The timetabling subproblem of bus transit network planning determines the departure times for all trips of the lines along the entire day. Most of the public transport networks consider planning periods identical for all lines. In this study we drop this strong assumption by introducing specific periods for each line, which is more realistic. Thus, we propose the multiperiod synchronization bus timetabling (MSBT) problem, which specifies the departure times of the trips of all lines where each line has its own planning periods along the day, with the objective of optimizing synchronization events: maximize passenger transfers and minimize bus bunching along the network. We propose an integer linear programming formulation for the MSBT problem and analyze the structural properties of this formulation by a constraint propagation methodology. These properties are the basis for different operators that lead to the design of efficient metaheuristics for solving the problem. We empirically obtain high-quality feasible solutions for real size instances and show that by considering a multiperiod approach, synchronization events of trips belonging to different planning periods are not ignored, as it is the case when several single period timetables are merged.

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The urban transport planning process has four main activities: Network design, Timetable construction, Vehicle scheduling and Crew scheduling; each activity has subactivities. In this paper the authors work with the subactivities of timetable construction: minimal frequency calculation and departure time scheduling. The authors propose to solve both subactivities in an integrated way. The developed mathematical model allows multi-period planning and it can also be used for multimodal urban transportation systems. The authors consider demand uncertainty and the authors employ fuzzy programming to solve the problem. The authors formulate the urban transportation timetabling construction problem as a bi-objective problem: to minimize the total operational cost and to maximize the number of multi-period synchronizations. Finally, the authors implemented the SAUGMECON method to solve the problem.

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We address the portfolio selection of social projects in public organizations considering interdependencies (synergies) affecting project funds requirements and tasks. A mixed integer linear programming model is proposed incorporating the most relevant aspects of the problem found in the literature. The model supports both complete (all or nothing) and partial (a certain amount from a given interval of funding) resource allocation policies. Numerical results for large-scale problem instances are presented.

Nowadays, Supply Chain success and competitiveness heavily depend on the integration of its components and adaptability to deal with a changing environment. This article suggests the integration of design and management of a Supply Chain from an outcome-driven perspective. We propose a two-phase decision-making support methodology: first suppliers are pre-screened by solving a multi criteria sorting problem, and then a design and management plan is generated by solving a Mixed Integer Linear Programming Model. Experimentally we showed that the proposed methodology can efficiently solve to optimality the most popular benchmark instances published in previous paper moreover our model also includes problem characteristics that have not been addressed together in previous published papers. Keywords:.Supply chain design; supply chain planning; Mixed Linear Integer Programming; multi-criteria sorting problem; outcome-driven. RESUMENActualmente, el éxito y competitividad de las cadenas de suministro depende en gran medida de la integración de sus componentes y la capacidad de adaptación a los cambios que se presenten. En este artículo se propone la integración del diseño y planeación de la cadena de suministro desde una perspectiva dirigida a resultados. Se propone una metodología de apoyo a la decisión de dos fases: en la primera fase de preselección los proveedores son pre-seleccionados resolviendo un problema de ordenamiento y en la segunda fase de diseño y planeación un modelo lineal entero mixto es resuelto. Experimentalmente se muestra que la metodología propuesta puede resolver de manera óptima instancias publicadas en artículos previos, por otra parte nuestro modelo incluye características que no han tratado en conjunto en los trabajos publicados anteriormente.

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A project portfolio can be defined as a set of project proposals that are selected according to one or more criteria by a decision-maker (individual or group). Regularly, the portfolio selection involves different decision problems, among those evaluation, selection, scheduling, and resource allocation. In published scientific literature, these problems have been addressed mainly separately giving as a result suboptimal solutions (portfolios). In addition, elements as partial allocation and project representation through tasks constitute relevant characteristics in practice that remain unaddressed in depth. The proposal of this research is to integrate the project selection and project scheduling, incorporating all relevant elements of both decision problems through the scheduling of tasks allowing to determine when the task will be funded and executed. The main impact of precedence rules at the task level in the portfolio is also studied. In this work, Project Portfolio Selection and Scheduling Problem (PPSS) is studied and solved through a new mixed-integer linear programming (MILP) model. The model incorporates renewable and nonrenewable resource allocation, along with partial and total funding policies, project divisibility, and interdependences. Scheduling is integrated into the model, both at the project level and at the project task level, which allows scheduling in noncontiguous periods. Small instances (up to 64 projects) and medium instances (up to 128 projects) were solved optimally in very short times. The relationship between the quality of near-optimal solutions and the solution computing time by modifying the parameters of the solver employed was researched. No significant change in the solution’s quality was perceived, but a significant reduction in solution computing time was achieved. Furthermore, the main effects of precedence rules on solution times and portfolio impact were studied. Results show that even if few precedence rules were introduced, the resource allocation of tasks changed significantly, even though the portfolio impact or the number of projects of the selected portfolios remains the same.