Designing a multi-agent approach system for distributed course timetabling

This paper proposes tackling the difficult course timetabling problem using a multi-agent approach. The proposed design seeks to deal with the problem using a distributed solution environment in which a mediator agent coordinates various timetabling agents that cooperate to improve a common global solution. Initial timetables provided to the multi-agent system are generated using several hybrid heuristics that combine graph colouring heuristics and local search in different ways. The hybrid heuristics are capable of generating feasible timetables for all instances of the two sets of benchmark problems used here. We discuss how these initialisation hybrid heuristics can be incorporated into the proposed multi-agent approach in order to conduct distributed timetabling. This preliminary work serves as a solid basis towards the design of an effective multi-agent distributed timetabling system

Non-linear great deluge with learning mechanism for solving the course timetabling problem

International audienc

Learning and Cooperating Multi-Agent Scheduling Repair Using a Provenance-Centred Approach

The timetabling problem is to find a timetable solution by assigning time and resources to sessions that satisfy a set of constraints. Traditionally, research has focused on optimization towards a final solution but this paper focuses on minimizing disturbance impact due to changing conditions. A Multi-Agent System (MAS) is proposed in which users are represented as autonomous agents negotiating with one another to repair a timetable. From repeated negotiations, agents learn to develop a model of other agent's preferences. The MAS is simulated on a factorial experiment set up and varying the cooperation level, learning model and selection strategy. A provenance-centred approach is adopted to improve the human aspect of timetabling to allow users to derive the steps towards a solution and make changes to influence the solution

Using Distributed Agents to Create University Course Timetables Addressing Essential & Desirable Constraints and Fair Allocation of Resources

In this study, the University Course Timetabling Problem (UCTP) has been investigated. This is a form of Constraint Satisfaction Problem (CSP) and belongs to the NP-complete class. The nature of a such problem is highly descriptive, a solution therefore involves combining many aspects of the problem. Although various timetabling algorithms have been continuously developed for nearly half a century, a gap still exists between the theoretical and practical aspects of university timetabling. This research is aimed to narrow the gap. We created an agent-based model for solving the university course timetabling problem, where this model not only considers a set of essential constraints upon the teaching activities, but also a set of desirable constraints that correspond to real-world needs. The model also seeks to provide fair allocation of resources. The capabilities of agents are harnessed for the activities of decision making, collaboration, coordination and negotiation by embedding them within the protocol designs. The resulting set of university course timetables involve the participation of every element in the system, with each agent taking responsibility for organising of its own course timetable, cooperating together to resolve problems. There are two types of agents in the model; these are Year-Programme Agent and Rooms Agent. In this study, we have used four different principles for organising the interaction between the agents: First-In-First-Out & Sequential (FIFOSeq), First-In-First-Out & Interleaved (FIFOInt), Round-Robin & Sequential (RRSeq) and Round-Robin & Interleaved (RRInt). The problem formulation and data instances of the third track of the Second International Timetabling Competition (ITC-2007) have been used as benchmarks for validating these implemented timetables. The validated results not only compare the four principles with each other; but also compare them with other timetabling techniques used for ITC-2007. The four different principles were able to successfully schedule all lectures in different periods, with no instances of two lectures occupying the same room at the same time. The lectures belonging to the same curriculum or taught by the same teacher do not conflict. Every lecture has been assigned a teacher before scheduling. The capacity of every assigned room is greater than, or equal to, the number of students in that course. The lectures of each course have been spread across the minimum number of working days with more than 98 percent success, and for more than 75 percent of the lectures under the same curriculum, it has been possible to avoid isolated deliveries. We conclude that the RRInt principle gives the most consistent likelihood of ensuring that each YPA in the system gets the best and fairest chance to obtain its resources

Automated university lecture timetable using Heuristic Approach

There are different approaches used in automating course timetabling problem in tertiary institution. This paper present a combination of genetic algorithm (GA) and simulated annealing (SA) to have a heuristic approach (HA) for solving course timetabling problem in Federal University Wukari (FUW). The heuristic approach was implemented considering the soft and hard constraints and the survival for the fittest. The period and space complexity was observed. This helps in matching the number of rooms with the number of courses. Keywords: Heuristic approach (HA), Genetic algorithm (GA), Course Timetabling, Space Complexity

Hybrid heuristic for multi-carrier transportation plans

This paper describes a hybrid heuristic approach to construct transportation plans for a singlecustomer multi-carrier scenario that arises at 3T Logistics Ltd, a UK company that provides outsourced transportation planning and management services. The problem consists on planning the delivery, using a set of carrier companies, of a set of shipments from a warehouse to different consignees across the UK. The problem tackled resembles a vehicle routing problem with time windows but there are several differences in our scenario. The hybrid heuristic algorithm described here combines a clustering algorithm, constructive and local search heuristics, and exact assignment based on integer programming. This approach is being currently evaluated at the company and results so far indicate the suitability of the algorithm to produce practical transportation plans at reduced cost compared to current practice

Using fuzzy c-means clustering algorithm for common lecturer timetabling among departments

University course timetabling problem is one of the hard problems and it must be done for each term frequently which is an exhausting and time consuming task. The main technique in the presented approach is focused on developing and making the process of timetabling common lecturers among different departments of a university scalable. The aim of this paper is to improve the satisfaction of common lecturers among departments and then minimize the loss of resources within departments. The applied method is to use a collaborative search approach. In this method, at first all departments perform their scheduling process locally; then two clustering and traversing agents are used where the former is to cluster common lecturers among departments and the latter is to find unused resources among departments. After performing the clustering and traversing processes, the mapping operation in done based on principles of common lecturers constraint in redundant resources in order to gain the objectives of the problem. The problem’s evaluation metric is evaluated via using fuzzy c-means clustering algorithm on common lecturer constraints within a multi agent system. An applied dataset is based on meeting the requirements of scheduling in real world among various departments of Islamic Azad University, Ahar Branch and the success of results would be in respect of satisfying uniform distribution and allocation of common lecturers on redundant resources among different departments

Intelligent maintenance management in a reconfigurable manufacturing environment using multi-agent systems

Thesis (M. Tech.) -- Central University of Technology, Free State, 2010Traditional corrective maintenance is both costly and ineffective. In some situations it is more cost effective to replace a device than to maintain it; however it is far more likely that the cost of the device far outweighs the cost of performing routine maintenance. These device related costs coupled with the profit loss due to reduced production levels, makes this reactive maintenance approach unacceptably inefficient in many situations. Blind predictive maintenance without considering the actual physical state of the hardware is an improvement, but is still far from ideal. Simply maintaining devices on a schedule without taking into account the operational hours and workload can be a costly mistake. The inefficiencies associated with these approaches have contributed to the development of proactive maintenance strategies. These approaches take the device health state into account. For this reason, proactive maintenance strategies are inherently more efficient compared to the aforementioned traditional approaches. Predicting the health degradation of devices allows for easier anticipation of the required maintenance resources and costs. Maintenance can also be scheduled to accommodate production needs. This work represents the design and simulation of an intelligent maintenance management system that incorporates device health prognosis with maintenance schedule generation. The simulation scenario provided prognostic data to be used to schedule devices for maintenance. A production rule engine was provided with a feasible starting schedule. This schedule was then improved and the process was determined by adhering to a set of criteria. Benchmarks were conducted to show the benefit of optimising the starting schedule and the results were presented as proof. Improving on existing maintenance approaches will result in several benefits for an organisation. Eliminating the need to address unexpected failures or perform maintenance prematurely will ensure that the relevant resources are available when they are required. This will in turn reduce the expenditure related to wasted maintenance resources without compromising the health of devices or systems in the organisation

APPLICATIONS OF ENVIRONMENT-BASED DESIGN (EBD) METHODOLOGY

A product’s environments play a significant role in its development. In other words, any alteration in the environment surrounding a product leads to changes in its features. Hence, having a systematic procedure to analyze the product’s environments is a crucial need for industries. Environment-Based Design (EBD) methodology describes the environment of the product (excluding the product itself) and presents a rational approach to analyze it. In order to achieve an efficient product design and development process, EBD utilizes different tools. Recursive Object Model (ROM) diagram, Cause and Effect Analysis, Life Cycle Analysis, Asking Right Question and Answering are EBD’s major tools and technics. In this research, we aim to represent EBD’s capabilities for product evolution analysis, complex products development and human-centered products development. In order to demonstrate EBD’s competences for product evolution analysis, we conduct a case study of braking systems evolution analysis through analyzing the environments around them. Afterward, we perform environment analysis for aerospace design methodology in order to propose a novel design methodology for the aerospace industries. Finally, we propose a course scheduling model based on environment analysis of the academic schedules and we verify our model using Concordia University’s courses

Overview of Infrastructure Charging, part 4, IMPROVERAIL Project Deliverable 9, “Improved Data Background to Support Current and Future Infrastructure Charging Systems”

Improverail aims are to further support the establishment of railway infrastructure management in accordance with Directive 91/440, as well as the new railway infrastructure directives, by developing the necessary tools for modelling the management of railway infrastructure; by evaluating improved methods for capacity and resources management, which allow the improvement of the Life Cycle Costs (LCC) calculating methods, including elements related to vehicle - infrastructure interaction and external costs; and by improving data background in support of charging for use of railway infrastructure. To achieve these objectives, Improverail is organised along 8 workpackages, with specific objectives, responding to the requirements of the task 2.2.1/10 of the 2nd call made in the 5th RTD Framework Programme in December 1999.This part is the task 7.1 (Review of infrastructure charging systems) to the workpackage 7 (Analysis of the relation between infrastructure cost variation and diversity of infrastructure charging systems).Before explaining the economic characteristics of railway and his basic pricing principles, authors must specify the objectives of railways infrastructure charging.principle of pricing ; rail infrastructure charging ; public service obligation ; rail charging practice ; Europe ; Improverail