Jean Berger

Defence Research and Development Canada · C2I

Discrete search and rescue path planning for a moving target problem is computationally hard. Despite heuristic methods proposed so far, for which efficiency is further plagued by random episodic target kinematic behavior, qualifying real solution goodness and specifying optimality gap still remain elusive. In this paper a new alternate formulation...

Multi-satellite scheduling often involves generating a fixed number of potential task schedules, evaluating them all, and selecting the path that yields the highest expected reward. Unfortunately, this approach, however accurate, is difficult to scale up and applied to large realistic problems due to combinatorial explosion. Furthermore, it is cost...

In practice, the execution of plans with vehicle routing components is often subjected to external events since the transporting vehicles can be exposed to various risk factors. This may lead to delivery failure, vehicle breakdown, commodity loses, etc. In this setting, the stakeholders can benefit from logistic planning techniques whereby potentia...

Search path planning is critical to achieve efficient information-gathering tasks in dynamic uncertain environments. Given task complexity, most proposed approaches rely on various strategies to reduce computational complexity, from deliberate simplifications or ad hoc constraint relaxation to fast approximate global search methods utilization ofte...

Demand for earth observation satellite imagery is pervasive and increasing rapidly across multiple domains, highlighting the key problem to optimally schedule satellite image acquisition/collection, subject to ground and on-board constraints. Despite the variety of reported approaches to solving the NP-hard multi-satellite scheduling problem (m-Sat...

A new framework¹ mixing evolutionary approach, discrete-event simulation and deep neural networks is proposed to achieve multi-asset collection/image acquisition scheduling in a surveillance context. It combines an extended graph-based hybrid genetic algorithm (GA) used for satellite image acquisition scheduling, with a predictive simulation-based...

Many heuristics and meta-heuristics problem-solving methods have been proposed so far to solve the NP-hard multi-satellite collection scheduling problem (m-SatCSP). In particular, genetic algorithms (GAs), well-suited for large scale problems, its simplicity and low cost implementation have been pervasive. However, most contributions largely emphas...

This chapter introduces novel integrated management of multiple heterogeneous satellite missions for the purpose of intelligence collection. The focus is on optimization of acquisition planning and scheduling for various missions including single satellites and satellite constellations. The relevant optimization problem and its mathematical program...

This paper presents a solution approach to optimize vehicle routes for a multi-depot, multi-vehicle, pickup and delivery problem over a large ground transportation network. More precisely, we address ground transportation of orders for the Canadian Department of National Defence using heterogeneous vehicle fleets. The fleets consist of limited numb...

Asset localization represents an important application over wireless sensor networks (WSN) with a wide area of applicability ranging from network surveillance to search and rescue operations. In this paper, we address a research problem of network management where resource constrained sensors, in terms of capacity, sensing range and energy, are ass...

Graph partitioning is an optimization problem that deals with dividing a large geographical transportation network into subnetworks in favor of balancing the workload and minimizing the communication among them. Over the past decades, various models have been developed in such a way to satisfy a multi-objective problem such as delivery time and man...

As discrete multi-agent static open-loop target search path planning known to be computationally hard recently proved to be solvable in practice in the homogeneous case, its heterogeneous problem counterpart still remains very difficult. The heterogeneous problem introduces broken symmetry reflected by dissimilar sensing ability/capacity, agent cap...

The dynamic vehicle routing and scheduling problem is a well-known complex combinatorial optimization problem that drew significant attention over the past few years. This paper presents a novel algorithm introducing a new strategy to integrate anticipated future visit requests during plan generation, aimed at explicitly improving customer satisfac...

Discrete search path planning is known to be a NP-hard problem, and problem-solving methods proposed so far rely on heuristics with no way to reasonably estimate solution quality for practical size problems. Departing from traditional nonlinear model formulations, a novel information theoretic based approach using integer linear programming (ILP) i...

Search and rescue path planning is known to be computationally hard, and most techniques developed to solve practical size problems have been unsuccessful to estimate an optimality gap. A mixed-integer linear programming (MIP) formulation is proposed to optimally solve the multi-agent discrete search and rescue (SAR) path planning problem, maximizi...

Logistics and supply-chain management may generate notable operational cost savings with increased reliance on shared serving of customer demands by multiple agents. However, traditional logistics planning exhibits an intrinsic limitation in modeling and implementing shared commodity delivery from multiple depots using multiple agents. In this pape...

This paper presents the approach, architecture and implementation guidelines for a Recognized Operational Support Picture (ROSP) framework suitable for the aggregation and presentation of up to date information related to the Canadian Armed Forces/Department of National Defence (CAF/DND) assets based on the requirements of the decision makers withi...

A key functionality in military logistics is the concept of sense and respond logistics to provide asset visibility, rapid response, and advanced agility tailored to all military decision levels: strategic, operational, and tactical. Future military forces do require a near-real-time in-transit and total visibility of assets, personnel, and vital s...

A new information-theoretic-based evolutionary approach is proposed to solve the dynamic search path planning problem. Path planning is achieved using an open-loop model with anticipated feedback while dynamically capturing incoming new requests and real action outcomes/observations as exogenous events, to timely adjust search path plans using coev...

Joint plan execution is gaining momentum due to its benefits in terms of cost effectiveness and operational agility. In this paper, we introduce a lightweight gossip based multi-agent distributed protocol for plan execution monitoring in a dynamic environment characterized by unreliable communication links and exogenous events. The information obta...

Distributed monitoring is challenging yet essential in order to address scalability issues observed in the context of large-scale plan execution. A formal framework can be very helpful in analyzing and reasoning about plan spec- ification, execution, and monitoring. In this paper, we elaborate on a distributed monitoring calculus framework that all...

Discrete search path planning in time-constrained uncertain environment relying upon imperfect sensors is known to be hard, and current problem-solving techniques proposed so far to compute near real-time efficient path plans are mainly bounded to provide a few move solutions. A new information-theoretic –based open-loop decision model explicitly i...

Recent progress in information technology brings new challenges in dealing with the dynamic vehicle routing problem with time windows (DVRPTW). However, few alternate methods to the well-known Tabu Search –based techniques have been proposed so far to solve the DVRPTW efficiently. In this paper, a new hybrid genetic approach (HGA-LCS) to address th...

Humanitarian logistics is defined as the process of planning, implementing and controlling the efficient, cost effective flow and storage of goods and materials as well as related information from the point of origin to the point of consumption for the purpose of alleviating the suffering of vulnerable people. The function encompasses a range of ac...

Discrete search and rescue path planning is known to be hard, and problem-solving techniques proposed so far mainly fail to properly assess optimality gap for practical size problems. A new mixed-integer linear programming (MIP) formulation is proposed to optimally solve the single agent discrete search and rescue (SAR) path planning problem. The a...

Discrete search path planning in time-constrained uncertain environment relying upon imperfect sensors is known to be hard, and current problem-solving techniques proposed so far to compute near real-time efficient path plans are mainly bounded to provide a few move solutions. A new information-theoretic –based open-loop decision model explicitly i...

Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far rely on key assumptions and heuristic procedures to reduce problem complexity. In this paper, a new model is proposed to solve the discrete rescue path planning problem for a single agent...

We develop a scheduling methodology of humanitarian reliefs distribution using military tactical logistics. We address some issues related to storage and distribution of heterogeneous commodities in a theatre of operations using a combination of heterogeneous transportation assets. The scheduling problem is modeled as a multi-objective Integer Line...

This paper presents a preliminary analysis and a comparison of existing information technologies that could be used to implement functionalities required to support operational logistics decisions. Two decision environments have been considered in this study: military and business-civilian environments. The considered functionalities have been spli...

Discrete static open-loop target search path planning is known to be a NP (non-deterministic polynomial) -Hard problem, and problem-solving methods proposed so far rely on heuristics with no way to properly assess solution quality for practical size problems. Departing from traditional nonlinear model frameworks, a new integer linear programming (I...

Service delivery planning may often involve plan specific ordering or sequencing especially in non-euclidean transit environments. Common approaches for vehicle routing fall short in adequately handling compound ad-hoc plan dependent constraints. We address multi-agent service provisioning by starting from different initial nodes of shared irregula...

In this paper, we present a strategyproof mechanism design to tackle the multi-depot vehicle routing problem in multi-agent setting. In a small-world network environment (any node is connected to every other node through a short path) wherein different self-interested agencies are controlling their own vehicle fleets, we use an innovative game theo...

Solving multi-depot vehicle routing problem (MDVRP) in centralized setting has known scalability issues. This paper presents an innovative multi-agent and multi-round reinforcement learning procedure over adaptive elitist solutions selected from an evolving population pool, to near optimally solve MDVRP in a distributed setting. The paper contribut...

There are a number of sources of randomness that arise in military airlift operations. However, the cost of uncertainty can be difficult to estimate, and is easy to overestimate if we use simplistic decision rules. Using data from Canadian military airlift operations, we study the effect of uncertainty in customer demands as well as aircraft failur...

Future military operations rely on increasingly complex joint and multinational environments. This calls for innovative concepts, doctrine, and technologies to support the emergence of new planning and execution systems that are more flexible, adaptive, interoperable, and responsive to a time-varying uncertain environment. The ability to conduct jo...

We introduce a Multi-Point Stochastic Insertion Cost Gradient Descent (MuPSICGD) heuristic algorithm to solve multi-depot split-delivery vehicle routing problem (MDSD- VRP) through an innovative approach. We also describe two solution improvement techniques that can further enhance a fairly good solution. Our contribution is threefold: First we pre...

Complex problems involving multiple agents exhibit varying degrees of cooperation. The levels of cooperation might reflect both differences in information as well as differences in goals. In this research, we develop a general mathematical model for distributed, semi-cooperative planning and suggest a solution strategy which involves decomposing th...

In this context, an effi cient management of resources is a key element for the success for any distributed and decentralized operation. The decentralization of operations makes it even more diffi cult to monitor their execution. The information is often transmitted through legacy systems that lack integration and synchronization and cause lack of...

This paper is devoted to describing the broad range of application domains which implement many of the coordination strategies and techniques from the field of multi-agent systems. The domains include defense, transportation, health care, telecommunication and e-business, emergency management, etc. The paper describes the diversity of the applicati...

Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far relies on key assumptions and heuristic procedures to reduce problem complexity. In this paper, a new model and a hybrid genetic algorithm are proposed to solve the rescue path planning p...

Mobile cooperative sensor networks are increasingly used for surveillance and reconnaissance tasks to support domain picture compilation. However, efficient distributed information gathering such as target search by a team of autonomous unmanned aerial vehicles (UAVs) remains very challenging in constrained environment. In this paper, we propose a...

Sophisticated multi-unmanned aerial vehicle (UAV) simulation environments developed so far intrinsically paid significant attention to high-fidelity flight control system components to realistically account for low-level decision support. However, the use of these simulators often incurs a large overhead when focusing on cooperative high-level deci...

This paper presents a methodology framework for the design of robust and effective military supply networks integrating various supply chain management dimensions. The proposed network design approach accounts for dynamic market demand, capacity, supply and resource conditions in a time-varying uncertain environment. The framework is based upon a t...

Persistent surveillance and reconnaissance tasks in mobile cooperative sensor networks are key to constructing recognized domain pictures over a variety of civilian and military problem instances. However, efficient information gathering for a task such as target search by a team of autonomous unmanned aerial vehicles (UAVs) still remains a major c...

Coordination is a central issue in agent-oriented distributed systems. Coordinating adequately the activities attributed to a large number of heterogeneous entities in agent-oriented engineering research remains a fundamental, challenging, and complex issue. This Chapter strongly suggests that coordination is central to distributed systems. It expl...

Industrial applications and future commercial opportunities offer a useful perspective and provide the ability to identify key issues and future trends for multi-agent research in coordination. This chapter focuses on highlighting discrepancies between current and future needs and existing coordination technologies. In this context, it is organized...

This chapter investigates emerging challenges related to the coordination of large-scale distributed systems. It highlights two key design factors that must be considered when building practical and robust distributed systems, namely scalability and performance. Hence, the present chapter is devoted to the discussion of scalability and performance...

The purpose of this chapter is to describe the broad range of application domains which implement many of the coordination strategies and techniques from the field of multi-agent systems. The domains include defense, transportation, health care, telecommunication and e-business. The objective of this chapter is to describe the diversity of the appl...

Coordination in distributed problem-solving and planning systems presents multiple dimensions to be notionally characterized. This chapter first introduces distributed problem-solving properties and motivations through various applications. It then explores fundamental notions, problems and issues associated to task and result -sharing as well as c...

Adaptive learning techniques can automate the large-scale coordination of multi-agent systems and enhance their robustness in dynamic environments. Several learning approaches have been developed to address the different aspects of coordination, from learning coordination rules to the integrated learning of trust and reputation in order to facilita...

The previous chapters have identified, highlighted, and addressed different issues and key aspects pertaining to coordination such as the need to understand scalability issues, as well as examples of the context in which coordination can occur, specifically, within the domain of distributed problem solving. With these issues and examples in mind, t...

This chapter is devoted to providing a broad overview of the various strategies and techniques designed and implemented to enable the coordination of autonomous entities within organizations, processes, and distributed multi-agent systems. In addition, examples are incorporated to illustrate the use of these strategies and techniques in various sof...

Distributed Intelligent Systems: A Coordination Perspective addresses and comprehensively answers commonly asked questions about coordination in agent-oriented distributed systems. Characterizing the state-of-the-art research in the field of coordination with regard to the development of distributed agent-oriented systems is a particularly complex...

In this paper we address a dynamic distributed patrolling problem where a team of autonomous unmanned aerial vehicles (UAVs) patrolling moving targets over a large area must coordinate. We propose a hybrid approach com- bining multi-agent geosimulation and reinforcement learn- ing enabling a group of agents to find near optimal solu- tions in reali...

The Canadian Forces (CF) relies on a supply network to support Canada's international disaster relief, humanitarian assistance, peacekeeping and peace enforcement roles. Internal and external warehousing, maintenance and transportation assets support the deployment, sustainment and redeployment phases of each mission. Currently, this supply network...

Network Centric Operations (NCO) encompasses a plethora of concepts that constitute a paradigm shift where battle-space entities are no longer stovepipes but rather a collection of network-enabled systems or databases that communicate and interoperate in a seamless and synergistic way. It stands for the embodiment of information age in military int...

Serial and parallel versions of a new memetic algorithm to address the Vehicle Routing Problem with Time Windows are presented. The underlying approach involves parallel co-evolution of two populations. The first population evolves individuals to minimize total traveled distance while the second focuses on minimizing temporal constraint violation t...

Network Centric Operations (NCO) encompasses a plethora of concepts that constitute a paradigm shift where battle space entities are no longer stovepipes but rather a collection of network-enabled systems or databases that communicate and interoperate in a seamless and synergistic way. It stands for the embodiment of information age in military int...

This paper introduces an extension of the multi-start local search framework [1, 2, 3], named Systematic Diversification for the Vehicle Routing Problem with Time Windows, where the objective is to design least cost routes for a fleet of vehicles from one depot to a set of geographically scattered points. The routes must be designed in such a way t...

A system called COLMAS (COordination Learning in Multi-Agent System) has been developed to investigate how the integration of realistic geosimulation and reinforcement learning might support a decision-maker in the context of cooperative patrolling. COLMAS is a model-driven automated decision support system combining geosimulation and reinforcement...

Past initiatives to address surveillance and reconnaissance mission planning mainly focused on low-level control aspects such as real-time path planning and collision avoidance algorithms in limited environment. However, few efforts have been spent on high-level real-time task allocation. It is believed that automated decision capabilities supporti...

The main intent of this paper is to address the issue of middleware in network centric operations. To this end, we characterize a set of Information Technology capabilities that such a middleware should implement. Afterwards, we will discuss the design and architectural aspects of these capabilities. This will lead us to an efficient and practical...

SSTR operations are becoming a priority for the Department of Defense (DoD) and are being given the same stature as the more traditional combat operations. The near-term goal of SSTR is to provide the local populace with security, restore essential services, and meet humanitarian needs, while in the longer term ensure a stable infrastructure. Large...

A colored Petri Net implementation of a high-level defence model is presented. The model is being used to represent the real-time aspects and behavior of critical system components and assess the performance of various weapon assignment decision policies. Simulation results for two candidate weapon allocation strategies are briefly discussed. A com...

The importance of- fresh data is of paramount importance especially in missions involving safety/security critical context. The present infrastructure for service integration implements a one-time execution of a multi-task business process. Business process languages compose various services but the end-result corresponds to a static instance of th...

The Decision Scheduling System (DSS) has been developed by the GERAD team under a research and development program. The DSS's aim is to solve the airline assignment problem designated as the Air mobility Line Tasking Problem (LTP). Moreover, to be able to solve the LTP problem, the DSS required the Gencol software commercialized by the AdOpt firm....

A parallel version of a new hybrid genetic algorithm for the vehicle routing problem with time windows is presented. The route-directed hybrid genetic approach is based upon the simultaneous evolution of two populations of solutions focusing on separate objectives subject to temporal constraint relaxation. While the first population evolves individ...

Recently proved successful for variants of the vehicle routing problem (VRP) involving time windows, genetic algorithms have not yet shown to compete or challenge current best search techniques in solving the classical capacitated VRP. A new hybrid genetic algorithm to address the capacitated VRP is proposed. The basic scheme consists in concurrent...

A route-directed hybrid genetic approach to address the Vehicle Routing Problem with Time Windows is presented. The proposed scheme relies on the concept of simultaneous evolution of two populations pursuing different objectives subject to partial constraint relaxation. The first population evolves individuals to minimize total traveled distance wh...

Threshold Accepting, a variant of Simulated Annealing, is applied for the first time to a set of 356 benchmark instances for the vehicle routing with time windows. The threshold accepting metaheuristic is used to improve upon results obtained with a recent parallel genetic algorithm and a multi-start local search to illustrate its value as a post-p...

Recently proved successful for variants of the vehicle routing problem (VRP) involving time windows, genetic algorithms have not yet shown to compete or challenge current best search techniques in solving the classical capacitated VRP. In this paper, a hybrid genetic algorithm to address the capacitated vehicle routing problem is proposed. The basi...

The purpose of this paper is to present a new method for solving the Vehicle Routing Problem with Time Windows (VRPTW). VRPTW can be described as the problem of designing least cost routes from one depot to a set of geographically scattered points. The routes must be designed in such a way that each point is visited only once by exactly one vehicle...

An improved hybrid genetic algorithm to address the vehicle routing problem with time windows (VRPTW) is presented. Based upon a previous algorithm, the extended approach captures some of the key concepts emerging from recent promising techniques, to efficiently combine exploitation and exploration. It focuses on the design of new and revisited gen...

Dynamic real-time scheduling applications are characterized by rapidly changing requirements and the need for timely response. We employ an approach combining Constraint Programming and Anytime Algorithms within a Multiagent framework. Agents implement various heuristic optimization algorithms which work concurrently on the same scheduling problem....

A variety of hybrid genetic algorithms has been recently proposed to address the vehicle routing problem with time windows (VRPTW), a problem known to be NP-hard. However, very few genetic-based approaches exploit implicit knowledge provided by the structure of the intermediate solutions computed during the evolutionary process to explore the solut...

A neural network-based optimization algorithm to solve an open-loop resource allocation problem is presented. The approach used is well suited to represent the structure of the model in which the occurrence of asynchronous outcome and decision events are explicitly incorporated. Mainly inspired from the principles of Hopfield neural networks, the a...

In a coalition context, there are tasks synchronization issues since missions are carried out by different players (agents) distributed over the environment. In [1], a general framework has been proposed to monitor the execution of different sub-plans by a group of agents in a coalition context. The monitoring is performed by using a temporal const...

Thèse (M. Sc. A.)--École polytechnique, 1987. "Mémoire présenté en vue de l'obtention du grade de Maître ès sciences appliquées (M. Sc.)."