Examinando por Autor "Alonso-Ayuso, Antonio"

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A heuristic approach for the online order batching problem with multiple pickers
(Elsevier, 2021) Gil-Borrás, Sergio; G.Pardo, Eduardo; Alonso-Ayuso, Antonio; Duarte, Abraham
The Online Order Batching Problem with Multiple Pickers (OOBPMP) consists of optimizing the operations related to the picking process of orders in a warehouse, when the picking policy follows an order batching strategy. In this case, this variant of the well-known Order Batching Problem considers the existence of multiple workers in the warehouse and an online arrival of the orders. We study three different objective functions for the problem: minimizing the completion time, minimizing the picking time, and minimizing the differences in the workload among the pickers. We have identified and classified all previous works in the literature for the OOBPMP. Finally, we propose a multistart procedure hybridized with a Variable Neighborhood Descent metaheuristic to handle the problem. We test our proposal over well-known instances previously reported in the literature by empirically comparing the performance of our proposal with previous methods in the state of the art. The statistical tests corroborated the significance of the results obtained.
A solution method for the shared resource-constrained multi-shortest path problem
(Elsevier, 2021-11-15) García-Heredia, David; Molina, Elisenda; Laguna, Manuel; Alonso-Ayuso, Antonio
We tackle the problem of finding, for each network within a collection, the shortest path between two given nodes, while not exceeding the limits of a set of shared resources. We present an integer programming (IP) formulation of this problem and propose a parallelizable matheuristic consisting of three phases: (1) generation of feasible solutions, (2) combination of solutions, and (3) solution improvement. We show that the shortest paths found with our procedure correspond to the solution of some type of scheduling problems such as the Air Traffic Flow Management (ATFM) problem. Our computational results include finding optimal solutions to small and medium-size ATFM instances by applying Gurobi to the IP formulation. We use those solutions to assess the quality of the output produced by our proposed matheuristic. For the largest instances, which correspond to actual flight plans in ATFM, exact methods fail and we assess the quality of our solutions by means of Lagrangian bounds. Computational results suggest that the proposed procedure is an effective approach to the family of shortest path problems that we discuss here.
An approach for Strategic Supply Chain Planning under Uncertainty based on Stochastic 0-1 Programming
(Springer Verlag, 2003) Alonso-Ayuso, Antonio; Escudero, Laureano F; Garín, Araceli; Ortuño, M Teresa; Pérez, Gloria
We present a two-stage stochastic 0-1 modeling and a related algorithmic approach for Supply Chain Management under uncertainty, whose goal consists of determining the production topology, plant sizing, product selection, product allocation among plants and vendor selection for raw materials. The objective is the maximization of the expected benefit given by the product net profit over the time horizon minus the investment depreciation and operations costs. The main uncertain parameters are the product net price and demand, the raw material supply cost and the production cost. The first stage is included by the strategic decisions. The second stage is included by the tactical decisions. A tight 0-1 model for the deterministic version is presented. A splitting variable mathematical representation via scenario is presented for the stochastic version of the model. A two-stage version of a Branch and Fix Coordination (BFC) algorithmic approach is proposed for stochastic 0-1 program solving, and some computational experience is reported for cases with dozens of thousands of constraints and continuous variables and hundreds of 0-1 variables.
BFC, A Branch-and-Fix Coordination Algorithmic Framework for solving Some Types of Stochastic Pure and Mixed 0-1 Programs
(Elsevier, 2003) Alonso-Ayuso, Antonio; Escudero, Laureano F; Ortuño, M Teresa
We present a framework for solving some types of $0-1$ multi-stage scheduling/planning problems under uncertainty in the objective function coefficients and the right-hand-side. A scenario analysis scheme with full recourse is used. The solution offered for each scenario group at each stage takes into account all scenarios but without subordinating to any of them. The constraints are modelled by a splitting variables representation via scenarios. So, a $0-1$ model for each scenario is considered plus the non-anticipativity constraints that equate the $0-1$ variables from the same group of scenarios in each stage. The mathematical representation of the model is very amenable for the proposed framework to deal with the $0-1$ character of the variables. A Branch-and-Fix Coordination approach is introduced for coordinating the selection of the branching nodes and branching variables in the scenario subproblems to be jointly optimized. Some computational experience is reported for different types of problems.
Collision Avoidance in the ATM Problem: A Mixed Integer Linear Optimization Approach
(IEEE, 2011) Alonso-Ayuso, Antonio; Escudero, Laureano F; Martín-Campo, F Javier
This paper tackles the collision avoidance problem in ATM. The problem consists in deciding the best strategy for new aircraft configurations (velocity and altitude changes) such that all conflicts in the airspace are avoided; a conflict being the loss of the minimum safety distance that has to be kept between two aircrafts. A mixed 0-1 linear optimization model based on geometric transformations for collision avoidance between an arbitrary number of aircrafts in the airspace is developed. Knowing initial coordinates, angle direction and level flight, the new configuration for each aircraft is established by minimizing several objectives like velocity variation and total number of changes (velocity and altitude), and forcing to return to the original flight configuration when no aircrafts are in conflict. Due to the small computational time for the execution, the new configuration approach can be used in real time by using optimization software.
Conflict Avoidance: 0-1 linear models for Conflict Detection & Resolution
(Springer, 2013) Alonso-Ayuso, Antonio; Escudero, Laureano F; Olaso, Pablo; Pizarro, Celeste
The Conflict Detection and Resolution Problem for Air Traffic Flow Man- agement consists of deciding the best strategy for airborne aircraft so that there is guarantee that no conflict takes place, i.e., all aircraft maintain the minimum safety distance at every time instant. Two integer linear optimization models for conflict avoidance between any number of aircraft in the airspace are proposed, the first being a pure 0-1 linear which avoids conflicts by means of altitude changes, and the second a mixed 0-1 linear whose strategy is based on altitude and speed changes. Several ob- jective functions are established. Due to the small elapsed time that is required for solving both problems, the approach can be used in real time by using state-of-the-art mixed integer linear optimization software.
Mathematical Optimization models for Air Traffic Flow Management: A review
(2010-02-10) Agustín, Alba; Alonso-Ayuso, Antonio; Escudero, Laureano F.; Pizarro, Celeste
Congestion problems are becoming increasingly acute in many European and American airports and air sectors. To protect Air Traffic Control (ATC) from overload a planning activity called Air Traffic Flow Management (ATFM) tries to anticipate and prevent overload and limit resulting delays. When the traffic expects to exceed the airport arrival and departure capacities or the airsector capacity a delay in the flight arrival (so called-congestion) occurs. The casuistry to be considered in this field is very extensive. In general, most references to be found in the literature written some years ago refer to the simplest models, those which do not take into account airsector. This is so because this work was first studied in USA, where only the problems of congestion in airports basically occur. In the paper we present a state-of-the-art survey on the main optimization models encountered in the literature. They are classified as follows: (1) Single-Airport Ground-Holding Problem (SAGHP). The simplest of the methodologies of planning modelling studied proposes solutions to the problem of deciding the optimal planning for an arrival airport. (2) Multi-Airport Ground-Holding Problem (MAGHP). In this methodology the field of work is extended and the inter-relationship which exists between different airports is included. (3) Air Traffic Flow Management Problem (ATFMP). This methodology attempts to solve real situations that are much more complex than those which can be dealt with using the previous methodologies, since the air sector capacity is also considered. (4) Air Traffic Flow Management Rerouting Problem (ATFMRP). This methodology considers the more realistic situation where the flights can be diverted to alternative routes. (5) Air Traffic Flow Management Rerouting Problem (ATFMRP) with uncertainty. The ATFM problem is especially sensitive to changes in capacity. This leads to generalize the previous methodologies and to include generic uncertainty for these possible unforeseen changes in the parameters of the model, making way for stochastic methodologies. This type of problems are the most difficult ones, but alas the realistic ones.
Medium range optimization of copper extraction planning under uncertainty in future copper prices
(2011-11-24) Alonso-Ayuso, Antonio; Carvallo, Felipe; Escudero, Laureano F; Guignard, Monique; Pi, Jiaxing; Purammalka, Raghav; Weintraub, Andres
Deterministic mine planning models along a time horizon have proved to be very effective in supporting decisions on sequencing the extraction of material in copper mines. Some of these models have been developed for, and used successfully by CODELCO, the Chilean state copper company. These models are extremely large. In this paper, we wish to consider the uncertainty in a very volatile parameter of the problem, namely, the copper price along a given time horizon. We represent the uncertainty by a multistage scenario tree. The resulting stochastic model is then converted into a mixed 0-1 Deterministic Equivalent Model using a compact representation. We first introduce the stochastic model that maximizes the expected profit along the time horizon over all scenarios (i.e., as in a risk neutral environment). We then present several approaches for risk management, in a risk averse environment. Specifically, we consider the maximization of the Value-at-Risk and several variants of the Conditional Value-at-Risk, the maximization of the expected profit minus the weighted probability of having a ``bad" scenario in the solution provided by the model, and the maximization of the expected profit subject to stochastic dominance constraints for a set of profiles given by the pairs of target profits and bounds on either the probability of not reaching them or the expected profit deficit over the targets. We present an extensive computational experience on the actual problem, by comparing the risk neutral approach, the tested risk averse strategies and the performance of the traditional deterministic approach that uses the expected value of the uncertain parameters. The results clearly show the advantage of using any risk neutral strategy over the traditional deterministic approach, as well as the advantage of using the risk averse strategy over the risk neutral one, although the plain use of the MIP solvers should be replaced by decomposition algorithms.
On a selection and scheduling problem in automatic storage and retrieval warehouses
(Taylor and Francis, 2013) Alonso-Ayuso, Antonio; Tirado, Gregorio; Udías, Ángel
Warehousing is one of the main components of the supply chain and its optimization is crucial to achieve global efficiency. Warehouse operations in- volve receiving, shipping, storing and order picking, among others, and the coordinated optimization of all these different operations is highly complex. This paper approaches a selection and scheduling real problem that arises in an automatic storage/retrieval warehouse system involving the scheduling of forklifts pickup operations. The objective is to minimize the total loading time of the vehicles performing transportation, while respecting their departure due dates. This complex problem is approached through a two-phase decomposi- tion method, combining both exact and heuristic procedures. The performance of the proposed solution method is evaluated through extensive computational results performed on several scenarios from a real case study built from data from a real mattress warehouse.
On modeling the air traffic control coordination in the collision avoidance problem by mixed integer linear optimization
(Springer, 2014-09) Alonso-Ayuso, Antonio; Escudero, Laureano F; Martín-Campo, Francisco Javier
On the Product Selection and Plant Dimensioning Problem under uncertainty
(Elsevier, 2005) Alonso-Ayuso, Antonio; Escudero, Laureano F; Garín, Araceli; Ortuño, M Teresa; Pérez, Gloria
We present a two-stage full recourse model for strategic production planning under uncertainty, whose aim consists of determining product selection and plant dimensioning. The main uncertain parameters are the product price, demand and production cost. The benefit is given by the product net profit over the time horizon minus the investment depreciation and operation costs. The Value-at-Risk and the reaching probability are considered as risk measures in the objective function to be optimized as alternatives to the maximization of the expected benefit over the scenarios. The uncertainty is represented by a set of scenarios. The problem is formulated as a mixed 0--1 Deterministic Equivalent Model. The strategic decisions to be made in the first stage are represented by 0--1 variables. The tactical decisions to be made in the second stage are represented by continuous variables. An approach for problem solving based on a splitting variable mathematical representation via scenario is considered. The problem uses the Twin Node Family concept within the algorithmic framework known as Branch-and-Fix Coordination for satisfying the nonanticipativity constraints. Some computational experience is reported.
Order batching problems: Taxonomy and literature review
(Elsevier, 2023) G. Pardo, Eduardo; Gil-Borrás, Sergio; Alonso-Ayuso, Antonio; Duarte, Abraham
Order Batching is a family of optimization problems related to the process of picking items in a warehouse as part of supply chain management. Problems classified into this category are those whose picking policy consists of grouping the orders received in a warehouse into batches, prior to starting the picking process. Once the batches have been formed, all items within the same batch are picked together on a single picking route. In this survey we review the optimization problems known in this family, focusing on manual picking systems and rectangular-shaped warehouses with only parallel and cross aisles, which is the most common warehouse configuration in the literature. First, we identify the decisions within the strategic, tactical, and operational levels that influence the picking task. Then, we characterize the optimization problems belonging to this family, whose objective function might differ. The identified problems are classified into a taxonomy proposed in this paper, which is designed to host future problems within this family. We also review the most outstanding papers by category and the strategies and algorithms proposed for the most relevant activities: batching, routing, sequencing, waiting, and assigning. To conclude, we outline the open issues and future paths of the topic under study.
Semi-Lagrangian relaxation applied to the uncapacitated facility location problem
(Springer Verlag, 2010) Beltrán-Royo, César; Vial, Jean Philip; Alonso-Ayuso, Antonio
Structuring bilateral energy contract portfolios in competitive markets
(2010) Alonso-Ayuso, Antonio; Domenica, Nico di; Escudero, Laureano F; Pizarro, Celeste
Two alternative models for farm management: discrete versus continuous time horizo
(Elsevier, 2003) Vitoriano, Begoña; Ortuño, M Teresa; Recio, Beatriz; Rubio, Fernando; Alonso-Ayuso, Antonio
Crop production entails many decision making processes aimed at improving productivity and achieving the best yield from scarce resources. Assuming that there is a set of tasks to be carried out within a given time horizon, and each task can be performed in different ways, the problem consists of determining how and when to carry out each task, in such a way that the tasks are scheduled in sequence at the minimum cost, taking into account any precedence relationships among them, the time window constraints for performing the tasks and the resources availability. This paper presents two alternative mathematical models to attain the proposed objective. The first model splits the time into discrete units spread throughout the planning horizon; it is presented in connection with flexible manufacturing. The second model keeps a continuous time horizon; a scheduling model is used for which a family of {\it incompatibility conditions} is introduced to avoid a certain type of simultaneous usage of resources. This type of conditions require to introduce a new structure so-called conditional disjunction. Computational experience is reported for real life problems.