The Process Allocation Problem: a Survey of the Application of Graph-Theoretic and Integer Programming Approaches

The Process Allocation Problem, which consists of allocating a number of processes to a network of processors with the objective of minimizing the sum of communication (between processes residing on different processors) and execution costs, is presented and the relevant literature reviewed. Solution procedures employing graph-theoretic and mathematical programming methods are critically examined. Some heuristic algorithms are also presented.     

Automatic identification of embedded network rows in large-scale optimization models

The solution of a large-scale linear, integer, or mixed integer programming problem is often facilitated by the exploitation of special structure in the model. This paper presents heuristic algorithms for identifying embedded network rows within the coefficient matrix of such models. The problem of identifying a maximum-size embedded pure network is shown to be among the class of NP-hard problems. The polynomially-bounded, efficient algorithms presented here do not guarantee network sets of maximum size. However, upper bounds on the size of the maximum network set are developed and used to show that our algorithms identify embedded networks of close to maximum size. Computational tests with large-scale, real-world models are presented.     

An improved decomposition-based heuristic to design a water distribution network for an irrigation system

In this paper the authors address a pressurized water distribution network design problem for irrigation purposes. Two mixed binary nonlinear programming models are proposed for this NP-hard problem. Furthermore, a heuristic algorithm is presented for the problem, which considers a decomposition sequential scheme, based on linearization of the second model, coupled with constructive and local search procedures designed to achieve improved feasible solutions. To evaluate the robustness of the method we tested it on several instances generated from a real application. The best solutions obtained are finally compared with solutions provided by standard software. These computational experiments enable the authors to conclude that the decomposition sequential heuristic is a good approach to this difficult real problem.     

The Feeder-bus Network-design Problem

The potential for improving the cost-effectiveness of public transport operations by designing better integrated feeder-bus/rail rapid transit systems has been widely recognized. This paper defines the feeder-bus network-design problem (FBNDP) as that of designing a feeder-bus network to access an existing rail system. The FBNDP is considered under two different demand patterns, many-to-one (M-to-1) and many-to-many (M-to-M). We present a mathematical programming model for the M-to-1 FBNDP, and show that it can be generalized to the M-to-M FBNDP. The FBNDP is a large and difficult vehicle-routeing-type problem with an additional decision variable—operating frequency. A heuristic model is presented, which generalizes the ‘savings approach’ to incorporate operating frequency. The computational analysis shows that the proposed heuristic provides reasonable feeder-bus networks and consistent responses to ‘what if’ questions. A comparison indicates that the proposed heuristic provides solutions that are superior to manually designed networks. The advantages of this heuristic are particularly significant under variable demand.     

Design of a recovery network for WEEE collection: the case of Galicia,Spain

This research aims to optimize the design of the reverse logistic network for the collection of Waste of Electric and Electronic Equipment (WEEE), in the Spanish region of Galicia. As a basis for our study a three-phase hierarchical approach is proposed. In the first phase a facility location problem is formulated and solved by means of a mixed integer linear programming; in the second phase a new integer programming formulation for the corresponding heterogeneous fleet vehicle routing problem is presented, and a savings-based heuristic algorithm is developed to efficiently solve the related collection routing problems; in the third phase a simulation study is performed on the collection routes in order to assess the overall performance of the recovery system. The results show a good performance of the proposed procedure, and an improved configuration of the recovery network compared to the one currently in use (particularly transportation costs are reduced by 29.2%).     

Pricing and revenue maximization over a multicommodity transportation network: the nonlinear demand case

This paper is concerned with the design of efficient exact and heuristic algorithms for addressing a bilevel network pricing problem where demand is a nonlinear function of travel cost. The exact method is based on the piecewise linear approximation of the demand function, yielding mixed integer programming formulations, while heuristic procedures are developed within a bilevel trust region framework.     

军事物流系统中定位-运输路线安排模型及算法

考虑到战时物资需求的紧迫性和保障资源的有限性,从决策者的角度出发,以军事物流系统总体供应时间最短为目标,构建了两级军事配送网络的定位-运输路线安排模型,并给出一种启发式算法.算法分为两个阶段,首先利用蚁群算法和线性规划的方法解决运输路线安排问题,然后运用贪婪搜索算法解决军事物流配送中心选址问题.最终,将两种算法结合起来进行逐步搜索,从而得到模型的解,并运用实例说明了算法的有效性和可行性.     

An efficient computational approach for railway booking problems

This paper deals with the determination of seat allocations for a rail booking system. It is assumed that demand for each trip in the network can be divided into two segments, namely a full fare segment and a discounted fare segment. A constrained nonlinear integer programming model is formulated to deal with this problem. The purpose of this paper is to develop an efficient heuristic approach to develop the booking limits for all ticket types in the railway network. The solutions obtained by the heuristic approach are compared with those found by the Lingo software and the DICOPT solver. Numerical results show that the proposed heuristic approach only require a small number of CPU time to obtain superior solutions.     

Production planning and warehouse management in supply networks with inter-facility mold transfers

This paper, considers with the problem of production capacity and warehouse management in a supply network in which inter-plant mold transfers are enabled. The supply network has a limited number of very expensive molds which can be transferred from a plant to another making it possible for each plant to produce the entire product gamut. It is assumed that warehouses in this supply network can be activated and deactivated as required, and that material transfers from a warehouse to another are also possible. The objective is to develop a capacity and warehouse management plan that satisfies the expected market demands with the lowest possible cost. A mixed integer programming model for the problem is suggested and its properties are discussed. A linear programming-based heuristic that combines Lagrangian relaxation and linear programming duality to generate lower and upper bounds for the problem is proposed. Finally, based on a designed experiment the performance of the heuristic on a set of generated test problems is reported and discussed.     

Large-scale mixed integer programming: Benders-type heuristics

The limited success of exact methods for solving integer programming problems has prompted the development of heuristic procedures which have performed surprisingly well in their search for near-optimal solutions. The present paper constitutes an attempt to generalize these procedures to the mixed integer case. The approach is based on the utilization of the Benders partitioning method (BPM) which separates the integer from the continuous variables. A number of alternatives are presented for integrating IP heuristics in the BPM thus alleviating its main limitation: the necessity of solving a sequence of integer master problems. The rationale and its usefulness are illustrated on large-scale applications from the fields of power systems and network flows.     

Optimal placement,scheduling, and routing to maximize lifetime in sensor networks

A wireless sensor network is a network consisting of distributed autonomouselectronic devices called sensors. Sensors have limited energy and capabilityfor sensing, data processing, and communicating, but they can collectivelybehave to provide an effective network that monitors an area and transmitinformation to gateway nodes or sinks, either directly or through other sensornodes. In most applications the network must operate for long periods of time,so the available energy resources of the sensors must be managed efficiently. Inthis paper, we first develop a mixed integer linear programming model tomaximize network lifetime by optimally determining locations of sensors andsinks, activity schedules of deployed sensors, and data flow routes from sensorsto sinks over a finite planning horizon subject to coverage, flow conservation,energy consumption, and budget constraints. Unfortunately, it is difficult tosolve this model exactly even for small instances. Therefore, we propose twoapproximate solution methods: a Lagrangean heuristic and a two-stage heuristicin which sensors are deployed and an activity schedule is found in the firststage, whereas sinks are located and sensor-to-sink data flow routes aredetermined in the second stage. Computational experiments performed on varioustest instances indicate that the Lagrangean heuristic is both efficient andaccurate and also outperforms the two-stage heuristic.     

A heuristic for scheduling problems,especially for scheduling farm operations

Scheduling problems in agriculture are often solved using techniques such as linear programming (the multi-period formulation) and dynamic programming. But it is difficult to obtain an optimal schedule with these techniques for any but the smallest problems, because the model is unwieldly and much time is needed to solve the problem. Therefore, a new algorithm, a heuristic, has been developed to handle scheduling problems in agriculture. It is based on a search technique (i.e. hill-climbing) supported by a strong heuristic evaluation function. In this paper the heuristic performance is compared with dynamic programming. The heuristic offers near-optimal solutions and is much faster than the dynamic programming model. When tested against dynamic programming the difference in results was about 3%. This heuristic could probably also be applied in an industrial environment (e.g. agribusiness or road construction).     

An addendum to the hierarchical network design problem

The hierarchical network design problem is the problem to find a spanning tree of minimum total weight, when the edges of the path between two given nodes are weighted by an other cost function than the tree edges not on this path. We point out that a dynamic programming oriented heuristic can already be found in literature. Further we report on possible extensions and improvements.     

A heuristic approach for combined equipment-planning and routing in multi-layer SDH/WDM networks

The paper deals with a multi-layer network design problem for a high-speed telecommunication network based on Synchronous Digital Hierarchy (SDH) and Wavelength Division Multiplex (WDM) technology. The network has to carry a certain set of demands with the objective of minimizing the investment in the equipment. The different layers are the fiber-layer, 2.5 Gbit/s-, 10 Gbit/s- and WDM-systems. Several variations of the problem including path-protected demands and specific types of cross-connect equipment are considered. The problem is described as a mixed integer linear programming model and some results for small networks are presented. Two greedy heuristics, a random start heuristic and a GRASP-like approach are implemented to solve large real world problems.     

Telecommunication Link Restoration Planning with Multiple Facility Types

To ensure uninterrupted service, telecommunication networks contain excess (spare) capacity for rerouting (restoring) traffic in the event of a link failure. We study the NP-hard capacity planning problem of economically installing spare capacity on a network to permit link restoration of steady-state traffic. We present a planning model that incorporates multiple facility types, and develop optimization-based heuristic solution methods based on solving a linear programming relaxation and minimum cost network flow subproblems. We establish bounds on the performance of the algorithms, and discuss problem instances that nearly achieve these worst-case bounds. In tests on three real-world problems and numerous randomly-generated problems containing up to 50 nodes and 150 edges, the heuristics provide good solutions (often within 0.5% of optimality) to problems with single facility type, in equivalent or less time than methods from the literature. For multi-facility problems, the gap between our heuristic solution values and the linear programming bounds are larger. However, for small graphs, we show that the optimal linear programming value does not provide a tight bound on the optimal integer value, and our heuristic solutions are closer to optimality than implied by the gaps.     

Management of periodic demands in distribution systems

The problem of the optimal scheduling of periodic demands for a given facility or commodity is presented and some properties of an integer programming model are discussed. Algorithms (both of implicit enumeration type and heuristic) are also given.     

A column generation based heuristic for sensor placement,activity scheduling and data routing in wireless sensor networks

A wireless sensor network is a network consisting of distributed autonomous electronic devices called sensors. In this work, we develop a mixed-integer linear programming model to maximize the network lifetime by optimally determining locations of sensors and sinks, sensor-to-sink data flows, and activity schedules of the deployed sensors subject to coverage, flow conservation, energy consumption and budget constraints. Since solving this model is difficult except for very small instances, we propose a heuristic method which works on a reformulation of the problem. In the first phase of this heuristic, the linear programming relaxation of the reformulation is solved by column generation. The second phase consists of constructing a feasible solution for the original problem using the columns obtained in the first phase. Computational experiments conducted on a set of test instances indicate that both the accuracy and the efficiency of the proposed heuristic is quite promising.     

Minimum power multicasting in wireless networks under probabilistic node failures

In this paper we deal with a probabilistic extension of the minimum power multicast (MPM) problem for wireless networks. The deterministic MPM problem consists in assigning transmission powers to the nodes, so that a multihop connection can be established between a source and a given set of destination nodes and the total power required is minimized. We present an extension to the basic problem, where node failure probabilities for the transmission are explicitly considered. This model reflects the necessity of taking uncertainty into account in the availability of the hosts. The novelty of the probabilistic minimum power multicast (PMPM) problem treated in this paper consists in the minimization of the assigned transmission powers, imposing at the same time a global reliability level to the solution network. An integer linear programming formulation for the PMPM problem is presented. Furthermore, an exact algorithm based on an iterative row and column generation procedure, as well as a heuristic method are proposed. Computational experiments are finally presented.     

The maximin HAZMAT routing problem

The hazardous material routing problem from an origin to a destination in an urban area is addressed. We maximise the distance between the route and its closest vulnerable centre, weighted by the centre’s population. A vulnerable centre is a school, hospital, senior citizens’ residence or the like, concentrating a high population or one that is particularly vulnerable or difficult to evacuate in a short time. The potential consequences on the most exposed centre are thus minimized. Though previously studied in a continuous space, the problem is formulated here over a transport (road) network. We present an exact model for the problem, in which we manage to significantly reduce the required variables, as well as an optimal polynomial time heuristic. The integer programming formulation and the heuristic are tested in a real-world case study set in the transport network in the city of Santiago, Chile.     

Solving a nurse scheduling problem with knapsacks,networks and tabu search

This paper illustrates how a modern heuristic and two classical integer programming models have been combined to provide a solution to a nurse rostering problem at a major UK hospital. Neither a heuristic nor an exact approach based on a standard IP package was able to meet all the practical requirements. This was overcome by using a variant of tabu search as the core method, but applying knapsack and network flow models in pre- and post-processing phases. The result is a successful software tool that frees senior nursing staff from a time consuming administrative task.