Abdeslem Boukhtouta

The Effect of Robust Decisions on the Cost of Uncertainty in Military Airlift Operations

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 failures, on the overall cost. The system is first analyzed using the types of myopic decision rules widely used in the research literature. The performance of the myopic policy is then compared to the results obtained using robust decisions that account for the uncertainty of future events. These are obtained by modeling the problem as a dynamic program, and solving Bellman’s equations using approximate dynamic programming. The experiments show that even approximate solutions to Bellman’s equations produce decisions that reduce the cost of uncertainty.

Customer satisfaction in dynamic vehicle routing problem with time windows

A dynamic vehicle routing problem focusing on customer satisfaction is solved.An evolutionary approach coupled to a customer satisfaction strategy is proposed.The strategy inserts future anticipated visits favoring customer satisfaction.Results show the value of advance planning and strategy on customer satisfaction. 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 satisfaction. An evaluation of the proposed strategy is performed using a hybrid genetic algorithm previously designed for the dynamic vehicle problem with time windows that we modified to capture customer satisfaction over multiple visits. Simulations compare the value of the revisited algorithm exploiting the new strategy, clearly demonstrating its impact on customer satisfaction level.

What middleware for network centric operations

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 decision support system that we call a digital cockpit. The latter is essentially a multi-tier IT platform that provides a plethora of services such as: data and service integration, monitoring, analysis, and process optimization. Moreover, the platform uses advanced display mechanisms to render the information in a structured and navigational representation that offers the possibility to drill down into the details. A significant subgoal of the paper is to discuss the quality attributes of such an NCO middleware. Finally, we present the results of an implementation of the aforesaid platform architecture.

A hybrid genetic algorithm for rescue path planning in uncertain adversarial environment

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 problem for a single vehicle navigating in uncertain adversarial environment. We present a simplified mathematical linear programming formulation aimed at minimizing traveled distance and threat exposure. As an approximation to the basic problem, the user-defined model allows to specify a lower bound on the optimal solution for some particular survivability conditions. Hard problem instances are then solved using a novel hybrid genetic algorithm relaxing some of the common assumptions considered by previous path construction methods. The algorithm evolves a population of solution combining genetic operators with a new stochastic path generation technique, providing guided local search, while improving solution quality. The value of the problem-solving approach is shown for simple cases and compared to an alternate heuristic.

Modeling and simulation of military tactical logistics distribution

The military tactical logistics planning problem addresses the issue of distributing heterogeneous commodities in a theater of operations using a combination of heterogeneous transportation assets such as logistics trucks and tactical helicopters. The Canadian Forces requires a decision support tool to examine the trade-off between the cost of the support and its effectiveness during sustainment operations. In this study, a mathematical optimization algorithm and a simulation module to build cost efficient and effective military tactical logistics are developed. Details of the optimization algorithm along with several example applications are presented to demonstrate the methodology. The simulation results are focused on the trade-off between cost and lead-time within which demands are required, and on the optimal fleet mix of transportation assets to respond to the different requirements of deployed forces

A decentralized heuristic for multi-depot split-delivery vehicle routing problem

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 present a heuristic-based mechanism to solve multi-depot, multi-vehicle per depot routing problems in split-delivery setting. Second, unlike related meta-heuristics approaches, we construct solutions from connecting fragments. This can be very helpful in projecting a fitting solution estimate during the searching mechanism along with the potential for adaptability to exogenous events during routing execution. Third, the approach is suitable for decentralized implementation as long as the operating nodes cooperate on solving a common problem instance. In this respect, we elaborate the decentralization procedure. The proposed technique is also resilient to the loss or addition of computing nodes. We also provide a case study, implementation guidelines and suitable benchmarks based on known problem instances.

An adaptive-learning framework for semi-cooperative multi-agent coordination

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 the system into subproblems, each of which is specified at a certain period in time and controlled by an agent. The agents communicate marginal values of resources to each other, possibly with distortion. We design experiments to demonstrate the benefits of communication between the agents and show that, with communication, the solution quality approaches that of the ideal situation where the entire problem is controlled by a single agent.

Mechanism design for decentralized vehicle routing problem

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 theoretic approach to distribute products to customers without any central authority. The game is using a reverse Vickrey auction that takes place in several rounds until all customers are assigned. Payments are given to depots as incentive for fairness of serving cost offering. The procedure leads to overall near-optimal routing for serving all customers. We briefly describe the use of a heuristic approach to solve the game-based procedure in bounded time and memory. We also provide benchmarks for several known problem instances.

A framework for the design of a military operational supply network

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 two-level decomposition scheme combining design and user model components. The proposed stochastic multi-stage design model problem consists of determining the number and location of facilities (depots) required to satisfy an anticipated set of customers demands and customer allocation (mission) to depots over a given time horizon. The user model is exploited to produce scenario-based anticipations to the design model required for network design problem-solving, and to assess network design solutions. The user model component mixes lot-sizing decisions with transportation assets assignments. Simulation is expected to be used to dynamically generate stochastic events supporting the construction of solution at both levels. Preliminary results on a military operational support hubs case study are reported and briefly analyzed for a simplified asset pre-positioning problem.

A column-and-cut generation algorithm for planning of Canadian armed forces tactical logistics distribution

The military tactical logistics planning problem addresses the issue of distributing heterogeneous commodities (e.g., food, medical supplies, construction material, ammunition, etc.) to forward operating bases in a theatre of operations using a combination of heterogeneous transportation assets such as logistics trucks and tactical helicopters. Minimizing the logistics operating cost while satisfying the operational demands under time and security constraints is of high importance for the Canadian Armed Forces. In this study, a logistics planning model is developed to explore the trade-offs between the effectiveness and efficiency in military tactical logistics distribution. A mathematical optimization algorithm based on Column-and-Cut generation techniques is developed to find the fleet mix and size of transportation assets to meet different Quality-of-Support (QoS) parameters. This paper presents details of a new column generation decomposition approach and a solution algorithm along with an application example to demonstrate the methodology. Extensive computational results are presented in order to measure the degree of efficiency and scalability of the proposed approach, and to analyze the trade-offs between: (1) delivery time and operating cost; (2) security and operating cost.