Adel Guitouni

The design of robust value-creating supply chain networks: A critical review

This paper discusses Supply Chain Network (SCN) design problem under uncertainty, and presents a critical review of the optimization models proposed in the literature. Some drawbacks and missing aspects in the literature are pointed out, thus motivating the development of a comprehensive SCN design methodology. Through an analysis of supply chains uncertainty sources and risk exposures, the paper reviews key random environmental factors and discusses the nature of major disruptive events threatening SCN. It also discusses relevant strategic SCN design evaluation criteria, and it reviews their use in existing models. We argue for the assessment of SCN robustness as a necessary condition to ensure sustainable value creation. Several definitions of robustness, responsiveness and resilience are reviewed, and the importance of these concepts for SCN design is discussed. This paper contributes to framing the foundations for a robust SCN design methodology.

Dynamic Resource Allocation in Computing Clouds Using Distributed Multiple Criteria Decision Analysis

In computing clouds, it is desirable to avoid wasting resources as a result of under-utilization and to avoid lengthy response times as a result of over-utilization. In this paper, we propose a new approach for dynamic autonomous resource management in computing clouds. The main contribution of this work is two-fold. First, we adopt a distributed architecture where resource management is decomposed into independent tasks, each of which is performed by Autonomous Node Agents that are tightly coupled with the physical machines in a data center. Second, the Autonomous Node Agents carry out configurations in parallel through Multiple Criteria Decision Analysis using the PROMETHEE method. Simulation results show that the proposed approach is promising in terms of scalability, feasibility and flexibility.

Multi-objectives Tabu Search based algorithm for progressive resource allocation

Abstract Military course of action planning involves time and space synchronization as well as resource and asset allocation. A mission could be seen as a defined set of logical ordered tasks with time and space constraints. The resources to task rules require that available assets should be allocated to each task. A combination of assets might be required to execute a given task. The couple (task, resources) constitutes an action. This problem is formulated as a multi-objectives scheduling and resource allocation problem. Any solution is assessed based on a number of conflicting and heterogeneous objectives. In fact, military planning officers should keep perfecting the plan based on the Commander’s criteria for success. The scheduling problem and resource allocation problem are considered as NP-Hard Problems [A. Guitouni, B. Urli, J.-M. Martel, Course of action planning: A project based modelling, Working Paper, Faculte des sciences de l’ administration, Universite Laval, Quebec, 2005]. This paper is concerned with the multi-objectives resource allocation problem. Our objective is to find adequate resource allocation for given courses of action schedule. To optimize this problem, this paper investigates non-exact solution methods, like meta-heuristic methods for finding potential efficient solutions. A progressive resource allocation methodology is proposed based on Tabu Search and multi-objectives concepts. This technique explores the search space so as to find a set of potential efficient solutions without aggregating the objectives into a single objective function. It is guided by the principle of maximizing the usage of any resource before considering a replacement resource. Thus, a given resource is allocated to the maximum number of tasks for a given courses of action schedule. A good allocation is a potential efficient solution. These solutions are retained by applying a combination of a dominance rule and a multi-criteria filtering method. The performance of the proposed Pareto-based approach is compared to two aggregation approaches: weighted-sum and the lexicographic techniques. The result shows that a Pareto-based approach is providing better solutions and allowing more flexibility to the decision-maker.

A general decomposition approach for multi-criteria decision trees

In this paper, we address the dynamic and multi-criteria decision-making problems under uncertainty, generally represented by multi-criteria decision trees. Decision-making consists of choosing, at each period, a decision that maximizes the decision-maker outcomes. These outcomes should often be measured against a set of heterogeneous and conflicting criteria. Generating the set of non-dominated solutions is a common approach considered in the literature to solve the multi-criteria decision trees, but it becomes very challenging for large problems. We propose a new approach to solve multi-criteria decision trees without generating the set of all non-dominated solutions, which should reduce the computation time and the cardinality of the solution set. In particular, the proposed approach combines the advantages of decomposition with the application of multi-criteria decision aid (MCDA) methods at each decision node. A generalization of the Bellman’s principle of decomposition to the multi-criteria context is put forward. A decomposition theorem is therefore proposed. Under the sufficient conditions stated by the theorem, the principle of decomposition will generate the set of best compromise strategies. Seven MCDA methods are then characterized (lexicographic, weighted sum, multi-attribute value theory, TOPSIS, ELECTRE III, and PROMETHEE II) against the conditions of the theorem of decomposition and against other properties (neutrality, anonymous, fidelity, dominance, independency), in order to confirm or infirm their applicability with the proposed decomposition principle. Moreover, the relationship between independency and temporal consistence is discussed as well as the effects of incomparableness, rank reversals, and use of thresholds. Two conjectures resulted from this characterization.

Variable Dwell Time Task Scheduling for Multifunction Radar

Efficient utilization of resources is an important issue in the operation of modern radar systems. This paper develops a generalized framework for the radar task scheduling problem as an optimization model. In the proposed method, all radar task parameters are treated as variables, thereby allowing greater scheduling flexibility and the ability to handle more targets using a single radar. An efficient heuristic scheduling method is also proposed and computational results are presented to asses the performance of the proposed method. Note to Practitioners - This paper deals with scheduling a sequence of tasks for a radar system in a limited time-window. Previous work in the literature has addressed this problem by modeling each task as having a fixed duration. In an earlier paper of one of the authors, the task duration was modeled as a variable, which allowed for some flexibility in the task duration, enabling enhanced utilization of the radar timeline. However, a simplified radar task model was used that did not account for the internal structure of a radar task. This work adds a practical dimension by further extending the radar task model to allow for some flexibility in the task duration and also explicitly account for the internal structure of the task. It is shown that utilization of the radar timeline can be thus enhanced through monitoring the interplay of adjusting the dwell times of the internal components of a task.

Deriving a minimum distance-based collective preorder: a binary mathematical programming approach

Deriving the “closest” (minimal distance) collective judgment to all individual opinions is a complex aggregation problem that has been widely studied in group decision-making literature. However, most of the existing literature does not consider individual opinions expressed as partial preorders (i.e., a preference system which includes the incomparability relation). In this paper, we propose a method based on binary linear programming to derive a minimum distance-based collective preorder from individual preferences relational systems (p.r.s.). This method is threefold. First, each member determines a preorder (partial or total) over the set of alternatives. Second, an aggregation algorithm is proposed to derive at least one collective and not necessary transitive p.r.s. at minimum distance from all individual preorders. Third, a binary linear programming optimization will transform each non-transitive collective p.r.s. into a collective preorder (i.e. a transitive p.r.s.). The proposed method has three main advantages: (1) it deals with incomparability (partial preorders), (2) the relative importance of the members is explicitly considered and (3) the collective p.r.s. obtained after the aggregation step might be “exploited” according to different decision-making problematics (i.e. ranking, choice and sorting).

A multi-objective optimization approach for resource assignment and task scheduling problem: Application to maritime domain awareness

Large volume surveillance missions are characterized by the employment of mobile and fixed surveillance assets to a large geographic operation area in order to perform surveillance activities. Finding efficient management solutions should be investigated to optimize assets allocation and tasks achievement. In this paper, we propose to model this optimization problem as a multi-objective, multi-mode assignment and scheduling problem. Resources are to be assigned to accomplish the tasks. Then, surveillance tasks should be scheduled onto successive periods. The problem is designed to consider two conflicting objective functions: minimizing the makespan and minimizing the total cost. As the problem is NP-Hard, a bi-colony ant based approach is proposed. The empirical validation is done using a simulation environment Inform Lab. The experimental results show that the computational time remains polynomial with respect to the problems size.

A genetic algorithm for a multi-objective nodes placement problem in heterogeneous network infrastructure for surveillance applications

In this paper, we adress a Multi-objective communication nodes (e.g., antennas, relays…) placement problem for heterogeneous network infrastructure. The proposed model considers three conflicting objective functions: maximizing the communication coverage, minimizing the cost of nodes placement and communication devices and the maximizing of the total capacity bandwidth in the network. The empirical validation of the model is done in a simulation environment called “Inform Lab”. We consider a large volume of surveillance missions. To solve such an NP-Hard problem, we propose a Multi-objective Genetic Algorithm (MOGA). The empirical results show that the proposed algorithm has good performance with good qualitys result in a practicable CPU time.

Engineering Situation Analysis Decision Support Systems

This paper explores a new approach to model-driven engineering (MDE) of situation analysis decision support systems for Marine Safety & Security Operations. Realistic situation analysis scenarios routinely deal with complex dynamic situations involving multiple mobile agents and events distributed in space and time. The work presented here builds on Abstract State Machine (ASM) modeling paired with Creams tool support to analyze and validate ASM models experimentally. The proposed approach facilitates analysis of the problem space and supports reasoning about design decisions and conformance criteria so as to ensure they are properly established and well understood prior to building the system. We provide an extension to Core ASM for the Marine Safety & Security domain, specifically for capturing rendezvous scenarios and illustrate the application of the proposed modeling approach using sample scenarios.

A genetic algorithm based decision support system for the multi-objective node placement problem in next wireless generation network

Abstract The node placement problem involves positioning and configuring infrastructure for wireless networks. Applied to next generation networks, it establishes a new wireless architecture able to integrate heterogeneous components that can collaborate and exchange data. Furthermore, the heterogeneity of wireless networks makes the problem more intractable. This paper presents a novel multi-objective node placement problem that optimizes concurrently four objectives: maximizing communication coverage, minimizing the active structures’ costs, maximizing of the total capacity bandwidth and minimizing the noise level in the network. Known to be NP -hard, the problem can be approached by applying heuristics mainly for large problem instances. As the number of nodes to place is not determined beforehand; we propose to apply a multi-objective variable-length genetic algorithm (VLGA) that simultaneously searches for the optimal number, positions and nature of heterogeneous nodes and communication devices. The performance of the VLGA is highlighted through the implementation of a decision support system (DSS) applied to the surveillance maritime problem using real data instances. We compare the ability of the proposed algorithm with an existing multi-objective model from the literature in order to validate its effectiveness in dealing with heterogeneous components. The results show that the proposed model well fits the network architecture constraints with a better balance between the objectives applied to the surveillance problem.