A Multi-agent Based Self-adaptive Genetic Algorithm for the Long-term Car Pooling Problem

Rising vehicles number and increased use of private cars have caused significant traffic congestion, noise and energy waste. Public transport cannot always be set up in the non-urban areas. Car pooling, which is based on the idea that sets of car owners having the same travel destination share their vehicles has emerged to be a viable possibility to reduce private car usage around the world. In this paper, we present a multi-agent based self-adaptive genetic algorithm to solve long-term car pooling problem. The system is a combination of multi-agent system and genetic paradigm, and guided by a hyper-heuristic dynamically adapted by a collective learning process. The aim of our research is to solve the long-term car pooling problem efficiently with limited exploration of the search space. The proposed algorithm is tested using large scale instance data sets. The computational results show that the proposed method is competitive with other known approaches for solving long-term car pooling problem.     

A new VRPPD model and a hybrid heuristic solution approach for e-tailing

We analyze a business model for e-supermarkets to enable multi-product sourcing capacity through co-opetition (collaborative competition). The logistics aspect of our approach is to design and execute a network system where “premium” goods are acquired from vendors at multiple locations in the supply network and delivered to customers. Our specific goals are to: (i) investigate the role of premium product offerings in creating critical mass and profit; (ii) develop a model for the multiple-pickup single-delivery vehicle routing problem in the presence of multiple vendors; and (iii) propose a hybrid solution approach. To solve the problem introduced in this paper, we develop a hybrid metaheuristic approach that uses a Genetic Algorithm for vendor selection and allocation, and a modified savings algorithm for the capacitated VRP with multiple pickup, single delivery and time windows (CVRPMPDTW). The proposed Genetic Algorithm guides the search for optimal vendor pickup location decisions, and for each generated solution in the genetic population, a corresponding CVRPMPDTW is solved using the savings algorithm. We validate our solution approach against published VRPTW solutions and also test our algorithm with Solomon instances modified for CVRPMPDTW.     

A bilinear approach to the pooling problem†

In this paper we present an algorithm for the pooling problem in refinery optimization based on a bilinear programming approach. The pooling problem occurs frequently in process optimization problems, especially refinery planning models. The main difficulty is that pooling causes an inherent nonlinearity in the otherwise linear models. We shall define the problem by formulating an aggregate mathematical model of a refinery, comment on solution methods for pooling problems that have been presented in the literature, and develop a new method based on convex approximations of the bilinear terms. The method is illustrated on numerical examples     

Designing WDM Optical Networks Using Branch-and-Price

In this paper, we present an exact solution procedure for the design of two-layer wavelength division multiplexing (WDM) optical networks with wavelength changers and bifurcated flows. This design problem closely resembles the traditional multicommodity flow problem, except that in the case of WDM optical networks, we are concerned with the routing of multiple commodities in two network layers. Consequently, the corresponding optimization models have to deal with two types of multicommodity variables defined for each of the network layers. The proposed procedure represents one of the first branch-and-price algorithms for a general WDM optical network setting with no assumptions on the number of logical links that can be established between nodes in the network. We apply our procedure in a computational study with four different network configurations. Our results show that for the three tested network configurations our branch-and-price algorithm provides solutions that are on average less than 5 % from optimality. We also provide a comparison of our branch-and-price algorithm with two simple variants of the upper bounding heuristic procedure HLDA that is commonly used for WDM optical network design.     

营救设备数量受限的应急疏散模型和算法

考虑在实际中可能面临着某些救援活动,必须借助于营救设备或者依赖营救人员的引导才能得以完成．针对这种情况,给出了设备数量受限的应急疏散模型．由于目标函数是疏散时间最小化,在考虑路径容量限制时,首先通过优先饱和最短路径来确定可行路径集合,把可行路径集合中的k短路作为初始解,再以每条路径上流量与旅行时间的比值流速作为更新路径的准则,每步迭代通过保留流速较大的路径来保存当前疏散时间最小的路径集合,从而确定疏散方案．最后通过算例验证了该算法的有效性和可行性．     

Dynamic Network Flow with Uncertain Arc Capacities: Decomposition Algorithm and Computational Results

In a multiperiod dynamic network flow problem, we model uncertain arc capacities using scenario aggregation. This model is so large that it may be difficult to obtain optimal integer or even continuous solutions. We develop a Lagrangian decomposition method based on the structure recently introduced in G.D. Glockner and G.L. Nemhauser, Operations Research, vol. 48, pp. 233–242, 2000. Our algorithm produces a near-optimal primal integral solution and an optimum solution to the Lagrangian dual. The dual is initialized using marginal values from a primal heuristic. Then, primal and dual solutions are improved in alternation. The algorithm greatly reduces computation time and memory use for real-world instances derived from an air traffic control model.     

An MPEC formulation and its cutting constraint algorithm for continuous network design problem with multi-user classes

Continuous network design problem (CNDP) is to determine the set of link capacity expansions and the corresponding equilibrium flows for which the measures of performance index for the network is optimal. Conventionally, CNDP assumed users to be homogeneous, that is, all travelers on the same link of the network are identical insofar as congestion effect and they have the same value of time (VOT). In fact, it does not accord with the real situation that all have the same VOT. So, multiple user classes with different VOT should be considered. This paper examines the CNDP with different VOT for multiple user classes, which is generally expressed as a mathematical programming with equilibrium constraint (MPEC). Then, the cut constraint algorithm (CCA) is presented to solve the problem. The numerical experiments on the examples from the literature are illustrated to demonstrate that our model and algorithm are feasible.     

Pricing substitutable flights in airline revenue management

We develop a Markov decision process formulation of a dynamic pricing problem for multiple substitutable flights between the same origin and destination, taking into account customer choice among the flights. The model is rendered computationally intractable for exact solution by its multi-dimensional state and action spaces, so we develop and analyze various bounds and heuristics. We first describe three related models, each based on some form of pooling, and introduce heuristics suggested by these models. We also develop separable bounds for the value function which are used to construct value- and policy-approximation heuristics. Extensive numerical experiments show the value- and policy-approximation approaches to work well across a wide range of problem parameters, and to outperform the pooling-based heuristics in most cases. The methods are applicable even for large problems, and are potentially useful for practical applications.     

A parametric algorithm for convex cost network flow and related problems

The convex cost network flow problem is to determine the minimum cost flow in a network when cost of flow over each arc is given by a piecewise linear convex function. In this paper, we develop a parametric algorithm for the convex cost network flow problem. We define the concept of optimum basis structure for the convex cost network flow problem. The optimum basis structure is then used to parametrize v, the flow to be transsshipped from source to sink. The resulting algorithm successively augments the flow on the shortest paths from source to sink which are implicitly enumerated by the algorithm. The algorithm is shown to be polynomially bounded. Computational results are presented to demonstrate the efficiency of the algorithm in solving large size problems. We also show how this algorithm can be used to (i) obtain the project cost curve of a CPM network with convex time-cost tradeoff functions; (ii) determine maximum flow in a network with concave gain functions; (iii) determine optimum capacity expansion of a network having convex arc capacity expansion costs.     

Cost optimal periodic train scheduling

For real world railroad networks, we consider minimizing operational cost of train schedules which depend on choosing different train types of diverse speed and cost. We develop a mixed integer linear programming model for this train scheduling problem. For practical problem sizes, it seems to be impossible to directly solve the model within a reasonable amount of time. However, suitable decomposition leads to much better performance. In the first part of the decomposition, only the train type related constraints stay active. In the second part, using an optimal solution of this relaxation, we select and fix train types and try to generate a train schedule satisfying the remaining constraints. This decomposition idea provides the cornerstone for an algorithm integrating cutting planes and branch-and-bound. We present computational results for railroad networks from Germany and the Netherlands.     

Multi-service multi-facility network design under uncertainty

The problem of designing high speed networks using different modules of link capacities, in the same model, in order to meet uncertain demands obtained from different probability distribution functions (PDF) is a very hard and challenging real network design problem. The novelty of the new model, compared to previous ones, is to allow installing more than one module per link having equal or different capacities. Moreover, the scenarios of traffic can be generated, according to practical observations, from the main classes of uncertain demands (multi-service) simulated from different PDFs, including heavy tailed ones. These classes of traffic are considered simultaneously for the scenario generation, different from related works in the literature that use only one probability distribution function to simulate the scenarios of traffic. In this work we present the problem formulation and report computational results using branch-and-bound and L-shaped decomposition solution approaches. We consider in the same model up to three different types of modular capacities (multi-facility), since it seems that using more than this can lead to an intractable model. The objective is to minimize penalty (in case of unmet demands) and investment costs. We obtain confidence intervals (with 95% of covering rate) on the expected optimal solution value for the resulting two-stage stochastic integer-modular problem and discuss when they are meaningful. Numerical experiments show that our model can handle up to medium real size instances.     

Allocation of jobs and identical resources with two pooling centers

We examine the resource allocation problem of partitioning identical servers into two parallel pooling centers, and simultaneously assigning job types to pooling centers. Each job type has a distinct Poisson arrival rate and a distinct holding cost per unit time. Each pooling center becomes a queueing system with an exponential service time distribution. The goal is to minimize the total holding cost. The problem is shown to be polynomial if a job type can be divided between the pooling centers, and NP-hard if dividing job types is not possible. When there are two servers and jobs cannot be divided, we demonstrate that the two pooling center configuration is rarely optimal. A heuristic which checks the single pooling center has an upper bound on the relative error of 4/3. The heuristic is extended for the multiple server problem, where relative error is bounded above by the number of servers.      

Target market selection and marketing effort under uncertainty: The selective newsvendor

We consider a firm that markets, procures, and delivers a good with a single selling season in a number of different markets. The price for the good is market-dependent, and each market has an associated demand distribution, with parameters that depend on the amount of marketing effort applied. Given long procurement lead-times, the firm must decide which markets it will serve prior to procuring the good. We develop a profit maximizing model to address the firm’s integrated market selection, marketing effort, and procurement decisions. The model implicitly accounts for inventory pooling across markets, which reduces safety stock costs but increases model complexity. The resulting model is a nonlinear integer optimization problem, for which we develop specialized solution methods. For the case in which budget constraints exist, we provide a novel solution approach that uses a tailored branch-and-bound algorithm. Our approach solves a broad range of 3000 test instances in an average of less than 2 seconds, significantly outperforming a leading commercial global optimization solver.     

Comparison of Algorithms for the Degree Constrained Minimum Spanning Tree

The Degree Constrained Minimum Spanning Tree (DCMST) on a graph is the problem of generating a minimum spanning tree with constraints on the number of arcs that can be incident to vertices of the graph. In this paper we develop three heuristics for the DCMST, including simulated annealing, a genetic algorithm and a method based on problem space search. We propose alternative tree representations to facilitate the neighbourhood searches for the genetic algorithm. The tree representation that we use for the genetic algorithm can be generalised to other tree optimisation problems as well. We compare the computational performance of all of these approaches against the performance of an exact solution approach in the literature. In addition, we also develop a new exact solution approach based on the combinatorial structure of the problem. We test all of these approaches using standard problems taken from the literature and some new test problems that we generate.     

Periodic airline fleet assignment with time windows,spacing constraints,and time dependent revenues

Given the sets of flights and aircraft of an airline carrier, the fleet assignment problem consists of assigning the most profitable aircraft type to each flight. In this paper we propose a model for the periodic fleet assignment problem with time windows in which departure times are also determined. Anticipated profits depend on the schedule and the selection of aircraft types. In addition, short spacings between consecutive flights which serve the same origin–destination pair of airports are penalized. We propose a non-linear integer multi-commodity network flow formulation. We develop new branch-and-bound strategies which are embedded in our branch-and-price solution strategy. Finally, we present computational results for periodic daily schedules on three real-world data sets.     

A novel algorithm for area traffic capacity control with elastic travel demands

A non-linear area traffic control system with limited capacity is considered in this paper. Optimal signal settings and link capacity expansions can be determined while trip distribution and network flow are in equilibrium. This problem can be formulated as a non-linear mathematical program with equilibrium constraints. For the objective function a non-linear constrained optimization program for signal settings and link capacity expansion is determined. For the constraint set the elastic user equilibrium traffic assignment obeying Wardrop’s first principle can be formulated as a variational inequality. Since the constrained optimization problem is non-convex, only local optima can be obtained. In this paper, a novel algorithm using a non-smooth trust region approach is proposed. Numerical tests are performed using a real data city network and various example test networks in which the effectiveness and robustness of the proposed method are confirmed as compared to other well-known solution methods.     

A mathematical programming viewpoint for solving the ultimate pit problem

In 1965 Helmut Lerchs and Ingo Grossmann presented to the mining community an algorithm to find the optimum design for an open pit mine. In their words, “the objective is to design the contour of a pit so as to maximize the difference between total mine value of the ore extracted and the total extraction cost of ore and waste”. They modeled the problem in graph theoretic terms and showed that an optimal solution of the ultimate pit problem is equivalent to finding the maximum closure of their graph based model. In this paper, we develop a network flow algorithm based on the dual to solve the same problem. We show how this algorithm is closely related to Lerchs and Grossmann's and how the steps in their algorithm can be viewed in mathematical programming terms. This analysis adds insight to the algorithm of Lerchs and Grossmann and shows where it can be made more efficient. As in the case Lerchs and Grossmann, our algorithm allows us to use very efficient data structures based on graphs that represent the data and constraints.. 1782 1528 V 3     

A hybrid metaheuristic algorithm for dynamic rail car fleet sizing problem

The aim of this paper is to present a model and a solution method for rail freight car fleet sizing problem. The mathematical model is dynamic and multi-periodic and car demands and travel times are assumed deterministic, and the proposed solution method is hybridization of genetic algorithms and simulated annealing algorithms. Experimental analysis is conducted using several test problems. The results of the proposed algorithm and CPLEX software are compared. The results show high efficiency and effectiveness of the proposed algorithm. The solution method is applied to solve fleet sizing problem in the Iran Railways as a case study.     

Supply chain scheduling in a collaborative manufacturing mode: model construction and algorithm design

In collaborative manufacturing, the supply chain scheduling problem becomes more complex according to both multiple product demands and multiple production modes. Aiming to obtain a reasonable solution to this complexity, we analyze the characteristics of collaborative manufacturing and design some elements, including production parameters, order parameters, and network parameters. We propose four general types of collaborative manufacturing networks and then construct a supply chain scheduling model composed of the processing costs, inventory costs, and two penalty costs of the early completion costs and tardiness costs. In our model, by considering the urgency of different orders, we design a delivery time window based on the least production time and slack time. Additionally, due to the merit of continuously processing orders belonging to the same product type, we design a production cost function by using a piecewise function. To solve our model efficiently, we present a hybrid ant colony optimization (HACO) algorithm. More specifically, the Monte Carlo algorithm is incorporated into our HACO algorithm to improve the solution quality. We also design a moving window award mechanism and dynamic pheromone update strategy to improve the search efficiency and solution performance. Computational tests are conducted to evaluate the performance of the proposed method.

     

Network flow interdiction on planar graphs

The network flow interdiction problem asks to reduce the value of a maximum flow in a given network as much as possible by removing arcs and vertices of the network constrained to a fixed budget. Although the network flow interdiction problem is strongly NP-complete on general networks, pseudo-polynomial algorithms were found for planar networks with a single source and a single sink and without the possibility to remove vertices. In this work, we introduce pseudo-polynomial algorithms that overcome various restrictions of previous methods. In particular, we propose a planarity-preserving transformation that enables incorporation of vertex removals and vertex capacities in pseudo-polynomial interdiction algorithms for planar graphs. Additionally, a new approach is introduced that allows us to determine in pseudo-polynomial time the minimum interdiction budget needed to remove arcs and vertices of a given network such that the demands of the sink node cannot be completely satisfied anymore. The algorithm works on planar networks with multiple sources and sinks satisfying that the sum of the supplies at the sources equals the sum of the demands at the sinks. A simple extension of the proposed method allows us to broaden its applicability to solve network flow interdiction problems on planar networks with a single source and sink having no restrictions on the demand and supply. The proposed method can therefore solve a wider class of flow interdiction problems in pseudo-polynomial time than previous pseudo-polynomial algorithms and is the first pseudo-polynomial algorithm that can solve non-trivial planar flow interdiction problems with multiple sources and sinks. Furthermore, we show that the k-densest subgraph problem on planar graphs can be reduced to a network flow interdiction problem on a planar graph with multiple sources and sinks and polynomially bounded input numbers.