Scheduling Assignment Research Articles

A decomposition approach for integrated locomotive scheduling and driver assignment in rail freight transport

In this work, we consider the integrated problem of locomotive scheduling and driver assignment in rail freight companies. Our aim is to compute an optimal simultaneous assignment of locomotives and drivers to the trains listed in a given order-book. Mathematically, this leads to the combination of a set-packing problem with compatibility constraints and a multi-commodity-flow problem. We develop a binary-programming formulation to model the given task and improve it by performing a clique-based tightening of the original set-packing inequalities. The objective function of this model makes sure that as many trains as possible are running. To handle the computational complexity of the problem, we introduce a novel decomposition approach which decomposes the problem into a master locomotive scheduling problem and a subproblem for driver assignment. It exploits the fact that the master problem is empirically much easier to solve than the subproblem. For any fixed solution of the master problem, we can use the subproblem to either confirm feasibility of the master solution or to derive valid inequalities from various constraint classes to cut the infeasible master solution off and reiterate. To further improve solution times, we also develop a presolve heuristic. We demonstrate the potential of the presented method by solving a large-scale real-world problem instance provided by our industry partner DB Cargo Polska S.A., as well as a set of derived realistic instances.

A novel bi-objective model for sustainable and efficient airport logistics management: A case study on Copenhagen airport

Due to the growing volume of air transportation and the scarcity of airport resources, aviation planners are paying more attention to the efficiency of airport resource management. Optimizing the assignment and scheduling of ground facilities plays an important role in reducing ground congestion, flight delays, and passenger dissatisfaction. To prevent any collisions among aircraft on the apron, taxi network, and runways, safety considerations should be strictly followed by traffic controllers. Minimizing taxi time is another matter of importance in airport logistics management that helps mitigate environmental concerns (e.g., fuel consumption, tire abrasion, and noise pollution). In this study, a bi-objective mixed-integer linear programming model is developed to optimize the ground resource scheduling problem, taking into account the challenges of gate assignment, taxi planning, and runway scheduling. The model is developed in a way that includes safety and sustainability considerations. The validation of the proposed model is investigated through several examples based on Copenhagen airport using the augmented ε-constraint method. Since the simultaneous scheduling of multiple resources is an NP-hard problem, two multi-objective metaheuristic algorithms are employed to deal with the large-scale problems. The performance of the algorithms is then compared in terms of coverage and convergence rates. Additionally, the results are discussed in detail to provide managerial implications for airport authorities.

A Bilevel Programming Approach for Optimizing Multi-Satellite Collaborative Mission Planning.

With the burgeoning of remote sensing and space technology, multi-satellite collaborative mission planning, which is the key to achieving efficient Earth observation, has become increasingly intricate due to the expanding complexity and volume of observation missions. Addressing the multi-satellite collaborative mission planning problem, which is characterized by its two-stage decision-making process involving mission assignment and resource scheduling, this study investigates a comprehensive joint decision making that encompasses both mission assignment and resource scheduling and comprehensively optimizes the mission completion rate, the mission profit rate, and the satellite resource utilization rate. Considering the interaction of these decisions, we formulate the problem as a bilevel programming model from a game-theoretic perspective and propose a nested bilevel improved genetic algorithm (NBIGA) for its solution. Simulation experiments substantiate the applicability of the bilevel programming model in joint decision making for the stages of mission assignment and resource scheduling in multi-satellite collaborative mission planning, as well as the robustness of the NBIGA. A comparative analysis with the nested bilevel genetic algorithm (NBGA) confirms that the algorithm proposed in this study can achieve superior optimization outcomes and higher solving efficiency.

Addressing staffing challenges through improved planning: Demand-driven course schedule planning and instructor assignment in higher education

This paper presents a novel decision support system (DSS) to address the University Course Timetabling Problem (UCTP). The solution decomposes the NP-complete UCTP into two sub-problems, allowing a structured approach to addressing the complexities inherent in the UCTP process. A mixed integer linear programming (MILP) model is proposed to integrate academic year course schedule planning and instructor assignment, accommodating various constraints to meet student demands. The model optimizes the number of course sections and strategically schedules instructors, aiming to reduce the number of new and distinct courses assigned to them. Historical data from an academic department encompassing multiple disciplines, including Computer Information Systems, Business Management, and Business Analytics, at a large public university in the U.S. is used to develop the model, and the results are compared with the actual course schedule and instructor assignment. The results demonstrate that the proposed DSS would result in a 14 % reduction in the number of course sections offered, translating to approximately $130,000 in annual savings. Additionally, it could significantly reduce the number of new courses assigned to instructors by up to 81 % and the number of distinct course sections assigned to them by 29 %.

Co-optimizing electric bus dispatching and charging considering limited resources and battery degradation

This paper aims to formulate a mathematical model for a multi-type electric bus scheduling problem to determine the optimal fleet composition, bus-to-trip assignment, and partial charging schedule, where the battery degradation, nonlinear charging, and the constraint of charging station capacity are considered. A time-expanded network is proposed to represent the bus-to-trip assignment and partial charging. An adaptive large neighborhood search algorithm is designed to solve the problem. Using a multi-line bus network in Nanjing as the case, empirical operational data is used to generate monthly timetable samples to simulate the uncertainty of trip travel time and energy consumption. The result shows that the charging station capacity can be reduced from 20 (real-world case) to 12, considering the cost-effectiveness and robustness of the bus system. The result of this study also provides suggestions on the charging duration choices and the starting state-of-charge for different periods of the day. In peak and off-peak hours, 20-30-minute charging is recommended for electric buses with state-of-charge lower than 30 %, and 10-minute charging is more recommended when the state-of-charge of the electric bus is between 30 % and 70 %.

College Management System using Python & Django

The College Management System (CMS) is an advanced web-based application designed to streamline administrative and academic processes within educational institutions. Developed using the Django framework for backend functionalities and HTML, CSS, and JavaScript for frontend presentation, the CMS offers a comprehensive solution for managing various tasks efficiently. Leveraging SQLite as the databadminisratiase management system ensures robust data management capabilities, scalability, and ease of maintenance. The CMS provides a wide array of features to automate routine tasks and enhance collaboration among students, faculty, and administrators. Student admission and enrollment management, course scheduling, curriculum planning, and faculty assignment are seamlessly handled by the system, reducing manual workload and minimizing errors. Integrated messaging systems and announcement boards facilitate communication, fostering a collaborative learning environment. The examination management module automates the entire examination process, including exam scheduling, creation of question papers, venue allocation, and result processing. Online exam registration and automated grading streamline the process, ensuring accuracy and transparency while reducing administrative burden. Attendance tracking is simplified with the CMS, allowing faculty to record and monitor student attendance easily. Integration with biometric systems or RFID technology enhances accuracy and eliminates manual attendance taking. Comprehensive attendance reports provide insights into student progress and compliance with attendance policies. Resource management is optimized through the CMS, which enables efficient allocation and utilization of classrooms, laboratories, library resources, and equipment. Administrators can manage inventory, track asset availability, and schedule resource bookings to prevent conflicts and ensure optimal utilization. Reporting and analytics features empower administrators with real-time insights into various aspects of college operations. Customizable dashboards provide information on enrollment statistics, academic performance, financial data, and more, enabling informed decision-making and continuous improvement. In conclusion, the College Management System developed with Django, HTML/CSS/JS, and SQLite revolutionizes educational institution management. By combining advanced technology with intuitive design, the CMS enhances efficiency, transparency, and collaboration, ultimately contributing to the success and growth of the institution.

Project scheduling and staff assignment of selected manufacturing firms in Osun state, Nigeria

Project scheduling and staff assignment of selected manufacturing firms in Osun state, Nigeria

Common due window assignment and single machine scheduling with delivery time, resource allocation, and job-dependent learning effect

Common due window assignment and single machine scheduling with delivery time, resource allocation, and job-dependent learning effect

Influence of Parameter Deviations Vis-à-Vis Assignment Schedule on Thermally Stressed State of Main Thermal Power Plant Equipment

Influence of Parameter Deviations Vis-à-Vis Assignment Schedule on Thermally Stressed State of Main Thermal Power Plant Equipment

Joint decision-making for divisional seru scheduling and worker assignment considering process sequence constraints

Joint decision-making for divisional seru scheduling and worker assignment considering process sequence constraints

Aircraft routing using dynamic programming and reinforcement learning: A customer-centric approach

Over the past few decades, the airline industry has evolved into a sophisticated business, with conventional objectives such as operational efficiency, effective fleet assignment, flight punctuality, and equitable crew scheduling being the primary focus. Nevertheless, given the growing accessibility to customer feedback (either through public forums or private data), it is crucial to make business decisions that cater to the customers’ requirements in addition to conventional goals. Within aircraft operations, there is a series of decisions to be made, such as flight scheduling, fleet assignment, aircraft routing, and crew scheduling. After designating a fleet type to each flight leg, airlines ascertain the sequence of flight legs, or strings, to be operated by individual aircrafts, while complying with obligatory maintenance checks. This constitutes the aircraft routing problem.We introduce a novel approach to aircraft routing problem, by infusing traditional methodologies with insightful customer feedback to create a more responsive and customer-centric model. Unlike conventional techniques of constructing routes, in our work, we construct maintenance feasible strings. Traditional aircraft routing methodologies typically concentrate on minimizing propagated delay. However, by integrating customer feedback, more effective prioritization to minimize delays for flight legs with higher customer dissatisfaction can be achieved. We propose an optimization model that strives to minimize propagated delay while prioritizing flights based on customer feedback, thus resulting in customer-aware routes. To solve the proposed optimization model, we design an iterative alternating optimization scheme where feasible strings are first constructed from the pool of available flight legs using a string generation procedure, followed by solving an integer linear optimization problem over only the feasible set of strings obtained in the first stage. To construct feasible strings of flights at each iteration, we use two different methods. The first method is based on a dynamic programming approach which is useful for mean delay information. To handle practical cases where delays are uncertain, we propose a reinforcement learning (RL) approach to construct the strings. We provide experiments of our proposed methods on synthetic and real data sets and demonstrate the effectiveness of our RL model in comparison to dynamic programming based model. Our experiments affirm that routing decisions informed by customer feedback prioritize minimizing delays for specific flight legs, resulting in routes that are inherently customer-friendly.

CrowdBot: An Open-Environment Robot Management System for On-Campus Services

In contemporary campus environments, the provision of timely and efficient services is increasingly challenging due to limitations in accessibility and the complexity and openness of the environment. Existing service robots, while operational, often struggle with adaptability and dynamic task management, leading to inefficiencies. To overcome these limitations, we introduce CrowdBot, a robot management system that enhances service in campus environments. Our system leverages a hierarchical reinforcement learning-based cloud-edge hybrid scheduling framework (REDIS), for efficient online streaming task assignment and dynamic action scheduling. To verify the REDIS framework, we have developed a digital twin simulation platform, which integrates large language models and hot-swapping technology. This facilitates seamless human-robot interaction, efficient task allocation, and cost-effective execution through the reuse of robot equipment. Our comprehensive simulations corroborate the system's remarkable efficacy, demonstrating significant improvements with a 24.46% reduction in task completion times, a 9.37% decrease in travel distances, and up to a 3% savings in power usage. Additionally, the system achieves a 7.95% increase in the number of tasks completed and a 9.49% reduction in response time. Real-world case studies further affirm CrowdBot's capability to adeptly execute tasks and judiciously recycle resources, thereby offering a smart and viable solution for the streamlined management of campus services.

A simulation-based genetic algorithm for a semi-automated warehouse scheduling problem with processing time variability

For warehouse operations, efficiently scheduling the available resources is crucial to improve the productivity and customer satisfaction. This paper proposes a simulation-based evolutionary algorithm for order scheduling and multi-robot task assignment in a robotic mobile fulfillment system. The algorithm proactively deals with the effects of the processing time variability by evaluating schedules based on both its system performance as well as its robustness under uncertain conditions. The algorithm implements an efficient resource allocation method and a variance reduction technique to reduce the overall computational burden. The experimental results show that the techniques to reduce the computational time are effective and can significantly reduce the amount of simulations required for the fitness evaluation. If a candidate schedule is allocated insufficient simulation replications it can lead to an inaccurate estimate of its long-term average performance. This could lead to an average performance loss of 7.3 %. Furthermore, the proactive scheduler is able to generate schedules that are more robust compared to deterministically generated. A reduction in the average operational cost of about 5 % can be reached, compared to a deterministically generated schedule. The paper reveals the relevance of identifying and modeling uncertainty when designing schedules in an operational system, rather than looking for optimal schedules for ideal scenarios.

Age of information based user scheduling and data assignment in multi-user mobile edge computing networks: An online algorithm

Age of information based user scheduling and data assignment in multi-user mobile edge computing networks: An online algorithm

A Reinforcement Learning Approach for Flexible Job Shop Scheduling Problem With Crane Transportation and Setup Times.

Flexible job shop scheduling problem (FJSP) has attracted research interests as it can significantly improve the energy, cost, and time efficiency of production. As one type of reinforcement learning, deep Q-network (DQN) has been applied to solve numerous realistic optimization problems. In this study, a DQN model is proposed to solve a multiobjective FJSP with crane transportation and setup times (FJSP-CS). Two objectives, i.e., makespan and total energy consumption, are optimized simultaneously based on weighting approach. To better reflect the problem realities, eight different crane transportation stages and three typical machine states including processing, setup, and standby are investigated. Considering the complexity of FJSP-CS, an identification rule is designed to organize the crane transportation in solution decoding. As for the DQN model, 12 state features and seven actions are designed to describe the features in the scheduling process. A novel structure is applied in the DQN topology, saving the calculation resources and improving the performance. In DQN training, double deep Q-network technique and soft target weight update strategy are used. In addition, three reported improvement strategies are adopted to enhance the solution qualities by adjusting scheduling assignments. Extensive computational tests and comparisons demonstrate the effectiveness and advantages of the proposed method in solving FJSP-CS, where the DQN can choose appropriate dispatching rules at various scheduling situations.

The integrated planning of outgoing coil selection for retrieval, multi-crane scheduling, and location assignment to the incoming and blocking coils in steel coil warehouses

In this paper, the problem of scheduling crane-operated warehouses in a triangle stacking style is investigated. In a steel supply chain, warehouses play a significant role in establishing a connection between the producer and the customers. Therefore, improving their performance results in a competitive advantage in the supply chain. One of the challenges of steel coil warehouses is the selection of outgoing coils out of the candidate coils for retrieval. Despite its importance, this problem has not been investigated in the studies relevant to steel coil warehouses. In this study, in addition to the selection of outgoing coils, sequencing of the operations on the incoming, outgoing, and blocking coils, location assignment to the incoming and blocking coils, and crane scheduling are considered in an integrated manner. First, a mixed integer linear programming model is proposed. Further, a Greedy heuristic algorithm and two metaheuristics are developed to solve large-sized instances of the problem. Metaheuristics include a Greedy Randomized Adaptive Search Procedure (GRASP) and a hybrid algorithm that combines Ant Colony System with the GRASP (ACS-GRASP). Finally, performances of the proposed algorithms are compared over randomly generated instances as well as instances which are generated based on the real data obtained from Isfahan’s Mobarakeh Steel Company. To conduct this analysis, various performance evaluation metrics are applied. Computational results indicate that ACS-GRASP converges slower than GRASP, but is more reliable and efficient. On the other hand, in large-sized instances, the Greedy algorithm runs extremely fast and can find solutions which are comparable to the metaheuristic solutions.

An intelligent optimization strategy for nurse-patient scheduling in the Internet of Medical Things applications

In the last years, the world has witnessed a potential increasing in the patient number resulted from the increasing number of aged persons along with the emergence of new virus and diseases. This imposes a high pressure on hospitals that suffer from a shortage of medical staff, personal equipment and adequate interventions to overcome such a challenge. Particularly, nurse scheduling is becoming a crucial operation to hospitals in order to efficiently handing the patents and increasing the performance of health system. In this paper, we present an efficient Nurse-Patient Scheduling (NPS) mechanism that is based on the patient classification according to the severity levels of their vital signs. The main goal of NPS is to balance the workload of nurses and it consists of three phases: patient monitoring, patient classification, and nurse scheduling. The first phase aims to periodically monitor the patients using a configurable window time and collect their vital signals through a set of biomedical sensors. The second phase allows for the extraction of prospective features among the collected data then to classify them according to a set of predefined criteria such as patient criticality level, patient age, and the allocated treatment time of each patient. In the last phase, we propose a novel scheduling algorithm that combines both genetic and particle swarm optimization methods in order to find the best scheduling assignment of nurses over patients. We performed simulations based on real health data and we demonstrated the performance of NPS mechanism in terms of obtaining optimal of nurses to patients according to the predefined criteria.

Utilizing a Poster Project as an Assessment in an Introductory Abstract Algebra Course

ABSTRACT Though posters have been used as a pedagogical tool in a variety of fields, the literature suggests that the use of posters as an educational assessment tool by the mathematics community has been limited. This is unfortunate given the numerous potential benefits of a poster, including the development of oral communication skills and the ability to examine a mathematical topic at a deeper level. We describe the use of a poster project in an introductory abstract algebra course with specific implementation guidance including a schedule of assignments and assessment criteria. We also include reflections on the success of this project over several semesters as well as advice for adapting the project to a remote course. Samples of student posters are available upon request to the authors.

Common due-date assignment problems with fixed-plus-linear earliness and tardiness costs

We study single machine scheduling and common due-date assignment problems. The cost considered is fixed plus linear, i.e., jobs are penalized by two cost components: (i) a variable cost which is proportional to their earliness/tardiness values, (ii) a fixed cost which is independent of their actual earliness/tardiness values. For the general version of this problem, three heuristics are introduced and tested. We also study a number of modifications and special cases. We focus first on the special case of identical job processing times. Both versions of minsum and minmax are shown to have a polynomial time solution, even with the additional option of job rejection. Another special case of variable earliness cost and fixed tardiness cost is also studied. For this NP-hard problem, a pseudo-polynomial dynamic programming algorithm is introduced and tested. An extension to the case of bounded rejection cost, and a modified version with an additional bound on the total tardiness cost, are also studied. The solution algorithms for these problems are tested numerically as well. The last studied problem is that of job-independent cost components. This version, which generalizes the first and most cited due-date assignment model, is shown to have a polynomial time solution.

A lexicographic approach for solving bi-criteria bottleneck assignment problems

The suitability of agents for tasks and ranked preference of tasks for agents play a significant role in problems where several agents are available to perform various tasks. The agents should be allocated to various tasks so that the corresponding assignment schedules must provide solution points on the Pareto front in the criteria space of the problem. This study discusses a bi-objective assignment problem of this kind, related to positioning players in a sports ground, and an algorithm based on a lexicographic technique is developed to solve this problem. The two objectives of the problem are to maximize the minimum suitability of players among positions and to minimize the maximum rank of positions among players, simultaneously. The proposed algorithm efficiently finds all the Pareto-optimal/non-dominated assignment schedules and hence, the solution points of the problem. This is an improved algorithm than the one available in the literature for this problem. A numerical example demonstrates the applicability of the proposed algorithm and shows less computational complexity than an existing algorithm.