Optimize class time tabling by using genetic algorithm technique in UTHM

Timetable scheduling in academic institutions is a major challenge for the institutions, especially with a large number of students and courses offered. This becomes more challenging when classrooms are limited and needs to consider the meeting time of students with the lecturers. These academic institutions such as schools, colleges and universities need timetables to make sure that the students have enough time for each subject in a week without clashing with other subjects or other classes. There are elements that need to be considered in order to make a timetable. These elements include students, teachers or lecturers, rooms, period and also the subjects involved. A new branch of university which is Universiti Tun Hussein Onn Malaysia (UTHM) Pagoh will also have a problem to schedule timetables. Since the branch is new, therefore the problem of lacking in facilities, the number of classrooms and the number of students or classes will arise. In order to schedule timetables, reshuffling and arranging classrooms need to be done and may lead to the complexity of classrooms scheduling. In existing research, many problems involving scheduling have been solved by using genetic algorithm method. There are many other methods that were also being used such as linear programming, integer linear programming, tabu search, ant colony optimization (ACO) algorithm and goal programming. This research is about optimization problem and it proposes a heuristic approach for timetabling optimization, in order to improve and enhance the efficiency of classroom planning. A new algorithm was produced to handle the timetabling problem in the university. This research used genetic algorithm (GA) that was applied to java programming languages with a goal of reducing conflict and optimizing the fitness. Therefore, the general problem was being solved and the best solutions were obtained with lower number of conflicts and maximum fitness value. The timetables for the FAST firstyear students from the Mathematics Department and Statistics Department were also being solved with less conflict and maximum fitness value. A further analysis was done and the results provided the best solutions as well. This research gives an idea about timetable scheduling and also about the optimization method of GA. This research can also become a reference for other timetable scheduling

The Management of a Bottling Service: Modelling Schedules to Optimise Capacity

Purpose The purpose of this research is to explore the feasibility of building a bottling plant in the East Midlands region to serve micro-breweries in this region. A capacity planning and scheduling analysis shall be undertaken to help realise the goal. Recommendations from the analysis will be made on how best to schedule capacity of the plant. Design/Methodology/Approach To achieve the goal, a scheduling model shall be built to help optimise capacity. The model shall use quantitative data obtained from observations, a survey and meetings with the stakeholder. Qualitative data will be used to beef up the recommendations with data from the industry. Findings A linear programming model was created using some techniques from the Vehicle Routing Problem. The model was solved on Excel Solver to find the schedule that best optimises capacity. Value A model was created using techniques from Vehicle Routing Problem and linear programming. To my knowledge, this has not been done before. All formulas are retained in the model making it user friendly. Keywords Capacity planning, capacity strategies, scheduling, Vehicle Routing Problem, Linear programming

Water Supply Planning under Interdependence of Actions: Theory and Application

An ongoing water supply planning problem in the Regional Municipality of Waterloo, Ontario, Canada, is studied to select the best water supply combination, within a multiple-objective framework, when actions are interdependent. The interdependencies in the problem are described and shown to be essential features. The problem is formulated as a multiple-criteria integer program with interdependent actions. Because of the large number of potential actions and the nonconvexity of the decision space, it is quite difficult to find nondominated subsets of actions. Instead, a modified goal programming technique is suggested to identify promising subsets. The appropriateness of this technique is explained, and the lessons learned in applying it to the Waterloo water supply planning problem are described

Optimising economic, environmental, and social objectives: a goal-programming approach in the food sector

The business-decision environment is increasingly complicated by the emergence of competing economic, environmental, and social goals, a notion typified by the current pressures of global economic instability and climate-change targets. Trade-offs are often unclear and contributions by different actors and stakeholders in the supply chain may be unequal but, due to the interdependencies between businesses and stakeholders in relation to total environmental or social impact, a whole chain, simultaneous, and strategic approach is required. After a review of relevant literature and the identification of knowledge gaps, the author introduces and illustrates the use of goal programming as a technique that could facilitate this approach and uses real case evidence for alternative food supply chain strategies, at local, regional, and national levels. It is shown that the method can simplify a complex simultaneous decision situation into a useful and constructive decision and planning framework. Results show how a priori beliefs may be challenged and how operational and resource efficiency could be improved through the use of such a model, which enables a broad stakeholder appreciation and the opportunity to explore and test new environmental or social challenges

A review of portfolio planning: Models and systems

In this chapter, we first provide an overview of a number of portfolio planning models which have been proposed and investigated over the last forty years. We revisit the mean-variance (M-V) model of Markowitz and the construction of the risk-return efficient frontier. A piecewise linear approximation of the problem through a reformulation involving diagonalisation of the quadratic form into a variable separable function is also considered. A few other models, such as, the Mean Absolute Deviation (MAD), the Weighted Goal Programming (WGP) and the Minimax (MM) model which use alternative metrics for risk are also introduced, compared and contrasted. Recently asymmetric measures of risk have gained in importance; we consider a generic representation and a number of alternative symmetric and asymmetric measures of risk which find use in the evaluation of portfolios. There are a number of modelling and computational considerations which have been introduced into practical portfolio planning problems. These include: (a) buy-in thresholds for assets, (b) restriction on the number of assets (cardinality constraints), (c) transaction roundlot restrictions. Practical portfolio models may also include (d) dedication of cashflow streams, and, (e) immunization which involves duration matching and convexity constraints. The modelling issues in respect of these features are discussed. Many of these features lead to discrete restrictions involving zero-one and general integer variables which make the resulting model a quadratic mixed-integer programming model (QMIP). The QMIP is a NP-hard problem; the algorithms and solution methods for this class of problems are also discussed. The issues of preparing the analytic data (financial datamarts) for this family of portfolio planning problems are examined. We finally present computational results which provide some indication of the state-of-the-art in the solution of portfolio optimisation problems

Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method

Approaches to integrated strategic/tactical forest planning

Traditionally forest planning is divided into a hierarchy of planning phases. Strategic planning is conducted to make decisions about sustainable harvest levels while taking into account legislation and policy issues. Within the frame of the strategic plan, the purpose of tactical planning is to schedule harvest operations to specific areas in the immediate few years and on a finer time scale than in the strategic plan. The operative phase focuses on scheduling harvest crews on a monthly or weekly basis, truck scheduling and choosing bucking instructions. Decisions at each level are to a varying degree supported by computerized tools. A problem that may arise when planning is divided into levels and that is noted in the literature focusing on decision support tools is that solutions at one level may be inconsistent with the results of another level. When moving from the strategic plan to the tactical plan, three sources of inconsistencies are often present; spatial discrepancies, temporal discrepancies and discrepancies due to different levels of constraint. The models used in the papers presented in this thesis approaches two of these discrepancies. To address the spatial discrepancies, the same spatial resolution has been used at both levels, i.e., stands. Temporal discrepancies are addressed by modelling the tactical and strategic issues simultaneously. Integrated approaches can yield large models. One way of circumventing this is to aggregate time and/or space. The first paper addresses the consequences of temporal aggregation in the strategic part of a mixed integer programming integrated strategic/tactical model. For reference, linear programming based strategic models are also used. The results of the first paper provide information on what temporal resolutions could be used and indicate that outputs from strategic and integrated plans are not particularly affected by the number of equal length strategic periods when more than five periods, i.e. about 20 year period length, are used. The approach used in the first paper could produce models that are very large, and the second paper provides a two-stage procedure that can reduce the number of variables and preserve the allocation of stands to the first 10 years provided by a linear programming based strategic plan, while concentrating tactical harvest activities using a penalty concept in a mixed integer programming formulation. Results show that it is possible to use the approach to concentrate harvest activities at the tactical level in a full scale forest management scenario. In the case study, the effects of concentration on strategic outputs were small, and the number of harvest tracts declined towards a minimum level. Furthermore, the discrepancies between the two planning levels were small