David Canca

Manufacturing cell formation using a new self-organizing neural network

Cellular manufacturing consists of grouping similar machines in cells and dedicating each of them to process a family of similar part types. In this paper, grouping parts into families and machines into cells is done in two steps: first, part families are formed and then machines are assigned. In phase one, weighted similarity coefficients are computed and parts are clustered using a new self-organizing neural network. In phase two, a linear network flow model is used to assign machines to families. To test the proposed approach, different problems from the literature have been solved. As benchmarks we have used a Maximum Spanning Tree heuristic.

Application of centralised DEA approach to capital budgeting in Spanish ports

In this paper, we propose a number of non-radial, output-oriented, centralised DEA models to determine individual and collective output target levels, input slacks and input reallocations as well as additional inputs acquisitions under a capital budget constraint. The application of the proposed approach to the Spanish Port Agency is presented. The overall amount of inefficiency currently found in the system allows for the determination of potential total output increases ranging from 24% to 114% without additional resources. Considering inputs reallocation would allow for an additional 20% output expansion. The acquisition of additional input resources would make feasible to expand outputs further, to levels whose exact values monotonously depend on the capital budget considered.

Exact formulations and algorithm for the train timetabling problem with dynamic demand

In this paper we study the design and optimization of train timetabling adapted to a dynamic demand environment. This problem arises in rapid train services which are common in most important cities. We present three formulations for the problem, with the aim of minimizing passenger average waiting time. The most intuitive model would consider binary variables representing train departure times but it yields to non-linear objective function. Instead, we introduce flow variables, which allow a linear representation of the objective function. We provide incremental improvements on these formulations, which allows us to evaluate and compare the benefits and disadvantages of each modification. We present a branch-and-cut algorithm applicable to all formulations. Through extensive computational experiments on several instances derived from real data provided by the Madrid Metropolitan Railway, we show the advantages of designing a timetable adapted to the demand pattern, as opposed to a regular timetable. We also perform an extensive computational comparison of all linear formulations in terms of size, solution quality and running time.

Machine grouping using sequence-based similarity coefficients and neural networks

Abstract Most neural network approaches to the cell formation problem do not use information on the sequence of operations on part types. They only use as input the binary part-machine incidence matrix. In this paper we investigate two sequence-based neural network approaches for cell formation. The objective function considered is the minimization of transportation costs (including both intracellular and intercellular movements). Constraints on the minimum and maximum number of machines per cell can be imposed. The problem is formulated mathematically and shown to be equivalent to a quadratic programming integer program that uses symmetric, sequence-based similarity coefficients between each pair of machines. Of the two energy-based neural network approaches investigated, namely Hopfield model and Potts Mean Field Annealing, the latter seems to give better and faster solutions, although not as good as a Tabu Search algorithm used for benchmarking.

An adaptive neighborhood search metaheuristic for the integrated railway rapid transit network design and line planning problem

Abstract We model and solve the Railway Rapid Transit Network Design and Line Planning (RRTNDLP) problem, which integrates the two first stages in the Railway Planning Process. The model incorporates costs relative to the network construction, fleet acquisition, train operation, rolling stock and personnel management. This implies decisions on line frequencies and train capacities since some costs depend on line operation. We assume the existence of an alternative transportation system (e.g. private car, bus, bicycle) competing with the railway system for each origin–destination pair. Passengers choose their transportation mode according to the best travel times. Since the problem is computationally intractable for realistic size instances, we develop an Adaptive Large Neighborhood Search (ALNS) algorithm, which can simultaneously handle the network design and line planning problems considering also rolling stock and personnel planning aspects. The ALNS performance is compared with state-of-the-art commercial solvers on a small-size artificial instance. In a second stream of experiments, the ALNS is used to design a railway rapid transit network in the city of Seville.

A general rapid network design, line planning and fleet investment integrated model

Traditionally, network design and line planning have been studied as two different phases in the planning process of public transportation. At the strategic level approaches dealing with the network design problem minimize travel time or maximize trip coverage, whereas at the tactical level, in the case of line planning, most models minimize cost or the number of transfers. The main novelty of this paper is the integration of the strategic and tactical phases of the rapid transit planning process. Specifically, a mathematical programming model that simultaneously determines the infrastructure network, line planning, train capacity of each line, fleet investment and personnel planning is defined. Moreover, the demand is assumed to be elastic and, therefore it is split into the rapid transit network and a competing mode according to a generalized cost. A rigorous analysis for the calibration of the different concepts that appear as consequence of the integration of phases is presented. Our approach maximizes the total profit of the network by achieving a balance between the maximum trip coverage and the minimum total cost associated to the network. Numerical results taking into account data based on real-world instances are presented.

A short-turning policy for the management of demand disruptions in rapid transit systems

Rapid transit systems timetables are commonly designed to accommodate passenger demand in sections with the highest passenger load. However, disruptions frequently arise due to an increase in the demand, infrastructure incidences or as a consequence of fleet size reductions. All these circumstances give rise to unsupplied demand at certain stations, which generates passenger overloads in the available vehicles. The design of strategies that guarantee reasonable user waiting time with small increases of operation costs is now an important research topic. This paper proposes a tactical approach to determine optimal policies for dealing with such situations. Concretely, a short-turning strategy is analysed, where some vehicles perform short cycles in order to increase the frequency among certain stations of the lines and to equilibrate the train occupancy level. Turn-back points should be located and service offset should be determined with the objective of diminishing the passenger waiting time while preserving certain level of quality of service. Computational results and analysis for a real case study are provided.

Macroscopic attraction-based simulation of pedestrian mobility: A dynamic individual route-choice approach

This paper presents a dynamic distribution and assignment simulation model based on discrete time simulation techniques and dynamic route assignment for planning, engineering design, and operation analysis of big exhibition events from a pedestrian circulation perspective. Both, the distribution and assignment stages are incorporated in an interlaced way with a dynamic behavior along a specific time horizon. In the proposed model, the individual route choice is dynamically determined as consequence of facilities attractiveness and network congestion. Therefore, in contrast with other simulation approaches, it does not require the usual origin–destination trip matrices to describe the transportation demand or the specification of different paths to be followed by visitors. This modeling approach turns out to be very appropriate for the simulation of these big exhibition events where each visitor usually has multiple and a priori unordered destination choices after entering the scenario.

Deformability properties of timolol-loaded transfersomes based on the extrusion mechanism. Statistical optimization of the process

Abstract The purpose of this work was to analyze the deformability properties of different timolol maleate (TM)-loaded transfersomes by extrusion. This was performed because elastic liposomes may contribute to the elevation of amount and rate of drug permeation through the corneal membrane. This paper describes the optimization of a transfersome formulation by use of Taguchi orthogonal experimental design and two different statistical analysis approaches were utilized. The amount of cholesterol (F1), the amount of edge-activator (F2), the distribution of the drug into the vesicle (F3), the addition of stearylamine (F4) and the type of edge-activator (F5) were selected as causal factors. The deformability index, the phosphorous recovery, the vesicle size, the polydispersity index, the zeta potential and percentage of drug entrapped were fixed as the dependent variables and these responses were evaluated for each formulation. Two different statistical analysis approaches were applied. The better statistical approach was determined by comparing their prediction errors, where regression analysis provided better optimized responses than marginal means. From the study, an optimized formulation of TM-loaded transfersomes was prepared and obtained for the proposed ophthalmic delivery for the treatment of open angle glaucoma. It was found that the lipid to surfactant ratio and type of surfactant are the main key factors for determining the flexibility of the bilayer of transfersomes. From in vitro permeation studies, we can conclude that TM-loaded transfersomes may enhance the corneal transmittance and improve the bioavailability of conventional TM delivery.

Counting and enumerating feasible rotating schedules by means of Gröbner bases

This paper deals with the problem of designing and analyzing rotating schedules with an algebraic computational approach. Specifically, we determine a set of Boolean polynomials whose zeros can be uniquely identified with the set of rotating schedules related to a given workload matrix subject to standard constraints. These polynomials constitute zero-dimensional radical ideals, whose reduced Grobner bases can be computed to count and even enumerate the set of rotating schedules that satisfy the desired set of constraints. Thereby, it enables to analyze the influence of each constraint in the same.