Final Schedule Research Articles

Scheduling jobs using queries to interactively learn human availability times

The solution to a job scheduling problem that involves humans as well some other shared resource has to consider the humans’ availability times. For practical acceptance of a scheduling tool, it is crucial that the interaction with the humans is kept simple and to a minimum. It is rarely practical to ask users to fully specify their availability times or to let them enumerate all possible starting times for their jobs. In the scenario we are considering, users initially only propose a single starting time for each of their jobs and a feasible and optimized schedule shall then be found within a small number of interaction rounds. In each such interaction round, our scheduling approach may propose each user a small number of alternative time intervals for scheduling the user’s jobs, and then the user may accept or reject these. To make the best out of these limited interaction possibilities, we propose an approach that utilizes integer linear programming and an exact and computationally efficient probability calculation for the users’ availabilities assuming two different stochastic models. In this way, educated proposals of alternative time intervals for performing the jobs are determined based on the computed availability probabilities and the improvements these time intervals would enable. The approach is experimentally evaluated on a variety of artificial benchmark scenarios, and different variants are compared with each other and to diverse baselines. Results show that an initial schedule can usually be quickly improved over few interaction rounds even when assuming a quite simple stochastic model, and the final schedule may come close to the solution of the full-knowledge case despite the strongly limited interaction.

Exploring fair scheduling aspects – Through final exam scheduling

AbstractFair treatment of individuals in a scheduling task is essential. Unfairness can cause dissatisfaction among workers, faster obsolescence of work tools and underutilization of others. The literature's definitions vary, and there is no clear definition of general scheduling tasks.This article explores fair scheduling through the lens of final exams, aiming to extend decision support system methodologies. It proposes a method based on Lipschitz mapping to measure fairness and presents a pseudo-algorithm for estimating optimal trend lines.The model and the algorithm are demonstrated using the example of final exam schedules. In this way, two feasible solutions can be measured and compared in terms of fairness.

Final Examination Time Table Scheduling using Integer Programming with Average Time Slot Gap Minimization

Final Examination Time Table Scheduling using Integer Programming with Average Time Slot Gap Minimization

A study on constructing an efficient examination scheduling system

The objective of this study is to investigate the final exam scheduling process at the Ho Chi Minh City Open University and develop an automated exam scheduling application. Our primary objectives are to prevent students from having conflicting exam schedules and to ensure that no student has to take more than two exams on the same day. This research focuses on applying graph coloring algorithms to the problem of automatic exam scheduling. Our research findings indicate that the graph coloring algorithm is highly effective for automated exam scheduling. This study has the potential to expand and support the development of an automatic exam scheduling and management system, in line with our overall goals. We conduct the experiments on the practical data at HCMCOU and obtain promising results.

A Dynamic Multi-objective Scheduling Approach for Gradient-Based Reinforcement Learning

In manufacturing dynamic scheduling is a complex task which is influenced by various factors, including several possibly contrary optimization objectives. While multi-objective optimization approaches are not novel anymore, prevailing deep learning solutions lack in the ability to dynamically adjust the preferences of the different objectives. For this reason, we developed a collaborate human-AI scheduling algorithm where Reinforcement Learning (RL) is utilized for the optimization of single objectives, while the human is left in control to flexibly mix and weight the influence of each objective into the final schedule. The approach performs on par with prevailing RL multi-objective approaches. However, it surpasses the later in its ability to dynamically adjust the preferences of the objectives without retraining.

Rolling horizon policies for multi-stage stochastic assemble-to-order problems

Assemble-to-order approaches deal with randomness in demand for end items by producing components under uncertainty, but assembling them only after demand is observed. Such planning problems can be tackled by stochastic programming, but true multistage models are computationally challenging and only a few studies apply them to production planning. Solutions based on two-stage models are often short-sighted and unable to effectively deal with non-stationary demand. A further complication may be the scarcity of available data, especially in the case of correlated and seasonal demand. In this paper, we compare different scenario tree structures. In particular, we enrich a two-stage formulation by introducing a piecewise linear approximation of the value of the terminal inventory, to mitigate the two-stage myopic behaviour. We compare the out-of-sample performance of the resulting models by rolling horizon simulations, within a data-driven setting, characterised by seasonality, bimodality, and correlations in the distribution of end item demand. Computational experiments suggest the potential benefit of adding a terminal value function and illustrate interesting patterns arising from demand correlations and the level of available capacity. The proposed approach can provide support to typical MRP/ERP systems, when a two-level approach is pursued, based on master production and final assembly scheduling.

Scheduling operations in a large hospital by multiple agents

The scheduling of operations in a large hospital is performed jointly by several groups of people, each with its own objective and constraints. It is a two-phase process, starting with the allocation of operating rooms to wards, and followed by the scheduling of operations in each operating room of the hospital on each day. The final schedule must satisfy all inter-ward hard constraints, such as the allocation of anesthetists, nurses, and equipment to operations that are taking place in parallel, and ideally, it should also address soft constraints such as taking the urgency and complexity of operations into consideration.This study contributes to the ongoing effort of adapting multi-agent optimization models and algorithms to real-world applications by modeling the problems in both phases as distributed constraint optimization problems (DCOPs), with different properties. The first phase includes partially cooperative ward-representing agents, allocating operating rooms for daily usage among themselves. In the second phase, ward-representing agents interact with agents representing constraining elements, in order to generate daily operation schedules for each operating room, thus forming a unique bipartite constraint graph. On one side are the ward representatives, while on the other are the agents representing the constraining resources. Each agent has a non-trivial local problem to solve, and its solution serves as the proposed assignment in the distributed algorithm.The study begins by discussing the properties required of the algorithms needed to solve the two phases. It then proposes adjustments to existing distributed partially cooperative algorithms and local search algorithms to solve these problems, and compares the results of different variants of these algorithms. The results obtained for both phases emphasize that successful collaboration is predicated on two requirements: that agents hold consistent information regarding their peers’ states and that the degree of exploration undertaken by the algorithm is restricted in order to produce high-quality solutions.

RANCANG BANGUN SISTEM INFORMASI TUGAS AKHIR MENGGUNAKAN METODE WATERFALL DI FAKULTAS TEKNIK UNIVERSITAS ISLAM NUSANTARA

One of the graduation requirements to get a bachelor's degree is to complete the final project. The implementation of the final assignment consists of several stages, starting with the submission of the proposal, proposal presentations, final project guidance, final project presentations, and final project defense. The Faculty of Engineering (FTEK) is one of the faculties at the Nusantara Islamic University (UNINUS). The implementation of the final assignment at FTEK UNINUS has not been well integrated because it still uses several different tools at each stage, making proposal seminar schedules, final project presentation schedules, and final project defense schedules is also still done manually by education staff (tendik). One of the ways to integrate the implementation of the final project is through information system development. Therefore, a website-based final project information system application was developed using the waterfall software development method with UML, the JavaScript programming language using the NextJS framework, and the PostgreSQL database. The result of this study is the final project information system website application, which is expected to help related parties at FTEK UNINUS implement their final project step by step

Port integrated scheduling under uncertain operation time and cascade effects: A complex network structure entropy solution

The integrated scheduling of quay cranes, yard cranes, and internal vehicles in a port allows for further optimization of resource allocation on a global scale. Since the scheduling objects are all mobile equipment and their mutual waiting time needs to be reduced as much as possible, new requirements are brought for integrated scheduling. Uncertain operation time is a common phenomenon in ports. How to ensure the effectiveness and robustness of integrated schedules under uncertainties is worth studying. Moreover, although the cascading effects of operation time fluctuation is an important factor affecting the robustness of schedules, less attention has been paid to it. To solve this problem, firstly, a mixed integer nonlinear programming model for integrated scheduling in a port that allows internal vehicles to travel deep into the yard is formulated. A genetic algorithm for solving the model is designed. Then, “gaps” between adjacent operations in schedules are focused on, and the complex network structural entropy theory is used to construct an anti-cascade effect and robustness evaluation index of schedules. In addition, a scheduling plan generation framework that gives a way to use the evaluation index is proposed. Specifically, the anti-cascade effect and robustness evaluation indices of scheduling plans whose objective function values are within a given threshold are calculated, and the final scheduling plan with the largest index is selected. Finally, the feasibility and effectiveness of the proposed method are verified by extensive experiments.

Multi-Timescale Optimal Operation Strategy for Renewable Energy Power Systems Based on Inertia Evaluation

To enhance the operational dependability of renewable energy power systems with high proportions, this study proposes a multi-timescale optimization strategy based on the inertia evaluation model. Firstly, the inertia evaluation model is established based on the factors influencing the inertia demand of the power system, and the concept of the inertia margin coefficient is introduced. Secondly, to address the uncertainties associated with sustainable energy output and the cost of carbon emissions, a multi-timescale optimization operation model is formulated for day-ahead, intraday, and real-time operations, aimed at economic optimization. The output status of each unit is obtained and adjusted in a timely manner in the next stage, while meeting the system’s inertia demand, to derive the final scheduling strategy. Lastly, a sensitivity analysis of the inertia margin coefficient is conducted through simulations to validate the effectiveness and cost-efficiency of the proposed scheduling strategy.

Computer-Aided Generation of N-shift RWS

Generating schedules for shift workers is essential for many employers, whether the employer is a small or a large industrial complex, research laboratory, or other businesses involving shift works. Previous methods for creating rotational workforce schedules included interactions between the schedule maker and the algorithm, including defining the length of sequences of consecutive days of working shifts. In this method, an algorithm takes into account inputs (or constraints) from the schedule maker and then presents the possible solutions (incl. that all shifts must be filled, working hours per week, minimal resting time, etc.) in a first phase. The schedule maker can then select which solutions are most feasible to proceed with in the second phase, where the final schedules are then constructed and exported.

Dual-layer self-healing strategy for standalone building energy systems: A case study of a tropical island

Due to dynamic building demand and environment, the state of each energy subsystem changes frequently in an integrated energy system (IES), which seriously threatens its operation security. Reliable self-healing operation ability is guarantee of stable energy supply. A dual-layer self-healing strategy considering decision boundaries is proposed to maximize resilience for stand-alone IES. In disturbance identification stage, pressure and release risk model is adopted to detect anomalies and risk state. In scheduling decision stage, decision boundaries of device layer resolve internal strategies conflict within a single device. Decision boundaries of system layer coordinate task and energy allocation among different devices. In scheduling response stage, final scheduling decision is performed to prevent and mitigate impact of potential threats on IES. Results indicate that dual-layer self-healing strategy improves system resilience by 54 % – 163 % in eight fault scenarios compared to fault isolation method. It also has advantages in energy supply reliability, fault absorption and energy storage capacity. Moreover, reduction of decision repetition rate speeds up the real-time scheduling efficiency. The proposed self-healing strategy helps system achieve stronger adaptability and reliability under dynamic conditions. It has a positive effect on practical application of IES for buildings in isolated areas.

A modular fuzzy expert system for chemotherapy drug dose scheduling

This study presents a modular Fuzzy Expert System (FES) for chemotherapy drug dose scheduling. The computational model considers the patient’s Body Surface Area (BSA) and experts’ opinions to calculate chemo doses following the clinical practice. A proper balance between reducing cancerous cells and toxic side effects is required for effective drug scheduling. Still, in many cases, traditional clinical approaches fail to determine appropriate therapeutical doses that balance all restrictions. In our proposed system, FES-1 is developed to determine primary drug doses based on experts’ opinions and competing treatment objectives. To adjust the dose, FES-2 is developed based on clinical practices, the patient’s BSA, and experts’ opinions. The final chemotherapy drug dose schedule is generated by combining the outputs of FES-1 and FES-2, which is the proposed modular FES. A growth model is used in this work to observe response due to administration of chemotherapy drug doses and to determine the following doses by considering cancer patients’ three weight patterns (increasing, decreasing, and random order). Extensive simulation results and comparative assessment with other current computational chemotherapy drug scheduling models validate the effectiveness and the superiority of the model proposed in this study over the other methods reported in relevant studies.

The Optimization Model of Final Project Examination Scheduling in Two Different Locations: A Case Study

The Nursing Undergraduate Study Program of the Vocational Faculty of Universitas Airlangga has two classes in two different cities: Gresik and Lamongan. The final project examination scheduling in the study program should be scheduled simultaneously to minimize the round-trip of the examiner. Currently, the scheduling process is done manually without a certain systematic method or approach. In this paper, an optimization model is developed to solve the scheduling problem. Several factors are considered in the model, namely the number of examiners, examination room availability, and the home campus of eachexaminer. The model employed an integer programming approach with the objective function of maximizing the totalassignment of the examiners in the time slot. The model cansolve the scheduling problem more efficiently than manual scheduling.

Bicriteria multi-machine scheduling with equal processing times subject to release dates

<abstract><p>This paper addresses the problem of scheduling $ n $ equal-processing-time jobs with release dates non-preemptively on identical machines to optimize two criteria simultaneously or hierarchically. For simultaneous optimization of total completion time (and makespan) and maximum cost, an algorithm is presented which can produce all Pareto-optimal points together with the corresponding schedules. The algorithm is then adapted to solve the hierarchical optimization of two min-max criteria, and the final schedule has a minimum total completion time and minimum makespan among the hierarchical optimal schedules. The two algorithms provided in this paper run in $ O(n^3) $ time.</p></abstract>

INFORMATION TECHNOLOGY FOR THE SCHEDULE GENERATION BASED ON THE ALGEBRA OF ADDITIVE-DISJUNCTIVE FORMS AND THE MODIFIED METHOD OF PERMANENT DECOMPOSITION

To improve the information technology of drawing up class schedules, there is a need to develop methods that allow significantly reduce the number of combinatorial objects in the process of algorithms for generating schedules matrices. For example, the result of applying the method of permanent decomposition is a collection of combinatorial objects - permutations, combinations, and placements. For the task of drawing up lesson schedules in the part of forming timetable matrices, the method provides a memory-recorded set of all possible systems of various representatives of sets, which are the columns of the timetable matrices (SRPS). Since the algorithm of permanent decomposition gives all possible SRPS, it creates the problem of forming the final schedule based on SRPS or all possible variants of schedules and requires the development of special algorithms. Certain known approaches to solving such a problem are associated with significant computational complexity in the general case. This also applies to the approach based on the order relation of the set of decomposition matrices.
 The basis of the information technology proposed in the work is the further modification of the incidence matrices and, accordingly, such a modification of the permanent decomposition method, which allows generating ready versions of the schedule matrices at the output. This is achieved due to the introduction of a special algebra of additive-disjunctive forms and, accordingly, the possibility of generating such forms in the process of permanent decomposition. In fact, in this context, ADF is a formal representation of a ready-made version of an admissible schedule that satisfies some additional requirements.

Integrated automated guided vehicle dispatching and equipment scheduling with speed optimization

The loading and unloading processes at quay and yard cranes are often studied separately, despite that integrated scheduling of the two processes could benefit in improving productivity and increasing profit. We consider an automated container terminal, in which the discharging and loading tasks at quay and yard cranes are fulfilled by a homogeneous fleet of automated guided vehicles (AGVs), and the traveling speed of AGVs can be adjusted. We formulate the problem into a mixed integer programming model that minimizes the total makespan of cranes and energy consumption of AGVs simultaneously. To address the problem, we develop a three-phase approach, where the problem is firstly reformulated as a digraph, and then a adjusting heuristic and a branch-and-bound procedure are used to optimize the assignment of tasks to AGVs and yard cranes. After the aforementioned two steps, the speed optimization process is executed to obtain the final schedule. Numerical results demonstrate the efficiency and effectiveness of the proposed approach and also justify the value of taking account of the AGVs speed optimization.

A Heuristic Construction Neural Network Method for the Time-Dependent Agile Earth Observation Satellite Scheduling Problem

The agile earth observation satellite scheduling problem (AEOSSP), as a time-dependent and arduous combinatorial optimization problem, has been intensively studied in the past decades. Many studies have proposed non-iterative heuristic construction algorithms and iterative meta-heuristic algorithms to solve this problem. However, the heuristic construction algorithms spend a relatively shorter time at the expense of solution quality, while the iterative meta-heuristic algorithms accomplish a high-quality solution with a lot of time. To overcome the shortcomings of these approaches and efficiently utilize the historical scheduling information and task characteristics, this paper introduces a new neural network model based on the deep reinforcement learning and heuristic algorithm (DRL-HA) to the AEOSSP and proposes an innovative non-iterative heuristic algorithm. The DRL-HA is composed of a heuristic construction neural network (HCNN) model and a task arrangement algorithm (TAA), where the HCNN aims to generate the task planning sequence and the TAA generates the final feasible scheduling order of tasks. In this study, the DRL-HA is examined with other heuristic algorithms by a series of experiments. The results demonstrate that the DRL-HA outperforms competitors and HCNN possesses outstanding generalization ability for different scenario sizes and task distributions. Furthermore, HCNN, when used for generating initial solutions of meta-heuristic algorithms, can achieve improved profits and accelerate interactions. Therefore, the DRL-HA algorithm is verified to be an effective method for solving AEOSSP. In this way, the high-profit and high-timeliness of agile satellite scheduling can be guaranteed, and the solution of AEOSSP is further explored and improved.

Completion and Installation of the ITER Lower Poloidal Field Coils PF5 & 6

The poloidal magnetic field of ITER is provided by 6 Niobium–Titanium (NbTi) coils mainly for plasma shaping and position control. The fabrication of the two lower coils PF6 from China ASIPP and PF5 from European F4E has been completed and the coils installed in the bottom of the Tokamak pit in 2021. This paper presents the challenge of the manufacturing process, which involves manufacturing of several dozens of superconducting double pancakes, requiring bending, insulating and welding steps to keep accuracy of a few mm on dimensions up to 25m. The reproducibility and reliability of the manufacturing processes to manage magnet winding/insulation and cryogenic skills are essential for final quality and schedule robustness. After having successfully completed final thermal cycles for site acceptance in Cadarache, the PF6 and PF5 have been delivered simultaneously and handed over to IO. This paper demonstrates the assembly scenario of the two coils, including assembly tools for transportation, preparation for lifting, lifting tooling design, temporary supports design in cryostat and alignment of positioning on supports from assembly hall to the tokamak pit. The coils are sensitive components and preservation steps are needed to protect them in the pit during other assembly operations. In future, the two coils will sit in pit for several years until mounted onto the TF coils through the support then connected to feeders at the coil terminals. PF6, PF5 will firstly rest on temporary supports, and later shift up by jacks and conical guides to align onto TFC. The alignment & shimming processes and tightening will be presented to show the practical functionality and avoidance of interferences with the other components of the ITER tokamak which achieving high accuracy to avoid error fields.

Stochastic Matrix Modelling and Scheduling Algorithm of Distributed Intelligent Computing System

Parallel and distributed processing has always been a hot field of scientific and technological research, development, and application. It is an important solution in the fields of scientific computing and data service processing, such as weather prediction, wind tunnel Reynolds numerical calculation, and financial services. Intelligent cloud computing has higher requirements for high-capacity and efficient computing. The ability of existing computing system has been difficult to meet its needs. It is necessary to establish an intelligent computing system with the self-organizing ability and realize efficient task scheduling. Since the coordination of computing and storage resource scheduling becomes the key to scheduling, this study designs scheduling tasks based on a large-scale multi-task distributed system, establishes the model of distributed intelligent computing system and the multi-objective optimization model of the task scheduling problem, and designs the IPSO algorithm combined with improved particle swarm optimization algorithm according to the model. First, the particle swarm optimization algorithm is used to generate the initial scheduling scheme, then the ant colony algorithm is initialized, and the final scheduling results are generated. Simulation results show that the performance of the algorithm has obvious performance advantages compared with the improved particle swarm optimization algorithm and the improved ant colony algorithm. In addition, this study presents the task migration conditions and optimization methods under the dual objectives of makespan and availability. This optimization operation increases the system availability without increasing the scheduling length. In the distributed system with heterogeneous availability, the algorithm is effective in the dual objective performance optimization of task completion time and system availability.