In this article, we propose a new bi-level approach for the location and sizing of charging stations (CSs) based on the integration of power distribution and traffic networks. At the lower level, the user choices and the energy demands are modeled through the user equilibrium (UE) traffic assignment conditions that are derived for the case of electric vehicles (EVs). At the higher level, an optimization problem is formalized for the optimal planning of locations, sizes, and unit prices of a set of new charging stations in a territory, which embeds the UE conditions and the power flow model as constraints. The main novelties lie in the integration of traffic and electric networks, the optimization problem formalization, and the inclusion of detailed electrical issues [such as the complete alternating current (AC) power flow equations and the modeling of CSs with different phases]. The developed model has been applied to a case study in the Genoa Municipality.Note to Practitioners—In the past decade, particular attention has been posed on EVs and renewable resources to reduce pollutants’ emissions. Critical issues regard the setting of adequate recharging infrastructure and the integration of huge distributed and intermittent loads and production into the electrical grid. This article aims to provide a bi-level optimization model that can be used by companies or public administrations that want to install charging stations in a territory. In particular, the transportation and the electrical networks are integrated: at a lower level, the UE traffic assignment conditions are derived to represent users’ choices and to assess the energy demand. At the upper level, an optimization problem is formalized, to minimize costs of installation and power losses and to maximize benefits, which embeds as constraints the UE conditions for the case of EVs and the model of the electrical grid (load flow equations, bounds over-voltage, apparent power, etc.).

The optimal planning and sizing of charging stations (CSs) over a territory is an interdisciplinary issue that should consider the transportation and electrical networks, and the characteristics of CSs. A new bi-level approach is here proposed that takes into account both transportation and energy demands. At the higher level, the optimal locations, sizes, and unit prices must be found for new CSs in a territory with existing CSs. The decision model includes, as constraints, the behavior of EVs (Electric Vehicles) at the lower level, i.e. the User Equilibrium traffic assignment conditions derived for the case of EVs. The developed model has been applied to a case study in the Genoa Municipality.

A multi-objective optimization problem is here formalized for a distribution grid in which are present both traditional generators and aggregators of microgrids, buildings, or general local prosumers. At the lower level, generators and aggregators want to maximize their profit, taking into account the limits of the flexibility they can sell. At the higher level, the distribution system operator (DSO) receives power/price bids from market participants and, based on technical and economic considerations, decides which bid to accept. For the lower-level decision problem, the Karush Khun Tucker (KKT) conditions are developed and included as constraints within the higher-level optimization problem. The resulting optimization problem is applied to a modified IEEE 13-bus system.

Smart grid planning and control is becoming a theme of high interest in the last years. This is due to the presence of distributed generation, power from renewable resources and storage systems, to the different actors present over the territory, and to the difficulty of defining appropriate models for decision \nsupport. A bilevel optimal control scheme is proposed for grids characterized by renewable and traditional power production, bidirectional power flows, dynamic storage systems, and stochastic modeling issues. In this scheme, the upper level decision maker (UDM) views the lower level decision makers (LDMs) or microgrids as single nodes. In the statement of the UDM problem, the LDM control strategies are structurally and parametrically constrained inside a nonlinear optimization problem that includes load flow equations. Then, the LDMs can follow references from the UDM and use available information at the local level to solve a stochastic optimization problem. The proposed control architecture has been applied to a specific case study (Savona, Italy)

A specific class of production systems is considered in this paper with the aim of minimizing inventory and production costs, over a finite horizon. In such a class of systems, all processes (arrival and departure of parts, production, demand to be satisfied) are modelled as piece-wise constant functions that change their values at asynchronous time instants. The decision variables are the constant rate at which raw parts enter the system and the service rate at the various time instants. In the paper, both the case of completely known demand and that of incomplete demand is taken into account. In both cases, a solution algorithm is provided in order to determine the optimal (or sub-optimal, in case of incomplete demand) solution of the optimization problem.

In this paper, supply chain nodes are considered, with the aim of optimizing the production of parts belonging to different classes (the case of two classes is here taken into account). The production node is modelled by a discrete-event model, being the system state, consisting of the continuous level of input and output inventories, affected by the asynchronous processes relevant to part arrivals and product departures. Moreover, also the resource capacity of the node is represented by a continuous variable (to be shared among the two classes). In the paper, two optimization problems are proposed: the former is only stated due to its intrinsic complexity, whereas the latter (a simplified version of the former) is stated and solved. In the latter problem, an optimization is performed each time an event occurs, in order to determine which the next event is, when the next event will occur, how many raw parts/products will arrive/depart, and which portions of production capacity are assigned to the two classes.

In this paper, a novel methodology aiming at the management and control of traffic flows within urban areas, and specifically at the reduction of traffic congestion, is presented. The propose methodology is based on a multi-level system architecture integrating two decision levels, namely the dynamic route guidance level and the traffic signaling control level. The former level implements an approach for the determination of the optimal turning rates of vehicles traveling within the considered urban area. Basing on these values, in the latter level, a mathematical programming-based methodology determines the optimal duration of the phases of various traffic signaling systems in the considered area. A third level, namely the data collection and state estimation level, is also included in the proposed architecture and discussed in the paper.

In this paper, supply chain networks are considered in order to solve decision problems at the tactical level. The proposed MRP multi-site model is characterized by the presence of several production sites, several raw material suppliers, and by non-negligible transportation times and costs. Decision problems, as the choice of a particular supplier (competing with the others) or the determination of the site over which to perform the next operation, are based on the formulation of a mathematical programming problem that minimizes all costs involved in the network.

In this paper, the formalization of performance optimization problems for flexible manufacturing systems is considered. In the adopted model, production resources are multipurpose, in the sense that they are able to perform more than one kind of operation; on the other hand, an operation can in general be carried out by more than one resource. This flexibility leads to the problem of optimally managing the part routing (i.e., the assignment of an operation to a resource) and the local scheduling (i.e., the choice of the next operation in a resource operation sequence). Thus, the performance optimization of such systems requires the solution of combinatorial optimization-problems. In this connection, the introduction of maximum gap policies makes it possible to reduce the number of combinatorial degrees of freedom, by writing constraints,. deriving from the structure of such policies, on the dynamic behaviour of the system. The formalization of the optimization problem is carried out by using a system representation built via the use of deterministically transition-timed Petri net.

The optimal control of a single machine processing a certain number of jobs and modelled as a discrete-event dynamic system is considered. The sequence of jobs is fixed, whereas their timing and sizes represent the control variables of the system. The objective function to be optimised is a weighted sum of the inventory cost and of the quadratic deviations of job lot-sizes and completion times from those specified by the production demand. The problem is formalised as a multistage optimal control problem and solved by a procedure making use of dynamic programming techniques; in this way, a proper real time control scheme is obtained.

We provide a formal approach to obtain a discrete event system (DES) representation of a flexible manufacturing system (FMS). The complexity of real FMSs typically yields formidable problems in modeling such systems and in their representation by means of any predefined formalism, for instance Petri nets (PNs). A technique which allows one to obtain the PN from a description of a system is called a synthesis procedure. A synthesis procedure is presented which is based on a modular conception: a generic FMS can be easily divided, from a physical point of view, into a set of connected resources totally independent one from the other. Once such resources have been identified, we represent them by means of a set of independent subnets, the Petri net modules (PNMs), and then we can apply the synthesis procedure. For this reason, the proposed procedure has been called modular structural synthesis.

This paper formalizes a unique decisional framework to state different problems relevant to automated manufacturing systems, such as production planning, material requirements planning, and online management. Since the manufacturing systems are modelled as discrete-event systems, the (timed) Petri net formalism can be adopted to represent their dynamics. Moreover, in order to make the decisional problems addressed computationally tractable, it is assumed that the manufacturing systems can be represented by means of a particular class of timed Petri nets-the timed event graphs. Two particular optimization problems are discussed in the paper: the first one is relevant to the minimization of production and inventory costs, while satisfying a pre-specified production demand; and the second one refers to an online management problem in which the deviations between the actual system behaviour and a pre-defined nominal behaviour are sought to be minimized.

A model of manufacturing systems is considered in which several classes of products are repeatedly manufactured, according to the requirements specified by customer orders. The problem considered is the development of control strategies capable of reacting to deviations of the system behaviour from a nominal behaviour defined solving a suitable problem at the scheduling level. The degrees of freedom allowed for the control problem are: the execution times of some elementary operations, the lot-sizes, and the variables expressing the timing of the production process. It is shown that, under suitable hypotheses about the nominal behaviour, the system can be represented by means of a particular class of timed Petri nets. Moreover, the structure of the control problem is that of a continuous variable problem with quadratic cost and linear (or sometimes quadratic) constraints.

A model of a cyclic manufacturing system representable as a deterministic timed event graph is considered in this paper. It is possible to apply analytical techniques for performance evaluation of such systems. On this basis, a two-level optimization problem is considered. The higher level refers to the maximization of the system productivity with respect to the assignment of operations to machines, the lot sizes, and the service sequences at the various machines, assuming that unlimited work-in-progress and buffers of finite dimension are available. The lower level deals with the minimization of an aggregate cost taking into account the work-in-progress and the buffer dimensions.

The authors deal with optimal control problems of deterministic queuing networks. The class of networks considered is characterized by a single-class population of customers, each of which has to undergo the same sequence of operations. The network is composed of a number of identically structured islands, where a decisional node has to route incoming customers to one between two different servers. With reference to the minimization of the mean flow time in the overall network, the optimality of an informationally decentralized, threshold-type, and quasi-stationary policy is proved.>

The construction of an expert system based on the knowledge of a group of experts can be viewed as a multiperson decision process. The problem of inferring the truth value of the rules for a production system structure is considered. Several possible models are described that utilize the knowledge of the various experts, and fuzzy sets are used to represent uncertainty.