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Maximizing Carsharing Profits: An Optimization Model to Support the Carsharing Planning

Procedia Manufacturing 2019年 39卷 1968-1976页

作者： Cristiano Martins Monteiro Claudia A. Soares Machado Mariana de Oliveira Lage Fernando Tobal Berssaneti Clodoveu Augusto Davis Jr. José Alberto Quintanilha Departament of Computer Science Federal University of Minas Gerais Belo Horizonte Brazil Department of Transportation Engineering Polytechnic School University of São Paulo São Paulo Brazil Institute of Energy and Environment University of São Paulo São Paulo Brazil Department of Production Engineering Polytechnic School University of São Paulo São Paulo Brazil

Carsharing services aim to offer short-term car rentals, including round-trip and one-way alternatives. Round-trip clients must deliver the rented car at the same station where the rental has started. One-way clients can return the vehicle in a different station. This work proposes a mixed-integer Linear programming Model to optimize the fleet-sizing of a carsharing service for the one-way and round-trip alternatives, seen as utilization scenarios. The proposed model aims to maximize the company’s profit, finding the best number of vehicles to be allocated to each carsharing station. Different scenarios were analyzed for the one-way and round-trip settings, varying service costs, rental prices, number of clients, rental duration and driven distance. Simulations were performed using real spatial data from the city of São Paulo, Brazil. Results showed that round-trip profits can benefit from rentals with higher durations, and that one-way profits can overcome the profits from round-trip if user demand and number of available vehicles are enough.

关键词： Carsharing Planning Optimization of Fleet Sizing mixed-integer programming

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AUTOMATIC SCHEDULING OF HYPERMEDIA DOCUMENTS WITH ELASTIC TIMES

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Parallel Processing Letters 2004年第1期14卷 45-59页

作者： MAIRA T. MEDINA CELSO C. RIBEIRO LUIZ F. G. SOARES Department of Computer Science Catholic University of Rio de Janeiro R. Marquês de São Vicente 225 Rio de Janeiro RJ 22453-900 Brazil

The problem of automatic scheduling hypermedia documents consists in finding the optimal starting times and durations of objects to be presented, to ensure spatial and temporal consistency of a presentation while respecting limits on shrinking and stretching the ideal duration of each object. The combinatorial nature of the minimization of the number of objects whose duration is modified makes it the most difficult objective to be tackled by optimization algorithms. We formulate this scheduling problem as a mixed integer programming problem and report some preliminary investigations. We propose an original approach to the minimization of the number of objects which are shrinked or stretched. A simple primal heuristic based on variable fixations along the solution of a sequence of linear relaxations of the mixed integer programming formulation is described. Computational experiments on realistic size problems are reported. The effectiveness of the heuristic in finding good approximate solutions within very small processing times makes of it a quite promising approach to be integrated within existing document formatters to perform compile time scheduling or even run time adjustments. We also discuss results obtained by Lagrangean relaxation and propose a dual heuristic using the modified costs, which consistently improves the solutions found by the primal heuristic.

关键词： hypermedia scheduling heuristics mixed-integer programming

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Exact and heuristic approaches to Truck-Drone Delivery Problems

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EURO JOURNAL ON TRANSPORTATION AND LOGISTICS 2023年 12卷

作者： Freitas, Julia C. Penna, Puca Huachi V. Toffolo, Tulio A. M. Univ Fed Ouro Preto Dept Comp Ouro Preto Brazil

Collaborative delivery employing drones in last-mile delivery has been an extensively studied topic in recent years. In this paper, it is studied Truck-Drone Delivery Problems (TDDPs) in which a traditional delivery truck is gathered with a drone to cut delivery times and costs. The vehicles work together in a hybrid operation involving one drone launching from a larger vehicle that operates as a mobile depot and a recharging platform. The drone launches from the truck with a single package to deliver to a customer. Each drone must return to the truck to recharge batteries, pick up another package, and launch again to a new customer location. This work proposes a novel mixed integer programming (MIP) formulation and a heuristic approach to address the problem. The proposed MIP formulation yields better linear relaxation bounds than previously proposed formulations for all instances, and was capable of optimally solving several unsolved instances from the literature. A hybrid heuristic based on the General Variable Neighborhood Search metaheuristic combining Tabu Search concepts is employed to obtain high-quality solutions for large-size instances. The efficiency of the algorithm was evaluated on 1415 benchmark instances from the literature, and over 80% of the best known solutions were improved.

关键词： Unmanned Aerial Vehicle Traveling Salesman Problem Drone delivery mixed-integer programming General Variable Neighborhood Search

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Optimization of earthwork allocation with multiple soil types

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AEJ - Alexandria Engineering Journal 2003年第1期42卷 111-123页

作者： Ammar, Mohammad A. Eldosouky, Adel I. Jarad, Fayez A. Faculty of Eng. Tanta University Tanta Egypt

Earthmoving is the process of moving and processing soil from one location to another to alter an existing land surface into a desired configuration. Highways, dams, and airports are typical examples of heavy earthmoving projects. Over the years, construction managers have devised methods to determine the quantities of material to be moved from one place to another. Various types of soil (soft earth, sand, hard clay, ... etc.) create different levels of difficulty of the problem. The earthmoving problem has traditionally been solved using mass diagram or variety of operational research techniques. However, existing models do not present realistic solution for the problem. Multiple soil types are usually found in cut areas and specific types of soil are required in fill sections. Some soil types in cut areas are not suitable for use in fill sections and must be disposed-off. In this paper a new mathematical programming model is developed to find-out the optimum allocation of earthmoving materials. In developing the proposed model, different soil types are considered as well as variations of unit cost with earth quantities moved. Suggested borrow pits and/or disposal sites are introduced to minimize the overall earthmoving cost. The proposed model is entirely formulated using the programming capabilities of VB6 while LINDO is used to solve the formulated model. An example project is presented to show how the model can be implemented. A case study project is analyzed using the developed model and a sensitivity analysis is then performed.

关键词： Construction management Earthmoving problem Integrated programs mixed-integer programming Optimization

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Formulation and Solution Method on Flexible Transmission Expansion Planning

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IFAC Proceedings Volumes 2011年第1期44卷 11685-11689页

作者： Zechun Hu Furong Li Department of Electrical Engineering Tsinghua University Beijing China University of Bath Bath BA2 7AY UK

Abstract In this paper, a transmission expansion planning formulation is proposed that simultaneously considers investment in phase shifters and in primary network assets, such as lines and transformers. Based on the classical network expansion planning formulation, a new mixed-integer linear programming (MILP) formulation is built to deal with the placement of phase shifter along with adding new branches, while the N-1 security criterion is considered. The curtailment of wind farm output is also discussed. The whole problem is solved by Benders’ decomposition method. Simulations have been done on the Garver 6-bus system. The planning schemes obtained from the proposed method showing cost reduction offered by phase shifters over the traditional circuit expansion.

关键词： Benders’ decomposition mixed-integer programming Network planning Phase shifter Wind power

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A data-driven bi-objective matheuristic for energy-optimising timetables in a passenger railway network

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JOURNAL OF RAIL TRANSPORT PLANNING & MANAGEMENT 2023年 26卷

作者： Als, Matthias Villads Hinsch Madsen, Mathias Bejlegaard Jensen, Rune Moller IT Univ Copenhagen Rued Langgaards Vej 7 DK-2300 Copenhagen S Denmark

Energy-efficient train timetabling (EETT) is essential to achieve the full potential of energy -efficient train control, which can reduce operating costs and contribute to a reduction in CO2 emissions. This article proposes a bi-objective matheuristic to address the EETT problem for a railway network. To our knowledge, this article is the first to suggest using historical data from train operation to model the actual energy consumption, reflecting the different driving behaviours. The matheuristic employs a genetic algorithm (GA) based on NSGA-II. The GA uses a warm-start method to generate the initial population based on a mixed-integer program. A greedy first-come-first-served fail-fast repair heuristic is used to ensure feasibility throughout the evolution of the population. The objectives taken into account are energy consumption and passenger travel time. The matheuristic was applied to a real-world case from a large North European train operating company. The considered network consists of 107 stations and junctions, and 18 periodic timetables for 9 train lines. Our results show that for an entire network, a reduction up to 3.3% in energy consumption and 4.64% in passenger travel time can be achieved. The results are computed in less than a minute, making the approach suitable for integration with a decision support tool.

关键词： Multi-objective optimisation Energy-efficient train timetabling Matheuristic mixed-integer programming Genetic algorithm Decision support

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Modeling social, economic, and health perspectives for optimal pandemic policy decision-making

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SOCIO-ECONOMIC PLANNING SCIENCES 2023年 86卷 101472页

作者： Soltanisehat, Leili Gonzalez, Andres Barker, Kash Univ Oklahoma Sch Ind & Syst Engn Norman OK 73019 USA

While different control strategies in the early stages of the COVID-19 pandemic have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative economic impact of control strategies. This paper proposes a novel multi-objective mixed-integer linear programming (MOMILP) formulation, which results in the optimal timing of closure and reopening of states and industries in each state to mitigate the economic and epidemiological impact of a pandemic. The three objectives being pursued include: (i) the epidemiological impact, (ii) the economic impact on the local businesses, and (iii) the economic impact on the trades between industries. The proposed model is implemented on a dataset that includes 11 states, the District of Columbia, and 19 industries in the US. The solved by augmented e-constraint approach is used to solve the multi-objective model, and a final strategy is selected from the set of Pareto-optimal solutions based on the least cubic distance of the solution from the optimal value of each objective. The Paretooptimal solutions suggest that for any control decision (state and industry closure or reopening), the economic impact and the epidemiological impact change in the opposite direction, and it is more effective to close most states while keeping the majority of industries open during the planning horizon.

关键词： Pandemic SIRD model Maximum network flow problem mixed-integer programming

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Erratum: Lot-Sizing with Stock Upper Bounds and Fixed Charges

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SIAM Journal on Discrete Mathematics 2011年第2期25卷 731-732页

作者： Marco Di Summa Laurence A. Wolsey

The purpose of this erratum is to correct the computational results reported in [M. Di Summa and L. A. Wolsey, SIAM J. Discrete Math., 24 (2010), pp. 853–875].

关键词： 90C11 90C57 mixed-integer programming discrete lot-sizing stock fixed costs mixing sets

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Using multiple reference vectors and objective scaling in the Feasibility Pump

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EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION 2023年第1期11卷

作者： Mexi, Gioni Berthold, Timo Salvagnin, Domenico Zuse Inst Berlin Takustr 7 D-14195 Berlin Germany Fair Isaac Deutschland GmbH Welle 10 D-60322 Frankfurt Germany Univ Padua Via Gradenigo 6-B I-35131 Padua Italy

The Feasibility Pump (FP) is one of the best-known primal heuristics for mixed-integer programming (MIP): more than 15 papers suggested various modifications of all of its steps. So far, no variant considered information across multiple iterations, but all instead maintained the principle to optimize towards a single reference integer point. In this paper, we evaluate the usage of multiple reference vectors in all stages of the FP algorithm. In particular, we use LP-feasible vectors obtained during the main loop to tighten the variable domains before entering the computationally expensive enumeration stage, a procedure we refer to as mRENS. Moreover, we consider multiple integer reference vectors to explore further optimizing directions and introduce alternative objective scaling terms to balance the contributions of the distance functions and the original MIP objective. Our computational experiments demonstrate that the new method can improve performance on general MIP test sets. In detail, our modifications provide a 29.3% solution quality improvement and 4.0% running time improvement in an embedded setting, needing 16.0% fewer iterations over a large test set of MIP instances. In addition, the method's success rate increases considerably within the first few iterations. In a standalone setting, we also observe a moderate performance improvement, which makes our version of FP suitable for the two main use-cases of the algorithm.(c) 2023 The Author(s). Published by Elsevier Ltd on behalf of Association of European Operational Research Societies (EURO). This is an open access article under the CC BY-NC-ND license (http:// creativecommons .org /licenses /by -nc -nd /4 .0/).

关键词： mixed-integer programming Primal heuristics

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Dynamic rebalancing for Bike-sharing systems under inventory interval and target predictions

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EURO JOURNAL ON TRANSPORTATION AND LOGISTICS 2024年 13卷

作者： Liang, Jiaqi Silva, Maria Clara Martins Aloise, Daniel Jena, Sanjay Dominik Polytech Montreal 2500 Chemin Polytech Montreal PQ H3T 1J4 Canada Univ Quebec Montreal Sch Management 315 Rue St Catherine Est Montreal PQ H2X 3X2 Canada Ctr Interuniv Rech Reseaux Entreprise Logist & Tra 2920 Chemin Tour Montreal PQ H3T 1J4 Canada Canada Excellence Res Chair Data Sci Real time Dec 2500 Chemin Polytech Montreal PQ H3T 1J4 Canada Grp Etud & Rech Anal decis GERAD 2920 Chemin Tour Montreal PQ H3T 1N8 Canada

Bike-sharing systems have become a popular transportation alternative. Unfortunately, station networks are often unbalanced, with some stations being empty, while others being congested. Given the complexity of the underlying planning problems to rebalance station inventories via trucks, many mathematical optimizations models have been proposed, mostly focusing on minimizing the unmet demand. This work explores the benefits of two alternative objectives, which minimize the deviation from an inventory interval and a target inventory, respectively. While the concepts of inventory intervals and targets better fit the planning practices of many system operators, they also naturally introduce a buffer into the station inventory, therefore better responding to stochastic demand fluctuations. We report on extensive computational experiments, evaluating the entire pipeline required for an automatized and data-driven rebalancing process: the use of synthetic and real-world data that relies on varying weather conditions, the prediction of demand and the computation of inventory intervals and targets, different reoptimization modes throughout the planning horizon, and an evaluation within a fine-grained simulator. Results allow for unanimous conclusions, indicating that the proposed approaches reduce unmet demand by up to 34% over classical models.

关键词： Bike-sharing systems Dynamic rebalancing Inventory intervals Target inventories Reoptimization modes mixed-integer programming

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