Generation Of Optimal Schedules Research Articles

Improving the Performance of the General Course Scheduling System at UPN Veteran Jawa Timur through the Application of the IWCFPSO Algorithm

Algorithm selection is the main key in producing a system, especially an artificial intelligence-based system, for example, a course scheduling system that involves many constraints in producing an optimal schedule. This research develops a scheduling system using the ICWFPSO algorithm which is a development from previous research, namely the development of a scheduling system using the MIPSO algorithm. The use of inertia weight in the MIPSO algorithm which is used as a scheduling algorithm in previous research can be optimized by using a combination of inertia weight and constriction factor. Scheduling system development is carried out using the waterfall method with research steps namely problem and needs analysis, data collection and literature study, system design, system implementation, and ends with testing the performance of the algorithms that are applied to the scheduling system that is built. Based on the tests carried out, it can be concluded that the ICWFPSO algorithm provides 2 times better performance compared to the MIPSO algorithm in terms of optimal schedule generation time. Meanwhile, tests carried out on CPU and RAM usage showed that there was no significant impact on these two parameters through the implementation of MIPSO and ICWFPSO in the course scheduling system created.
 
 Keywords: best scheduling, ICWFPSO, number of iterations

Optimal schedule generation for single-channel crude transfer using a multi-model approach

This paper presents three techniques for scheduling for crude transfer between a port and a refinery on a single pipeline in the presence of stringent flow constraints. The three techniques are based on metaheuristics (business rules), mixed integer linear programming and reinforcement learning. In addition to comparing the three techniques, we also demonstrate how knowledge gleaned from one technique (in our case, the metaheuristics) can be used to design an algorithm based on another technique (in our case, reinforcement learning). A novel feature of our approach to reinforcement learning, in particular, is the use of low-fidelity, reduced-order simulators for training the scheduler and supporting it with a post-processor based either on business rules or on integer programming for ensuring compatibility with the constraints. The set of constraints considered here includes temporal restrictions on the use of the pipeline, flow constraints in the tanks that feed the column distillation units in the refinery, and the need to ensure a certain minimum residence time for crude in a given tank for dewatering.

A flexible and optimal approach for appointment scheduling in healthcare

ABSTRACTAppointment scheduling is generally applied in outpatient clinics and other healthcare services. The challenge in scheduling is to find a strategy for dealing with variability and unpredictability in service duration and patient arrivals. The consequences of an ineffective strategy include long waiting times for patients and idle time for the healthcare provider. In turn, these have implications for the perceived quality, cost‐efficiency, and capacity of healthcare services. The generation of optimal schedules is a notoriously intractable problem, and earlier attempts at designing effective strategies for appointment scheduling were based on approximation, simulation, or simplification. We propose a novel strategy for scheduling that exploits three tactical ideas to make the problem manageable. We compare the proposed strategy to other approaches, and show that it matches or outperforms competing methods in terms of flexibility, ease of use, and speed. More importantly, it outperforms competing approaches nearly uniformly in approaching the desired balance between waiting and idle times as specified in a chosen objective function. Therefore, the strategy is a good basis for further enrichments.

Multi-Agents Based Coordinated Scheduling in Hospitals

Patient Scheduling is the process of scheduling the patients by creating an optimal schedule for the patients in using the resources in hospitals. The real time scheduling with various constraints like emergency situations like accident case makes this problem complex. The manually scheduling algorithms and the traditional approaches needs more coordination, time and manpower to achieve its efficiency. Agents are a natural choice because they are known to represent a distributed environment well and they are capable of coordinating with each other in dynamic environment. The architectural components like Belief that maintains the internal state of the system, Desires that represents the motivational state of the agent and Intentions that helps to deliberate the state of the goal make this autonomous system adaptable and fault tolerant. In the proposed system each resources and patient is constructed to be software agents, which has the ability to coordinate among them. The optimal schedule is calculated in minimal time using the Genetic Algorithm (GA), an efficient search-based optimization technique. When a patient arrives, he/she is served by a common agent where the priority, resource requirement, processing time, and sequence are assigned to each patient. Hence in this highly dynamic domain optimal schedule generation with minimal time and hardware/software requirements will provide the customers satisfaction with their visit to the hospitals.

Scheduling of Energy-Integrated Batch Process Systems Using a Pattern-Based Framework

In this paper, a novel pattern-based method is developed for the generation of optimal schedules for energy-integrated batch process systems. The proposed methodology is based on the analysis of available schedules for the identification of repetitive patterns. It is shown that optimal schedules of energy-integrated batch processes are composed of several repeating sections (or building blocks), and their sizes and relative positions are dependent on the scheduling horizon and constraints. Based on such a decomposition, the proposed pattern-based algorithm generates an optimal schedule by computing the number and sequence of these blocks. The framework is then integrated with rigorous optimization-based approach wherein it is shown that the learning from the pattern-based solution significantly improves the performance of rigorous optimization. The main advantage of the pattern-based method is the significant reduction in computational time required to solve large scheduling problems, thus enabling the possibility of on-line rescheduling. Three literature examples were considered to demonstrate the presence of repeating patterns in optimal schedules of energy-integrated batch systems. The effectiveness of the proposed methodology was illustrated using an integrated reactor-separator system.

The Multiprocessor Scheduling of Precedence-Constrained Task Systems in the Presence of Interprocessor Communication Delays

The problem of scheduling precedence-constrained task systems characterized by interprocessor communication delays is addressed. It is assumed that task duplication is permitted. The target machine is a homogenous multiprocessor with an unbounded number of processors. The general problem is known to be NP-hard; however, when communication delays are small relative to task execution times, the C.P.M. based approach of Colin and Chrétienne (1991) yields an optimal schedule in polynomial time. Extensions to this method of Colin and Chrétienne are presented here, which allow for polynomial-time optimal schedule generation for certain categories of task systems with arbitrary precedence relations, processing times, and communication delays.

A design synthesis system for recursive DSP algorithms represented by fully specified flow graphs

This paper describes a design synthesis environment which can generate an efficient VLSI layout from a recursive DSP algorithm specified by a graph. The design synthesis environment is divided into three parts: optimal schedule generation, circuit synthesis, and VLSI layout generation (silicon compilation). The scheduler first computes optimality conditions for a given input algorithm and then finds a schedule which satisfies the optimality conditions. We have employed a cyclo-static optimal multiprocessor compiler as a scheduler. The circuit synthesis component translates the optimal schedule into a structural specification, including the control structures, for an circuit realization. In the final part, a VLSI layout is generated from the structural specification. We have chosen the LAGER system for the silicon compilation. This paper illustrates the design synthesis process with complete details of a simple, complete example, a second order Direct Form II IIR filter.

Optimal schedule generation for a single-product production line—I. Problem formulation

Optimal schedule generation for a single-product production line is an important subproblem of the multipurpose plant scheduling problem. An analysis o

Optimal schedule generation for a single-product production line—II. Identification of dominant unique path sequences

Many of the feasible path assignments for the MINLP formulation of the production line scheduling problem presented in Part I result in equivalent schedules. A dominant set of path assignments is identified which allows the path assignments for several stages to be determined simultaneously. A combinatorial analysis of the set of all feasible path assignments reveals there are O ( NP) equivalent assignments for each operationally and rotationally-unique simple path sequence. An enumeration procedure is developed to identify those assignments which result in unique path sequences. The overall solution methodology is illustrated with an example.

A methodology for generation of optimal schedules for an underground railway system

This paper presents a methodology developed for the automatic generation of optimal schedules for the north-south line of the Metro Company of São Paulo. An analytical model was created to represent the system behavior: train and passenger movements. Based on the model characteristics, the goal-coordination method, due to Lasdon and Tamura, was utilized to produce the Optimal reference schedule by considering comfort levels for passengers, the number of trains in the line, and the performance of trains. The optimal schedule for the morning peak period was generated and a great improvement over the current strategy was obtained.