Stefano Giordani

Finding minimum and equitable risk routes for hazmat shipments

This paper deals with the generation of minimal risk paths for the road transportation of hazardous materials between an origin–destination pair of a given regional area. The main considered issue is the selection of paths that minimize the total risk of hazmat shipments while spreading the risk induced on the population in an equitable way. The problem is mathematically formulated, and two heuristic algorithms are proposed for its solution. Substantially, these procedures are modified versions of Yens algorithm for the k-shortest path problem, which take into due consideration the risk propagation resulting from close paths and spread the risk equitably among zones of the geographical region in which the transportation network is embedded. Furthermore, a lower bound based on a Lagrangean relaxation of the given mathematical formulation is also provided. Finally, a series of computational tests, referring to a regional area is reported.

A market-based multi-agent system model for decentralized multi-project scheduling

We consider a multi-project scheduling problem, where each project is composed of a set of activities, with precedence relations, requiring specific amounts of local and shared (among projects) resources. The aim is to complete all the project activities, satisfying precedence and resource constraints, and minimizing each project schedule length. The decision making process is supposed to be decentralized, with as many local decision makers as the projects. A multi-agent system model, and an iterative combinatorial auction mechanism for the agent coordination are proposed. We provide a dynamic programming formulation for the combinatorial auction problem, and heuristic algorithms for both the combinatorial auction and the bidding process. An experimental analysis on the whole multi-agent system model is discussed.

Scheduling models for air traffic control in terminal areas

We propose a job-shop scheduling model with sequence dependent set-up times and release dates to coordinate both inbound and outbound traffic flows on all the prefixed routes of an airport terminal area and all aircraft operations at the runway complex. The proposed model is suitable for representing several operational constraints (e.g., longitudinal and diagonal separations in specific airspace regions), and different runway configurations (e.g., crossing, parallel, with or without dependent approaches) in a uniform framework. The complexity and the highly dynamic nature of the problem call for heuristic approaches. We propose a fast dynamic local search heuristic algorithm for the job-shop model suitable for considering one of the different performance criteria and embedding aircraft position shifting control technique to limit the controllers/pilots’ workload. Finally, we describe in detail the experimental analysis of the proposed model and algorithm applied to two real case studies of Milan-Malpensa and Rome-Fiumicino airport terminal areas.

Minimizing total completion time subject to release dates and sequence‐dependentprocessing times

We consider the problem of scheduling jobs with release dates and sequence‐dependentprocessing times on a single machine to minimize the total completion time. We show thatthis problem is equivalent to the Cumulative Traveling Salesman Problem with additionaltime constraints. For this latter problem, we give a dynamic programming formulation fromwhich lower bounds are derived. Two heuristic algorithms are proposed. Performanceanalysis of both lower bounds and heuristics on randomly generated test problems are carriedout. Moreover, the application of the model and algorithms to the real problem of sequencinglanding aircraft in the terminal area of a congested airport is analyzed. Computational resultson realistic data sets show that heuristic solutions can be effective in practical contexts.

Scheduling Models and Algorithms for TMA Traffic Management

In this paper, models and algorithms for real-time control of the TMA are proposed. We consider two cases: in the first one (static) we assume that there is a set of aircraft to be sequenced for which we know in advance their entry time in the terminal area; in the second one (dynamic), the entry times of future aircraft are unknown and the sequence of aircraft is recomputed whenever a new aircraft approaches the terminal area. For the static case, we model the sequencing problem as a Cumulative Traveling Salesman Problem with Ready Times and propose two lower bounds for testing heuristic solutions. For the dynamic case, where only a limited knowledge of the arrivals is assumed, we add to the basic model a set of constraints which allow the controller to maintain given patterns of the landing sequences previously generated. For both cases, heuristic algorithms are proposed and computational results are discussed.

Flow Shop No-Wait Scheduling With Sequence Dependent Setup Times And Release Dates

We consider the problem of scheduling no-wait jobs, with release dates and sequence dependent setup times, on a flow shop to minimize the makespan. We show that this problem is equivalent to the asymmetric traveling salesman problem with additional visiting time constraints. For this latter problem we give a mathematical formulation and two lower bounds. Two heuristic algorithms for solving the scheduling problem are proposed. Performance analysis of both lower bounds and heuristics on randomly generated test problems are carried out.

A tabu search approach for scheduling hazmat shipments

Vehicle routing and scheduling are two main issues in the hazardous material (hazmat) transportation problem. In this paper, we study the problem of managing a set of hazmat transportation requests in terms of hazmat shipment route selection and actual departure time definition. For each hazmat shipment, a set of minimum and equitable risk alternative routes from origin to destination points and a preferred departure time are given. The aim is to assign a route to each hazmat shipment and schedule these shipments on the assigned routes in order to minimize the total shipment delay, while equitably spreading the risk spatially and preventing the risk induced by vehicles traveling too close to each other. We model this hazmat shipment scheduling problem as a job-shop scheduling problem with alternative routes. No-wait constraints arise in the scheduling model as well, since, supposing that no safe area is available, when a hazmat vehicle starts traveling from the given origin it cannot stop until it arrives at the given destination. A tabu search algorithm is proposed for the problem, which is experimentally evaluated on a set of realistic test problems over a regional area, evaluating the provided solutions also with respect to the total route risk and length.

A distributed algorithm for the multi-robot task allocation problem

In this work we address the Multi-Robot Task Allocation Problem (MRTA). We assume that the decision making environment is decentralized with as many decision makers (agents) as the robots in the system. To solve this problem, we developed a distributed version of the Hungarian Method for the assignment problem. The robots autonomously perform different substeps of the Hungarian algorithm on the base of the individual and the information received through the messages from the other robots in the system. It is assumed that each robot agent has an information regarding its distance from the targets in the environment. The inter-robot communication is performed over a connected dynamic communication network and the solution to the assignment problem is reached without any common coordinator or a shared memory of the system. The algorithm comes up with a global optimum solution in O(n3) cumulative time (O(n2) for each robot), with O(n3) number of messages exchanged among the n robots.

A distributed multi-agent production planning and scheduling framework for mobile robots

Inspired by the new achievements in mobile robotics having as a result mobile robots able to execute different production tasks, we consider a factory producing a set of distinct products via or with the additional help of mobile robots. This particularly flexible layout requires the definition and the solution of a complex planning and scheduling problem. In order to minimize production costs, dynamic determination of the number of robots for each production task and the individual robot allocation are needed. We propose a solution in terms of a two-level decentralized Multi-Agent System (MAS) framework: at the first, production planning level, agents are tasks which compete for robots (resources at this level); at the second, scheduling level, agents are robots which reallocate themselves among different tasks to satisfy the requests coming from the first level. An iterative auction based negotiation protocol is used at the first level while the second level solves a Multi-Robot Task Allocation (MRTA) problem through a distributed version of the Hungarian Method. A comparison of the results with a centralized approach is presented.

Route guidance: bridging system and user optimization in traffic assignment

Abstract In this paper we study the problem of the assignment of road paths to vehicles. Due to the assumption that a low percentage of vehicles follow the routes proposed by route guidance systems (RGS) and the increase of the use of the same, the conventional RGS might shortly result obsolete. Assuming a complete road network information at the disposal of RGSs, their proposed paths are related with user optimization which in general can be arbitrarily more costly than the system optimum. However, the user optimum is fair for the drivers of the same Origin–Destination (O–D) pair but it does not guarantee fairness for different O–D pairs. Contrary, the system optimum can produce unfair assignments both for the vehicles of the same as of different O–D pairs. This is the reason why, in this paper, we propose an optimization model which bridges this gap between the user and system optimum, and propose a new mathematical programming formulation based on Nash Welfare optimization which results in a good egalitarian and utilitarian welfare for all O–D pairs. To avoid the issues with the lack of robustness related with the centralized implementation, the proposed model is highly distributed. We test the solution approach through simulation and compare it with the conventional user- and system-optimization.