A heuristic for nonlinear global optimization

We propose a new heuristic for nonlinear global optimization combining a variable neighborhood search framework with a modified trust-region algorithm as local search. The proposed method presents the capability to prematurely interrupt the local search if the iterates are converging to a local minimum that has already been visited or if they are reaching an area where no significant improvement can be expected. The neighborhoods, as well as the neighbors selection procedure, are exploiting the curvature of the objective function. Numerical tests are performed on a set of unconstrained nonlinear problems from the literature. Results illustrate that the new method significantly outperforms existing heuristics from the literature in terms of success rate, CPU time, and number of function evaluations

Kicking the habit is hard: A hybrid choice model investigation into the role of addiction in smoking behavior

Use of choice models is growing rapidly in tobacco research. These models are being used to answer key policy questions. However, certain aspects of smokers' choice behavior are not well understood. One such feature is addiction. Here, we address this issue by modeling data from a choice experiment on the US smokers. We model addiction using a latent variable. We use this latent variable to understand the relationship between choices and addiction, giving attention to nicotine levels. We find that more addicted smokers have stronger preferences for cigarettes and are unwilling to switch to e‐cigarettes. Addicted smokers value nicotine in tobacco products to a much greater extent than those that are less addicted. Lastly, we forecast short‐term responses to lowering nicotine levels in cigarettes. The results suggest that current nicotine‐focused policies could be effective at encouraging addicted smokers to less harmful products and lead to substantial public health gains

Testing for saliency-led choice behavior in discrete choice modeling: an application in the context of preferences towards nuclear energy in Italy

This work proposes a discrete choice model that jointly accounts for heterogeneity in preferences and in decision making procedures adopted by respondents, as well as for non-linearities in the utility function, allowing for the potential effect of salient attributes in choice experiments. We present an innovative application in the context of preferences towards nuclear energy, with data obtained from a nationwide online survey conducted in Italy. Results show that most of the variation in the choice data is indeed due to heterogeneity in the decision process, where the saliency heuristic plays an important role. Furthermore, the proposed model provides more conservative monetary valuations as opposed to standard models, potentially leading to sub-stantial differences in cost-benefit analysis. Implications for choice modeling practitioners are discussed, emphasizing the need to account for saliency effects when modeling the choice data

Preferences for coastal and marine conservation in Vietnam: Accounting for differences in individual choice set formation

This paper has two objectives. The first is to estimate the value of implementing new coastal and marine conservation measures in Vietnam, focussing on the relative benefits of water quality improvements, coral conservation and control of marine plastic pollution. The second is to explicitly model any tendency of respondents to fail to give consideration to the “opt-out” or status quo option in a choice experiment, due to social and cultural factors. The analysis employs the independent availability logit model with random coefficients to simultaneously account for heterogeneity of preferences and choice set formation. Results show significantly improved model fit when consideration set heterogeneity is taken into account. However, estimates of preference weights and marginal willingness to pay for marine conservation measures are unaffected by this modelling choice

Quantum probability: A new method for modelling travel behaviour

There has been an increasing effort to improve the behavioural realism of mathematical models of choice, resulting in efforts to move away from random utility maximisation (RUM) models. Some new insights have been generated with, for example, models based on random regret minimisation (RRM, μ-RRM). Notwithstanding work using for example Decision Field Theory (DFT), many of the alternatives to RUM tested on real-world data have however only looked at only modest departures from RUM, and differences in results have consequently been small. In the present study, we address this research gap again by investigating the applicability of models based on quantum theory. These models, which are substantially different from the state-of-the-art choice modelling techniques, emphasise the importance of contextual effects, state dependence, interferences and the impact of choice or question order. As a result, quantum probability models have had some success in better explaining several phenomena in cognitive psychology. In this paper, we consider how best to operationalise quantum probability into a choice model. Additionally, we test the quantum model frameworks on a best/worst route choice dataset and demonstrate that they find useful transformations to capture differences between the attributes important in a most favoured alternative compared to that of the least favoured alternative. Similar transformations can also be used to efficiently capture contextual effects in a dataset where the order of the attributes and alternatives are manipulated. Overall, it appears that models incorporating quantum concepts hold significant promise in improving the state-of-the-art travel choice modelling paradigm through their adaptability and efficient modelling of contextual changes

Modifications of the variable neighborhood search method and their applications to solving the file transfer scheduling problem

Metoda promenljivih okolina se u praksi pokazala vrlo uspesnom za resavanje pro- blema diskretne i kontinualne optimizacije. Glavna ideja ove metode je sistematska promena okolina unutar prostora resenja u potrazi za boljim resenjem. Za opti- mizaciju funkcija vise promenljivih koriste se metode koje nalaze lokalni minimum polazeci od zadate pocetne tacke. U slucaju kada kontinualna funkcija ima mnostvo lokalnih minimuma, nalazenje globalnog minimuma obicno nije lak zadatak jer najcesce dostignuti lokalni minimumi nisu optimalni. Kod uobicajenih implementa- cija sa ogranicenim okolinama razlicitih dijametara iz proizvoljne tacke nije moguce dostici sve tacke prostora resenja. Zbog toga je strategija koriscenja konacnog broja ogranicenih okolina primenjiva na probleme kod kojih optimalno resenje pripada nekom unapred poznatom ogranicenom podskupu skupa IRn. U cilju prevazilazenja pomenutog ogranicenja predlozena je nova varijanta meto- de, Gausovska metoda promenljivih okolina. Umesto denisanja niza razlicitih okolina iz kojih ce se birati slucajna tacka, u ovoj metodi se sve okoline pokla- paju sa celim prostorom resenja, a slucajne tacke se generisu koriscenjem razlicitih slucajnih raspodela Gausovog tipa. Na ovaj nacin se i tacke na vecem rastojanju od tekuce tacke mogu teorijski dostici mada sa manjom verovatnocom. U osnovnoj verziji metode promenljivih okolina neophodno je unapred denisati sistem okolina, njihov ukupan broj i velicinu, kao i tip raspodele koja ce se koristiti za odabir slucajne tacke unutar tih okolina. Gausovska metoda promenljivih okolina za razliku od osnovne verzije ima manje parametara jer su sve okoline teorijski iste velicine (jednake celom prostoru pretrage) i imaju jedinstvenu jednoparametarsku familiju raspodela Gausovu raspodelu slucajnih brojeva sa promenljivom dispe- rzijom. Problem raspored-ivanja prenosa datoteka (File transfer scheduling problem - FTSP) je optimizacioni problem koji svoju primenu pronalazi u mnogim oblastima poput telekomunikacijama, LAN i WAN mrezama, raspored-ivanju u okviru MIMD (multiple instruction multiple data) racunarskih sistema i dr. Spada u klasu NP teskih problema za cije resavanje se uobicajeno koriste heuristicke metode. Za- datak optimizacije FTSP sastoji se u trazenju odgovarajuceg rasporeda pojedinacnih prenosa datoteka, tj. vremenskih trenutaka kada ce svaka datoteka zapoceti svoj prenos tako da duzina vremenskog intervala od trenutka kada prva datoteka zapocne prenos do trenutka u kom poslednja zavrsi bude sto manja...The Variable neighborhood search method proved to be very successful for solving discrete and continuous optimization problems. The basic idea is a systematic change of neighborhood structures in search for the better solution. For optimiza- tion of multiple variable functions, methods for obtaining the local minimum starting from certain initial point are used. In case when the continuous function has many local minima, nding the global minimum is usually not an easy task since the obta- ined local minima in most cases are not optimal. In typical implementations with bounded neighborhoods of various diameters it is not possible, from arbitrary point, to reach all points in solution space. Consequently, the strategy of using the nite number of neighborhoods is suitable for problems with solutions belonging to some known bounded subset of IRn. In order to overcome the previously mentioned limitation the new variant of the method is proposed, Gaussian Variable neighborhood search method. Instead of dening the sequence of dierent neighborhoods from which the random point will be chosen, all neighborhoods coincide with the whole solution space, but with die- rent probability distributions of Gaussian type. With this approach, from arbitrary point another more distant point is theoretically reachable, although with smaller probability. In basic version of Variable neighborhood search method one must dene in advance the neighborhood structure system, their number and size, as well as the type of random distribution to be used for obtaining the random point from it. Gaussian Variable neighborhood search method has less parameters since all the neighborhoods are theoretically the same (equal to the solution space), and uses only one distribution family - Gaussian multivariate distribution with variable dispersion. File transfer scheduling problem (FTSP) is an optimization problem widely appli- cable to many areas such as Wide Area computer Networks (WAN), Local Area Ne- tworks (LAN), telecommunications, multiprocessor scheduling in a MIMD machines, task assignments in companies, etc. As it belongs to the NP-hard class of problems, heuristic methods are usually used for solving this kind of problems. The problem is to minimize the overall time needed to transfer all les to their destinations for a given collection of various sized les in a computer network, i.e. to nd the le transfer schedule with minimal length..

Dealing with Correlations in Discrete Choice Models

The focus of this thesis is to develop methods to address research challenges related to correlation patterns in discrete choice models. In the context of correlations within alternatives, we extend the novel methodology of the multiple indicator solution (MIS) to deal with endogeneity, and show, through its theoretical derivation, that it is applicable when there are interactions between observed and unobserved variables. In the context of correlations between alternatives, we discuss the importance of using models that can capture them, such as cross nested logit models. We show, through real world examples, that ignoring these correlation patterns can have severe impacts on the obtained demand indicators, and that this can lead to wrong decisions by practitioners. We also address the challenge of using revealed preference data, where the attributes of the non-chosen alternatives are unavailable, and propose a solution based on multiple imputations of their empirical distributions. In the thesis, we also contribute to the existing literature by gaining a better understanding of private motorized modes, in terms of modal split and purchases of new cars. Related to modal split, we use a mode choice case study in low density areas of Switzerland. We find that ignoring the car-loving attitude of individuals leads to incorrect value of time estimates and elasticities, which might have severe implications in the pricing schemes of public transportation, for example. Related to the purchase of new cars, we use data from new car acquisitions in France in 2014, and focus on hybrid and electric vehicles. We find elasticities to price that are in line with the literature, and willingness to pay values in line with the market conditions. We also study the impact of different future policy scenarios and find that the sales of new electric vehicles could reach around 1% as a result of a major technological innovation that would render electric vehicles less expensive. In the last part of the thesis, we propose the discrete-continuous maximum likelihood (DCML) framework, which consists in estimating discrete and continuous parameters simultaneously. This innovative idea, opens the door to new research avenues, where decisions that were usually taken by the analyst can now be data driven. As an illustration, we show that correlations between alternatives can be identified at the estimation level, and do not need to be assumed by the analyst. The DCML framework consists in a mixed integer linear program (MILP) in which the log-likelihood estimator is linearized. This linearization might be useful to estimate parameters of other discrete choice models for which the log-likelihood function is not concave (and therefore global optimality is not insured by the optimization algorithms), since for an MILP, a global optimum is guaranteed. We use a simple mode choice case study for the proof-of-concept of the DCML framework, and use it to investigate its strengths and limitations. The preliminary results presented in the thesis seem very promising. To summarize, we develop methods to deal with correlations in discrete choice models that are relevant to real world problems, and show their applicability by using transportation examples. The contributions are therefore both theoretical and applied. The new methods proposed open the door to new research directions in the discrete choice field

A Heuristic for Nonlinear Global Optimization

We propose a new heuristic for nonlinear global optimization combining a variable neighborhood search framework with a modified trust-region algorithm as local search. The proposed method presents the capability to prematurely interrupt the local search if the iterates are converging to a local minimum that has already been visited or if they are reaching an area where no significant improvement can be expected. The neighborhoods, as well as the neighbors selection procedure, are exploiting the curvature of the objective function. Numerical tests are performed on a set of unconstrained nonlinear problems from the literature. Results illustrate that the new method significantly outperforms existing heuristics from the literature in terms of success rate, CPU time, and number of function evaluations

A heuristic for nonlinear global optimization relevant to discrete choice models estimation

In most applications related to transportation, it is of major importance to be able to identify the global optimum of the associated optimization problem. The work we present in this paper is motivated by the optimization problems arising in the maximum likelihood estimation of discrete choice models. Estimating those models becomes more and more problematic as several issues may occur in the estimation. We focus our interest on the non-concavity of the log-likelihood function which can present several (and often many) local optima in the case of advanced models. In this context, we propose a new heuristic for nonlinear global optimization combining a variable neighborhood search framework with a modified trust-region algorithm as local search. The proposed method presents the capability to prematurely interrupt the local search if the iterates are converging to a local minimum which has already been visited or if they are reaching an area where no significant improvement can be expected. The neighborhoods as well as the neighbors selection procedure are exploiting the curvature of the objective function. Numerical tests are performed on a set of unconstrained nonlinear problems from the literature. Results illustrate that the new method significantly outperforms existing heuristics from the literature in terms of success rate, CPU time, and number of function evaluations