<p>The choice of an efficient and effective production control mechanism (PCM)along with the appropriate buffer allocation pattern is very important for anyproduction engineer/decision maker when designing a production line in order toattain the required system performance. This project work aims to give an insightwith different PCMs, different buffer allocation patterns and arrangement ofworkers of different capability to help the production engineers/decision makersto select the right mechanism and pattern. This study has been performed withmulti-objective simulation optimisation (MOSO) tool. The result from manyexperiments have shown that the ascending buffer allocation pattern stands outas the prominent choice when the goal was to attain maximum throughput (TP)and simultaneously keeping minimum cycle time (CT) and work in process (WIP).The PCMs and workers imbalance patterns performance is different in differentregions of the Pareto-optimal CT-TP data plots obtained from MOSO so theirselection is depending on the interest of the desired level of throughput togetherwith the limit of cycle time.</p>

We study simulation optimization methods for the stochastic economic lot scheduling problem. In contrast to prior research, we focus on methods that treat this problem as a black box. Based on a large-scale numerical study, we compare approximate dynamic programming with a global search for parameters of simple control policies. We propose two value function approximation schemes based on linear combinations of piecewise- constant functions as well as control policies that can be described by a small set of parameters. While approximate value iteration worked well for small problems with three products, it was clearly outperformed by the global policy search as soon as problem size increased. The most reliable choice in our study was a globally optimized fixed-cycle policy. An additional analysis of the response surface of model parameters on optimal average cost revealed that the cost effect of product diversity was negligible. (authors' abstract)

We consider the stochastic economic lot scheduling problem (SELSP) with lost sales and random demand, where switching between products is subject to sequence-dependent setup times. We propose a solution based on simulation optimization using an iterative two-step procedure which combines global policy search with local search heuristics for the traveling salesman sequencing subproblem. To optimize the production cycle, we compare two criteria: minimizing total setup times and evenly distributing setups to obtain a more regular production cycle. Based on a numerical study, we find that a policy with a balanced production cycle leads to lower cost than other policies with unbalanced cycles. (authors' abstract)

Storage of freshwater in saline aquifers has a strategic importance in water deficit countries. The freshwater stored in these aquifers may be the only source of water available during times of crisis. Coupled simulation and optimization type groundwater management models have been developed that will achieve the optimal control of the storage of freshwater in a stagnant manner for constant density and variable density flows in hypothetical single- and multi-layered saline aquifers. The study is carried out in two stages. In the first stage, a transient model of five years is simulated that allows sufficient time for the freshwater mound to be created. In the second stage, an optimization model is formulated which minimizes the pumping/injection rates of a set of hydraulic gradient control wells subject to a set of constraints consisting of systems response equations, demand requirements, hydraulic gradient controls, pumping and injection limitations. The optimization models select which wells to be pumped and which ones to be injected and decide on their pumping/injection schedules to maintain the freshwater mound from migration. The results of the optimization models showed that the mound is successfully contained in its original location by controlling the hydraulic gradient via pumping/injection wells.

Thesis (MEng)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Efficient and reliable energy generation capability is vital to any country's economic growth. Many strategic, tactical and operational decisions take place along the energy supply chain. Shortcomings in South Africa's electricity production industry have led to the development of an energy ow simulator. The energy ow simulator is claimed to incorporate all significant factors involved in the energy ow process from primary energy to end-use consumption. The energy ow simulator thus provides a decision support system for electric utility planners. The original aim of this study was to develop a global optimisation model and integrate it into the existing energy ow simulator. After gaining an understanding of the architecture of the energy ow simulator and scrutinising a large number of variables, it was concluded that global optimisation was infeasible. The energy ow simulator is made up of four modules and is operated on a module-by-module basis, with inputs and outputs owing between modules. One of the modules, namely the primary energy module, lends itself well to optimisation. The primary energy module simulates coal stockpile levels through Monte Carlo simulation. Classic inventory management policies were adapted to fit the structure of the primary energy module, which is treated as a black box. The coal stockpile management policies that are introduced provide a prescriptive means to deal with the stochastic nature of the coal stockpiles. As the planning horizon continuously changes and the entire energy ow simulator has to be re-run, an efficient algorithm is required to optimise stockpile management policies. Optimisation is achieved through the rapidly converging cross-entropy method. By integrating the simulation and optimisation model, a prescriptive capability is added to the primary energy module. Furthermore, this study shows that coal stockpile management policies can be improved. An integrated solution is developed by nesting the primary energy module within the optimisation model. Scalability is incorporated into the optimisation model through a coding approach that automatically adjusts to an everchanging planning horizon as well as the commission and decommission of power stations. As this study is the first of several research projects to come, it paves the way for future research on the energy ow simulator by proposing future areas of investigation. / AFRIKAANSE OPSOMMING: Effektiewe en betroubare energie-opwekkingsvermoë is van kardinale belang in enige land se ekonomiese groei. Baie strategiese, taktiese en operasionele besluite word deurgaans in die energie-verskaffingsketting geneem. Tekortkominge in Suid-Afrika se elektrisiteitsopwekkingsindustrie het tot die ontwikkeling van 'n energie-vloei-simuleerder gelei. Die energie-vloei-simuleerder vervat na bewering al die belangrike faktore wat op die energie-vloei-proses betrekking het van primêre energieverbruik tot eindgebruik. Die energie-vloei-simuleerder verskaf dus 'n ondersteuningstelsel aan elektrisiteitsdiensbeplanners vir die neem van besluite. Die oorspronklike doel van hierdie studie was om 'n globale optimeringsmodel te ontwikkel en te integreer in die bestaande energie-vloeisimuleerder. Na 'n begrip aangaande die argitektuur van die energievloei- simuleerder gevorm is en 'n groot aantal veranderlikes ondersoek is, is die slotsom bereik dat globale optimering nie lewensvatbaar is nie. Die energie-vloei-simuleerder bestaan uit vier eenhede en werk op 'n eenheid-tot-eenheid basis met insette en uitsette wat tussen eenhede vloei. Een van die eenhede, naamlik die primêre energiemodel, leen dit goed tot optimering. Die primêre energiemodel boots steenkoolreserwevlakke deur Monte Carlo-simulering na. Tradisionele voorraadbestuursbeleide is aangepas om die primêre energiemodel se struktuur wat as 'n swartboks hanteer word, te pas. Die steenkoolreserwebestuursbeleide wat ingestel is, verskaf 'n voorgeskrewe middel om met die stogastiese aard van die steenkoolreserwes te werk. Aangesien die beplanningshorison deurgaans verander en die hele energie-vloei-simulering weer met die energie-vloei-simuleerder uitgevoer moet word, word 'n effektiewe algoritme benodig om die re-serwebestuursbeleide te optimeer. Optimering word bereik deur die vinnige konvergerende kruis-entropie-metode. 'n Geïntegreerde oplossing is ontwikkel deur die primêre energiemodel en die optimering funksie saam te voeg. Skalering word ingesluit in die optimeringsmodel deur 'n koderingsbenadering wat outomaties aanpas tot 'n altyd-veranderende beplanningshorison asook die ingebruikneem en uitgebruikstel van kragstasies. Aangesien hierdie studie die eerste van verskeie navorsingsprojekte is, baan dit die weg vir toekomstige navorsing oor die energie-vloeisimuleerder deur ondersoekareas vir die toekoms voor te stel.

In recent years, the project management is very popular tool for management of companies and their processes, and this system is utilized in companies of practically any specialisation. In this diploma paper the principles of the project management are applied on a real construction project, which is one of the most common applications. During project preparation, another two disciplines of the operations research are applied on the precarious parts of the project; namely simulation modelling, and optimization via mathematical programming. Simulation modelling allows the analyst to predict behaviour of the modelled real system without the need for any time or costs to be spent on its observation. In this paper the simulation covers the system of vertical transport during execution of the project. Optimization is meant to set such parameters of any model in order for the watched criteria to reach optimal values. In this paper, the optimization is applied on total budget of the project to minimize the total costs.

This thesis presents three novel contributions to the application as well as development of ranking and selection procedures. Ranking and selection is an important topic in the discrete event simulation literature concerned with the use of statistical approaches to select the best or set of best systems from a set of simulated alternatives. Ranking and selection is comprised of three different approaches: subset selection, indifference zone selection, and multiple comparisons. The methodology addressed in this thesis focuses primarily on the first two approaches: subset selection and indifference zone selection. Our first contribution regards the application of existing ranking and selection procedures to an important body of literature known as system reliability design. If we are capable of modeling a system via a network of arcs and nodes, then the difficult problem of determining the most reliable network configuration, given a set of design constraints, is an optimization problem that we refer to as the network reliability design problem. In this thesis, we first present a novel solution approach for one type of network reliability design optimization problem where total enumeration of the solution space is feasible and desirable. This approach focuses on improving the efficiency of the evaluation of system reliabilities as well as quantifying the probability of correctly selecting the true best design based on the estimation of the expected system reliabilities through the use of ranking and selection procedures, both of which are novel ideas in the system reliability design literature. Altogether, this method eliminates the guess work that was previously associated with this design problem and maintains significant runtime improvements over the existing methodology. Our second contribution regards the development of a new optimization framework for the network reliability design problem that is applicable to any topological and terminal configuration as well as solution sets of any sizes. This framework focuses on improving the efficiency of the evaluation and comparison of system reliabilities, while providing a more robust performance and user-friendly procedure in terms of the input parameter level selection. This is accomplished through the introduction of two novel statistical sampling procedures based on the concepts of ranking and selection: Sequential Selection of the Best Subset and Duplicate Generation. Altogether, this framework achieves the same convergence and solution quality as the baseline cross-entropy approach, but achieves runtime and sample size improvements on the order of 450% to 1500% over the example networks tested. Our final contribution regards the development and extension of the general ranking and selection literature with novel procedures for the problem concerned with the selection of the -best systems, where system means and variances are unknown and potentially unequal. We present three new ranking and selection procedures: a subset selection procedure, an indifference zone selection procedure, and a combined two stage subset selection and indifference zone selection procedure. All procedures are backed by proofs of the theoretical guarantees as well as empirical results on the probability of correct selection. We also investigate the effect of various parameters on each procedure's overall performance.

PhD thesis deals with application of simulation and optimization methods in the field of waste and biomass utilization for energy purposes. Current situation in this field is described in the introductory. Following chapters deal with approaches of creating mathematical models of apparatus and processes used in the field of interest for simulation and optimization purposes. Stochastic methods, which are widely applied for real problems solution, are mentioned as well. The core of the thesis consists in proposal of systematic approach and its application for simulation and optimization model building used in the field of interest. The application is demonstrated through two case studies. The first one deals with the building of simulation model of an existing waste-to-energy plant using its operation data. The second study deals with optimization model building and its application for a problem regarding utilization of biomass in an existing energy system.

No description available.

No description available.