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Ferdinando PEZZELLA

Pubblicazioni

Ferdinando PEZZELLA

 

65 pubblicazioni classificate nel seguente modo:

Nr. doc. Classificazioni
31 4 Contributo in Atti di Convegno (Proceeding)
24 1 Contributo su Rivista
4 2 Contributo in Volume
3 3 Libro
2 7 Curatele
1 5 Altro
Anno Risorsa
2018 A two-phase optimization method for a multiobjective vehicle relocation problem in electric carsharing systems
JOURNAL OF COMBINATORIAL OPTIMIZATION
Autore/i: Bruglieri, Maurizio; Pezzella, Ferdinando; Pisacane, Ornella
Classificazione: 1 Contributo su Rivista
Abstract: 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.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/258366 Collegamento a IRIS

2018 A Path-based solution approach for the Green Vehicle Routing Problem
COMPUTERS & OPERATIONS RESEARCH
Autore/i: Bruglieri, Maurizio; Mancini, Simona; Pezzella, Ferdinando; Pisacane, Ornella
Classificazione: 1 Contributo su Rivista
Abstract: The Green Vehicle Routing Problem concerns routing alternative fuel vehicles, based at a single depot, to handle a subset of customers while minimizing the total travel distance. Due to limited fuel autonomy, each vehicle may need to stop at Alternative Fuel Stations (AFSs) during its trip. We propose a two-phase solution approach in which a route is seen as the composition of paths, each one handling a subset of customers without intermediate stops at AFSs. In the first phase, all feasible paths are generated limiting their number through dominance rules. In the second phase, the paths are selected and properly combined to generate the routes via Mixed Integer Linear Programming. Our approach, tested on small- to medium-sized benchmark instances, outperforms the existing exact methods obtaining always the optimal solution in a smaller average computational time. Furthermore, the approach was converted into a heuristic one considering in the first phase only a subset of feasible non-dominated paths. In this way, we can also address larger- sized instances outperforming, in terms of solution quality, the best heuristic approach in the literature.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/261980 Collegamento a IRIS

2018 An Adaptive Large Neighborhood Search for Relocating Vehicles in Electric Carsharing Services
DISCRETE APPLIED MATHEMATICS
Autore/i: Bruglieri, M.; Pezzella, F.; Pisacane, O.
Classificazione: 1 Contributo su Rivista
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 prot, 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 a bounding procedure to evaluate the solution quality when the optimal solution is not available.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/256998 Collegamento a IRIS

2017 An Adaptive Large Neighborhood Search for Relocating Vehicles in Electric Carsharing Services
Metaheuristics: Proceeding of the MIC and MAEB 2017 Conferences
Autore/i: Bruglieri, Maurizio; Pezzella, Ferdinando; Pisacane, Ornella
Editore: Universitat Pompeu Fabra
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
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 two real-like benchmark sets of instances and compared with both a previous Ruin and Recreate metaheuristic and the optimal results obtained through a Mixed Integer Linear Programming formulation, when available
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/253692 Collegamento a IRIS

2017 A path-based Mixed Integer Linear Programming formulation for the Green Vehicle Routing Problem
Book of Abstracts of The sixth meeting of the EURO Working Group on Vehicle Routing and Logistics optimization
Autore/i: Pisacane, Ornella; Bruglieri, Maurizio; Mancini, Simona; Pezzella, Ferdinando
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The Green Vehicle Routing Problem (G-VRP) is a variant of the classical Vehicle Routing Problem (VRP) in which Green Vehicles (GVs), such as those with alternative fuel propulsion, are considered. Since GVs are characterized by a limited driving range, one or more stops at Refueling Stations (RSs) may be required along their trip. The goal of the problem is to serve a set of customers exploiting a fleet of identical GVs and minimizing their total travelled distance. Each vehicle leaves from the depot and returns to it. A maximum limit is imposed on the route duration. We propose a path-based Mixed Integer Linear Programming formulation for the G-VRP. In classical VRPs, paths enumeration techniques cannot be adopted due to the exponential number of feasible paths. On the contrary, in the G-VRP, given the GV autonomy constraints, the number of feasible paths is somehow limited. We generate all the feasible paths between the depot and each RS and between two RSs. We also introduce some rules to a priori exclude dominated paths from the feasible set. Such a feasible set is given in input to a Set-Partitioning formulation with the aim of selecting a subset of paths that, properly combined, compose the solution routes for the G-VRP. Computational results, carried out on benchmark instances, show that our approach is much faster than every exact method already presented in the literature, and it is also suitable to detect the optimal solutions in almost all the test cases.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/253693 Collegamento a IRIS

2017 The Green Vehicle Routing Problem with Capacitated Alternative Fuel Stations
Book of Abstracts of The sixth meeting of the EURO Working Group on Vehicle Routing and Logistics optimization
Autore/i: Bruglieri, Maurizio; Mancini, Simona; Pezzella, Ferdinando; Pisacane, Ornella
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This work addresses the problem of efficiently routing a set of Alternative Fuel Vehicles (AFVs), considering that, during their trips, some stops at Alternative Fuel Stations (AFSs) have to be planned. Every AFV leaves from a common depot and returns to it, after serving a subset of customers. Due to some forms of contract with the drivers, an upper bound is usually imposed on the duration of each route. The aim is to dene the optimal routing of the AFVs in order to minimize the total traveled distance. This problem is known in the literature as the Green Vehicle Routing Problem (G-VRP). Several Mixed Integer Linear Programming (MILP) formulations have been already presented to model it. The G-VRP assumes that an unlimited number of vehicles may be simultaneously refueled at the same AFS. This hypothesis is not realistic, since AFSs typically have a very small number of refueling locations. To manage this issue, we propose an extension of the G-VRP that models the more realistic situation where a capacity is associated with every AFS, bounding the number of vehicles that can simultaneously refuel. The capacity constraint makes more challenging the scheduling of the stops at the AFSs, since now the AFSs become a shared resource of the problem. For this new version of the GVRP, we propose a MILP formulation and a heuristic approach. Preliminary numerical results have been carried out on some benchmark instances, properly adapted to this extension of the G-VRP.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/253694 Collegamento a IRIS

2017 A three-phase matheuristic for the time-effective electric vehicle routing problem with partial recharges
ELECTRONIC NOTES IN DISCRETE MATHEMATICS
Autore/i: Bruglieri, Maurizio; Mancini, Simona; Pezzella, Ferdinando; Pisacane, Ornella; Suraci, Stefano
Classificazione: 1 Contributo su Rivista
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.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/247420 Collegamento a IRIS

2017 Heuristic algorithms for the operator-based relocation problem in one-way electric carsharing systems
DISCRETE OPTIMIZATION
Autore/i: Bruglieri, Maurizio; Pezzella, Ferdinando; Pisacane, Ornella
Classificazione: 1 Contributo su Rivista
Abstract: 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 user's 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.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/245664 Collegamento a IRIS

2016 A multi-objective optimization for relocating electric vehicles in car-sharing services
Book of Abstracts of the Fifth meeting of the EURO working group on Vehicle Routing and Logistics optimization (VeRoLog 2016)
Autore/i: Pisacane, Ornella; Bruglieri, Maurizio; Pezzella, Ferdinando
Luogo di pubblicazione: Nantes
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Nowadays, also thanks to the Information and Communication Technology, the sharing mobility represents a significant part of the sharing economy. In particular, the Car Sharing Services (CSSs), in which the user rents a car for short time, paying according to the time of use, support the sustainable mobility, reducing the number of parked vehicles and consequently, the traffic congestion, noise and air pollution. These two last advantages are more guaranteed in CSSs with Electric Vehicles (EVs). In fact, the EVs guarantee zero local CO2 emissions and are less noisy than the traditional combustion engine vehicles. In particular, in One-way CSSs (OCSS), a user can drop off a vehicle in a parking station different from the pickup one. However, the OCSSs suffer of possible imbalances between the demand and the supply of vehicles, leading to a Vehicle RElocation Problem (VREP). We address a VREP in OCSSs with EVs in which the relocation is operator-based: the CSS operators relocate the EVs by directly driving them from a station of pickup to one of delivery and move from a station of delivery to one of pickup by folding bikes. To balance the good quality of service assured to the users (maximizing the number of EV requests satisfied), the cost reduction and the load balancing among the operators, a multiobjective VREP is solved. Firstly, a set of feasible solutions is heuristically generated and then, through the epsilon-constraint method, a three-objective non-overlapping model is solved. Numerical results are carried out on some benchmark instances.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/245666 Collegamento a IRIS

2016 On the Green Vehicle Routing Problem
Book of Abstract 46th Annual Conference of the Italian Operational Research Society
Autore/i: Bruglieri, Maurizio; Mancini, Simona; Pezzella, Ferdinando; Pisacane, Ornella
Luogo di pubblicazione: Tireste
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The road transport impacts signicantly on the worsening of the air pollution as highlighted by recent studies. The use of Alternative Fuel Vehicles (AFVs) contributes to the reduction of the harmful emissions but it is currently limited by the short driving range so that an AFV may require many refuels in a trip. In addition, the poor availability of the Alternative Fuel Stations (AFSs) on the networks limits the usage of AFVs also in urban contexts. Therefore, the problem of efficiently routing the AFVs to provide eco-sustainable transport solutions arises. It is not new to the Operations Research community and it was introduced in the literature by [1] as the Green Vehicle Routing Problem (G-VRP). The G-VRP deals with the planning of the routes of a fleet of AFVs, based on a single depot, serving a set of customers, geographically distributed, while minimizing the total travel distance. Each AFV starts/ends from/to the depot, respecting both the limited cargo and fuel tank capacity. For refueling reasons, intermediate stops to the AFSs have been also planned to prevent drivers remaining without the minimum fuel level to either reach an AFS or return to depot. The G-VRP has been addressed from both the modeling and methodological point of view and generally, to allow multiple visits at the AFSs, dummy copies of them are introduced consequently increasing the problem complexity. In this work, a new Mixed Integer Linear Programming formulation for the GVRP is proposed in which the visits to the AFSs are only implicitly considered, avoiding dummy copies. Moreover, the number of variables is reduced also by pre-computing, for each pair of customers, an efficient set of AFSs, given by only those that may be actually used in an optimal solution. Numerical experiments, carried out on benchmark instances, extending those presented in [3], show that our model, solved through an optimization tool software, outperforms the previous ones proposed in the literature [1,2]. Moreover, it allows certifying optimal solutions also for instances previously not solved to optimality.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/245668 Collegamento a IRIS

2016 A Three-Phase Matheuristic for the Time-Effective Electric Vehicle Routing Problem with Partial Recharges
Book of abstracts of the 4th International Conference on Variable Neighborhood Search
Autore/i: Bruglieri, Maurizio; Mancini, Simona; Pezzella, Ferdinando; Pisacane, Ornella; Suraci, Stefano
Luogo di pubblicazione: Malaga
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
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 .
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/245757 Collegamento a IRIS

2016 A pick-up and delivery problem with time windows by electric vehicles
INTERNATIONAL JOURNAL OF PRODUCTIVITY AND QUALITY MANAGEMENT
Autore/i: Grandinetti, Lucio; Guerriero, Francesca; Pezzella, Ferdinando; Pisacane, Ornella
Classificazione: 1 Contributo su Rivista
Abstract: In the pick-up and delivery problem with time windows (PDPTW), each transportation service is delivered, from an origin to a destination, satisfying both the time windows and the precedence constraints. This paper addresses the related vehicle routing problem by using only electric vehicles (EVs) and by introducing the recharging stations (RSs). The problem is formulated as a multi-objective mixed integer linear model for minimising the total travel distance, the total cost for the EVs used and the total penalty cost for the unsatisfied time windows. In addition, length constraints on the routes are imposed in order to include several aspects such as the limited availability of the RSs. The weighted sum method is adopted and, to properly set the weights, three methods, derived from the analytical hierarchical process, are compared. Computational experiments on some instances are carried out, in order to assess the behaviour of our approach in terms of solution quality
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/245662 Collegamento a IRIS

2016 A new Mathematical Programming Model for the Green Vehicle Routing Problem
ELECTRONIC NOTES IN DISCRETE MATHEMATICS
Autore/i: Bruglieri, Maurizio; Mancini, Simona; Pezzella, Ferdinando; Pisacane, Ornella
Classificazione: 1 Contributo su Rivista
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
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/245663 Collegamento a IRIS

2016 A new Mathematical Programming Model for the Green Vehicle Routing Problem
Proceedings of 14th Cologne Twente Workshop (CTW 2016)
Autore/i: Bruglieri, Maurizio; Mancini, Simona; Pezzella, Ferdinando; Pisacane, Ornella
Editore: Università degli Studi di Milano
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
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.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/245667 Collegamento a IRIS

2015 Solving the Electric Vehicle Routing Problem with Time Windows and Partial Recharges
Fourth meeting of the EURO Working Group on Vehicle Routing and Logistics Optimization (VeRoLog 2015)
Autore/i: Pisacane, Ornella; Bruglieri, Maurizio; Pezzella, Ferdinando; Suraci, Stefano
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Electromobility aims promoting transportation solutions employing the Electric Vehicles (EVs) in place of the traditional internal combustion engine vehicles in order to reduce the harmful CO2 emissions that are polluting more and more the big cities. In addition, the recent technological progresses concerning the EVs allow also partial battery recharges. In this context, the aim of our work is to efficiently route a fleet of EVs, exploiting such recent technological advancements, in order to handle a set of customers within their time windows. Each EV route starts/ends from/at a common depot. Moreover, along each route, intermediate stops at the recharging stations for (also partial) battery recharges are allowed. The problem, known as Electric Vehicle Routing Problem with Time Windows, is here mathematically formulated as a Mixed Integer Linear Program (MILP) with the aim of firstly minimizing the number of EVs used and then, of optimizing the total time spent by the EVs outside the depot i.e., the total recharging, traveling and waiting times. In order to handle the problem hardness and to find good quality solutions in real life settings, a matheuristic, based on the Variable Neighborhood Search, is proposed. Numerical results, carried out on some benchmark instances, are shown for the solutions found by both the proposed MILP and the matheuristic.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/245665 Collegamento a IRIS

2015 A Variable Neighborhood Search Branching for the Electric Vehicle Routing Problem with Time Windows
ELECTRONIC NOTES IN DISCRETE MATHEMATICS
Autore/i: Bruglieri, Maurizio; Pezzella, Ferdinando; Pisacane, Ornella; Suraci, Stefano
Classificazione: 1 Contributo su Rivista
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 Achille's 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
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/225069 Collegamento a IRIS

2014 Multi-objective Optimization in Dial-a-ride Public Transportation
TRANSPORTATION RESEARCH PROCEDIA
Autore/i: Guerriero, F.; Pezzella, F.; Pisacane, O.; Trollini, L.
Classificazione: 1 Contributo su Rivista
Abstract: In the Dial-a-Ride public transportation systems, each customer requirement is specified in terms of a pickup (origin), of a delivery (destination) and of a time window within it has to be satisfied. The aim is to find a set of routes, each assigned to a vehicle, in order to satisfy the set of requests, under capacity, time windows, precedence and pairing conditions. It is usually assumed that the demand of a request, picked up at its origin, is exactly delivered at its destination (one-to-one service) and that the fleet of the vehicles is based at a single depot. From a modelling point of view, the problem could be addressed as a one-to-one capacitated Pickup and Delivery Problem with Time Windows (PDPTW) and therefore, the mathematical formulation presents, beyond the traditional capacity constraints on the vehicles, also the pairing, the precedence and the time windows conditions. In particular, the pairing conditions guarantee that each couple (pickup, delivery) has to belong to the same route while the precedence constraints impose that each pickup has to be served before the associated delivery. This paper addresses the problem with the aim of optimizing, at the same time, the maximum total ride time and the total waiting time. Then, a bi-objective PDPTW with a constraint on the maximum duration of each route is proposed and solved by a two-step approach. In particular, the first step determines a set of feasible routes by meta-heuristics. These routes are used in second step in a bi-objective set partitioning formulation solved by the epsilon-constraint method to generate efficient solutions. The parameters of the meta-heuristics are properly set by a racing procedure. Computational experiments on some benchmark instances are carried out to assess the performance of the proposed approach
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/201711 Collegamento a IRIS

2014 A Variable Neighborhood Search Branching for the Electric Vehicle Routing Problem with Time Windows
Abstract Booklet- 3rd International Conference on Variable Neighborhood Search
Autore/i: Bruglieri, M.; Pezzella, Ferdinando; Pisacane, O.; Suraci, Stefano
Editore: MODILS, FSEG, Sfax University
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
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 Achille’s 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.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/221713 Collegamento a IRIS

2014 The multi-objective multi-vehicle pickup and delivery problem with time windows
PROCEDIA: SOCIAL & BEHAVIORAL SCIENCES
Autore/i: Grandinetti, L.; Guerriero, F.; Pezzella, F.; Pisacane, O.
Classificazione: 1 Contributo su Rivista
Abstract: The Single Objective Single Vehicle Pickup and Delivery Problem (SOSV-PDP) is a Vehicle Routing Problem (VRP)in which the vehicle, based at the depot, has to visit exactly once a set of customers with known demand. Each request specifies two locations: an origin for the picking and one for the delivery. The vehicle must start and finish at the depot and the total handled demand must not exceed its capacity. Moreover, for each request, the origin must precede the destination (precedence constraints). In the SOSV-PDP with Time Windows (SOSV-PDPTW), each request specifies also a time window. Therefore, the vehicle has to serve the customer within the time window (time window constraint). The Single Objective Multiple Vehicle-PDPTW (SOMV-PDPTW) is an extension of SOSV-PDPTW where customers are served by a fleet (usually homogeneous) of vehicles. Therefore, together with the precedencies, for each request, the origin and the destination have to belong to the same route (pairing constraints). In the traditional SOMV-PDPTW, only one objective is optimized (usually, the total travel cost); while, in the literature, few multi-objective MOMV-PDPTW exist that optimize at most three criteria simultaneously. The contribution of this paper consists in addressing the MOMV-PDPTW from both a modeling and methodological point of view. In fact, the MOMV-PDPTW is firstly modeled with the aim of optimizing the number of vehicles, the total travel cost and the longest travel cost, simultaneously; then, a two-step solution approach is proposed. In particular, in the first step, a set of feasible routes is generated by properly adapting some meta-heuristics proposed in literature for the SOMV-PDPTW, then, set partitioning optimization problems are solved within an c-constraint framework. More specifically, each set partitioning problem selects the routes from the feasible set, optimizing one criterion at time, constraining the remaining ones by appropriate upper bounds and satisfying customer requirements. Finally, the second step finds the set of efficient solutions for approximating the Pareto Fronts. Computational experiments, carried out on some instances generated in literature, show that our approach determines good quality Efficient Pareto Fronts (in terms of number of efficient solutions) and also provides well-diversified efficient sets. This last aspect is properly evaluated by computing the Spread metric on each of the instances.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/155307 Collegamento a IRIS

2014 Multi-objective optimization in dial-and-ride public transportation
Book of abstract of 17th meeting of the EURO WORKING GROUP ON TRANSPORTATION
Autore/i: Guerriero, F.; Pezzella, F.; Pisacane, O.; Trollini, L.
Editore: University of Seville
Luogo di pubblicazione: Siviglia
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In the Dial-And-Ride public transportation systems, each customer requirement is specified in terms of a pickup (origin), of a delivery (destination) and of a time window within it has to be satisfied. The aim is to find a set of routes, each assigned to a vehicle, in order to satisfy the set of requests, under capacity, time windows, precedence and pairing conditions. In fact, it is usually assumed that the service demand of a request, picked up at its origin, is exactly delivered at its destination (one-to-one service) and that the fleet of the vehicles is based at a single-depot. From a modeling point of view, the problem could be addressed as a one-to-one capacitated Pickup and Delivery Problem with Time Windows (PDPTW) and therefore, the mathematical formulation presents, beyond the traditional capacity constraints on the vehicles, also the pairing, the precedence and the time windows conditions. In particular, the pairing conditions guarantee that each couple (pickup, delivery) has to belong to the same route while the precedence constraints impose that each pickup has to be served before the associated delivery. The contribution of this paper mainly consists in addressing the problem with the aim of finding a set of feasible routes by optimizing, at the same time, two objectives such as the maximum ride time and the total waiting time. Therefore, a bi-objective time constrained PDPTW is proposed and solved by implementing a two-step approach. In particular, the first step heuristically determines a set of feasible routes, used by the second step based on a set partitioning mathematical formulation and the constraint method to generate efficient solutions. The control parameters of the heuristics, used in the first step, are properly set by adopting a F-Race based approach. Computational experiments on some benchmark instances are carried out to assess the behavior of the proposed approach in finding good quality Pareto Efficient solutions.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/182102 Collegamento a IRIS

2013 A pickup and delivery problem with time Windows by electric vehicles
Proceedings of XVIII Summer School "Francesco Turco"
Autore/i: Grandinetti, L.; Guerriero, F.; Pezzella, F.; Pisacane, O.
Editore: AIDI - Italian Association of Industrial Operations Professors
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The Pickup and Delivery Problem with Time Windows (PDPTW) is a Vehicle Routing Problem with Time Windows (VRPTW) in which each customer, together with a demand and a time window for the service, specifies also an origin (pickup) and a destination (delivery). Our work manly extends the PDPTW to the case in which the fleet consists of electric vehicles (E-PDPTW), in order to exploit their significant advantages in terms of energy saving and sustainability. The E-PDPTW is then modeled as a multi-objective optimization problem in order to minimize the total travel distance, the total cost due to the used electric vehicles and the penalties due to the delayed services. In addition, beyond the classical vehicle routing constraints, in order to consider the practical difficulties due to the limited battery life of the electric vehicles (EVs) and to the poor availability of the recharging stations, some additional constraints are also imposed. The problem is then formulated as a multiobjective mathematical programming model and solved by applying the Weighted Sum Method (WSM) with weights determined by an approach derived from the Analytical Hierarchical Process (AHP).
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/155318 Collegamento a IRIS

2013 The multi-objective multi-vehicle pickup and dlivery problem with time windows
Book of abstracts of EURO Working Group on Transportation Conference (EWGT 2013)
Autore/i: Grandinetti, L.; Guerriero, F.; Pezzella, Ferdinando; Pisacane, O.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/155324 Collegamento a IRIS

2013 A Heuristic and an Exact Method for the Gate Matrix Connection Cost Minimization Problem
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
Autore/i: PEZZELLA F.; DE GIOVANNI L.; MASSI G.; PFETSHC M.E.; RINALDI G.; VENTURA P.
Classificazione: 1 Contributo su Rivista
Abstract: Special Issue on ALIO - EURO 2011 - Porto, Portugal, May 4 - 6, 2011
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/63155 Collegamento a IRIS

2013 An Adaptive Genetic Algorithm for Large-Size Open Stack Problems
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Autore/i: PEZZELLA F. ; DE GIOVANNI L.; MASSI G.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/55584 Collegamento a IRIS

2012 An LP-based tabu search for batch scheduling in a cutting process with finite buffers
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
Autore/i: C. Arbib; F. Marinelli; F. Pezzella
Classificazione: 1 Contributo su Rivista
Abstract: This paper addresses a cutting stock problem under typical resource constraints that arise when working centres with nesting capabilities are associated with automatic feeders/stackers. The critical resource is the number of buffers available to host the batches built up by the centre. To cope with it, pattern and batch sequencing problems must be addressed simultaneously. A tabu-search algorithm exploring batch output sequences is proposed. The algorithm never opens more stacks than buffers, respects batch compatibility/precedence constraints, and keeps the maximum order spread under control. To demonstrate its effectiveness and efficiency, a computational study was set up, solving 920 test problems derived from the literature. The study enabled a proper tuning of the method and offered encouraging results: in 228 cases an optimum was found; in nearly all, the gap from the optimum was below 1%. Computation times range from fractions of seconds to a couple of minutes in the worst cases. Compared to existing methods, the algorithm provides on average the same solution quality, with the advantage of solving a problem which is more general and hence closer to application. The paper includes a discussion on the method extensions required to deal with asynchronous stacking and heterogeneous batches.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/55585 Collegamento a IRIS

2012 Dynamic allocation and resource planning in the management of a bus terminal
Book of Abstract Annual Conference AIRO
Autore/i: Massi. G.; Pezzella F.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/155303 Collegamento a IRIS

2011 Robust Platform Assignment in Bus Stations
Abstract Book - Annual Conference of the Italian Operational Research Society
Autore/i: PEZZELLA F.; MASSI G.; MORGANTI G.
Editore: Università degli Studi di Brescia
Luogo di pubblicazione: Brescia
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/63152 Collegamento a IRIS

2011 A Heuristic and an Exact Method for Pattern Sequencing Problems
Proceedings book ALIO/EURO Conference
Autore/i: PEZZELLA F.; DE GIOVANNI L.; MASSI G.; PFETSCH E.; RINALDI G.; VENTURA P.
Editore: University of Porto
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/55070 Collegamento a IRIS

2010 A computational study on the duty generation for the multi-depot bus driver scheduling problem
Proceeding of the XVIII EURO Working Group on Locational Analisys
Autore/i: Marinelli F.; Pezzella F.; Rosetti R.
Editore: Fridericiana Editrice Universitaria
Luogo di pubblicazione: NAPOLI
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/54637 Collegamento a IRIS

2010 Batch scheduling in a cutting process with finite buffers
Autore/i: Arbib C.; Marinelli F.; Pezzella F.
Luogo di pubblicazione: L'Aquila
Classificazione: 5 Altro
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/79564 Collegamento a IRIS

2010 An improved genetic algorithm for distributed and flexible job -shop scheduling problem
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Autore/i: F. PEZZELLA; DE GIOVANNI L
Classificazione: 1 Contributo su Rivista
Abstract: The Distributed and Flexible job-shop Scheduling problem (DFJS) considers the scheduling of distributed manufacturing environments, where jobs are processed by a system of several Flexible Manufacturing Units (FMUs). Distributed scheduling problems deal with the assignment of jobs to FMUs and with determining the scheduling of each FMU, in terms of assignment of each job operation to one of the machines able to work it [job-routing flexibility) and sequence of operations on each machine. The objective is to minimize the global makespan over all the FMUs. This paper proposes an Improved Genetic Algorithm to solve the Distributed and Flexible job-shop Scheduling problem. With respect to the solution representation for non-distributed job-shop scheduling, gene encoding is extended to include information on job-to-FMU assignment, and a greedy decoding procedure exploits flexibility and determines the job routings. Besides traditional crossover and mutation operators, a new local search based operator is used to improve available solutions by refining the most promising individuals of each generation. The proposed approach has been compared with other algorithms for distributed scheduling and evaluated with satisfactory results on a large set of distributed-and-flexible scheduling problems derived from classical job-shop scheduling benchmarks. (C) 2009 Elsevier B.V. All rights reserved.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/30094 Collegamento a IRIS

2009 Controlling open stacks and flow time in a cutting process
6th ESICUP Meeting
Autore/i: ARBIB C; MARINELLI F; F. PEZZELLA
Editore: Universitat de València
Luogo di pubblicazione: València
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/44145 Collegamento a IRIS

2009 Sequencing cutting patterns by enhanced genetic algorithm
6th ESICUP Meeting
Autore/i: F. PEZZELLA; DE GIOVANNI L; MASSI G
Editore: Universitat de València
Luogo di pubblicazione: València
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/44144 Collegamento a IRIS

2008 A column generation approach for the multiple depot crew scheduling problem: a case study
Optimization and Logistics in Transportation and Communication Networks
Autore/i: Marinelli F.; Pezzella F.; Rosetti R.
Editore: Fridericiana Editrice Universitaria
Luogo di pubblicazione: Napoli
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/55637 Collegamento a IRIS

2008 Trim loss minimization under a given number of open stacks
Optimisation and Logistics in Transportation and Communication Networks
Autore/i: Arbib C.; Marinelli F.; Pezzella F.
Editore: Fridericiana Editrice Universitaria
Luogo di pubblicazione: Napoli
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/55636 Collegamento a IRIS

2008 Exact methods for solving the open stack problems
5th ESICUP Meeting
Autore/i: F. PEZZELLA; DE GIOVANNI L; PFETSCH M; RINALDI G; VENTURA P
Editore: Università degli Studi dell'Aquila
Luogo di pubblicazione: L'Aquila
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/45257 Collegamento a IRIS

2008 EVE-OPT: an hybrid algorithm for the capability vehicle routing problem
MATHEMATICAL METHODS OF OPERATIONS RESEARCH
Autore/i: F. PEZZELLA; PERBOLI G; TADEI R
Classificazione: 1 Contributo su Rivista
Abstract: This paper presents EVE-OPT, a Hybrid Algorithm based on Genetic Algorithms and Taboo Search for solving the Capacitated Vehicle Routing Problem. Several hybrid algorithms have been proposed in recent years for solving this problem. Despite good results, they usually make use of highly problem-dependent neighbourhoods and complex genetic operators. This makes their application to real instances difficult, as a number of additional constraints need to be considered. The algorithm described here hybridizes two very simple heuristics and introduces a new genetic operator, the Chain Mutation, as well as a new mutation scheme. We also apply a procedure, the k-chain-moves, able to increase the neighbourhood size, thereby improving the quality of the solution with negligible computational effort. Despite its simplicity, EVE-OPT is able to achieve the same results as very complex state-of-the art algorithms.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/30095 Collegamento a IRIS

2008 An adaptive genetic algorithm for the pattern sequencing problem
5th ESICUP Meeting
Autore/i: F. PEZZELLA; MASSI G; DE GIOVANNI L
Editore: Università degli Studi dell'Aquila
Luogo di pubblicazione: L'Aquila
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/45256 Collegamento a IRIS

2008 A genetic algorithm for flexible job-shop problem
COMPUTERS & OPERATIONS RESEARCH
Autore/i: F. PEZZELLA; CIASCHETTI G; MORGANTI G
Classificazione: 1 Contributo su Rivista
Abstract: In this paper, we present a genetic algorithm for the Flexible Job-shop Scheduling Problem (FJSP). The algorithm integrates different strategies for generating the initial population, selecting the individuals for reproduction and reproducing new individuals. Computational result shows that the integration of more strategies in a genetic framework leads to better results, with respect to other genetic algorithms. Moreover, results are quite comparable to those obtained by the best-known algorithm, based on tabu search. These two results, together with the flexibility of genetic paradigm, prove that genetic algorithms are effective for solving FJSP. (C) 2007 Elsevier Ltd. All rights reserved.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/30093 Collegamento a IRIS

2008 Heuristic and Exact Methods for the Open Stacks Problems
Optimization and Logistics in Transportation and Communication Networks
Autore/i: PEZZELLA F.; DE GIOVANNI L.; PFETSCH M.; RINALDI G.; VENTURA P.
Editore: FRIDERICIANA EDITRICE UNIVERSITARIA
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/55635 Collegamento a IRIS

2007 An hybrid genetic algorithm for distribute and scheduling job-shop problem
International Conference AIRO Winter
Autore/i: F. PEZZELLA; DE GIOVANNI L
Editore: AIRO
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/45253 Collegamento a IRIS

2007 A genetic approach for the cutting pattern sequencing problem
Book of Abstract
Autore/i: F. PEZZELLA; DE GIOVANNI L.
Editore: University of Economics
Luogo di pubblicazione: Prague
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Book of Abstracts
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/45252 Collegamento a IRIS

2007 An adaptive genetic algorithm for the cutting-pattern sequencing problem
Conference Proceedings AIRO 2007
Autore/i: F. PEZZELLA; DE GIOVANNI L.; MASSI G.
Editore: PRIMA Soc. Coop. a.r.l.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/55626 Collegamento a IRIS

2007 The open stack problem
Conference Proceedings AIRO 2007
Autore/i: F. PEZZELLA; DE GIOVANNI L.; PFETSCH M.; RINALDI G.; VENTURA P.
Editore: PRIMA Soc. Coop. a.r.l.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/55622 Collegamento a IRIS

2005 A genetic algorithm approach for flexible job-shop scheduling
Procedings of Annual Conference AIRO 2004
Autore/i: F. PEZZELLA; CIASCHETTI G; MORGANTI G
Editore: Università di Camerino
Luogo di pubblicazione: Camerino
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/45250 Collegamento a IRIS

2004 A genetic algorithm for distibuted and flexible job-shop scheduling problems
Proceedings of Annual Conference AIRO 2004
Autore/i: F. PEZZELLA; VICHI R.
Editore: Università di Lecce
Luogo di pubblicazione: Lecce
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/45249 Collegamento a IRIS

2003 A tabu search method for flexible job shop scheduling problems
Proceedings of the Annual Conference AIRO 2003
Autore/i: F. PEZZELLA
Editore: Università Cà Foscari di Venezia
Luogo di pubblicazione: Venezia
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/45196 Collegamento a IRIS

2002 A tabu search approach for job shop scheduling in flexible manufacturing systems
Proceedings of the Annual Confrence AIRO 2002
Autore/i: F. PEZZELLA
Editore: Università degli Studi dell'Aquila
Luogo di pubblicazione: L'Aquila
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/45195 Collegamento a IRIS

2001 A bilevel programming approach for the production planning in decentralized systems
Proceedings of the Annual Conference AIRO 2011
Autore/i: PEZZELLA F.
Editore: CLUEC Editrice
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/55632 Collegamento a IRIS

2000 A tabu search method guided by shifting bottleneck for the job-shop scheduling problem
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Autore/i: F. PEZZELLA; MERELLI E.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/28879 Collegamento a IRIS

1999 An algorithm for the optimal planning of the decentralized production
Simulation and optimization in operations management
Autore/i: PEZZELLA F.
Editore: Edizioni Scientifiche Italiane s.p.a
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/75348 Collegamento a IRIS

1999 Ricerca Operativa: problemi di gestione della produzione
Autore/i: F. PEZZELLA; FAGGIOLI E
Editore: Pitagora Editrice
Luogo di pubblicazione: BOLOGNA
Classificazione: 3 Libro
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/42139 Collegamento a IRIS

1997 Modelli decisionali per la pianificazione della produzione
Logistica su calcolatore per la pianificazione della produzione nelle piccole e medie imprese
Autore/i: F. PEZZELLA
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Atti del corso di formazione AIRO a cura di Ferdinando Pezzella pubblicati con il supporto finanziario del CNR
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/39533 Collegamento a IRIS

1997 Logistica su calcolatore per la pianificazione della produzione nelle piccole e medie imprese
Autore/i: Pezzella F.
Editore: Stampa Nova s.n.c.
Luogo di pubblicazione: Jesi (AN)
Classificazione: 7 Curatele
Abstract: Atti del Corso di formazione AIRO, Ancona 4-6 Dicembre 1996 a cura del Prof. F. Pezzella - Stampato con contributo finanziario del Consiglio Nazionale delle Ricerche
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/73327 Collegamento a IRIS

1997 Solving large set covering problems for crew scheduling
TOP
Autore/i: F. PEZZELLA; FAGGIOLI E
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/29106 Collegamento a IRIS

1995 Enterprise systems: management of technological and organizational changes
Autore/i: Pezzella F.
Editore: Stampa Nova s.n.c.
Luogo di pubblicazione: Jesi (AN)
Classificazione: 7 Curatele
Abstract: Proceedings of the Annual Conference AIRO 20-22 Settembre '95 - Volume stampato con il contributo del Consiglio Nazionale delle Ricerche
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/73326 Collegamento a IRIS

1995 Un metodo euristico per problemi di set covering a grandi dimensioni
RICERCA OPERATIVA
Autore/i: Pezzella F.; Faggioli E.
Classificazione: 1 Contributo su Rivista
Abstract: Special Issue Ferrovie Airo Set-covering TendER (FASTER) competition papers
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/73323 Collegamento a IRIS

1993 Ricerca operativa: problemi ed applicazioni aziendali
Autore/i: Pezzella F.; Faggioli E.
Editore: CLUA Edizioni
Luogo di pubblicazione: Ancona
Classificazione: 3 Libro
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/73325 Collegamento a IRIS

1984 Confidence intervals in the solution of stochastic integer linear programming problems
ANNALS OF OPERATIONS RESEARCH
Autore/i: Apolloni B.; Pezzella F.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/73321 Collegamento a IRIS

1981 A system approach to the optimal health-care districting
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Autore/i: Pezzella F.; Bonanno R.; Nicoletti B.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/73318 Collegamento a IRIS

1981 Modelling and optimization of local health units
JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES
Autore/i: Pezzella F.; Bonanno R.; Nicoletti B
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/73319 Collegamento a IRIS

1981 Elementi di programmazione lineare
Autore/i: Pezzella F.
Editore: Liguori Editore. Napoli
Luogo di pubblicazione: Napoli
Classificazione: 3 Libro
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/73324 Collegamento a IRIS

1979 Optimal information structure in a simple economic system
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Autore/i: Nicoletti B.; Mariani L.; Pezzella F.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/73317 Collegamento a IRIS

1977 Dynamic modelling in development planning
APPLIED MATHEMATICAL MODELLING
Autore/i: Grandinetti L.; La Bella A.; Pezzella F.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/73316 Collegamento a IRIS

1976 Optimal allocation of investment in a two- Region economy
Optimization Techniques, Modelling and Optimization in the Service of Man - Part I
Autore/i: Nicoletti B.; Pezzella F.; Raiconi G.
Editore: Ed. W. R. Mayr, Springer-Verlag, Berlin, Heidelber
Luogo di pubblicazione: Berlin
Classificazione: 2 Contributo in Volume
Abstract: Lecture Notes in Computer Science
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/73322 Collegamento a IRIS


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