Stochastic programming is an optimization technique that incorporates random variables as parameters. Because it better reflects the uncertain real world than its traditional deterministic counterpart, stochastic programming has drawn increasingly more attention among decision-makers, and its applications span many fields including financial engineering, health care, communication systems, and supply chain management. On the flip side, stochastic programs are usually very difficult to solve, which is further compounded by the fact that in many of the aforementioned applications, we also have discrete decisions, thereby rendering these problems even more challenging. In this dissertation, we study the class of two-stage stochastic mixed-integer programs (SMIP), which, as its name suggests, lies at the confluence of two formidable classes of problems. We design a novel algorithm for this class of problems, and also explore specialized approaches for two related real-world applications. Although a number of algorithms have been developed to solve two-stage SMIPs, most of them deal with problems containing purely integer or continuous variables in either or both of the two stages, and frequently require the technology and/or recourse matrices to be deterministic. As a ground-breaking effort, in this work, we address the challenging class of two-stage SMIPs that involve 0-1 mixed-integer variables in both stages. The only earlier work on solving such problems (Carøe and Schultz (1999)) requires the optimization of several non-smooth Lagrangian dual problems using subgradient methods in the bounding process, which turns out to be computationally very expensive. We begin with proposing a decomposition-based branch-and-bound (DBAB) algorithm for solving two-stage stochastic programs having 0-1 mixed-integer variables in both stages. Since the second-stage problems contain binary variables, their value functions are in general nonconvex and discontinuous; hence, the classical Benders' decomposition approach (or the L-shaped method) for solving two-stage stochastic programs, which requires convex subproblem value functions, cannot be directly applied. This motivates us to relax the second-stage problems and accompany this relaxation with a convexification process. To make this process computationally efficient, we propose to construct a certain partial convex hull representation of the two-stage solution space, using the relaxed second-stage constraints and the restrictions confining the first-stage variables to lie within some hyperrectangle. This partial convex hull is sequentially generated using a convexification scheme, such as the Reformulation-Linearization Technique (RLT), which yields valid inequalities that are functions of the first-stage variables and, of noteworthy importance, are reusable in the subsequent subproblems by updating the values of the first-stage variables. Meanwhile, since the first stage contains continuous variables, whenever we tentatively fix these variables at some given feasible values, the resulting constraints may not be facial with respect to the associated bounding constraints that are used to construct the partial convex hull. As a result, the constructed Benders' subproblems define lower bounds for the second-stage value functions, and likewise, the resulting Benders' master problem provides a lower bound for the original stochastic program defined over the same hyperrectangle. Another difficulty resulting from continuous first-stage variables is that when the given first-stage solution is not extremal with respect to its bounds, the second-stage solution obtained for a Benders' subproblem defined with respect to a partial convex hull representation in the two-stage space may not satisfy the model's binary restrictions. We thus need to be able to detect whether or not a Benders' subproblem is solved by a given fractional second-stage solution. We design a novel procedure to check this situation in the overall algorithmic scheme. A key property established, which ensures global convergence, is that these lower bounds become exact if the given first-stage solution is a vertex of the defining hyperrectangle, or if the second-stage solution satisfies the binary restrictions. Based on these algorithmic constructs, we design a branch-and-bound procedure where the branching process performs a hyperrectangular partitioning of the projected space of the first-stage variables, and lower bounds for the nodal problems are computed by applying the proposed modified Benders' decomposition method. We prove that, when using the least-lower-bound node-selection rule, this algorithm converges to a global optimal solution. We also show that the derived RLT cuts are not only reusable in subsequent Benders iterations at the same node, but are also inheritable by the subproblems of the children nodes. Likewise, the Benders' cuts derived for a given sub-hyperrectangle can also be inherited by the lower bounding master programs solved for its children nodes. Using these cut inheritance properties results in significant savings in the overall computational effort. Some numerical examples and computational results are presented to demonstrate the efficacy of this approach. The sizes of the deterministic equivalent of our test problems range from having 386 continuous variables, 386 binary variables, and 386 constraints, up to 1795 continuous variables, 1539 binary variables, and 1028 constraints. The results reveal an average savings in computational effort by a factor of 9.5 in comparison with using a commercial mixed-integer programming package (CPLEX 8.1) on a deterministic equivalent formulation. We then explore an important application of SMIP to enhance the traditional airline fleet assignment models (FAM). Given a flight schedule network, the fleet assignment problem solved by airline companies is concerned with assigning aircraft to flight legs in order to maximize profit with respect to captured path- or itinerary-based demand. Because certain related crew scheduling regulations require early information regarding the type of aircraft serving each flight leg, the current practice adopted by airlines is to solve the fleet assignment problem using estimated demand data 10-12 weeks in advance of departure. Given the level of uncertainty, deterministic models at this early stage are inadequate to obtain a good match of aircraft capacity with passenger demands, and revisions to the initial fleet assignment become naturally pertinent when the observed demand differs considerably from the assigned aircraft capacities. From this viewpoint, the initial decision should embrace various market scenarios so that it incorporates a sufficient look-ahead feature and provides sufficient flexibility for the subsequent re-fleeting processes to accommodate the inevitable demand fluctuations. With this motivation, we propose a two-stage stochastic programming approach in which the first stage is concerned with the initial fleet assignment decisions and, unlike the traditional deterministic methodology, focuses on making only a family-level assignment to each flight leg. The second stage subsequently performs the detailed assignments of fleet types within the allotted family to each leg under each of the multiple potential scenarios that address corresponding path- or itinerary-based demands. In this fashion, the initial decision of what aircraft family should serve each flight leg accomplishes the purpose of facilitating the necessary crew scheduling decisions, while judiciously examining the outcome of future re-fleeting actions based on different possible demand scenarios. Hence, when the actual re-fleeting process is enacted several weeks later, this anticipatory initial family-level assignment will hopefully provide an improved overall fleet type re-allocation that better matches demand. This two-stage stochastic model is complemented with a secondary model that performs adjustments within each family, if necessary, to provide a consistent fleet type-assignment information for accompanying decision processes, such as yield management. We also propose several enhanced fleet assignment models, including a robust optimization model that controls decision variation among scenarios and a stochastic programming model that considers the recapture effect of spilled demand. In addition to the above modeling concepts and framework, we also contribute in developing effective solution approaches for the proposed model, which is a large-scale two-stage stochastic 0-1 mixed-integer program. Because the most pertinent information needed from the initial fleet assignment is at the family level, and the type-level assignment is subject to change at the re-fleeting stage according to future demand realizations, our solution approach focuses on assigning aircraft families to the different legs in the flight network at the first stage, while finding relaxed second-stage solutions under different demand scenarios. Based on a polyhedral study of a subsystem extracted from the original model, we derive certain higher-dimensional convex hull as well as partial convex hull representations for this subsystem. Accordingly, we propose two variants for the primary model, both of which relax the binary restrictions on the second-stage variables, but where the second variant then also accommodates the partial convex hull representations, yielding a tighter, albeit larger, relaxation. For each variant, we design a suitable solution approach predicated on Benders' decomposition methodology. Using certain realistic large-scale flight network test problems having 900 flight legs and 1,814 paths, as obtained from United Airlines, the proposed stochastic modeling approach was demonstrated to increase daily expected profits by about 3% (which translates to about $160 million per year) in comparison with the traditional deterministic model in present usage, which considers only the expected demand. Only 1.6% of the second-stage binary variables turn out to be fractional in the first variant, and this number is further reduced to 1.2% by using the tighter variant. Furthermore, when attempting to solve the deterministic equivalent formulation for these two variants using a commercial mixed-integer programming package (CPLEX 8.1), both the corresponding runs were terminated after reaching a 25-hour cpu time limit. At termination, the software was still processing the initial LP relaxation at the root node for each of these runs, and no feasible basis was found. Using the proposed algorithms, on the other hand, the solution times were significantly reduced to 5 and 19 hours for the two variants, respectively. Considering that the fleet assignment models are solved around three months in advance of departure, this solution time is well acceptable at this early planning stage, and the improved quality in the solution produced by considering the stochasticity in the system is indeed highly desirable. Finally, we address another practical workforce planning problem encountered by a global financial firm that seeks to manage multi-category workforce for functional areas located at different service centers, each having office-space and recruitment-capacity constraints. The workforce demand fluctuates over time due to market uncertainty and dynamic project requirements. To hedge against the demand fluctuations and the inherent uncertainty, we propose a two-stage stochastic programming model where the first stage makes personnel recruiting and allocation decisions, while the second stage, based on the given personnel decision and realized workforce demand, decides on the project implementation assignment. The second stage of the proposed model contains binary variables that are used to compute and also limit the number of changes to the original plan. Since these variables are concerned with only one quality aspect of the resulting workforce plan and do not affect feasibility issues, we replace these binary variables with certain conservative policies regarding workforce assignment change restrictions in order to obtain more manageable subproblems that contain purely continuous variables. Numerical experiments reveal that the stochastic programming approach results in significantly fewer alterations to the original workforce plan. When using a commercial linear programming package CPLEX 9.0 to solve the deterministic equivalent form directly, except for a few small-sized problems, this software failed to produce solutions due to memory limitations, while the proposed Benders' decomposition-based solution approach consistently solved all the practical-sized test problems with reasonable effort. To summarize, this dissertation provides a significant advancement in the algorithmic development for solving two-stage stochastic mixed-integer programs having 0-1 mixed-integer variables in both stages, as well as in its application to two important contemporary real-world applications. The framework for the proposed solution approaches is to formulate tighter relaxations via partial convex hull representations and to exploit the resulting structure using suitable decomposition methods. As decision robustness is becoming increasingly relevant from an economic viewpoint, and as computer technological advances provide decision-makers the ability to explore a wide variety of scenarios, we hope that the proposed algorithms will have a notable positive impact on solving stochastic mixed-integer programs. In particular, the proposed stochastic programming airline fleet assignment and the workforce planning approaches studied herein are well-poised to enhance the profitability and robustness of decisions made in the related industries, and we hope that similar improvements are adapted by more industries where decisions need to be made in the light of data that is shrouded by uncertainty. / Ph. D.

Air transportation offers both passenger and freight services that are essential for economic growth and development. In a highly competitive environment, airline companies have to control their operating costs by managing their flights, aircraft, and crews effectively. This motivates the extensive use of analytical techniques to solve complex problems related to airline operations planning, which includes schedule design, fleet assignment, aircraft routing, and crew scheduling. The initial problem addressed by airlines is that of schedule design, whereby a set of flights having specific origin and destination cities as well as departure and arrival times is determined. Then, a fleet assignment problem is solved to assign an aircraft type to each flight so as to maximize anticipated profits. This enables a decomposition of subsequent problems according to the different aircraft types belonging to a common family, for each of which an aircraft routing problem and a crew scheduling or pairing problem are solved. Here, in the aircraft routing problem, a flight sequence or route is built for each individual aircraft so as to cover each flight exactly once at a minimum cost while satisfying maintenance requirements. Finally, in the crew scheduling or pairing optimization problem, a minimum cost set of crew rotations or pairings is constructed such that every flight is assigned a qualified crew and that work rules and collective agreements are satisfied. In practice, most airline companies solve these problems in a sequential manner to plan their operations, although recently, an increasing effort is being made to develop novel approaches for integrating some of the airline operations planning problems while retaining tractability. This dissertation formulates and analyzes three different models, each of which examines a composition of certain pertinent airline operational planning problems. A comprehensive fourth model is also proposed, but is relegated for future research. In the first model, we integrate fleet assignment and schedule design by simultaneously considering optional flight legs to select along with the assignment of aircraft types to all scheduled legs. In addition, we consider itinerary-based demands pertaining to multiple fare-classes. A polyhedral analysis of the proposed mixed-integer programming model is used to derive several classes of valid inequalities for tightening its representation. Solution approaches are developed by applying Benders decomposition method to the resulting lifted model, and computational experiments are conducted using real data obtained from a major U.S. airline (United Airlines) to demonstrate the efficacy of the proposed procedures as well as the benefits of integration. A comparison of the experimental results obtained for the basic integrated model and for its different enhanced representations reveals that the best modeling strategy among those tested is the one that utilizes a variety of five types of valid inequalities for moderately sized problems, and further implements a Benders decomposition approach for relatively larger problems. In addition, when a heuristic sequential fixing step is incorporated within the algorithm for even larger sized problems, the computational results demonstrate a less than 2% deterioration in solution quality, while reducing the effort by about 21%. We also performed an experiment to assess the impact of integration by comparing the proposed integrated model with a sequential implementation in which the schedule design is implemented separately before the fleet assignment stage based on two alternative profit maximizing submodels. The results obtained demonstrate a clear advantage of utilizing the integrated model, yielding an 11.4% and 5.5% increase in profits in comparison with using the latter two sequential models, which translates to an increase in annual profits by about $28.3 million and $13.7 million, respectively. The second proposed model augments the first model with additional features such as flexible flight times (i.e., departure time-windows), schedule balance, and demand recapture considerations. Optional flight legs are incorporated to facilitate the construction of a profitable schedule by optimally selecting among such alternatives in concert with assigning the available aircraft fleet to all the scheduled legs. Moreover, network effects and realistic demand patterns are effectively represented by examining itinerary-based demands as well as multiple fare-classes. Allowing flexibility on the departure times of scheduled flight legs within the framework of an integrated model increases connection opportunities for passengers, hence yielding robust schedules while saving fleet assignment costs. A provision is also made for airlines to capture an adequate market share by balancing flight schedules throughout the day. Furthermore, demand recapture considerations are modeled to more realistically represent revenue realizations. For this proposed mixed-integer programming model, which integrates the schedule design and fleet assignment processes while considering flexible flight times, schedule balance, and recapture issues, along with optional legs, itinerary-based demands, and multiple fare-classes, we perform a polyhedral analysis and utilize the Reformulation-Linearization Technique in concert with suitable separation routines to generate valid inequalities for tightening the model representation. Effective solution approaches are designed by applying Benders decomposition method to the resulting tightened model, and computational results are presented to demonstrate the efficacy of the proposed procedures. Using real data obtained from United Airlines, when flight times were permitted to shift by up to 10 minutes, the estimated increase in profits was about $14.9M/year over the baseline case where only original flight legs were used. Also, the computational results indicated a 1.52% and 0.49% increase in profits, respectively, over the baseline case, while considering two levels of schedule balance restrictions, which can evidently also enhance market shares. In addition, we measured the effect of recaptured demand with respect to the parameter that penalizes switches in itineraries. Using values of the parameter that reflect 1, 50, 100, or 200 dollars per switched passenger, this yielded increases in recaptured demand that induced additional profits of 2.10%, 2.09%, 2.02%, and 1.92%, respectively, over the baseline case. Overall, the results obtained from the two schedule balance variants of the proposed integrated model that accommodate all the features of flight retiming, schedule balance, and demand recapture simultaneously, demonstrated a clear advantage by way of $35.1 and $31.8 million increases in annual profits, respectively, over the baseline case in which none of these additional features is considered. In the third model, we integrate the schedule design, fleet assignment, and aircraft maintenance routing decisions, while considering optional legs, itinerary-based demands, flexible flight retimings, recapture, and multiple fare-classes. Instead of utilizing the traditional time-space network (TSN), we formulate this model based on a flight network (FN) that provides greater flexibility in accommodating integrated operational considerations. In order to consider through-flights (i.e., a sequence of flight legs served by the same aircraft), we append a set of constraints that matches aircraft assignments on certain inbound legs into a station with that on appropriate outbound legs at the same station. Through-flights can generate greater revenue because passengers are willing to pay a premium for not having to change aircraft on connecting flights, thereby reducing the possibility of delays and missed baggage. In order to tighten the model representation and reduce its complexity, we apply the Reformulation-Linearization Technique (RLT) and also generate other classes of valid inequalities. In addition, since the model possesses many equivalent feasible solutions that can be obtained by simply reindexing the aircraft of the same type that depart from the same station, we introduce a set of suitable hierarchical symmetry-breaking constraints to enhance the model solvability by distinguishing among aircraft of the same type. For the resulting large-scale augmented model formulation, we design a Benders decomposition-based solution methodology and present extensive computational results to demonstrate the efficacy of the proposed approach. We explored four different algorithmic variants, among which the best performing procedure (Algorithm A1) adopted two sequential levels of Benders partitioning method. We then applied Algorithm A1 to perform several experiments to study the effects of different modeling features and algorithmic strategies. A summary of the results obtained is as follows. First, the case that accommodated both mandatory and optional through-flight leg pairs in the model based on their relative effects on demands and enhanced revenues achieved the most profitable strategy, with an estimated increase in expected annual profits of $2.4 million over the baseline case. Second, utilizing symmetry-breaking constraints in concert with compatible objective perturbation terms greatly enhanced problem solvability and thus promoted the detection of improved solutions, resulting in a $5.8 million increase in estimated annual profits over the baseline case. Third, in the experiment that considers recapture of spilled demand from primary itineraries to other compatible itineraries, the different penalty parameter values (100, 50, and 1 dollars per re-routed passenger) induced average respective proportions of 3.2%, 3.4%, and 3.7% in recaptured demand, resulting in additional estimated annual profits of $3.7 million, $3.8 million, and $4.0 million over the baseline case. Finally, incorporating the proposed valid inequalities within the model to tighten its representation helped reduce the computational effort by 11% on average, while achieving better solutions that yielded on average an increase in estimated annual profits of $1.4 million. In closing, we propose a fourth more comprehensive model in which the crew scheduling problem is additionally integrated with fleet assignment and aircraft routing. This integration is important for airlines because crew costs are the second largest component of airline operating expenses (after fuel costs), and the assignment and routing of aircraft plus the assignment of crews are two closely interacting components of the planning process. Since crews are qualified to typically serve a single aircraft family that is comprised of aircraft types having a common cockpit configuration and crew rating, the aircraft fleeting and routing decisions significantly impact the ensuing assignment of cockpit crews to flights. Therefore it is worthwhile to investigate new models and solution approaches for the integrated fleeting, aircraft routing, and crew scheduling problem, where all of these important inter-dependent processes are handled simultaneously, and where the model can directly accommodate various work rules such as imposing a specified minimum and maximum number of flying hours for crews on any given pairing, and a minimum number of departures at a given crew base for each fleet group. However, given that the crew scheduling problem itself is highly complex because of the restrictive work rules that must be heeded while constructing viable duties and pairings, the formulated integrated model would require further manipulation and enhancements along with the design of sophisticated algorithms to render it solvable. We therefore recommend this study for future research, and we hope that the modeling, analysis, and algorithmic development and implementation work performed in this dissertation will lend methodological insights into achieving further advances along these lines. / Ph. D.

<p dir="ltr">The emergence of Autonomous Mobile Robots (AMR) signifies a pivotal shift in vehicle-based material handling systems, demonstrating their effectiveness across a broad spectrum of applications. Advancing beyond the traditional Automated Guided Vehicles (AGV), AMRs offer unprecedented flexibility in movement, liberated from electromagnetic guidance constraints. Their decentralized control architecture not only enables remarkable scalability but also fortifies system resilience through advanced conflict resolution mechanisms. Nevertheless, transitioning from AGV to AMR presents intricate challenges, chiefly due to the expanded complexity in path planning and task selection, compounded by the heightened potential for conflicts from their dynamic interaction capabilities. This dissertation confronts these challenges by fully leveraging the technological advancements of AMRs. A kinematic-enabled agent-based simulator was developed to replicate AMR system behavior, enabling detailed analysis of fleet dynamics and interactions within AMR intralogistics systems and their environments. Additionally, a comprehensive fleet management protocol was formulated to enhance the throughput of AMR-based intralogistics systems from an integrated perspective. A pivotal discovery of this research is the inadequacy of existing path planning protocols to provide reliable plans throughout their execution, leading to task allocation decisions based on inaccurate plan information and resulting in false optimality. In response, a novel machine learning enhanced probabilistic Multi-Robot Path Planning (MRPP) protocol was introduced to ensure the generation of dependable path plans, laying a solid foundation for task allocation decisions. The contributions of this dissertation, including the kinematic-enabled simulator, the fleet management protocol, and the MRPP protocol, are intended to pave the way for practical enhancements in autonomous vehicle-based material handling systems, fostering the development of solutions that are both innovative and applicable in industrial practices.<br></p>

The development of Digital Twin Technology, with a focus on addressing environmental concerns, has elevated the priority of Industry 4.0-based solutions. The study aimed to design a simulation model to optimize the electricity consumption of the electric bus fleet at Arlanda Airport as a subproject of the main Digital Twin project. The study found that there was no current model designed to simulate electricity consumption by formal methods, Z notation.  The research is guided by four primary objectives find power management strategies for e-buses, identify critical parameters affecting their energy consumption, create a Z Notation simulation model, and assess this model. Through a thorough review of the literature and methodical application, power management strategies were defined, and significant energy consumption parameters were identified. The model's usefulness in modelling and optimizing electricity usage was demonstrated by its careful construction using Z Notation and evaluation with Spivey's Fuzz Checker. The paper demonstrates the use of Design Science Research in creating a digital twin-based simulation, which has important implications for transportation systems as well as theoretical advances in simulation methodologies. Throughout the developed Z notations, it provide a proper insight into operational efficiency and sustainability in energy consumption.  The study also emphasizes the drawbacks of using Z Notation, such as its steep learning curve and limited community assistance. To improve the accuracy of electricity consumption forecasts, future research should use predictive analytics and fine-tune the model granularity. The thesis demonstrates how design science can be applied for preparing specification of services but not only in software development. This work lays the groundwork for more extensive applications in digital twin technologies and energy optimization, in addition to contributing to our understanding of e-bus power management at Arlanda Airport.

The goal of this study is to describe the way Austria-Hungary followed to create and develop a modern navy in 1897-1914, and to determine, which factors were decisive in creating the final composition of the fleet. As the base for the research, a number of works related to the subject were studied, but far more important was detailed research of primary sources located in Austrian State Archives. The main line of the research as well as of this study follows negotiations on navy's budgets because of the assumption that the very base for building ships and developing the navy are financial resources. Among the more important subtopics are the way the navy had to award producers form both state, Austria and Hungary, with adequate portion of contracts, the Austro-Italian naval arms race, and the structure of the Austro- Hungarian fleet. The research revealed, that the ideal fleet as imagined by the commanders of the navy was a product of theories concerning a decisive battle and naval supremacy then widely widespread, and of the rivalry with Italy. However, the extent to which the ideal could have been followed, depended on the economic situation of both states; in the process of discussing and voting navy's budgets, the decisive word belonged to governments, not to delegations. The way the navy had...

Thesis (MComm)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: FoodBank South Africa is an non-profit organisation formed to establish a national network of community foodbanks in urban and rural areas of South Africa, with all participants working towards the common goal of eliminating hunger and food insecurity. FoodBank Cape Town was the first of these community foodbanks launched in South Africa on 2 March 2009. The operations of FoodBank Cape Town include sourcing food and redistributing it to agencies (social services organisations running feeding programmes). Currently the majority of the food is sourced from the retail sector and then redistributed to approximately two hundred agencies. The logistics involved in both sourcing and distributing food are vital to the efficient functioning of FoodBank Cape Town. Since the costs associated with these logistics operations are very high, streamlining these operations has been identified as a priority area for efficiency improvement. The focus in this thesis is on the distribution logistics involved, specifically focussing on a facility location problem according to which FoodBank Cape Town can establish local distribution depots to which it delivers food and from which the agencies collect food assigned to them. A mixed-integer programming model is formulated for the above facility location problem and small test instances of the problem are solved using different exact and approximate solution methods in order to identify a suitable solution methodology for the full (large-scale) FoodBank Cape Town facility location problem. The full facility location problem is solved approximately by means of a meta-heuristic solution method in the more highly constrained instances, while an exact method is selected for solving the lesser constrained instances. The problem is first solved based on the distances between the warehouse and the depots as well as the distances between the agencies and the depots, for the twenty four instances where 17 to 40 depots are located. The model is then developed further to incorporate the cost of distribution. This cost-based facility location model is solved with a view to minimise the cost of food distribution from the warehouse to the depots and the cost of food distribution incurred by each agency to collect food from its assigned depot. A basic vehicle routing technique is applied to the cost-based facility location solution and the associated costs of the distribution are updated. This cost-based solution updating process is performed iteratively until the solution converges. Since the cost of food distribution depends on the vehicle fleet composition used, a vehicle fleet composition comparison of possible FoodBank Cape Town vehicles is performed to determine the most desirable vehicle fleet composition to be used for the distribution of food to depots. The results of the FoodBank Cape Town facility location problem and vehicle fleet composition comparison are presented and recommendations are made to FoodBank Cape Town regarding the preferred number of depots, the location of these depots and the preferred vehicle fleet composition. / AFRIKAANSE OPSOMMING: FoodBank South Africa is ’n nie-winsgewende organisasie wat ten doel het om ’n nasionale netwerk van gemeenskapsvoedselbanke in stedelike en landelike gebiede van Suid-Afrika op die been te bring, waarin al die deelnemers die gemeenskaplike doel nastreef om honger en voedselonsekerheid te elimineer. Foodbank Cape Town was die eerste van hierdie gemeenskapsvoedselbanke in Suid-Afrika en is op 2 Maart 2009 gestig. Die take van Foodbank Cape Town sluit in die versameling van voedsel en die verspreiding daarvan aan agentskappe (gemeenskapsorganisasies wat voedingsprogramme bestuur). Die oorgrote meerderheid voedsel is tans uit die kleinhandelsektor afkomstig en word aan ongeveer tweehonderd agentskappe versprei. Die logistiek wat met hierdie versamelings- en verspreidingsprosesse gepaard gaan, is sentraal tot die doeltreffende funksionering van FoodBank Cape Town. Aangesien die kostes verbonde aan hierdie logistieke prosesse baie hoog is, is hierdie aktiwiteite as ’n prioriteitsarea vir verbetering geidentifiseer. Die fokus in hierdie tesis val op die logistiek verbonde aan die verspreiding van voedsel deur FoodBank Cape Town, en meer spesifiek op die probleem van die plasing van ’n aantal lokale verspreidingsdepots waar FoodBank Cape Town voedsel kan aflewer en waar die agentskappe dan voedsel wat aan hulle toegeken is, kan gaan afhaal. ’n Gemengde heeltallige-programmeringsmodel word vir die bogenoemde plasingsprobleem geformuleer en klein gevalle van die model word deur middel van beide eksakte en benadere oplossingstegnieke opgelos om sodoende ’n geskikte oplossingsmetode vir die volle (grootskaalse) Food- Bank Cape Town plasingsmodel te identifiseer. Die volle plasingsmodel word aan die hand van ’n metaheuristiese oplossingstegniek benaderd opgelos vir hoogsbeperkte gevalle van die model, terwyl minder beperkte gevalle van die model eksak opgelos word. Die plasingsmodel word eers met die oog op die minimering van afstande tussen die pakhuis en verspreidingsdepots sowel as tussen die verspreidingsdepots en agentskappe vir die vier-en-twintig gevalle van die plasing van 17 tot 40 verspreidingsdepots opgelos. Die model word dan verder ontwikkel om ook die koste van die verspreiding van voedsel in ag te neem. Die koste-gebaseerde plasingsmodel word opgelos met die doel om die voedselbankkoste van voedselverspreiding vanaf die pakhuis na die lokale verspreidingsdepots sowel as die agentskapkoste van die afhaal van voedsel vanaf verspreidingsdepots te minimeer. ’n Basiese voertuigroeteringstegniek word op die koste-gebaseerde plasingsmodel toegepas en die verspreidingskoste word dienooreenkomstig aangepas. Hierdie aanpassingsproses van die koste-gebaseerde oplossing word herhaal totdat die oplossing konvergeer. Aangesien die koste van voedselverspreiding afhang van die voertuigvlootsamestelling, word ’n vergelyking tussen moontlike vlootsamestellings vir FoodBank Cape Town getref om die mees geskikte samestelling van voertuie vir die verspreiding van voedsel te vind. Die resultate van die FoodBank Cape Town verspreidingsdepot-plasingsprobleem en vlootsamestellingsvergelyking word aangebied en ’n aanbeveling word aan FoodBank Cape Town gemaak in terme van ’n geskikte aantal verspreidingsdepots, waar hierdie depots geleë behoort te wees, en ’n geskikte voertuigvlootsamestelling vir die verspreiding van voedsel.

Approved for public release, distribution is unlimited / This study analyzes the relationship between selection to major in the Marine Corps, and the survival of midgrade officers to the promotion point of major, by investigating the effects of billet assignments. Specifically, this study looks at the influence of the percentage of time spent in the Fleet Marine Forces (FMF), the percentage of time spent in primary military occupation (PMOS) billet assignments, and the effect of having served in combat, recruiting, security forces, joint, and drill field duties. Models were formulated using groundwork established in previous promotion, retention, and attrition studies. Assignment variables were then introduced to the models. To account for officers' choice for continued service vice forced attrition, the sample was restricted to officers who had attained five years of service. Probit regression was used to find the influence of career assignments on the probability of selection; Heckman's correction was used to control for self-selection bias; and, Cox proportionalhazard regression was used, utilizing the same assignment factors, to find the influence of assignments on the likelihood of attrition. The findings indicated that FMF and PMOS ratios above 60 percent had a negative effect on promotion and retention. Also indicated was that time spent outside the PMOS, in "B" billets, had a positive effect on retention. In a time of budgetary constraints, this information may provide assistance to personnel planners as an alternative to pecuniary measures used to maintain and shape the force. / Major, United States Marine Corps

Dans ce mémoire, nous étudions un problème de tournées de véhicules dans lequel une flotte privée de véhicules n’a pas la capacité suffisante pour desservir les demandes des clients. Dans un tel cas, on fait appel à un transporteur externe. Ce dernier n’a aucune contrainte de capacité, mais un coût est encouru lorsqu’un client lui est affecté. Il n’est pas nécessaire de mettre tous les véhicules de la flotte privée en service si cette approche se révèle plus économique. L’objectif consiste à minimiser le coût fixe des véhicules, puis le coût variable de transport et le coût chargé par le transporteur externe. Notre travail consiste à appliquer la métaheuristique de recherche adaptative à grand voisinage sur ce problème. Nous comparons nos résultats avec ceux obtenus précédemment avec différentes techniques connues sur les instances de Christofides et celles de Golden. / In this master thesis, we study a vehicle routing problem in which a private fleet does not have sufficient capacity to serve all customers. Therefore, an external common carrier is required. The external common carrier has no constraint of capacity, but there is a cost when a customer it assigned to it. It is not necessary for all the vehicles of the private fleet to be used. The objective is to minimize the sum of the fixed cost of the private fleet, the variable routing cost and the external carrier cost. Our work applies the adaptative large neighborhood search metaheuristic on this problem. We compare our results with those obtained previously with different well-known techniques on the benchmark instances of Christofides and Golden.

O tema de roteamento de veículos é de grande importância na literatura e tem sido amplamente estudada pela sua importância para muitas indústrias. Com a evolução na literatura, mais características foram adicionadas para torná-lo mais próximo de situações reais. Alinhado com esta tendência, este trabalho aborda o problema de roteamento de veículos quando há a terceirização da frota que realiza as entregas. Uma forma de cobrança do frete é por meio de custos escalonados, que são calculados de acordo com o tipo de veículo e a distância percorrida, com valores fixos para cada faixa de distância. Embora seja uma forma comum de trabalho na indústria, nenhum trabalho focado nesta característica foi encontrado na literatura. Este problema é o problema de roteamento de veículos com frota mista, janelas de tempo e custos escalonados (FSMVRPTWSC). Ao abordar este problema, este trabalho apresenta um modelo de programação linear inteira mista que é avaliado em um cenário real da indústria. Além disso, três heurísticas de inserção sequencial são propostas para lidar com problemas maiores. Estes métodos são examinados por meio de testes computacionais em 168 problemas de referência gerados para este problema. Os experimentos numéricos mostram que os métodos são robustos e eficientes, apresentando um bom desempenho em conjuntos de problemas com diversas características. / The theme of vehicle routing is of great importance in the literature and has been widely studied for its relevance to many industries and, throughout the literature, more characteristics have been added to make it closer to real situations. Aligned with this trend, this paper addresses the vehicle routing problem when there is outsourcing of the fleet that delivers goods. One form of freight charging is by scaled costs, which are calculated according to the type of vehicle and the distance traveled, with fixed values for each distance range. Though it is a common form of work in the industry, no work focused on this characteristic was found in the literature. This problem is the fleet size and mix vehicle routing problem with time windows and scaled costs (FSMVRPTWSC). In approaching this problem, this paper presents a mixed integer linear programming model that is evaluated under a real situation scenario. Furthermore, three sequential insertion heuristics are proposed in order to deal with larger problems. These methods are examined through a computational comparative study in 168 benchmark problems generated for this problem. The numerical experiments show that the methods are robust and efficient, performing well in different problem sets.

Esta pesquisa trata da aplicação de meta-heurísticas baseadas em busca em vizinhança variável em problemas de operação de transportes. Desta forma, buscou-se encontrar problemas complexos durante a operação de sistemas de transportes, nas grandes cidades, que possam ser resolvidos com a aplicação de meta-heurística baseada em busca em vizinhança variável. Este trabalho aborda dois diferentes problemas de planejamento e operação de transportes. O primeiro problema abordado neste trabalho é o Problema de Programação da Tabela de Horários, de Veículos e de Tripulantes de Ônibus, no qual as viagens que comporão a tabela de horários, os veículos que executarão as viagens e as tripulações que operarão os veículos são alocadas simultaneamente e de maneira integrada. O segundo problema a ser abordado é o problema de distribuição física, o qual envolve o agrupamento e a alocação de entregas a uma frota de veículos visando minimizar o frete total. Uma abordagem para a modelagem matemática deste problema é modelar como um problema de bin-packing, com bins de tamanho variável unidimensional (do inglês Variable Sized Bin-Packing Problem - VSBPP), ou seja, uma generalização do tradicional problema de bin-packing no qual bins (veículos) de diferentes capacidades e custos estão disponíveis para a alocação de um conjunto de objetos (cargas), de modo que o custo total dos bins (veículos) utilizados seja mínimo. A outra abordagem proposta para o problema de distribuição física é modelar o problema como um problema de bin-packing, com bins de tamanho variável bidimensional (do inglês Bidimensional Variable Sized Bin-Packing Problem BiD-VSBPP). Assim sendo, trata-se de uma expansão do problema de bin-packing com bins de tamanho variável unidimensional (VSBPP), no qual bins (veículos) de diferentes capacidades (capacidade volumétrica e capacidade de carga) e custos estão disponíveis para a alocação de um conjunto de objetos (cargas), os quais possuem as dimensões peso e volume, de modo que o custo total dos bins (veículos) utilizados seja mínimo. Durante a realização deste trabalho, foi desenvolvido um programa computacional em C++, o qual implementa a meta-heurística Busca em Vizinhança Variável (VNS) e duas meta-heurísticas baseadas em VNS. São apresentados resultados de experimentos computacionais com dados reais e dados benchmarking. Os resultados obtidos comprovam a eficácia das meta-heurísticas propostas. / This work approaches variable neighborhood search meta-heuristic applicate on transport operation problems. This way, we sought find complex transport operation problems in large cities that can be solved with the variable neighborhood search meta-heuristic application. This work approaches two different transport planning and operation problems. The first problem approached in this paper is the Bus Timetable Vehicle Crew Scheduling Problem, in which timetabling, bus and crew schedules are simultaneously determined in an integrated approach. The second problem to be approached is the physical distribution problem which comprises grouping and assigning deliveries to a heterogeneous fleet of vehicles aiming to minimize the total freight cost. The problem can be mathematical modeled as one-dimensional Variable Sized Bin-Packing Problem (VSBPP), a generalization of the traditional bin-packing problem, in which bins (vehicles) with different sizes and costs are available for the assignment of the objects (deliveries) such that the total cost of the used bins (vehicles) is minimized. Another proposed approach to the problem of physical distribution is model as two dimensional Variable Sized Bin-Packing Problem (BiD-VSBPP). Therefore, it is an expansion of the bin-packing problem with bins variable-length-dimensional (VSBPP), in which bins (vehicle) of different capacity (capacity and load carrying capacity) and costs are available for allocation a set of objects (loads), which have the dimensions weight and volume, so that minimized the total cost of bins (vehicle). In this work, was developed a C++ software implemented, which was implemented a meta-heuristic Variable Neighborhood Search (VNS) and two others meta-heuristics based on VNS. Computational results for real-world problems and benchmarking problems are presented, showing the effectiveness of these proposed meta-heuristics.