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.

Coastal zone is the region of rapid socio-economic development and the habitat of various marine lives. With the increasing complexity of land use, integrated spatial planning is important and indispensable to the development of coastal zone. Coastal environment is a complicated system with highly dynamic environment, coastal zone planning needs to achieve the objectives of environmental protection, economic development and ecological sustainability. To solve this planning problem, either analytic or simulation approaches have been used. However these two approaches have their own demerits in problem solving. The purpose of this research is to develop a model combing analytic and simulation methods to solve spatial planning problem in complicated dynamic marine environment. Through combined with Multi-Objective Programming, Fuzzy Sets Theory and System Dynamics, a spatial planning model of dynamic multi-objective programming approach to the coastal zone spatial planning will be constructed. It could be used in coastal zone development predicting, regional and city planning and marine policy decision making.

Multiple objectives are often used by agencies trying to manage pavement networks. Often alternative investment strategies can accomplish the agencies’ target objectives. If the goal is to achieve the target objectives at the minimum cost, an approach is needed to assist agencies in identifying investment strategies capable of meeting the targets while minimizing costs. The approach used by the agency should not be limited to an analytical method to mathematically solve the funding allocation problem. Finding mechanisms to ensure the sustainability and efficiency of the investment strategy over time is a great challenge that needs to be addressed by the approach. The challenge is even greater for local agencies where resources are usually limited. This research develops a multi-objective strategic management approach oriented to improving decisions for pavement management practices in local agencies. In this approach, target objectives are tied to key pavement network parameters in the management process. A methodology to identify the best combination of projects to meet target objectives at the minimum cost while maximizing treatment effectiveness is provided as a result of the research. Concepts from the pavement management program (PMP) of the Metropolitan Transportation Commission (MTC) of the San Francisco Bay Area were used as a basis for developing the methodology. Four pavement network parameters are considered for setting the target objectives over the agency’s planning horizon: the average network pavement condition index (PCI), average network remaining life, percent of the pavement network in good condition, and percent of the pavement network in poor and very poor condition. Results from a case study show that funding allocation methods influence the allocation of preservation and rehabilitation funds among pavement network groups, affecting budget estimates and future condition of the pavement network. It is also concluded that the use of mechanisms that facilitate data integration and the flow of knowledge across management levels can contribute to making better informed decisions. Hence, the adoption of the multi-objective strategic pavement management approach developed in this dissertation should lead to identifying more efficient investment strategies for achieving the pavement network state desired by a local agency at a minimum cost.

In this thesis, we address the route selection problem for Unmanned Air Vehicles (UAV) under multiple objectives. We consider a general case for this problem where the UAV has to visit several targets and return to the base. For this case, there are multiple combinatorial problems to be considered. First, the paths to be followed between any pairs of targets should be determined. This part can be considered as a multi-objective shortest path problem. Additionally, we need to determine the order of the targets to be visited. This in turn, is a multi-objective traveling salesperson problem. The overall problem is a combination of these two combinatorial problems. The route selection for UAVs has been studied by several researchers, mainly in the military context. They considered a linear combination of the two objectives / minimizing distance traveled and minimizing radar detection threat / and proposed heuristics for the minimization of the composite single objective problem. We treat these two objectives separately. We develop an evolutionary algorithm to determine the efficient tours. We also consider an exact interactive approach to identify the best paths and tours of a decision maker. We tested the two solution approaches on both small-sized and large-sized problem instances.

Desirability Functions continue to attract attention of scientists and researchers working in the area of multi-response optimization. There are many versions of such functions, differing mainly in formulations of individual and overall desirability functions. Derringer and Suich&rsquo / s desirability functions being used throughout this thesis are still the most preferred ones in practice and many other versions are derived from these. On the other hand, they have a drawback of containing nondifferentiable points and, hence, being nonsmooth. Current approaches to their optimization, which are based on derivative-free search techniques and modification of the functions by higher-degree polynomials, need to be diversified considering opportunities offered by modern nonlinear (global) optimization techniques and related softwares. A first motivation of this work is to develop a new efficient solution strategy for the maximization of overall desirability functions which comes out to be a nonsmooth composite constrained optimization problem by nonsmooth optimization methods. We observe that individual desirability functions used in practical computations are of mintype, a subclass of continuous selection functions. To reveal the mechanism that gives rise to a variation in the piecewise structure of desirability functions used in practice, we concentrate on a component-wise and generically piecewise min-type functions and, later on, max-type functions. It is our second motivation to analyze the structural and topological properties of desirability functions via piecewise max-type functions. In this thesis, we introduce adjusted desirability functions based on a reformulation of the individual desirability functions by a binary integer variable in order to deal with their piecewise definition. We define a constraint on the binary variable to obtain a continuous optimization problem of a nonlinear objective function including nondifferentiable points with the constraints of bounds for factors and responses. After describing the adjusted desirability functions on two well-known problems from the literature, we implement modified subgradient algorithm (MSG) in GAMS incorporating to CONOPT solver of GAMS software for solving the corresponding optimization problems. Moreover, BARON solver of GAMS is used to solve these optimization problems including adjusted desirability functions. Numerical applications with BARON show that this is a more efficient alternative solution strategy than the current desirability maximization approaches. We apply negative logarithm to the desirability functions and consider the properties of the resulting functions when they include more than one nondifferentiable point. With this approach we reveal the structure of the functions and employ the piecewise max-type functions as generalized desirability functions (GDFs). We introduce a suitable finite partitioning procedure of the individual functions over their compact and connected interval that yield our so-called GDFs. Hence, we construct GDFs with piecewise max-type functions which have efficient structural and topological properties. We present the structural stability, optimality and constraint qualification properties of GDFs using that of max-type functions. As a by-product of our GDF study, we develop a new method called two-stage (bilevel) approach for multi-objective optimization problems, based on a separation of the parameters: in y-space (optimization) and in x-space (representation). This approach is about calculating the factor variables corresponding to the ideal solutions of each individual functions in y, and then finding a set of compromised solutions in x by considering the convex hull of the ideal factors. This is an early attempt of a new multi-objective optimization method. Our first results show that global optimum of the overall problem may not be an element of the set of compromised solution. The overall problem in both x and y is extended to a new refined (disjunctive) generalized semi-infinite problem, herewith analyzing the stability and robustness properties of the objective function. In this course, we introduce the so-called robust optimization of desirability functions for the cases when response models contain uncertainty. Throughout this thesis, we give several modifications and extensions of the optimization problem of overall desirability functions.

In this study, a bi-objective capacitated facility location problem is presented which includes partial coverage concept and relocation of facility nodes. In partial coverage, a predefined distance between a demand node and a facility node is assumed to be fully covered. After the predefined distance, the service level commences to decay linearly. The problem is designed to consider the existence of already functioning facility nodes. It is allowed to close these existing facilities and open new facilities in potential sites. However, existing facility nodes are strongly favored against new facility nodes. The objectives are the maximization of the weighted total coverage and the minimization of number of facility nodes. A novel interactive multi-objective evolutionary algorithm is proposed to solve this problem, I-TREA. I-TREA is originated from NSGA-II and designed for interactive methods benefiting from quality infeasible solutions. The performance of I-TREA is benchmarked with a modified version of NSGA-II on randomly generated problems with various sizes and utility functions.

The architectures of the control devices in active control algorithm are an important fact in civil structural buildings. Traditional research has limitations in finding the optimal architecture of control devices such as using predefined numbers or locations of sensors and dampers within the 2-and 3-dimensional (3-D) model of the structure. Previous research using single-objective optimization only provides limited data for defining the architecture of sensors and control devices. The Linear Quadratic Gaussian (LQG) control algorithm is used as the active control strategy. The American Society of Civil Engineers (ASCE) control benchmark building definition is used to develop the building system model. The proposed gene manipulation genetic algorithm (GMGA) determines the near-optimal Pareto fronts which consist of varying numbers and locations of sensors and control devices for controlling the ASCE benchmark building by considering multi-objectives such as interstory drift and minimizing the number of the control devices. The proposed GMGA reduced the central processing unit (CPU) run time and produced more optimal Pareto fronts for the 2-D and 3-D 20-story building models. Using the GMGA provided several benefits: (1) the possibility to apply any presuggested multi-objective optimization mechanism; (2) the availability to perform a objective optimization problem; (3) the adoptability of the diverse encoding provided by the GA; (4) the possibility of including the engineering judgment in generating the next generation population by using a gene creation mechanisms; and (5) the flexibility of the gene creation mechanism in applying and changing the mechanism dependent on optimization problem. The near-optimal Pareto fronts obtained offer the structural engineer a diverse choice in designing control system and installing the control devices. The locations and numbers of the dampers and sensors in each story are highly dependent on the sensor locations. By providing near-Pareto fronts of possible solutions to the engineer that also consider diverse earthquakes, the engineer can get normalized patterns of architectures of control devices and sensors about random earthquakes.

<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>

<p>Most real-world optimization problems behave stochastically. Evolutionary optimization algorithms have to cope with the uncertainty in order to not loose a substantial part of their performance. There are different types of uncertainty and this thesis studies the type that is commonly known as noise and the use of resampling techniques as countermeasure in multi-objective evolutionary optimization. Several different types of resampling techniques have been proposed in the literature. The available techniques vary in adaptiveness, type of information they base their budget decisions on and in complexity. The results of this thesis show that their performance is not necessarily increasing as soon as they are more complex and that their performance is dependent on optimization problem and environment parameters. As the sampling budget or the noise level increases the optimal resampling technique varies. One result of this thesis is that at low computing budgets or low noise strength simple techniques perform better than complex techniques but as soon as more budget is available or as soon as the algorithm faces more noise complex techniques can show their strengths. This thesis evaluates the resampling techniques on standard benchmark functions. Based on these experiences insights have been gained for the use of resampling techniques in evolutionary simulation optimization of real-world problems.</p>

Optimization is a mathematical technique that concerns the finding of maxima or minima of functions in some feasible region. There is no business or industry which is not involved in solving optimization problems. A variety of optimization techniques compete for the best solution. Particle Swarm Optimization (PSO) is a relatively new, modern, and powerful method of optimization that has been empirically shown to perform well on many of these optimization problems. It is widely used to find the global optimum solution in a complex search space. This thesis aims at providing a review and discussion of the most established results on PSO algorithm as well as exposing the most active research topics that can give initiative for future work and help the practitioner improve better result with little effort. This paper introduces a theoretical idea and detailed explanation of the PSO algorithm, the advantages and disadvantages, the effects and judicious selection of the various parameters. Moreover, this thesis discusses a study of boundary conditions with the invisible wall technique, controlling the convergence behaviors of PSO, discrete-valued problems, multi-objective PSO, and applications of PSO. Finally, this paper presents some kinds of improved versions as well as recent progress in the development of the PSO, and the future research issues are also given.