O presente trabalho realiza o estudo e aplicação de um algoritmo de branch-and-price para a resolução de um problema de escalonamento de compressores. O problema é ligado à produção petrolífera, o qual consiste em definir um conjunto de compressores a serem ativados para fornecer gas de elevação a um conjunto de poços, atendendo toda demanda e minimizando os custos envolvidos. O problema é caracterizado por uma função objetivo não-convexa que é linearizada por partes de forma a ser formulada como um problema de programação inteira mista. A abordagem de geração de colunas é baseada na decomposição de Dantzig-Wolfe e apresenta melhores limitantes inferiores em relação à relaxação linear da formulação compacta. O branch-and-price é comparado ao solver CPLEX, sendo capaz de encontrar a solução ótima em menor tempo para um conjunto de instâncias, bem como melhores soluções factíveis para instâncias maiores em um período de tempo limitado. / This work presents the study and application of a branch-and-price algorithm for solving a compressor scheduling problem. The problem is related to oil production and consists of defining a set of compressors to be activated, supplying the gas-lift demand of a set of wells and minimizing the associated costs. The problem has a non-convex objective function, to which a piecewise-linear formulation has been proposed. This dissertation proposes a column generation approach based on the Dantzig-Wolfe decomposition, which achieves tighter lower bounds than the straightforward linear relaxation of the piecewise-linear formulation. The column generation was embedded in a branch-and-price algorithm and further compared with CPLEX, obtaining optimal solutions in lesser time for a set of instances. Further, the branch-and-price algorithm can find better feasible solutions for large instances under a limited processing time.

Drivers are essential factors affecting the efficiency and management level of a carrier. In this thesis, the driver assignment problem is investigated and methods for obtaining lower total operational costs are introduced for small and medium-sized truck freight transportation companies. Three interrelated research topics, including the following, have been systematically studied. Firstly, extending the traditional costing and Activity-Based Costing (ABC) method, the new Time-Driven Activity-Based Costing (TDABC) method, TDABC-FTC, has been introduced for truck freight companies. Detailed implementation process flow has been designed to streamline the easy incorporation of overhead cost. Fuel costs hold about one-third of the total operational costs of truck freight transportation, and drivers’ driving behaviors heavily influence the fuel consumption rate. In this work, the On-Board Diagnostics (OBD) Ⅱ, GPS tracker and Controller Area Network (CAN) bus are used to retrieve related truck operation data and transfer these data to a central database for later processing to obtain driving behavior parameters. An artificial neural network (ANN) model, built using MATLAB toolbox, is introduced to capture the relations between driving behavior and fuel consumption rate. The fuel consumption indicators for different drivers are then developed to reflect their relative fuel consumption rate quantitatively. The driver assignment problem is modeled as an optimization problem for minimizing the total operational cost of the truck, and the NP-hard problem is solved as a mixed integer programming problem. Two solution methods, Branch and Bound, and the Hungarian algorithm, are used to solve the formulated driver assignment problem. The Hungarian algorithm has been modified to address two particular situations in the driver assignment problem. Numerical experiments are conducted to validate the effectiveness of the newly introduced TDABC model, the fuel saving oriented optimal driver assignment method associating driver behavior to truck fuel consumption rate for different transportation tasks, and the solution methods for the special optimization problems formulated in this work. The newly introduced methods were tested using real truck fleet data, showing considerable benefit of the optimal scheduling techniques, and forming the foundation for further research in this area. / Graduate

A logística de contêiner vem aumentando sua participação em volume de cargas transportadas, tornando-se a parcela mais significativa do tráfego de mercadorias. Com isso, o gerenciamento dos altos custos envolvidos com a aquisição, manutenção, manipulação e transporte desses contêineres tornam-se um problema relevante para as organizações. As alocações dos contêineres cheios e vazios são comumente vistos como dois sistemas distintos e estáticos e não de forma intregada e dinâmica. Há um número restrito de trabalhos na literatura desenvolvendo heurísticas integrando os sistemas, porém não foi encontrada uma formulação ótima para o problema. Logo, a questão para a dissertação é quão próximo estão os resultados das heurísticas encontradas na literatura, para o problema da alocação de contêineres, dos resultados ótimos. O presente trabalho apresenta uma formulação matemática para o problema de alocação dinâmica, e integrada, para contêineres cheios e vazios. A formulação foi testada com diversos cenários, objetivando saber o limite computacional das instâncias para a formulação. Como o problema é um problema NP-Hard, heurísticas são comumente apresentadas na literatura. Demonstra-se como podem ser realizadas comparações entre os resultados das heurísticas e os resultados ótimos e visam a constatação da importância de uma formulação ótima para comparações. / Containers' Logistics has increased their importance in the goods transportion and nowadays, has the most important share of them. With that in mind, the management of high costs of acquisition, maintenance, manipulation and transportation of them became a significant problem to organizations. The problem of empty container allocation and load container allocation are commonly treated as two distinct, and static, systems, which means without integration and not dynamically. Just a couple of examples could be found of the two systems dynamically integrated, and no optimal model was found. So, the question here is how close heuristics' results are from the optimal results. A mathematical formulation is presented to the problem concerned with the integration and the dynamics associated to it. The formulation was tested with several scenarios to determine the maximum size that could be tested with optimal results, in an acceptable computacional time. Since the problem is a NP-Hard problem, heuristics approach are commonly used. Here is demonstrated how could be compare optimal solutions of the formulation and solutions from heuristics, and aim to demonstrate the significance of the optimal formulation.

Crew scheduling is very important, especially in continuous operating environments running 24 hours a day, 7 days a week, more so if the demand for staff is varying over each hour of the day. This thesis focuses on staff optimization in a fast food chain where special conditions for scheduling like flexible starting-times and shift lengths or heterogeneous crew are present. Two new models based on a mixed integer programming approach were designed, dealing with data from a particular restaurant with the aim of improving schedules and saving time spent on the creation of those schedules. At the end of the thesis the empiric schedules and results obtained are compared and the computational efficiency of both models is discussed.

Production scheduling is one of the most important issues in the planning and operation of manufacturing systems. Customers increasingly expect to receive the right product at the right price at the right time. Various problems experienced in manufacturing, for example low machine utilization and excessive work-in-process, can be attributed directly to inadequate scheduling. In this dissertation a production scheduling algorithm is developed for Optimatix, a South African-based company specializing in supply chain optimization. To address the complex requirements of the customer, the problem was modeled as a flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and production down time. The algorithm development process focused on investigating the application of both particle swarm optimization (PSO) and differential evolution (DE) to production scheduling environments characterized by multiple machines and multiple objectives. Alternative problem representations, algorithm variations and multi-objective optimization strategies were evaluated to obtain an algorithm which performs well against both existing rule-based algorithms and an existing complex flexible job shop scheduling solution strategy. Finally, the generality of the priority-based algorithm was evaluated by applying it to the scheduling of production and maintenance activities at Centurion Ice Cream and Sweets. The production environment was modeled as a multi-objective uniform parallel machine shop problem with sequence-dependent set-up times and unavailability intervals. A self-adaptive modified vector evaluated DE algorithm was developed and compared to classical PSO and DE vector evaluated algorithms. Promising results were obtained with respect to the suitability of the algorithms for solving a range of multi-objective multiple machine scheduling problems. Copyright / Dissertation (MEng)--University of Pretoria, 2009. / Industrial and Systems Engineering / unrestricted

This thesis deals with scheduling problems and algorithms usable to solve them. Scheduling algorithms seek an optimal allocation of resources over time while using constraints. Scheduling problems are often different in nature and type of constraint conditions. In the practical part is solved one particular type of scheduling problem, which is a constrained version of the common scheduling problem.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19290号 / 工博第4087号 / 新制||工||1630(附属図書館) / 32292 / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 谷口 栄一, 准教授 宇野 伸宏, 准教授 QURESHI,Ali Gul / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

The main goal of a scheduling process is to decide when and how to execute each of the project’s activities. Despite large variety of researched scheduling problems, the majority of them can be described as generalisations of the resource-constrained project scheduling problem (RCPSP). Because of wide applicability and challenging difficulty, RCPSP has attracted vast amount of attention in the research community and great variety of heuristics have been adapted for solving it. Even though these heuristics are structurally different and operate according to diverse principles, they are designed to obtain only one solution at a time. In the recent researches on RCPSPs, it was proven that these kind of problems have complex multimodal fitness landscapes, which are characterised by a wide solution search spaces and presence of multiple local and global optima. The main goal of this thesis is twofold. Firstly, it presents a variation of the RCPSP that considers optimisation of projects in an uncertain environment where resources are modelled to adapt to their environment and, as the result of this, improve their efficiency. Secondly, modification of a novel evolutionary computation method Cuckoo Search (CS) is proposed, which has been adapted for solving combinatorial optimisation problems and modified to obtain multiple solutions. To test the proposed methodology, two sets of experiments are carried out. Firstly, the developed algorithm is applied to a real-life software development project. Secondly, the performance of the algorithm is tested on universal benchmark instances for scheduling problems which were modified to take into account specifics of the proposed optimisation model. The results of both experiments demonstrate that the proposed methodology achieves competitive level of performance and is capable of finding multiple global solutions, as well as prove its applicability in real-life projects.

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No / The Job Shop Scheduling Problem (JSSP) is a hard combinatorial optimisation problem. This paper presents a heuristic-based Genetic Algorithm (GA) or Hybrid Genetic Algorithm (HGA) with the aim of overcoming the GA deficiency of fine tuning of solution around the optimum, and to achieve optimal or near optimal solutions for benchmark JSSP. The paper also presents a detail GA parameter analysis (also called sensitivity analysis) for a wide range of benchmark problems from JSSP. The findings from the sensitivity analysis or best possible parameter combination are then used in the proposed HGA for optimal or near optimal solutions. The experimental results of the HGA for several benchmark problems are encouraging and show that HGA has achieved optimal solutions for more than 90% of the benchmark problems considered in this paper. The presented results will provide a reference for selection of GA parameters for heuristic-based GAs for JSSP.