The Vehicle Routing Problem with Service Level Constraints

We consider a vehicle routing problem which seeks to minimize cost subject to service level constraints on several groups of deliveries. This problem captures some essential challenges faced by a logistics provider which operates transportation services for a limited number of partners and should respect contractual obligations on service levels. The problem also generalizes several important classes of vehicle routing problems with profits. To solve it, we propose a compact mathematical formulation, a branch-and-price algorithm, and a hybrid genetic algorithm with population management, which relies on problem-tailored solution representation, crossover and local search operators, as well as an adaptive penalization mechanism establishing a good balance between service levels and costs. Our computational experiments show that the proposed heuristic returns very high-quality solutions for this difficult problem, matches all optimal solutions found for small and medium-scale benchmark instances, and improves upon existing algorithms for two important special cases: the vehicle routing problem with private fleet and common carrier, and the capacitated profitable tour problem. The branch-and-price algorithm also produces new optimal solutions for all three problems

Positive multi-criteria models in agriculture for energy and environmental policy analysis

Environmental consciousness and accompanying actions have been paralleled by the evolution of multi-criteria methods which have provided tools to assist policy makers in discovering compromises in order to muddle through. This paper recalls the development of multi-criteria methods in agriculture, focusing on their contribution to produce input or output functions useful for environmental and/or energy policy. Response curves generated by MC models can more accurately predict farmers’ response to market and policy parameters compared with classic profit maximizing behavior. Concrete examples from recent literature illustrate the above statements and ideas for further research are provided.multi-criteria models, interval programming, supply curves, bio-energy, policy analysis

A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational biology

[Abstract] Background: We consider a general class of global optimization problems dealing with nonlinear dynamic models. Although this class is relevant to many areas of science and engineering, here we are interested in applying this framework to the reverse engineering problem in computational systems biology, which yields very large mixed-integer dynamic optimization (MIDO) problems. In particular, we consider the framework of logic-based ordinary differential equations (ODEs). Methods: We present saCeSS2, a parallel method for the solution of this class of problems. This method is based on an parallel cooperative scatter search metaheuristic, with new mechanisms of self-adaptation and specific extensions to handle large mixed-integer problems. We have paid special attention to the avoidance of convergence stagnation using adaptive cooperation strategies tailored to this class of problems. Results: We illustrate its performance with a set of three very challenging case studies from the domain of dynamic modelling of cell signaling. The simpler case study considers a synthetic signaling pathway and has 84 continuous and 34 binary decision variables. A second case study considers the dynamic modeling of signaling in liver cancer using high-throughput data, and has 135 continuous and 109 binaries decision variables. The third case study is an extremely difficult problem related with breast cancer, involving 690 continuous and 138 binary decision variables. We report computational results obtained in different infrastructures, including a local cluster, a large supercomputer and a public cloud platform. Interestingly, the results show how the cooperation of individual parallel searches modifies the systemic properties of the sequential algorithm, achieving superlinear speedups compared to an individual search (e.g. speedups of 15 with 10 cores), and significantly improving (above a 60%) the performance with respect to a non-cooperative parallel scheme. The scalability of the method is also good (tests were performed using up to 300 cores). Conclusions: These results demonstrate that saCeSS2 can be used to successfully reverse engineer large dynamic models of complex biological pathways. Further, these results open up new possibilities for other MIDO-based large-scale applications in the life sciences such as metabolic engineering, synthetic biology, drug scheduling.Ministerio de Economía y Competitividad; DPI2014-55276-C5-2-RMinisterio de Economía y Competitividad; TIN2016-75845-PGalicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2016/045Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2013/05

Rough-Cut Capacity Planning in Multimodal Freight Transportation Networks

A main challenge in transporting cargo for United States Transportation Command (USTRANSCOM) is in mode selection or integration. Demand for cargo is time sensitive and must be fulfilled by an established due date. Since these due dates are often inflexible, commercial carriers are used at an enormous expense, in order to fill the gap in organic transportation asset capacity. This dissertation develops a new methodology for transportation capacity assignment to routes based on the Resource Constrained Shortest Path Problem (RCSP). Routes can be single or multimodal depending on the characteristics of the network, delivery timeline, modal capacities, and costs. The difficulty of the RCSP requires use of metaheuristics to produce solutions. An Ant Colony System to solve the RCSP is developed in this dissertation. Finally, a method for generating near Pareto optimal solutions with respect to the objectives of cost and time is developed

Explicit Building-Block Multiobjective Genetic Algorithms: Theory, Analysis, and Developing

This dissertation research emphasizes explicit Building Block (BB) based MO EAs performance and detailed symbolic representation. An explicit BB-based MOEA for solving constrained and real-world MOPs is developed the Multiobjective Messy Genetic Algorithm II (MOMGA-II) which is designed to validate symbolic BB concepts. The MOMGA-II demonstrates that explicit BB-based MOEAs provide insight into solving difficult MOPs that is generally not realized through the use of implicit BB-based MOEA approaches. This insight is necessary to increase the effectiveness of all MOEA approaches. In order to increase MOEA computational efficiency parallelization of MOEAs is addressed. Communications between processors in a parallel MOEA implementation is extremely important, hence innovative migration and replacement schemes for use in parallel MOEAs are detailed and tested. These parallel concepts support the development of the first explicit BB-based parallel MOEA the pMOMGA-II. MOEA theory is also advanced through the derivation of the first MOEA population sizing theory. The multiobjective population sizing theory presented derives the MOEA population size necessary in order to achieve good results within a specified level of confidence. Just as in the single objective approach the MOEA population sizing theory presents a very conservative sizing estimate. Validated results illustrate insight into building block phenomena good efficiency excellent effectiveness and motivation for future research in the area of explicit BB-based MOEAs. Thus the generic results of this research effort have applicability that aid in solving many different MOPs

Heuristinen yhteistyöhaku ohjelmistoagenttien avulla

Parallel algorithms extend the notion of sequential algorithms by permitting the simultaneous execution of independent computational steps. When the independence constraint is lifted and executions can freely interact and intertwine, parallel algorithms become concurrent and may behave in a nondeterministic way. Parallelism has over the years slowly risen to be a standard feature of high-performance computing, but concurrency, being even harder to reason about, is still considered somewhat notorious and undesirable. As such, the implicit randomness available in concurrency is rarely made use of in algorithms. This thesis explores concurrency as a means to facilitate algorithmic cooperation in a heuristic search setting. We use agents, cooperating software entities, to build a single-source shortest path (SSSP) search algorithm based on parallelized A∗, dubbed A!. We show how asynchronous information sharing gives rise to implicit randomness, which cooperating agents use in A! to maintain a collective secondary ranking heuristic and focus search space exploration. We experimentally show that A! consistently outperforms both vanilla A∗ and a noncooperative, explicitly randomized A∗ variant in the standard n-puzzle sliding tile problem context. The results indicate that A! performance increases with the addition of more agents, but that the returns are diminishing. A! is observed to be sensitive to heuristic improvement, but also constrained by search overhead from limited path diversity. A hybrid approach combining both implicit and explicit randomness is also evaluated and found to not be an improvement over A! alone. The studied A! implementation based on vanilla A∗ is not as such competitive against state-of-the-art parallel A∗ algorithms, but rather a first step in applying concurrency to speed up heuristic SSSP search. The empirical results imply that concurrency and nondeterministic cooperation can successfully be harnessed in algorithm design, inviting further inquiry into algorithms of this kind.Rinnakkaisalgoritmit sallivat useiden riippumattomien ohjelmakäskyjen suorittamisen samanaikaisesti. Kun riippumattomuusrajoite poistetaan ja käskyjen suorittamisen järjestystä ei hallita, rinnakkaisalgoritmit voivat käskysuoritusten samanaikaisuuden vuoksi käyttäytyä epädeterministisellä tavalla. Rinnakkaisuus on vuosien saatossa noussut tärkeään rooliin tietotekniikassa ja samalla hallitsematonta samanaikaisuutta on yleisesti alettu pitää ongelmallisena ja ei-toivottuna. Samanaikaisuudesta kumpuavaa epäsuoraa satunnaisuutta hyödynnetään harvoin algoritmeissa. Tämä työ käsittelee käskysuoritusten samanaikaisuuden hyödyntämistä osana heuristista yhteistyöhakua. Työssä toteutetaan agenttien, yhteistyökykyisten ohjelmistokomponenttien, avulla uudenlainen A!-hakualgoritmi. A! perustuu rinnakkaiseen A∗ -algoritmiin, joka ratkaisee yhden lähteen lyhimmän polun hakuongelman. Työssä näytetään, miten ajastamaton viestintä agenttien välillä johtaa epäsuoraan satunnaisuuteen, jota A!-agentit kollektiivisesti hyödyntävät toissijaisen järjestämisheuristiikan ylläpitämisessä ja edelleen haun kohdentamisessa. Työssä näytetään kokeellisesti, kuinka A! suoriutuu niin tavanomaista kuin satunnaistettuakin A∗ -algoritmia paremmin n-puzzle pulmapelin ratkaisemisessa. Tulokset osoittavat, että A!-algoritmin suorituskyky kasvaa lisäagenttien myötä, mutta myös sen, että hyöty on joka lisäyksen jälkeen suhteellisesti pienempi. A! osoittautuu heuristiikan hyödyntämisen osalta verrokkeja herkemmäksi, mutta myös etsintäpolkujen monimuotoisuuden kannalta vaatimattomaksi. Yksinkertaisen suoraa ja epäsuoraa satunnaisuutta yhdistävän hybridialgoritmin ei todeta tuovan lisäsuorituskykyä A!-algoritmiin verrattuna. Empiiriset kokeet osoittavat, että hallitsematonta samanaikaisuutta ja epädeterminististä yhteistyötä voi onnistuneesti hyödyntää algoritmisuunnittelussa, mikä kannustaa lisätutkimuksiin näitä soveltavan algoritmiikan parissa

Markowitz Theory–Based Asset Allocation Strategies with Special Regard to Private Wealth Management

This dissertation concentrates on portfolio optimization problems in asset allocation strategies with special focus on Private Wealth Management. The research is incorporated in the framework of both utility theory and the Markowitz model. Using monthly returns of ten different indices from seven asset classes recorded from 1996 to 2007, this dissertation shows that utility maximization for portfolio optimization problems based on quadratic utility and other popular but more difficult utility functions leads to similar results

A Hybrid Tabu/Scatter Search Algorithm for Simulation-Based Optimization of Multi-Objective Runway Operations Scheduling

As air traffic continues to increase, air traffic flow management is becoming more challenging to effectively and efficiently utilize airport capacity without compromising safety, environmental and economic requirements. Since runways are often the primary limiting factor in airport capacity, runway operations scheduling emerge as an important problem to be solved to alleviate flight delays and air traffic congestion while reducing unnecessary fuel consumption and negative environmental impacts. However, even a moderately sized real-life runway operations scheduling problem tends to be too complex to be solved by analytical methods, where all mathematical models for this problem belong to the complexity class of NP-Hard in a strong sense due to combinatorial nature of the problem. Therefore, it is only possible to solve practical runway operations scheduling problem by making a large number of simplifications and assumptions in a deterministic context. As a result, most analytical models proposed in the literature suffer from too much abstraction, avoid uncertainties and, in turn, have little applicability in practice. On the other hand, simulation-based methods have the capability to characterize complex and stochastic real-life runway operations in detail, and to cope with several constraints and stakeholders’ preferences, which are commonly considered as important factors in practice. This dissertation proposes a simulation-based optimization (SbO) approach for multi-objective runway operations scheduling problem. The SbO approach utilizes a discrete-event simulation model for accounting for uncertain conditions, and an optimization component for finding the best known Pareto set of solutions. This approach explicitly considers uncertainty to decrease the real operational cost of the runway operations as well as fairness among aircraft as part of the optimization process. Due to the problem’s large, complex and unstructured search space, a hybrid Tabu/Scatter Search algorithm is developed to find solutions by using an elitist strategy to preserve non-dominated solutions, a dynamic update mechanism to produce high-quality solutions and a rebuilding strategy to promote solution diversity. The proposed algorithm is applied to bi-objective (i.e., maximizing runway utilization and fairness) runway operations schedule optimization as the optimization component of the SbO framework, where the developed simulation model acts as an external function evaluator. To the best of our knowledge, this is the first SbO approach that explicitly considers uncertainties in the development of schedules for runway operations as well as considers fairness as a secondary objective. In addition, computational experiments are conducted using real-life datasets for a major US airport to demonstrate that the proposed approach is effective and computationally tractable in a practical sense. In the experimental design, statistical design of experiments method is employed to analyze the impacts of parameters on the simulation as well as on the optimization component’s performance, and to identify the appropriate parameter levels. The results show that the implementation of the proposed SbO approach provides operational benefits when compared to First-Come-First-Served (FCFS) and deterministic approaches without compromising schedule fairness. It is also shown that proposed algorithm is capable of generating a set of solutions that represent the inherent trade-offs between the objectives that are considered. The proposed decision-making algorithm might be used as part of decision support tools to aid air traffic controllers in solving the real-life runway operations scheduling problem