A decision support tool for the location, districting and dimensioning of Community Health Houses.

Traffic grooming techniques are used to combine low-speed data streams onto high-speed lightpaths with the objective of minimizing the network cost, or maximizing the network throughput. In this paper, we first present an efficient integer linear program (ILP) formulation for traffic grooming on mesh WDM networks. Our formulation can be easily modified to implement different objective functions. Unlike previous formulations, our ILP formulation can be used for practical sized networks with several hundred requests. We then propose a second ILP for traffic grooming, with the simplifying assumption that RWA is not an issue. This second formulation is able to generate, in a reasonable time, grooming strategies, for networks with over 30 nodes, with hundreds and even thousands of low-speed data streams. Finally, we introduce a set of ILP formulations for traffic grooming, where the logical topology is specified. We have studied, using simulation, the time needed to determine grooming strategies, using the different ILP formulations.

Forests assume a great socioeconomic and environmental importance, requiring good management decisions to value and care for these natural resources. In Portugal, forest land use accounts for 34.5% of the continental area. The softwood species with the highest representation is maritime pine (Pinus pinaster Ait.). Traditionally, the species is managed as pure and even-aged stands for timber production, with a rotation age of 45 to 50 years. Depending on the initial stand density, the stands are thinned 2 to 4 times during the rotation period. Disturbances associated with forest fires have a negative impact on the age structure of stands over time, as they result in a narrow range of stand ages. This age homogenization over large forest areas increases with the recurrence and size of forest fires, bringing new challenges to forest management, namely the difficulty in ensuring the long-term sustainability of the wood supply. The problem aggravates with the increasing demand pressure on pine wood. This article aims to suggest a framework of DSS for Pinus pinaster that can effectively support the management of forest areas under these circumstances, i.e., narrow age ranges and high demand of harvested timber volume. A communal woodland area in the Northern region of Portugal affected by forest fires was selected as a study case. The Modispinaster model was used as the basis of the DSS, to simulate growth scenarios and interventions along the optional rotation period. Two clear-cut ages were considered: 25 and 40 years. The results obtained were the input data for an integer linear programming (ILP) model to obtain the plan that maximizes the volume of timber harvested in the study area, during the planning horizon. The ILP model has constraints bounding the area of clearings, and sustainability, operational and forestry restrictions. The computational results are a powerful tool for guidance in the decision-making of scheduling and forecasting the execution of interventions determining the set of stands that are exploited according to the different scenarios and the period in which the clear-cut is made throughout the planning horizon. Considering all constraints, the solution allows a balanced extraction of a total of 685 m3·ha−1, over the 50-year horizon, as well as the representation of all age classes at the end of the planning period.

Fast development of the Internet including increasing number of various streaming services, e.g., IPTV, gaming, distance learning, triggers the need to deploy robust methods to protect critical streaming sessions. Since the most effective way to provide streaming services in computer networks is multicasting, in this paper we focus on the problem multicast session protection. We propose to use the p-cycles approach to provide 100% protection against a single link failure. The key contribution of this paper is a new ILP (Integer Linear Programming) model for joint optimisation of working and spare capacity for link failure recovery problem in multicast flow protected by p-cycles proposed in [1]. The main idea of our approach is to model multicast sessions using predefined candidate trees, i.e., during the optimisation, one among several multicast candidate trees is chosen to realise each session. According to extensive numerical experiments, our new ILP model provides results very close comparing the previous ILP formulation (in some case even less than 3% worse), but the execution time is significantly lower.

Cost minimization of multi-class demands groomed over multi-rate OTN interfaces considering WDM-layer constraints

Digital micro fluidic biochips have become one of the most promising technologies in many biomedical fields. In a digital micro fluidic biochip, a group of (adjacent) cells in the micro fluidic array can be configured to work as storage, functional operations, as well as for transporting fluid droplets dynamically. There has been proposed a very efficient Integer Linear Programming (ILP)-based method to schedule operations with considering this dynamic reconfigurability. However, the method cannot directly deal with a case having an operation with multiple successors, which is necessary in some bioassays, e.g., Sample preparation applications. Sample preparation is a crucial preprocessing step to produce droplets of the desired target concentrations from input reagents. In every optimized application graphs for sample preparation, there are many operations with more than one successors. Thus, the previous method cannot be used to get an optimal scheduling, and thus we propose a new framework to use another ILP formulation for scheduling operations in a digital micro fluidic biochips. Our formulation can successfully deal with a case where there is a node with multiple successors. Also, our method can find a binding solution at the same time unlike the above-mentioned previous one thanks to our new ILP formulation. Although our ILP formulation seems to be a bit more complex than the previous one, a preliminary experiment demonstrates that the proposed ILP formulation can successfully find the optimal scheduling for practical sample preparation applications within a reasonable time.

The duplication-loss-coalescence (DLC) parsimony model is invaluable for analyzing the complex scenarios of concurrent duplication loss and deep coalescence events in the evolution of gene families. However, inferring such scenarios for already moderately sized families is prohibitive owing to the computational complexity involved. To overcome this stringent limitation, we make the first step by describing a flexible integer linear programming (ILP) formulation for inferring DLC evolutionary scenarios. Then, to make the DLC model more scalable, we introduce four sensibly constrained versions of the model and describe modified versions of our ILP formulation reflecting these constraints. Our simulation studies showcase that our constrained ILP formulations compute evolutionary scenarios that are substantially larger than scenarios computable under our original ILP formulation and the original dynamic programming algorithm by Wu et al. Furthermore, scenarios computed under our constrained DLC models are remarkably accurate compared with corresponding scenarios under the original DLC model, which we also confirm in an empirical study with thousands of gene families.

The classical Duplication-Loss-Coalescence parsimony model (DLC-model) is a powerful tool when studying the complex evolutionary scenarios of simultaneous duplication-loss and deep coalescence events in evolutionary histories of gene families. However, inferring such scenarios is an intrinsically difficult problem and, therefore, prohibitive for larger gene families typically occurring in practice. To overcome this stringent limitation, we make the first step by describing a non-trivial and flexible Integer Linear Programming (ILP) formulation for inferring DLC evolutionary scenarios. To make the DLC-model more practical, we then introduce two sensibly constrained versions of the model and describe two respectively modified versions of our ILP formulation reflecting these constraints. Using a simulation study, we showcase that our constrained ILP formulation computes evolutionary scenarios that are substantially larger than the scenarios computable under our original ILP formulation and DLCPar. Further, scenarios computed under our constrained DLC-model are overall remarkably accurate when compared to corresponding scenarios under the original DLC-model.

Planning and optimization of WDM networks has raised much interest among the research community in the last years. Integer linear programming (ILP) is the most used exact method to perform this task and many studies have been published concerning this issue. Unfortunately, many works have shown that, even for small networks, the ILP formulations can easily overwhelm the capabilities of today state-of-the-art computing facilities. So in this paper we focus our attention on ILP model computational efficiency in order to provide a more effective tool in view of direct planning or other benchmarking applications. Our formulation exploits flow aggregation and consists in a new ILP formulation that allows us to reach optimal solutions with less computational effort compared to other ILP approaches. This formulation applies to multifiber mesh networks with or without wavelength conversion. After presenting the formulation we discuss the results obtained in the optimization of case-study networks.

There has been considerable research interest in the design of survivable grooming capable networks in recent years. For such networks, protection may take place at the lightpath level or at the connection level. The vast majority of the current work can be classified into one of two categories (i) static grooming, where the demands are allocated for the entire duration of the network and (ii) dynamic grooming, where the start times and durations of demands are generated randomly based on certain traffic distributions. In this paper, we propose a new technique for survivable traffic grooming under the scheduled traffic model that exploits knowledge of the connection holding times of traffic demands to lead to more efficient resource allocation. We present efficient integer linear program (ILP) formulations for the complete survivable traffic grooming problem in WDM networks. Our formulations can solve the joint problem of the topology design, traffic routing and RWA, using path protection at lightpath level. Our aim is to design a stable logical topology that can accommodate a collection of low-speed traffic demands with specified setup and teardown times. The objective function, considered in our ILP formulation, is to minimize the resource requirements. This can be easily modified to maximize the throughput under a given set of resources. We also have proposed a simplified version of our ILP formulations that can solve the problem in a way that is computationally more efficient. To the best of our knowledge, this is the first paper to address the survivable traffic grooming problem under the scheduled traffic model.

Integer Linear Programming (ILP) is commonly used in high level and system level synthesis. It is an NP-Complete problem (in general cases). There exists some tools that give an optimal solution for small ILP formulations. Nevertheless, these tools may not give solutions for complex formulations. In this paper, we present a solution to overcome the problem of complexity in ILP formulations. We propose a partitioning methodology based on a constraint graph representing all the constraints included in any ILP formulation. To direct the partitioning, the constraint graph nodes are grouped to represent Data Flow Graph (DFG) nodes. This reduced constraint graph can be used to partition any ILP formulation based on DFG. We illustrate this method on ILP formulation for scheduling. Results on ILP scheduling formulations are promising.

We consider the following general graph clustering problem: given a complete undirected graph G=(V,E,c) with an edge weight function c:E->Q, we are asked to find a partition C of V that maximizes the sum of edge weights within the clusters in C. Owing to its high generality, this problem has a wide variety of real-world applications, including correlation clustering, group technology, and community detection. In this study, we investigate the design of mathematical programming formulations and constraint satisfaction formulations for the problem. First, we present a novel integer linear programming (ILP) formulation that has far fewer constraints than the standard ILP formulation by Groetschel and Wakabayashi (1989). Second, we propose an ILP-based exact algorithm that solves an ILP problem obtained by modifying our above ILP formulation and then performs simple post-processing to produce an optimal solution to the original problem. Third, we present maximum satisfiability (MaxSAT) counterparts of both our ILP formulation and ILP-based exact algorithm. Computational experiments using well-known real-world datasets demonstrate that our ILP-based approaches and their MaxSAT counterparts are highly effective in terms of both memory efficiency and computation time.

In this paper, we address the constrained two‐dimensional rectangular guillotine single large placement problem (2D_R_CG_SLOPP). This problem involves cutting a rectangular object to produce smaller rectangular items from orthogonal guillotine cuts. In addition, there is an upper limit on the number of copies that can be produced of each item type. To model this problem, we propose a new pseudopolynomial integer nonlinear programming (INLP) formulation and obtain an equivalent integer linear programming (ILP) formulation from it. Additionally, we developed a procedure to reduce the numbers of variables and constraints of the integer linear programming (ILP) formulation, without loss of optimality. From the ILP formulation, we derive two new pseudopolynomial models for particular cases of the 2D_R_CG_SLOPP, which consider only two‐staged or one‐group patterns. Finally, as a specific solution method for the 2D_R_CG_SLOPP, we apply Benders decomposition to the proposed ILP formulation and develop a branch‐and‐Benders‐cut algorithm. All proposed approaches are evaluated through computational experiments using benchmark instances and compared with other formulations available in the literature. The results show that the new formulations are appropriate in scenarios characterized by few item types that are large with respect to the object's dimensions.

In this paper we propose new simple integer linear programs (ILPs) formulations for minimizing capacity (in wavelength link) utilization in survivable WDM network. The study examines the performance of shared based protection schemes, such as path protection scheme and link protection scheme under single fiber failure. The numerical results obtained show a reduction in capacity utilization using random traffic compared to the reported ILP formulation. We also present the results using Poisson’s traffic to identify the frequently used links for the widely used NSF network. The proposed work not only reduces the wavelength consumption in different traffic scenarios but also efficient in terms of simulation time. In this paper we propose new simple integer linear programs (ILPs) formulations for minimizing capacity (in wavelength link) utilization in survivable WDM network. The study examines the performance of shared based protection schemes, such as path protection scheme and link protection scheme under single fiber failure. The numerical results obtained show a reduction in capacity utilization using random traffic compared to the reported ILP formulation. We also present the results using Poisson’s traffic to identify the frequently used links for the widely used NSF network. The proposed work not only reduces the wavelength consumption in different traffic scenarios but also efficient in terms of simulation time.

In this paper, we introduce the double traveling salesman problem with partial last‐in‐first‐out loading constraints (DTSPPL). It is a pickup‐and‐delivery single‐vehicle routing problem, where all pickup operations must be performed before any delivery operation because the pickup‐and‐delivery areas are geographically separated. The vehicle collects items in the pickup area and loads them into its container, a horizontal stack. After performing all pickup operations, the vehicle begins delivering the items in the delivery area. Loading and unloading operations must obey a partial last‐in‐first‐out (LIFO) policy, that is, a version of the LIFO policy that may be violated within a given reloading depth. The objective of the DTSPPL is to minimize the total cost, which involves the total distance traveled by the vehicle and the number of items that are unloaded and then reloaded due to violations of the standard LIFO policy. We formally describe the DTSPPL through two integer linear programming (ILP) formulations and propose a heuristic algorithm based on the biased random‐key genetic algorithm (BRKGA) to find high‐quality solutions. The performance of the proposed solution approaches is assessed over a broad set of instances. Computational results have shown that both ILP formulations have been able to solve only the smaller instances, whereas the BRKGA obtained good‐quality solutions for almost all instances, requiring short computational times.

This paper investigates the problem of optimal diverse routing for shared path-based protection in the complete routing information scenario on mesh optical networks, where a novel Integer Linear Programming (ILP) formulation is introduced such that the least-cost link-disjoint working and protection path-pair can be derived in a single step. The proposed ILP formulation is characterized by the facts that it is solvable with the commercially available Linear Programming (LP) solvers and that it can deal with the dependency between working and spare capacity in the network, which is a step ahead of the most state-of-the-art techniques in the design of diverse routing algorithms for shared protection. To verify the proposed ILP, an experiment is conducted to compare it with four reported schemes for end-to-end shared protection on two network topologies, namely APFPBC, MLR, ITSA, and ILP-2S, where blocking probability is taken as the performance metric with connection requests being dynamically launched into the networks. The simulation results show that the ILP formulation yields the best performance while the ILP-2S scheme investigating less network states yields the worst. We also use the results by the proposed ILP to evaluate the four heuristic-based schemes adopted in the simulation in terms of two performance indexes – the percentage of optimality (denoted as %opti) and the offset of optimality (denoted as Q).