Planning petrol station replenishment is an important logistics activity for all the major oil companies. The studied Multi-Depot Periodic Petrol Station Replenishment problem derives from a real case in which the company must replenish a set of petrol stations from a set of depots, during a weekly planning horizon. The company must ensure refuelling according to available visiting patterns, which can be different from customer to customer. A visiting pattern predefines how many times (days) the replenishment occurs during a week and in which visiting days a certain amount of fuel must be delivered. To fulfill the weekly demand of each petrol station, one of the available replenishment plans must be selected among a given set of visiting patterns. The aim is to minimize the total distance travelled by the fleet of tank trucks during the entire planning horizon. A matheuristic approach is proposed, based on the cluster-first route-second paradigm, to solve it. The proposed approach is thoroughly tested on a set of realistic random instances. Finally, a weekly large real instance is considered with 194 petrol stations and two depots.

E-commerce is a continuously growing sector worldwide, with important repercussions on the delivery system in urban areas and especially in the Business to Consumer (B2C) sector. The delivery of a package to a consumer's address involves not only high costs for couriers (greater number of kilometres travelled), but also increased congestion and greater environmental pollution (greater volume of pollutants released into the air). To rationalize deliveries in urban areas the use of collection points, equipped with lockers, to store the goods that users have ordered has been considered in literature. This work compares two alternative delivery options: deliveries to the consumer's home versus to Lockers. To make this comparison we used a cluster first route second math-heuristic approach. In the clustering phase, we experimented a new clustering function, while the routing phase consists in solving an instance of the Traveling Salesman Problem for each generated cluster. Finally, we applied the math-heuristic to a real case (the Italian municipality of Dolo near Venice) and compared the two delivery alternatives. We evaluate the performance considering two different fleets of vehicles, with small and medium capacity. In addition, since additional trips might be performed by consumers to pick up parcels at Lockers, a sensitivity analysis was carried out to analyse the sustainability of the proposed city logistics scheme.

This paper studies a scheduling problem application for the optimization of the employees used in aircrafts' refueling in a medium size airport. The problem is modelled as a particular resource leveling problem for which we provide a mixed integer mathematical formulation that we solve with CPLEX. The model allows to evaluate and analyse different scenarios that could be considered by the company in place of the current one in order to rearrange the available human resources used in refueling activity. Experimental results on a set of real test cases provided by an oil & gas company are discussed.

The process of aircraft refueling has crucial impact in the performance of an airport. It is in fact of common knowledge that one of the most important indicators for benchmarking an airport is the punctuality of flights departure. To assure high results, the airplane service activities such as passengers boarding, baggage handling and aircraft refueling must not delay one another and the overall departure time. The scope of the proposed study is to produce an instrument capable of simulating the process of the aircraft refueling in the airport environment and to consider different scenarios and evaluate their impact in the overall performance. This tool has significant relevance for the company whom process we have analyzed, allowing it to be able also to evaluate easily and in a short period of time complex changes in the process.

The petrol station replenishment problem consists in delivering fuel oils from a set of storage depots to a set of petrol stations during a few days planning horizon. This problem is addressed by an oil company which, for example, has to decide simultaneously the weekly fuel oil replenishment plan for each station, and, for each day of the week, the tank truck (vehicle) routes from depots to stations, in order to deliver the planned fuel oil replenishment amounts to petrol stations. Assuming a fleet of homogeneous tank trucks, the aim is to minimize the total distance travelled by tank trucks during the week, while loading tank trucks possibly near to their capacity in order to maximize the resource utilization. We model the problem as a generalization of the Multi-Depot Periodic Vehicle Routing Problem (MDPVRP) and provide a mathematical formulation. Due to the large size of the real instances which the company has to deal with, we solve the problem heuristically. We propose a hybrid genetic algorithm that successfully address the problem. The algorithm is derived from a known hybrid genetic algorithm for the MDPVRP, and adopts additional techniques and features tailored for the particular fuel oil distribution problem. It is specifically designed to deal with real instances derived from the fuel oil distribution in the European context that are profoundly different from the MDPVRP instances available from the literature. The proposed algorithm is evaluated on a set of real case studies and on a set of randomly generated instances that hold the same characteristics of the former.

Several researches in the scientific, industrial and commercial fields are supporting the reduction of traditional combustion cars' use. The main purpose is to increase the quality of life into the metropolitan cities through the reduction of CO2 emissions and global warming. Accordingly, one of the most successful models is the carpooling system. Currently, people are investigating the sustainability and durability of carpooling business model from both economic and organizational point of view. The present research aims to develop a Multicriteria Decision Support System (MDSS) in order to offer a carpooling system's platform based on different criteria. The MDSS is developed from driver's point of view and settled on two levels of optimization. Firstly, a genetic algorithm is proposed to solve an orienteering problem that optimizes the total revenue of driver based on the car's capability and the time schedule. Secondly, the best optimization solutions are compared with multicriteria analysis respect to other criteria not included in the first optimization. The outcome of MDSS is a schedule for drivers, which gives maximum satisfaction in terms of profitability, punctuality and comfort of the travel.

E-commerce is a sector in continual growth in all countries and, in particular, the increase in B2C (Business to Consumer) e-commerce market has important effects on last-mile deliveries in city areas. The delivery of a parcel to a consumer's address involves not only high costs for both couriers (extended car routes) and consumers (high prices) and also greater environmental pollution. The growing demand for deliveries in urban areas involves increases in traffic and congestion problems and, consequently, environmental issues. In recent years, many studies have focused on alternative measures to reduce the negative aspects and impact of last-mile deliveries. Good practice to rationalize last-mile delivery should involve the use of various systems, such as reception boxes, delivery boxes, controlled access systems, collection points and lockers. This paper compares two alternative options to home delivery. In particular, it makes comparisons between point-to-point and lockers, states the pro and cons of both, and defines the best positions to locate lockers to reduce consumers' deviations. The proposed method is applied to a real case: the Italian municipality of Dolo (near Venice).

The emergencies management in industrial plants is an issue widely discussed in the literature and in the European legislative framework. Despite the large interest shown by the different actors involved in emergencies management, neither scientific nor in industrial field, have developed intelligent tools to support the decisions in these particular contexts. This work, realized inside an Italian financed project (DIEM-SSP), faces the problem to evacuate the greater number of persons from a risky area and transfer them in a unique destination outside from this area using the available and limited resources. Supposing that these persons have problem of mobility, the problem to solve becomes this: collect the highest number of persons from several origins and bring them into a unique destination, using a limited number of capacitated vehicles respecting a time limit. This problem has been modelled as a Multi origins Capacitated Team Orienteering Problem (Mo-CTOP) and solved implementing Ants Colony Optimization algorithm (ACOa). Results and tests are given in order to validate the proposed model and to offer a solution for a real case treaty into the abovementioned project.

ThŒis paper is focused on the comparison of results obtained by the resolution of the capacitated vehicle routing problem. A modi€ed algorithm of classical arti€cial bee colony (ABC) is described and implemented in this article, in particular, a new type of neighborhood operator is introduced. Œe idea on the base of ABC is creating an algorithm of Swarm - Intelligence which mimics the behavior of a honey bee swarm. Œe performance of the proposed metaheuristic is evaluated on two sets of standard benchmark instances and is compared with the well-known results obtained by savings algorithm of Clarke and Wright and with those calculated using ABC-enhanced by Szeto et al. Œe computational results show that the proposed ABC outperforms the Saving algorithm and that it can produce good solutions when compared with the ABC-enhanced.

ThŒe emergencies management in industrial plants is an issue widely discussed in the literature and in the European legislative framework. Despite the large interest shown by the di‚erent actors involved in emergencies management, neither scienti€c nor in industrial €eld, have developed intelligent tools to support the decisions in these particular contexts. Œiswork, realized inside an Italian €nanced project (DIEM-SSP), faces the problem to evacuate the greater number of persons from a risky area and transfer them in a unique destination outside from this area using the available and limited resources. Supposing that these persons have problem of mobility, the problem to solve becomes this: collect the highest number of persons from several origins and bring them into a unique destination or multiple destination using a limited number of capacitated vehicles respecting a time limit. Œis problem has been modelled as a Multi origins Capacitated Team Orienteering Problem (Mo-CTOP) and solved implementing Optimization algorithm. At the same time the potentiality of multi-destination are explored in order to analyse the bene€ts in a real application. So that a €rst design a solution approach for a Multi origins and Multi destination Capacitated Team Orienteering Problem (Mo-Md-CTOP) is given. Œe team characteristic of the problem is due to need of simultaneous optimization of multi recovery vehicles. Results and tests are given on simple instances in order to validate the proposed model. Future research could explore the opportunities o‚ered by this tool if implemented in smartphone and tablet application. In this case in fact, the computational time could represent an important constraint to considerer in the algorithm€s design. Heuristics and metaheuristics approaches could be explored to give sub-optimal solutions to the problem in short time of computation. In this way the designed algorithm could be used also in real time situation in mobile applications available for rescue teams.

In this work a two level heuristic algorithm is described for a nearly real-time multi-vehicle many-to-many Dial-A-Ride Problem (DARP). This algorithm is ready to support a Demand Responsive Transportation System in which we face the problem of quickly evaluate a good-quality schedule for the vehicles and provide fast response to the users. The insertion heuristic is double dynamic nearly real-time and the objective function is to minimize the variance between the requested and scheduled time of pickup and delivery. In the first level, after a customer web-request, the heuristic returns an answer about the possibility to insert the request into the accepted reservations, and therefore in a vehicle schedule, or reject the request. In the second level, during the time elapsed between a request and the following, and after a reshuffling of the order of the incoming accepted requests, the same heuristic works for the whole set of accepted requests, trying to optimize the solution. We intensively tested the algorithm with a requests-generating software that has allowed us to show the competitive advantage of this web-based architecture.

The obstacles to the E-Mobility (EM)'s development are widely discussed both in scientific and in industrial fields. Approaches to overcome these obstacles are still not consolidate. At the same time, it is not so clear, what Business Models (BM) are more sustainable for the owners of Charging Infrastructure (CI). With the aim to support the development of charging network (CN), the authors propose a new BM based on intelligent, collaborative and digital services for all actors of the value chain. The implementation of this BM starts with the development of a decisional structure (DS) and the sharing of data and information among all operators that are involved into the charging process. The main elements of the model are explained and the first results of implementation are given. Future development are discussed to enrich the research and to supply at industrial field a useful tool to face decisions in the real context of CNs.

The authors propose a Multi Attributes approach to meet the demand of personalized tourist tours into cultural cities. Respecting to others works present into the literature, in this paper the decisional process includes two phases and a high number of variables that don't increase the complexity of the problem. A real application in an Italian city, Florence, is presented to demonstrate the great potential of this system into real context. The first phase of optimization is solved applying an innovative Genetic Algorithm, the second one a Multi Criteria Method, Analytic Hierarchy Process (AHP). The combination of these two approach gives flexibility to the system with respect to number of variables and allow to return a good solution for tourist in few second of computational time.

In this paper we consider the final distribution of fuel oil from a storage depot to a set of petrol stations faced by an oil company, which has to decide the weekly replenishment plan for each station, and determine petrol station visiting sequences (vehicle routes) for each day of the week, assuming a fleet of homogeneous vehicles (tankers). The aim is to minimize the total distance travelled by tankers during the week, while loading tankers possibly near to their capacity in order to maximize the resource utilization. The problem is modelled as a generalization of the Periodic Vehicle Routing Problem (PVRP). Due to the large size of the real instances which the company has to deal with, we solve the problem heuristically. We propose a hybrid genetic algorithm that successfully address the problem inspired to a known hybrid genetic algorithm from the literature for the PVRP. However, the proposed algorithm adopts some techniques and features tailored for the particular fuel oil distribution problem, and it is specifically designed to deal with real instances derived from the fuel oil distribution in the European context that are profoundly different from the PVRP instances available from literature. The proposed algorithm is evaluated on a set of real case studies and on a set of randomly generated instances that hold the same characteristics of the former.

The aim of this paper is to study a Bike Sharing Touring (BST) applying a mathematical model known in operation research as Orienteering Problem (OP). Several European Cities are developing BST in order to reduce the exhaust emissions and to improve the sustainability in urban areas. The authors offer a Decision Support Tool useful for the tourist and the service's manager to organize the tourists' paths on the basis of tourists' desires, subject to usable time, place of interest position and docking station location. The model analyzed presents two innovative aspects compared to a classic OP. The first one is that the start and the arrival point of routes aren't necessary coinciding and pre-conditioned. The last one is that the knowledge of tourist tours allows to book the visit to a point of interest and doing so to optimize efficiency of the whole system and not only of the single tourist tour.

In this work we proposed an integrated support system combining a meta-heuristic algorithm and a multicriteria decision analysis method to solve an orienteering problem applied to car-pooling system. For this purpose a Genetic Algorithm (GA), an Analytical Hierarchy Process (AHP) are implemented. The research is based on the awareness that decision makers (DMs) often face situations in which different conflicting viewpoints (goals or criteria) are to be considered. Current car-pooling web platforms are focused on the exchange of information among potential users and drivers. The aim of this work is to include in web platform a decision procedure to support driver to organize the tour considering more criteria. The driver has to decide which tour does and which users to take into the trip Preliminary test are given to validate the functionality and usefulness of created integrated decision support system.

This work describes two applications of the vehicle routing problem (VRP) to the design of fixed and periodic routes. The first application is an industrial case in the field of touristic cruise planning where point of interests should be visited within exactly one of multiple time windows on a weekly time basis. The second application is in retail distribution of fuel oils where petrol stations must be refueled with given fuel oil amounts periodically within a given time horizon. The work studies the VRP models for the two described applications evaluating intersections between them and studying possible unified modeling formulation.

The realization of innovative passengers transport services requires more and more often a greater flexibility and inexpensiveness of the service. To answer this request in many cases the physical solution is to realize a demand responsive transportation system (DRTS). A DRTS require the planning of travel paths (routing) and customers pick-up and drop-off times (scheduling) according to received requests, respecting the limited capacity of the fleet and time constraints (hard time windows) for each network's node, and the service time of the system. By the modelling point of view a DRTS can be effectively represented with a Dial-aride problem (DaRP). A DaRP derives from the Pick-up and Delivery Problem with Time Windows (PDPTW) and may operate according to a static or to a dynamic mode. In the static setting, all customers' requests are known beforehand and the DaRP returns the vehicles routing and the passengers pick up and drop off time scheduling. The static setting may be representative of a phase of reservation occurred the day before the execution of the service. But, if the reservation requests must be processed online, even during the booking process there may be a certain level ad dynamism. In fact, if the algorithm works online, it manages each and every incoming request separately, and accepts or refuses it immediately, without knowing anything about the following. The operative program is constantly updated after each received request without refusal to carry out previous accepted services. In the dynamic mode, customers' requests arrive when the service is already running and, consequently, the solution may change whilst the vehicle is already travelling. In this mode it is necessary that the schedule is updated when each new request arrives and that this is done in a short time to ensure that the potential customer will not leave the system before a possible answer. In this work, we describe a flexible people transport system capable of managing incoming transport demand in dynamic mode, using a solution architecture based on a two-stage algorithm to solve Dial-a-Ride Problem instances. In the first stage, a constructive heuristic algorithm quickly provides a feasible solution to accept the incoming demand. The algorithm in the second stage try to improve the solution evaluated at the first stage by using the time between two consecutive transportation events. The algorithm, unlike most of the works in the literature, use an objective function that optimizes the service punctuality.

One of the most important objectives of a manufacturing company is the optimization of the distribution of the produced goods considering the whole value chain. Unfortunately, in many companies the performance of the supply chain depends on many uncertain factors that are difficult to predict. The only way to face them is to adopt innovative solutions and tools that allow a swift response to the market changes. This paper analyzes the distribution processes managed by the logistics department of a large company producing and distributing petroleum products through the following main steps: crude oil's transportation typically from many countries to a refinery; refining process; maritime transportation from the refinery to three costal depots; road transport from depots to gas stations. The analyzed process is the primary supply, consisting in the maritime transport from the refinery to the coastal depots, liable to stochastic activities and events as weather condition. Through simulating the primary supply, we study the effects that the ship traffic generates on the overall variance of inventory levels at the costal depots with respect to specific inventory level targets, and analyze the impact of different tactical decision choices on the variance reduction. Reducing inventory's variance, through a better control of the distribution, allows the company to reduce inventory target levels and hence to reduce inventory costs in term of capital stock, while keeping the same risk level of stock out. The project is made of many phases: map all relevant processes to have a complete vision of transport's structure; conduct a statistical analysis to identify specific statistical distributions of every ships' process (delay, mooring, loading, etc.); model and simulate the primary supply using simulation software; use the model to make a "what-if" analysis. Within this project, it has been possible to realize a model that presents stochastic elements. All these phases are supported by six-sigma methodology, which focalizes on defects' process reduction by the control of its mean square deviation and following the stages of the DMAIC (Define Measure Analyze Improve Control). One of the what-if analysis which has been done consists in simulating the opening refinery's jetties h24, because currently these are closed during the night. Opening the jetties, will increase the capacity of some of the bottleneck resources for the oil distribution process, and thanks to the simulation model we can estimate quickly the effects on the oil transport system.

This paper presents an approach to model and simulate industrial plant accidents as well as the related emergency management; interoperable simulation is proposed as approach for applying High Level Architecture in this context. The authors are focusing their attention on the disaster simulation and its interaction with the emergency management. Modern simulation, by using technology enables such as mobile solution, enhance its support to first responders, the dynamic reaction to crisis evolution as well as the improvement in training and management of safe routing and handling of injured people. This paper introduces these elements as part of new research track devoted to get benefits from interoperable simulation in federating multidisciplinary models for industrial plant emergency management.