloud resource frequently happen in a cloud atmosphere. Task scheduling (TS), thus, means effective scheduling of rational allocation and computational actions of computing resource in certain limitations in the IaaS cloud network. Job scheduling was to allocate tasks to the most appropriate sources to reach more than one goal. Thus, choosing a suitable work scheduling technique for rising CC resource efficiency, whereas maintaining high quality of service (QoS) assurances, becomes a significant problem that remains to attract interest of researchers. Metaheuristic techniques shown remarkable efficacy in supplying near-optimal scheduling solutions for a complicated large-sized issues. Recently, a rising number of independent scholar has examined the QoS rendered by TS approaches. Therefore, this study develops an Energy Efficient Task Scheduling Strategy using Modified Coot Optimization Algorithm (EETSS-MCOA) for CC environment. The EETSS-MCOA method carries out the derivation of features and MCOA is applied to schedule tasks. In addition, the MCOA algorithm is derived by the combination of adaptive β hill climbing concept with the COA for enhanced task scheduling. The conventional COA is stimulated by the swarming characteristics of birds known as coots. The COA followed two distinct stages of bird movements on water surface. The experimental results of the EETSS-MCOA model are validated on CloudSim tool. The solutions attained by the EETSS-MCOA model are found to be better than the existing algorithms.

A study of concurrent multi-frontal solvers for modern massively parallel architectures

Leveraging Trace Theory, we investigate the efficient parallelization of direct solvers for large linear equation systems. Our focus lies on a multi-frontal algorithm, and we present a methodology for achieving near-optimal scheduling on modern massively parallel machines. By employing trace theory with Diekert Graphs and Foata Normal Form, we rigorously validate the correctness of our proposed solution. To establish a strong link between the mesh and elimination tree of the multi-frontal solver, we conduct extensive testing on matrices derived from the Finite Element Method (FEM). Furthermore, we assess the performance of computations on both GPU and CPU platforms, employing practical implementation strategies.

Optimized variable resource allocation framework for scheduling of fast-track industrial construction projects

Efficient scheduling and resource allocation for large-scale industrial projects is challenging due to their size and complexity, especially with fast-track contracts, which often lack detailed information during the early planning phase. This paper introduces a data-driven workface planning framework to enhance scheduling and resource allocation while accommodating uncertainties and constraints (e.g., minimum and maximum resource allocation curves, dynamic predecessor relationships, congestion limits). This framework employs an integrated approach, combining time-stepped simulation with graph-based optimization. By leveraging historical data and expert knowledge, the data-driven framework mitigates certain subjective assumptions, including durations and resource allocations. In practical application, the framework generates near-optimal schedules, even with limited information. Applying the framework to a fast-track industrial construction project case study demonstrated enhanced resource allocation. These findings offer practical benefits to industrial projects regarding time and cost savings and serve as a foundation for future research in data-driven project planning approaches.

Near-Optimal Scheduling for Crowdsourced Transit System With Skip-Stop Tactic

Efficient bus scheduling is a crucial component for the improvement of public transit services. Without systematic optimization and efficient shift arrangement, the bus scheduling system may suffer from poor vehicle loading rate or crowd onboard, resulting in wasted energy and passenger dissatisfaction. In this paper, we consider a crowdsourced bus service system (on a fixed route) that receives user requests as input and computes the scheduling of buses with flexible departure time and skip-stop to minimize the travel time of users. We first show that the general problem of computing the optimal scheduling is NP-hard. On the other hand, for the case when skip-stop is not adopted, we propose the Optimized Departure Time (ODT) algorithm that computes optimal scheduling. Our algorithm is built on an innovative reduction of the problem to some variants of the k-clustering problem and an efficient application of dynamic programming. On top of ODT, we further improve the effectiveness of the solution by utilizing the power of skip-stop tactic, named ODTS. Our experimental results demonstrate that ODT and ODTS dramatically outperform existing algorithms for the bus scheduling problem in terms of effectiveness and efficiency. Moreover, the solutions given by ODTS are very close to the optimum.

Scheduling a set of jobs with convex piecewise linear cost functions on a single-batch-processing machine

We investigate a general single-batch-processing machine scheduling problem of minimizing the total costs of all jobs with general convex piecewise linear cost functions, where the processing time, due dates, and sizes of jobs are non-uniform. Since the studied problem is NP-hard and the objective is irregular, we adopt a practical 3-step method that starts from a specific job sequence, finds the optimal and near-optimal schedules of the given job sequence, and then iteratively improves the schedules. For the restricted problem where the job sequence is given, we propose a dynamic programming algorithm to obtain the optimal schedule. To further reduce the running time, we also devise a span-limit tree search (SLTS) approach to find the near-optimal schedule. We then design a local search method to improve the solutions solved by SLTS. Finally, we design a branch and bound algorithm with a lower bound method for small-sized instances. All proposed methods are based on our analysis of the mathematical properties of the scheduling problem, and extensive numerical experiments are conducted to demonstrate their effectiveness and efficiency.

Optimization on operation sorting for HLS scheduling algorithms

High-Level Synthesis (HLS) automates hardware design from high-level language descriptions, enhancing design efficiency and reducing cycles. Operation scheduling, a critical step in HLS, determines system latency. Due to the NP-hard nature of scheduling problem, existing algorithms adopt heuristic approaches, scheduling operations sequentially. In this paper, we present a rigorous proof establishing the direct correspondence between the balanced state and the optimal scheduling. Additionally, we demonstrate the existence of operation scheduling sequences that achieve optimal results. Building on these findings, we propose a resource-constrained list scheduling algorithm. We introduce a novel priority function for the classic list scheduling algorithm to determine the scheduling order. Furthermore, we extend our approach to enhance the latency-constrained entropy-directed scheduling algorithm by altering the scheduling order. Experimental results validate the effectiveness of our novel priority function in addressing both resource-constrained and latency-constrained scheduling problems. Our algorithm achieves a near-optimal scheduling solution while maintaining low time complexity.

Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network

Smart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting, optimization, and control/scheduling algorithms that can handle the uncertain nature of demand and renewable generation. This paper proposes an advanced ML algorithm, called Recurrent Trend Predictive Neural Network based Forecast Embedded Scheduling (rTPNN-FES), to provide efficient residential demand control. rTPNN-FES is a novel neural network architecture that simultaneously forecasts renewable energy generation and schedules household appliances. By its embedded structure, rTPNN-FES eliminates the utilization of separate algorithms for forecasting and scheduling and generates a schedule that is robust against forecasting errors. This paper also evaluates the performance of the proposed algorithm for an IoT-enabled smart home. The evaluation results reveal that rTPNN-FES provides near-optimal scheduling 37.5 times faster than the optimization while outperforming state-of-the-art forecasting techniques.

GIẢI THUẬT TIẾN HÓA CHO BÀI TOÁN LẬP LỊCH VỚI TÀI NGUYÊN GIỚI HẠN MỚI

This paper proposes and states a novel problem called TDOS-RCPSP, a general case of the classical RCPSP problem. TDOS-RCPSP problem has many applications in science, especially in industrial production lines such as car assembly and textiles. In this study, the TDOS-RCPSP problem is proved to be NP-Hard. Besides, like other scheduling problems, TDOS-RCPSP will be classified and represented by Graham notation. Since the TDOS-RCPSP is NP-hard, a new evolutionary algorithm based on the Cuckoo Search strategy is proposed to find near-optimal schedules in a reasonable computation time. To evaluate the proposed algorithm, we perform an evaluation study using two datasets containing the iMOPSE dataset, the datasets of previous algorithms, and the TNG industrial sewing dataset. The experimental results show that the proposed algorithm has better performance than previous algorithms.

A stochastic programming approach to surgery scheduling under parallel processing principle

Parallel processing is a principle which enables simultaneous implementation of anesthesia induction and operating room (OR) turnover with the aim of improving OR utilization. In this article, we study the problem of scheduling surgeries for multiple ORs and induction rooms (IR) that function based on the parallel processing principle under uncertainty. We propose a two-stage stochastic mixed-integer programming model considering the uncertainty in induction, surgery and turnover durations. We sequence patients and set appointment times for surgeries in the first stage and assign patients to IRs at the second stage of the model. We show that an optimal myopic policy can be used for IR assignment decisions due to the special structure of the model. We minimize the expected total cost of patient waiting time, OR idle time and IR idle time in the objective function. We enhance the model formulation using bounds on variables and symmetry-breaking constraints. We implement a novel progressive hedging algorithm by proposing a penalty update method and a variable fixing mechanism. Based on real data of a large academic hospital, we compare our solution approach with several scheduling heuristics from the literature. We assess the additional benefits and costs associated with the implementation of parallel processing using near-optimal schedules. We examine how the benefits are inflated by increasing the number of IRs. Finally, we estimate the value of stochastic solution to underline the importance of considering uncertainty in durations.

A makespan minimization problem for versatile developers in the game industry

Today, the development of a modern video game draws upon multiple areas of expertise. Moreover, its development cost could be as high as tens of millions of dollars. Consequently, we should carefully schedule its jobs so as not to increase the total cost. However, project leaders traditionally treat developers alike or even schedule all the jobs manually. In this study, we consider a versatile-developer scheduling problem. The objective is to minimize the makespan of a game project. We propose a branch-and-bound algorithm (B&B) to generate the optimal schedules for small problem instances. On the other hand, an imperialist competitive algorithm (ICA) is proposed to obtain approximate schedules for large problem instances. Lastly, computational experiments are conducted to show the performances of both algorithms. When the problem size is small (e.g., n ≤ 12), B&B can generate the optimal schedules within 5 s. For some large problem instances (e.g., n = 600), near-optimal schedules can be obtained by ICA within 10 min. The final results imply that both algorithms converge quickly and are of high solution quality.

A Two-Stage Method of Dimensioning and Scheduling Service Providers under Patient Demand Uncertainty.

Many researchers have studied the problem of dimensioning service providers and making shift schedules and have proposed various methods to solve it. Considering the importance and complexity of health care, this research is conducted through the integrated dimensioning and scheduling of service providers under patient demand uncertainty. In the first stage, a robust approach is adopted to determine the minimum number of required service providers. In the second stage, a monthly schedule is devised for service providers, and a two-stage stochastic program is used to solve the problem. To this end, an improved sample average approximation method considers different contracts and skills to determine a near-optimal schedule by minimizing the service providers' regular working hours, overtime, and penalties for idle hours. In the first stage, considering the highest level of conservatism, equal to 7.6, a 19.38% cost increase is created compared to the nominal problem. In the second stage, by applying different clustering methods in the SAA algorithm and comparing them, the k-means++ algorithm obtains a good upper and lower bound and achieves a near-optimal solution in the shortest time. This research deals with the Iranian Health Control Center as a case study. The proposed method can yield the appropriate number of service providers based on monthly workloads and make the least undesirable schedules for service providers. Hence, managers can overcome patient issues' uncertainty by assigning various service providers to each scheduling period.

Reliability Versus Latency in IIoT Visual Applications: A Scalable Task Offloading Framework

In Industrial Internet of Things (IIoT), reliability and latency are two important performance indicators. However, these two performance indicators are contradictory with each other, which are difficult to be enhanced simultaneously. In reality, many IIoT applications are in video or image format with critical requirements of both reliability and latency. In this article, we propose a scalable task offloading scheme for IIoT visual applications considering the unique scalable feature compared with general content. The proposed scheme demonstrates that partial content can be adaptively offloaded to a specific computing node to meet the reliability and latency requirements, meanwhile obtaining an excellent tradeoff between them. For optimization, a utility function is defined to characterize the tradeoff between reliability and latency. Then, a greedy algorithm is introduced to solve the problem of maximizing the utility function, where a near-optimal scheduling policy is adopted to achieve offloading association and properly offload data volume. Simulation results reveal that the proposed scalable task offloading scheme performs better than other benchmark schemes in balancing reliability and latency in IIoT visual applications, especially when the network traffic is moderate and channel conditions are undesirable.

A String of Disjoint Job Block with Processing Time Associated with Probability in Two-Stage Weighted Open Shop Model

Open-shop scheduling problem (OSSP) is a well-known topic with wide industrial applications which belongs to one of the vital issues in the field of engineering. This paper deals with a two-stage open shop scheduling problem in which the processing time of jobs is allied with probabilities. The concept of a string of two job blocks which are disjoint in nature is considered so that the first block covers the jobs with a fixed route and the second block covers the jobs with an arbitrary path. Further, the weights of jobs are also introduced due to their applicability and relative importance in the real world. The objective of this study is to propose a heuristic which on execution, provides an optimal or near-optimal schedule to diminish the makespan. Several numerical illustrations are produced in MATLAB 2018a to demonstrate the effectiveness of the proposed approach, and to confirm the performance, the results are compared with the existing methods developed by Johnson and Palmer.

Near Interference-Free Space-Time User Scheduling for mmWave Cellular Network

The highly directional beams applied in millimeter wave (mmWave) cellular networks make it possible to achieve near interference-free (NIF) transmission under judiciously designed space-time user scheduling, where the power of intra-/ inter-cell interference between any two users is below a predefined threshold. In this paper, we investigate two aspects of the NIF space-time user scheduling in a multi-cell mmWave network with multi-RF-chain base stations. Firstly, given that each user has a requirement on the number of space-time resource elements, we study the NIF user scheduling problem to minimize the unfulfilled user requirements, so that the space-time resources can be utilized most efficiently and meanwhile all strong interferences are avoided. A near-optimal scheduling algorithm is proposed with performance close to the lower bound of unfulfilled requirements. Furthermore, we study the joint NIF user scheduling and power allocation problem to minimize the total transmit power under the constraint of rate requirements. Based on our proposed NIF scheduling, an energy-efficient joint scheduling and power allocation scheme is designed with limited channel state information, which outperforms the existing independent set based schemes, and has near-optimal performance as well.

Stochastic scheduling of chemotherapy appointments considering patient acuity levels

The uncertainty in infusion durations and non-homogeneous care level needs of patients are the critical factors that lead to difficulties in chemotherapy scheduling. We study the problem of scheduling patient appointments and assigning patients to nurses under uncertainty in infusion durations for a given day. We consider instantaneous nurse workload, represented in terms of total patient acuity levels, and chair availability while scheduling patients. We formulate a two-stage stochastic mixed-integer programming model with the objective of minimizing expected weighted sum of excess patient acuity, waiting time and nurse overtime. We propose a scenario bundling-based decomposition algorithm to find near-optimal schedules. We use data of a major university hospital to generate managerial insights related to the impact of acuity consideration, and number of nurses and chairs on the performance measures. We compare the schedules obtained by the algorithm with the baseline schedules and those found by applying several relevant scheduling heuristics. Finally, we assess the value of stochastic solution.

Simple Heuristics for Near-Optimal Appointment Scheduling in Primary Care.

In primary care allocating appointments to sequential requests can result in sub-optimal scheduling. Optimal scheduling requires hiring of consultants to analyze historical patterns. Many practices focus their resources on larger problems instead of optimizing appointment schedules. We simulate simple heuristics to compare their performance with optimal schedules uncovered using offline optimization models. We use uncapacitated appointment calendars for a nationally representative heterogeneous primary care panel to meet all patients' requests. The stochastic nature of appointment requests gives a distribution for daily appointments and for the uncovered optimal capacity. The First Minimum heuristic gives near-optimal schedules and can be easily implemented in small practices using pen-and-paper, without any investment in computer-systems.

Centralized and hierarchical scheduling frameworks for copper smelting process

Optimal scheduling of copper smelting process is an ongoing challenge due to conflicting objectives of the various process units and the inter-dependencies that exist among these units. To design a scheduling framework, two potential alternatives – centralized and hierarchical approaches – can address those inter-dependencies in this process. These approaches represent the two extremes and the choice depends on the accuracy, reliability, and complexity of the scheduling task. In this study, optimization-based centralized and hierarchical scheduling frameworks are developed to find an optimal schedule for the smelting process, considering the inter-dependencies among process units. We propose a practical and effective coordination scheme for the hierarchical framework that finds a near-optimal schedule with reasonable computational demands. Two case studies are presented to demonstrate that the proposed hierarchical framework is capable of finding a near plant-wide optimum for the copper smelting process and it can be used in similar plant-wide scheduling applications.