Ornella Pisacane

A Variable Neighborhood Search Branching for the Electric Vehicle Routing Problem with Time Windows

Abstract E-mobility plays a key role especially in contexts where the transportation activities impact a lot on the total costs. The Electric Vehicles (EVs) are becoming an effective alternative to the internal combustion engines guaranteeing cheaper and eco-sustainable transport solutions. However, the poor battery autonomy is still an Achilles hell since the EVs require many stops for being recharged. We aim to optimally route the EVs for handling a set of customers in time considering the recharging needs during the trips. A Mixed Integer Linear Programming formulation of the problem is proposed assuming that the battery recharging level reached at each station is a decision variable in order to guarantee more flexible routes. The model aims to minimize the total travel, waiting and recharging time plus the number of the employed EVs. Finally, a Variable Neighborhood Search Branching (VNSB) is also designed for solving the problem at hand in reasonable computational times. Numerical results on benchmark instances show the performances of both the mathematical formulation and the VNSB compared to the ones of the model in which the battery level reached at each station is always equal to the maximum capacity.

An approximate ϵ-constraint method for a multi-objective job scheduling in the cloud

Cloud computing is a hybrid model that provides both hardware and software resources through computer networks. Data services (hardware) together with their functionalities (software) are hosted on web servers rather than on single computers connected by networks. Through a device (e.g., either a computer or a smartphone), a browser and an Internet connection, each user accesses a cloud platform and asks for specific services. For example, a user can ask for executing some applications (jobs) on the machines (hosts) of a cloud infrastructure. Therefore, it becomes significant to provide optimized job scheduling approaches suitable to balance the workload distribution among hosts of the platform. In this paper, a multi-objective mathematical formulation of the job scheduling problem in a homogeneous cloud computing platform is proposed in order to optimize the total average waiting time of the jobs, the average waiting time of the jobs in the longest working schedule (such as the makespan) and the required number of hosts. The proposed approach is based on an approximate @e-constraint method, tested on a set of instances and compared with the weighted sum (WS) method. The computational results highlight that our approach outperforms the WS method in terms of a number of non-dominated solutions.

Heuristic algorithms for the operator-based relocation problem in one-way electric carsharing systems

This paper addresses an Electric Vehicle Relocation Problem (E-VReP), in one-way carsharing systems, based on operators who use folding bicycles to facilitate vehicle relocation. In order to calculate the economic sustainability of this relocation approach, a revenue associated with each relocation request satisfied and a cost associated with each operator used are introduced. The new optimization objective maximizes the total profit. To overcome the drawback of the high CPU time required by the Mixed Integer Linear Programming formulation of the E-VReP, two heuristic algorithms, based on the general properties of the feasible solutions, are designed. Their effectiveness is tested on two sets of realistic instances. In the first, all the requests have the same revenue, while, in the second, the revenue of each request has a variable component related to the users rent-time and a fixed part related to customer satisfaction. Finally, a sensitivity analysis is carried out on both the number of requests and the fixed revenue component. Economic sustainability of E-VReP in one way carsharing systems was addressed.Computational complexity and APX-hardness is addressed.Heuristics were designed to overcome the drawback of the high CPU times of the MILP.Variable revenues associated with requests satisfied were also considered.Sensitivity analysis was carried out on the number of requests and the revenue.

A new Mathematical Programming Model for the Green Vehicle Routing Problem

Abstract A new MILP formulation for the Green Vehicle Routing Problem is introduced where the visits to the Alternative Fuel Stations (AFSs) are only implicitly considered. The number of variables is also reduced by pre-computing for each couple of customers an efficient set of AFSs, only given by those that may be actually used in an optimal solution. Numerical experiments on benchmark instances show that our model outperforms the previous ones proposed in the literature.

An Adaptive Large Neighborhood Search for relocating vehicles in electric carsharing services

Abstract We propose an Adaptive Large Neighborhood Search metaheuristic to solve a vehicle relocation problem arising in the management of electric carsharing systems. The solution approach, aimed to optimize the total profit, is tested on three real-like benchmark sets of instances. It is compared with a Tabu Search, ad hoc designed for this work, with a previous Ruin and Recreate metaheuristic and with the optimal results obtained via Mixed Integer Linear Programming. We also develop bounding procedures to evaluate the solution quality when the optimal solution is not available.

A three-phase matheuristic for the time-effective electric vehicle routing problem with partial recharges

Abstract We propose a three-phase matheuristic, combining an exact method with a Variable Neighborhood Search local Branching (VNSB) to route a fleet of Electric Vehicles (EVs). EVs are allowed stopping at the recharging stations along their routes to (also partially) recharge their batteries. We hierarchically minimize the number of EVs used and the total time spent by the EVs, i.e., travel times, charging times and waiting times (due to the customer time windows). The first two phases are based on Mixed Integer Linear Programs to generate feasible solutions, used in a VNSB algorithm. Numerical results on benchmark instances show that the proposed approach finds good quality solutions in reasonable amount of time.

A two-phase optimization method for a multiobjective vehicle relocation problem in electric carsharing systems

The paper focuses on one-way electric carsharing systems, where the fleet of cars is made up of Electric Vehicles (EVs) and the users can pick-up the EV at a station and return it to a different one. Such systems require efficient vehicle relocation for constantly balancing the availability of EVs among stations. In this work, the EVs are relocated by workers, and the issue of finding a trade-off among the customers’ satisfaction, the workers’ workload balance and the carsharing provider’s objective is addressed. This leads to a three-objective optimization problem for which a two-phase solution approach is proposed. In the first phase, feasible routes and schedules for relocating EVs are generated by different randomized search heuristics; in the second phase, non-dominated solutions are found through epsilon-constraint programming. Computational results are performed on benchmark instances and new large size instances based on the city of Milan.

Cloud in Science

Models of morphological analysis and synthesis of word-forms are based on the morphology of natural languages, using a linguistic point of view. However, to use the models in natural language processing program systems requires their analysis from mathematical and algorithmic points of view. The purpose of the study is a formal description of models of morphological analysis and synthesis of word-forms and their analysis on various aspects. Aspects are methods of algorithmic realization of morphological analysis and synthesis of word-forms, used operations and its coupling with model objects, approaches to reduce the search space in morphological analysis. Item and Arrangement, Item and Process, Word and Paradigm models are analyzed, as well as our universal model of form-building. Mathematical and algorithmic formalization of models reveal their similarity. We conclude that in all models, except Item and Arrangement, the operations are explicitly unlimited and most often an affix adding or replacing of a part of the stem by the other. The models reduce the search space in morphological analysis.

Green Computing: A Dual Technology for HPC and Cloud Computing

Green computing refers to the practice and procedures of using computing resources in an environment friendly way while maintaining overall computing performance. Global warming is the continuing rise in the average temperature of the Earth’s climate system due to a range of factors. Scientific understanding of the various causes of global warming has been increasing since the last decade. Climate change and associated impacts vary from region to region across the globe. Nowadays, weather behaviour is becoming extremely unpredictable throughout the globe. United Nations Framework Convention on Climate Change (UNFCCC) is working relentlessly to achieve its objective of preventing dangerous anthropogenic (humaninduced) climate change. Owing to global warming, various regulations and laws related to environmental norms forces manufacturers of I.T equipments to meet various energy requirements. Green computing is a well balanced and sustainable approach towards the achievement of a greener, healthier and safer environment without compromising technological needs of the current and future generations. This paper is a survey of several important literature related to the field of green computing that emphasises the importance of green computing for sustainable development.

Economics of Cloud Computing

Cloud computing has its root deep into ground and in the market. The evolution of cloud computing is one of the major advances in the computing area as well as in economics of using computing. There are three major technologies which represent cloud computing: Software-asa-Service (SaaS), Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (Iaas). In our example we discuss pros and cons of implementing PaaS or SaaS. While there are quite a few papers covering technical aspects of cloud computing technologies, this paper will have focus on economics of the cloud. In this paper we will try to explain which criteria should be considered when deciding to move or not to move to cloud. There is also general view on Return on Investment shown which takes into account various intangible impacts of cloud computing, apart from cost. These impacts include better flexibility, scalability and faster time to market.