Emanuel Melachrinoudis

Controlled sink mobility for prolonging wireless sensor networks lifetime

This paper demonstrates the advantages of using controlled mobility in wireless sensor networks (WSNs) for increasing their lifetime, i.e., the period of time the network is able to provide its intended functionalities. More specifically, for WSNs that comprise a large number of statically placed sensor nodes transmitting data to a collection point (the sink), we show that by controlling the sink movements we can obtain remarkable lifetime improvements. In order to determine sink movements, we first define a Mixed Integer Linear Programming (MILP) analytical model whose solution determines those sink routes that maximize network lifetime. Our contribution expands further by defining the first heuristics for controlled sink movements that are fully distributed and localized. Our Greedy Maximum Residual Energy (GMRE) heuristic moves the sink from its current location to a new site as if drawn toward the area where nodes have the highest residual energy. We also introduce a simple distributed mobility scheme (Random Movement or RM) according to which the sink moves uncontrolled and randomly throughout the network. The different mobility schemes are compared through extensive ns2-based simulations in networks with different nodes deployment, data routing protocols, and constraints on the sink movements. In all considered scenarios, we observe that moving the sink always increases network lifetime. In particular, our experiments show that controlling the mobility of the sink leads to remarkable improvements, which are as high as sixfold compared to having the sink statically (and optimally) placed, and as high as twofold compared to uncontrolled mobility.

The dynamic relocation and phase-out of a hybrid, two-echelon plant/warehousing facility: A multiple objective approach

Abstract A real-world application involving the relocation and phase-out of a combined manufacturing plant and warehousing facility is presented in this paper. The relocation and phase-out decision was called for to adapt to dynamic changes in business environments surrounding the firms supply chain operations. Such changes include changes in supplier and customer bases, distribution networks, corporate re-engineering, business climate, and government legislation. To aid management in formulating more effective relocation strategy, we assess the viability of the proposed site from multi-echelon supply chain perspectives and determine the optimal timing of relocation and phase-out in the multiple planning horizon using a dynamic, multiple objective, mixed-integer programming model.

Bi-criteria models for all-uses test suite reduction

Using bi-criteria decision making analysis, a new model for test suite minimization has been developed that pursues two objectives: minimizing a test suite with regard to a particular level of coverage while simultaneously maximizing error detection rates. This new representation makes it possible to achieve significant reductions in test suite size without experiencing a decrease in error detection rates. Using the all-uses inter-procedural data flow testing criterion, two binary integer linear programming models were evaluated, one a single-objective model, the other a weighted-sums bi-criteria model. The applicability of the bi-criteria model to regression test suite maintenance was also evaluated. The data show that minimization based solely on definition-use association coverage may have a negative impact on the error detection rate as compared to minimization performed with a bi-criteria model that also takes into account the ability of test cases to reveal error. Results obtained with the bi-criteria model also indicate that test suites minimized with respect to a collection of program faults are effective at revealing subsequent program faults.

The relocation of a hybrid manufacturing/distribution facility from supply chain perspectives: a case study

In this paper we present a real-world case study involving the re-location of a combined manufacturing and distribution (warehousing) facility. The relocation decision was called to adapt to dynamic changes in business environments surrounding the firms supply chain operations. Such changes include changes in supplier and customer bases, distribution networks, corporate re-engineering, business climate and government legislation. To aid management in formulating a more efficient and effective relocation strategy, we designed the configuration of supply chain networks and assessed the viability of the proposed sites from supply chain perspectives using the analytic hierarchy process (AHP).

A dial-a-ride problem for client transportation in a health-care organization

Abstract We propose a double request dial-a-ride model with soft time windows and its application to the CAB Health and Recovery Services, Inc., a non-profit organization in the Boston Metropolitan area for the purpose of addressing the CAB clients’ transportation needs. The objective of the proposed model is to minimize a convex combination of total vehicle transportation costs and total clients’ inconvenience time. The latter consists of excess riding time, early/late delivery time before service and late pickup time after service. The proposed model was used to compare the benefits of coordination and central dispatching over the current system under which individual centers of the organization schedule their own clients’ appointments and route their own vehicles.

Aggregate Objective Functions and Pareto Frontiers: Required Relationships and Practical Implications

This paper addresses the problem of capturing Pareto optimal points on non-convex Pareto frontiers, which are encountered in nonlinear multiobjective optimization problems in computational engineering design optimization. The emphasis is on the choice of the aggregate objective function (AOF) of the objectives that is employed to capture Pareto optimal points. A fundamental property of the aggregate objective function, the admissibility property, is developed and its equivalence to the coordinatewise increasing property is established. Necessary and sufficient conditions for such an admissible aggregate objective function to capture Pareto optimal points are derived. Numerical examples illustrate these conditions in the biobjective case. This paper demonstrates in general terms the limitation of the popular weighted-sum AOF approach, which captures only convex Pareto frontiers, and helps us understand why some commonly used AOFs cannot capture desirable Pareto optimal points, and how to avoid this situation in practice. Since nearly all applications of optimization in engineering design involve the formation of AOFs, this paper is of direct theoretical and practical usefulness.

A MULTIOBJECTIVE MODEL FOR THE DYNAMIC LOCATION OF LANDFILLS

Abstract The passage of the Resource Conservation and Recovery Act of 1976 and its subsequent renewals triggered controversies over where potentially hazardous garbage should be deposited. Such controversies originated from decade-long conflicts between the government agency seeking cost savings and the general public seeking safe environments. The controversies will go on without a settlement, unless more systematic and realistic decision-aid tools for locating landfills are developed which consider a multitude of dynamic factors affecting the location decision and then make trade-offs among them. These factors may include garbage collection services for regional communities, the explicit and hidden costs of developing landfills, transporting and disposing garbage, long-term health and safety hazards for neighboring residents and ecosystems, negative impacts on the regional economy, equity concerns and so forth. As an effective decision-aid tool that can incorporate these conflicting factors, we develop a dynamic (multiperiod), multiobjective mixed integer programming model. The model is tested in a hypothetical case resembling a real world scenario and the results are interpreted to provide insights into the multiobjective and dynamic nature of the model. Efficient alternatives are generated, using the weighting method, and reduced using a filtering method.

Mathematical and Pragmatic Perspectives of Physical Programming

Physical programming (PP) is an emerging multiobjective and design optimization method that has been applied successfully in diverse areas of engineering and operations research. The application of PP calls for the designer to express preferences by defining ranges of differing degrees of desirability for each design metric. Although this approach works well in practice, it has never been shown that the resulting optimal solution is not unduly sensitive to these numerical range definitions. PP is shown to be numerically well conditioned, and its sensitivity to designer input (with respect to optimal solution) is compared with that of other popular methods. The important proof is provided that all solutions obtained through PP are Pareto optimal and the notion of Pareto optimality is extended to one of pragmatic implication. The important notion of P dominance that extends the concept of Pareto optimality beyond the cases minimize and maximize is introduced. P dominance is shown to lead to the important concept of generalized Pareto optimality. Numerical results are provided that illustrate the favorable numerical properties of physical programming.

A mixed integer knapsack model for allocating funds to highway safety improvements

This paper presents a methodology for allocating funds to highway safety improvements. Besides the commonly used binary variables that represent discrete interventions at specific points of a highway, continuous variables are introduced to represent the lengths of a highway over which continuous improvements, such as pavement resurfacing or lighting, are implemented. The problem is formulated as a mixed integer knapsack model with linear multiple choice constraints. Some insight into its solution properties is provided and an efficient branch and bound algorithm is proposed for its solution. A case study that illustrates the application of the model is also presented.

DYNAMIC EXPANSION AND LOCATION OF AN AIRPORT: A MULTIPLE OBJECTIVE APPROACH

In this paper we propose a dynamic, multi-objective, mixed integer programming model that aims to determine the optimal airport site under capacity and budgetary restrictions. In contrast with the existing models, the proposed model can also solve a practical size location-allocation problem without serious computational difficulty. As a practical example, the model has been applied to the airport expansion and construction problem facing the Massachusetts Port Authority and Aeronautics Commission.