Michał Pióro, Mariusz Mycek, Artur Tomaszewski, Amaro de Sousa
In software defined networks (SDN) packet data switches are configured by a limited number of SDN controllers, which respond to queries for packet forwarding decisions from the switches. To enable optimal control of switches in real time the placement of controllers at network nodes must guarantee that the controller-to-controller and switch-to-controller communications delays are bounded. Apart from the primary controllers that control the switches in the nominal state, separate backup controllers can be introduced that take over when the primary controllers are unavailable, and whose delay bounds are relaxed. In this paper, we present optimization models to jointly optimize the placement of primary and backup controllers in long-distance SDN networks, aimed at maximizing the network's resilience to node-targeted attacks. Applying the models to two well-known network topologies and running a broad numerical study we show that, when compared with the standard approach of using only primary controllers, the use of backup controllers provides significant resilience gains, in particular in case of tight delay bounds.
{"title":"Maximizing SDN resilience to node-targeted attacks through joint optimization of the primary and backup controllers placements","authors":"Michał Pióro, Mariusz Mycek, Artur Tomaszewski, Amaro de Sousa","doi":"10.1002/net.22201","DOIUrl":"https://doi.org/10.1002/net.22201","url":null,"abstract":"In software defined networks (SDN) packet data switches are configured by a limited number of SDN controllers, which respond to queries for packet forwarding decisions from the switches. To enable optimal control of switches in real time the placement of controllers at network nodes must guarantee that the controller-to-controller and switch-to-controller communications delays are bounded. Apart from the primary controllers that control the switches in the nominal state, separate backup controllers can be introduced that take over when the primary controllers are unavailable, and whose delay bounds are relaxed. In this paper, we present optimization models to jointly optimize the placement of primary and backup controllers in long-distance SDN networks, aimed at maximizing the network's resilience to node-targeted attacks. Applying the models to two well-known network topologies and running a broad numerical study we show that, when compared with the standard approach of using only primary controllers, the use of backup controllers provides significant resilience gains, in particular in case of tight delay bounds.","PeriodicalId":54734,"journal":{"name":"Networks","volume":"194 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We consider Hybrid fiber-coaxial (HFC) networks in which data is transmitted from a root node to a set of customers using a series of splitters and coaxial cable lines that make up a tree. The physical locations of the components in a HFC network are always known but frequently the cabling is not. This makes cable faults difficult to locate and resolve. In this study we consider time series data received by customer modems to reconstruct the topology of HFC networks. We assume that the data can be translated into a series of events, and that two customers sharing many connections in the network will observe many similar events. This approach allows us to use maximum parsimony to minimize the total number of character-state changes in a tree based on observations in the leaf nodes. Furthermore, we assume that nodes located physically close to each other have a larger probability of being closely connected. Hence, our objective is a weighted sum of data distance and physical distance. A variable-neighborhood search heuristic is presented for minimizing the combined distance. Furthermore, three greedy heuristics are proposed for finding an initial solution. Computational results are reported for both real-life and synthetic network topologies using simulated customer data with various degrees of random background noise. We are able to reconstruct large topologies with a very high precision.
{"title":"Topology reconstruction using time series data in telecommunication networks","authors":"David Pisinger, Siv Sørensen","doi":"10.1002/net.22196","DOIUrl":"https://doi.org/10.1002/net.22196","url":null,"abstract":"We consider Hybrid fiber-coaxial (HFC) networks in which data is transmitted from a root node to a set of customers using a series of splitters and coaxial cable lines that make up a tree. The physical locations of the components in a HFC network are always known but frequently the cabling is not. This makes cable faults difficult to locate and resolve. In this study we consider time series data received by customer modems to reconstruct the topology of HFC networks. We assume that the data can be translated into a series of events, and that two customers sharing many connections in the network will observe many similar events. This approach allows us to use maximum parsimony to minimize the total number of character-state changes in a tree based on observations in the leaf nodes. Furthermore, we assume that nodes located physically close to each other have a larger probability of being closely connected. Hence, our objective is a weighted sum of data distance and physical distance. A variable-neighborhood search heuristic is presented for minimizing the combined distance. Furthermore, three greedy heuristics are proposed for finding an initial solution. Computational results are reported for both real-life and synthetic network topologies using simulated customer data with various degrees of random background noise. We are able to reconstruct large topologies with a very high precision.","PeriodicalId":54734,"journal":{"name":"Networks","volume":"14 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138534109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pedro Henrique Fernandes da Silva, Hervé Kerivin, Juan Pablo Nant, Annegret K. Wagler
The routing and spectrum assignment problem in modern optical networks is an NP-hard problem that has received increasing attention during the last years. The majority of existing integer linear programming models for the problem uses edge-path formulations where variables are associated with all possible routing paths so that the number of variables grows exponentially with the size of the instance. To bypass this difficulty, precomputed subsets of all possible paths per demand are typically used, which cannot guarantee optimality of the solutions in general. Our contribution is to provide a framework for the use of edge-path formulations to minimize the spectrum width of a solution. For that, we select an appropriate subset of paths to operate on with the help of combinatorial properties in such a way that optimality of the solution can be guaranteed. Computational results indicate that our approach is indeed promising to solve the routing and spectrum assignment problem.
{"title":"Solving the routing and spectrum assignment problem, driven by combinatorial properties","authors":"Pedro Henrique Fernandes da Silva, Hervé Kerivin, Juan Pablo Nant, Annegret K. Wagler","doi":"10.1002/net.22195","DOIUrl":"https://doi.org/10.1002/net.22195","url":null,"abstract":"The routing and spectrum assignment problem in modern optical networks is an NP-hard problem that has received increasing attention during the last years. The majority of existing integer linear programming models for the problem uses edge-path formulations where variables are associated with all possible routing paths so that the number of variables grows exponentially with the size of the instance. To bypass this difficulty, precomputed subsets of all possible paths per demand are typically used, which cannot guarantee optimality of the solutions in general. Our contribution is to provide a framework for the use of edge-path formulations to minimize the spectrum width of a solution. For that, we select an appropriate subset of paths to operate on with the help of combinatorial properties in such a way that optimality of the solution can be guaranteed. Computational results indicate that our approach is indeed promising to solve the routing and spectrum assignment problem.","PeriodicalId":54734,"journal":{"name":"Networks","volume":"31 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138534106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Let <math altimg="urn:x-wiley:net:media:net22198:net22198-math-0001" display="inline" location="graphic/net22198-math-0001.png" overflow="scroll"> <semantics> <mrow> <mi>G</mi> <mo>=</mo> <mo stretchy="false">(</mo> <mi>V</mi> <mo>,</mo> <mi>E</mi> <mo stretchy="false">)</mo> </mrow> $$ G=left(V,Eright) $$</annotation> </semantics></math> be a graph with unit-length edges and nonnegative costs assigned to its vertices. Given a list of pairwise different vertices <math altimg="urn:x-wiley:net:media:net22198:net22198-math-0002" display="inline" location="graphic/net22198-math-0002.png" overflow="scroll"> <semantics> <mrow> <mi>S</mi> <mo>=</mo> <mo stretchy="false">(</mo> <msub> <mrow> <mi>s</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mrow> <mi>s</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>,</mo> <mi>…</mi> <mo>,</mo> <msub> <mrow> <mi>s</mi> </mrow> <mrow> <mi>p</mi> </mrow> </msub> <mo stretchy="false">)</mo> </mrow> $$ S=left({s}_1,{s}_2,dots, {s}_pright) $$</annotation> </semantics></math>, the <i>prioritized Voronoi diagram</i> of <math altimg="urn:x-wiley:net:media:net22198:net22198-math-0003" display="inline" location="graphic/net22198-math-0003.png" overflow="scroll"> <semantics> <mrow> <mi>G</mi> </mrow> $$ G $$</annotation> </semantics></math> with respect to <math altimg="urn:x-wiley:net:media:net22198:net22198-math-0004" display="inline" location="graphic/net22198-math-0004.png" overflow="scroll"> <semantics> <mrow> <mi>S</mi> </mrow> $$ S $$</annotation> </semantics></math> is the partition of <math altimg="urn:x-wiley:net:media:net22198:net22198-math-0005" display="inline" location="graphic/net22198-math-0005.png" overflow="scroll"> <semantics> <mrow> <mi>G</mi> </mrow> $$ G $$</annotation> </semantics></math> in <math altimg="urn:x-wiley:net:media:net22198:net22198-math-0006" display="inline" location="graphic/net22198-math-0006.png" overflow="scroll"> <semantics> <mrow> <mi>p</mi> </mrow> $$ p $$</annotation> </semantics></math> subsets <math altimg="urn:x-wiley:net:media:net22198:net22198-math-0007" display="inline" location="graphic/net22198-math-0007.png" overflow="scroll"> <semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>,</mo> <mi>…</mi> <mo>,</mo> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>p</mi> </mrow> </msub> </mrow> $$ {V}_1,{V}_2,dots, {V}_p $$</annotation> </semantics></math> so that, for every <math altimg="urn:x-wiley:net:media:net22198:net22198-math-0008" display="inline" location="graphic/net22198-math-0008.png" overflow="scroll"> <semantics> <mrow> <mi>i</mi> </mrow> $$ i $$</annotation> </semantics></math> with <math altimg="urn:x-wiley:net:media:net22198:net22198-math-0009" display="inline" location="graphic/net22198-math-0009.png" overflow="scroll"> <semantics> <mrow> <mn>1</mn> <mo>≤</mo> <mi>i</mi> <mo>≤</mo> <mi>p</mi> </mrow> $$ 1le ile p $$</annotation> </semant
设G=(V,E) $$ G=left(V,Eright) $$是一个边长度为单位且顶点代价为非负的图。给定一个成对不同顶点S=(s1,s2，…，sp) $$ S=left({s}_1,{s}_2,dots, {s}_pright) $$的列表，G $$ G $$相对于S $$ S $$的优先Voronoi图是G $$ G $$在p $$ p $$子集V1,V2，…，Vp $$ {V}_1,{V}_2,dots, {V}_p $$中的划分，使得对于每一个i $$ i $$, 1≤i≤p $$ 1le ile p $$，一个顶点v $$ v $$在Vi $$ {V}_i $$当且仅当si $$ {s}_i $$是S $$ S $$中最接近v $$ v $$的顶点，并且在S $$ S $$中在子集s1,s2，…，si−1$$ left{{s}_1,{s}_2,dots, {s}_{i-1}right} $$中没有最接近v {}$$ v $$的顶点。对于每一个i $$ i $$, 1≤i≤p $$ 1le ile p $$，顶点si $$ {s}_i $$的载荷等于Vi $$ {V}_i $$中所有顶点的代价之和。S $$ S $$的载荷等于S $$ S $$中一个顶点的最大载荷。我们研究了在S $$ S $$的末端再增加一个顶点v $$ v $$以最小化负载的问题。这一问题发生在考虑现有设施的同时，对新服务设施(如学校或医院)进行最佳定位，并以尽量减少站点的最大拥堵为目标的情况下。有一种蛮力算法可以在n个$$ n $$ -顶点m $$ m $$ -边图上的 (nm)时间内解决这个问题。对于m=n1+o(1) $$ m={n}^{1+o(1)} $$和p=1 $$ p=1 $$的特殊情况，假设Abboud等人的所谓命中集猜想，我们证明了一个匹配时间下界到次多项式因子。在积极的方面，我们提出了简单的线性时间算法，用于团，路径和循环问题，以及树，适当间隔图和(假设p $$ p $$是常数)有界树宽图的几乎线性时间算法。
{"title":"Balancing graph Voronoi diagrams with one more vertex","authors":"Guillaume Ducoffe","doi":"10.1002/net.22198","DOIUrl":"https://doi.org/10.1002/net.22198","url":null,"abstract":"Let <math altimg=\"urn:x-wiley:net:media:net22198:net22198-math-0001\" display=\"inline\" location=\"graphic/net22198-math-0001.png\" overflow=\"scroll\">\u0000<semantics>\u0000<mrow>\u0000<mi>G</mi>\u0000<mo>=</mo>\u0000<mo stretchy=\"false\">(</mo>\u0000<mi>V</mi>\u0000<mo>,</mo>\u0000<mi>E</mi>\u0000<mo stretchy=\"false\">)</mo>\u0000</mrow>\u0000$$ G=left(V,Eright) $$</annotation>\u0000</semantics></math> be a graph with unit-length edges and nonnegative costs assigned to its vertices. Given a list of pairwise different vertices <math altimg=\"urn:x-wiley:net:media:net22198:net22198-math-0002\" display=\"inline\" location=\"graphic/net22198-math-0002.png\" overflow=\"scroll\">\u0000<semantics>\u0000<mrow>\u0000<mi>S</mi>\u0000<mo>=</mo>\u0000<mo stretchy=\"false\">(</mo>\u0000<msub>\u0000<mrow>\u0000<mi>s</mi>\u0000</mrow>\u0000<mrow>\u0000<mn>1</mn>\u0000</mrow>\u0000</msub>\u0000<mo>,</mo>\u0000<msub>\u0000<mrow>\u0000<mi>s</mi>\u0000</mrow>\u0000<mrow>\u0000<mn>2</mn>\u0000</mrow>\u0000</msub>\u0000<mo>,</mo>\u0000<mi>…</mi>\u0000<mo>,</mo>\u0000<msub>\u0000<mrow>\u0000<mi>s</mi>\u0000</mrow>\u0000<mrow>\u0000<mi>p</mi>\u0000</mrow>\u0000</msub>\u0000<mo stretchy=\"false\">)</mo>\u0000</mrow>\u0000$$ S=left({s}_1,{s}_2,dots, {s}_pright) $$</annotation>\u0000</semantics></math>, the <i>prioritized Voronoi diagram</i> of <math altimg=\"urn:x-wiley:net:media:net22198:net22198-math-0003\" display=\"inline\" location=\"graphic/net22198-math-0003.png\" overflow=\"scroll\">\u0000<semantics>\u0000<mrow>\u0000<mi>G</mi>\u0000</mrow>\u0000$$ G $$</annotation>\u0000</semantics></math> with respect to <math altimg=\"urn:x-wiley:net:media:net22198:net22198-math-0004\" display=\"inline\" location=\"graphic/net22198-math-0004.png\" overflow=\"scroll\">\u0000<semantics>\u0000<mrow>\u0000<mi>S</mi>\u0000</mrow>\u0000$$ S $$</annotation>\u0000</semantics></math> is the partition of <math altimg=\"urn:x-wiley:net:media:net22198:net22198-math-0005\" display=\"inline\" location=\"graphic/net22198-math-0005.png\" overflow=\"scroll\">\u0000<semantics>\u0000<mrow>\u0000<mi>G</mi>\u0000</mrow>\u0000$$ G $$</annotation>\u0000</semantics></math> in <math altimg=\"urn:x-wiley:net:media:net22198:net22198-math-0006\" display=\"inline\" location=\"graphic/net22198-math-0006.png\" overflow=\"scroll\">\u0000<semantics>\u0000<mrow>\u0000<mi>p</mi>\u0000</mrow>\u0000$$ p $$</annotation>\u0000</semantics></math> subsets <math altimg=\"urn:x-wiley:net:media:net22198:net22198-math-0007\" display=\"inline\" location=\"graphic/net22198-math-0007.png\" overflow=\"scroll\">\u0000<semantics>\u0000<mrow>\u0000<msub>\u0000<mrow>\u0000<mi>V</mi>\u0000</mrow>\u0000<mrow>\u0000<mn>1</mn>\u0000</mrow>\u0000</msub>\u0000<mo>,</mo>\u0000<msub>\u0000<mrow>\u0000<mi>V</mi>\u0000</mrow>\u0000<mrow>\u0000<mn>2</mn>\u0000</mrow>\u0000</msub>\u0000<mo>,</mo>\u0000<mi>…</mi>\u0000<mo>,</mo>\u0000<msub>\u0000<mrow>\u0000<mi>V</mi>\u0000</mrow>\u0000<mrow>\u0000<mi>p</mi>\u0000</mrow>\u0000</msub>\u0000</mrow>\u0000$$ {V}_1,{V}_2,dots, {V}_p $$</annotation>\u0000</semantics></math> so that, for every <math altimg=\"urn:x-wiley:net:media:net22198:net22198-math-0008\" display=\"inline\" location=\"graphic/net22198-math-0008.png\" overflow=\"scroll\">\u0000<semantics>\u0000<mrow>\u0000<mi>i</mi>\u0000</mrow>\u0000$$ i $$</annotation>\u0000</semantics></math> with <math altimg=\"urn:x-wiley:net:media:net22198:net22198-math-0009\" display=\"inline\" location=\"graphic/net22198-math-0009.png\" overflow=\"scroll\">\u0000<semantics>\u0000<mrow>\u0000<mn>1</mn>\u0000<mo>≤</mo>\u0000<mi>i</mi>\u0000<mo>≤</mo>\u0000<mi>p</mi>\u0000</mrow>\u0000$$ 1le ile p $$</annotation>\u0000</semant","PeriodicalId":54734,"journal":{"name":"Networks","volume":"9 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138534108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Hamiltonian p-median problem consists of finding p( is given) non-intersecting Hamiltonian cycles in a complete edge-weighted graph such that each cycle visits at least three vertices and each vertex belongs to exactly one cycle, while minimizing the total cost of pcycles. In this work, we present an effective and scalable hybrid genetic algorithm to solve this computationally challenging problem. The algorithm combines an edge-assembly crossover to generate promising offspring solutions from high-quality parents, and a multiple neighborhood local search to improve each offspring solution. To promote population diversity, the algorithm applies a mutation operator to the offspring solutions and a quality-and-distance update strategy to manage the population. We compare the method to the best reference algorithms in the literature based on three sets of 145 popular benchmark instances (with up to 318 vertices), and report improved best upper bounds for eight instances. To evaluate the scalability of the method, we perform experiments on a new set of 70 large instances (with up to 1060 vertices). We examine the contributions of key components of the algorithm.
哈密顿p中值问题包括在一个完整的边加权图中找到p(p $$ p $$给定)个不相交的哈密顿环，使得每个环至少访问三个顶点，并且每个顶点恰好属于一个环，同时最小化环的总代价。在这项工作中，我们提出了一个有效的、可扩展的混合遗传算法来解决这个计算上具有挑战性的问题。该算法结合了边组装交叉算法，从高质量的亲本中生成有希望的子代解，并结合了多邻域局部搜索来改进每个子代解。为了提高种群多样性，该算法在子代解中引入变异算子，并采用质量-距离更新策略对种群进行管理。我们将该方法与文献中基于三组145个流行基准实例(最多318个顶点)的最佳参考算法进行比较，并报告了八个实例的最佳上界改进。为了评估该方法的可伸缩性，我们在一组新的70个大型实例(最多有1060个顶点)上执行实验。我们研究了算法的关键组成部分的贡献。
{"title":"A hybrid genetic algorithm for the Hamiltonian p-median problem","authors":"Pengfei He, Jin-Kao Hao, Qinghua Wu","doi":"10.1002/net.22197","DOIUrl":"https://doi.org/10.1002/net.22197","url":null,"abstract":"The Hamiltonian <i>p</i>-median problem consists of finding <i>p</i>(<math altimg=\"urn:x-wiley:net:media:net22197:net22197-math-0002\" display=\"inline\" location=\"graphic/net22197-math-0002.png\" overflow=\"scroll\">\u0000<semantics>\u0000<mrow>\u0000<mi>p</mi>\u0000</mrow>\u0000$$ p $$</annotation>\u0000</semantics></math> is given) non-intersecting Hamiltonian cycles in a complete edge-weighted graph such that each cycle visits at least three vertices and each vertex belongs to exactly one cycle, while minimizing the total cost of <i>p</i>cycles. In this work, we present an effective and scalable hybrid genetic algorithm to solve this computationally challenging problem. The algorithm combines an edge-assembly crossover to generate promising offspring solutions from high-quality parents, and a multiple neighborhood local search to improve each offspring solution. To promote population diversity, the algorithm applies a mutation operator to the offspring solutions and a quality-and-distance update strategy to manage the population. We compare the method to the best reference algorithms in the literature based on three sets of 145 popular benchmark instances (with up to 318 vertices), and report improved best upper bounds for eight instances. To evaluate the scalability of the method, we perform experiments on a new set of 70 large instances (with up to 1060 vertices). We examine the contributions of key components of the algorithm.","PeriodicalId":54734,"journal":{"name":"Networks","volume":"88 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138534125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julien Khamphousone, Fabian Castaño, André Rossi, Sonia Toubaline
Abstract The Ring Star Problem consists in selecting a subset of nodes called hubs including the depot and linking them with a cycle, the remaining nodes being connected to exactly one hub, at minimum cost. We study a survivable variant of the Ring Star Problem where at most one node in a given subset of so‐called uncertain nodes can fail if selected as a hub. We model this problem as an Integer Linear Program (ILP), that is also addressed with a Branch‐and‐Benders‐cut decomposition. The Benders subproblem is turned into a linear program with the addition of new inequalities that are shown to be facet‐defining, and several enhancements to both the ILP and Branch‐and‐Benders‐cut algorithm are also presented. Both approaches are compared on the basis of extensive numerical experiments that bring the following conclusions. First, the survivable variant is shown to be much harder than the original Ring Star Problem, and the extra cost induced by survivability is significant. Second, the ILP formulation tends to produce tighter lower bounds but memory issues are frequent for large instances. Finally, the Branch‐and‐Benders‐cut algorithm returns feasible solutions that are often of better quality than those produced by ILP, and is less frequently subjected to memory issues on the considered set of instances.
环形星问题包括选择一个称为集线器的节点子集，包括仓库，并将它们与一个循环连接，其余节点以最小的成本恰好连接到一个集线器。我们研究了环星问题的一个可生存变体，其中在给定的所谓不确定节点子集中最多有一个节点在被选为集线器时可能失效。我们将此问题建模为整数线性规划(ILP)，也可以用分支和弯刀分解来解决。通过添加新的面定义不等式，将Benders子问题转化为一个线性规划，并对ILP和Branch - and - Benders - cut算法进行了一些改进。在大量数值实验的基础上，对两种方法进行了比较，得出以下结论。首先，可生存的变体比原来的环形星问题要困难得多，并且由可生存性引起的额外成本是显著的。其次，ILP公式倾向于产生更严格的下界，但对于大型实例来说，内存问题很常见。最后，Branch - and - Benders - cut算法返回的可行解通常比ILP生成的解质量更好，并且在考虑的实例集上较少受到内存问题的影响。
{"title":"A survivable variant of the ring star problem","authors":"Julien Khamphousone, Fabian Castaño, André Rossi, Sonia Toubaline","doi":"10.1002/net.22193","DOIUrl":"https://doi.org/10.1002/net.22193","url":null,"abstract":"Abstract The Ring Star Problem consists in selecting a subset of nodes called hubs including the depot and linking them with a cycle, the remaining nodes being connected to exactly one hub, at minimum cost. We study a survivable variant of the Ring Star Problem where at most one node in a given subset of so‐called uncertain nodes can fail if selected as a hub. We model this problem as an Integer Linear Program (ILP), that is also addressed with a Branch‐and‐Benders‐cut decomposition. The Benders subproblem is turned into a linear program with the addition of new inequalities that are shown to be facet‐defining, and several enhancements to both the ILP and Branch‐and‐Benders‐cut algorithm are also presented. Both approaches are compared on the basis of extensive numerical experiments that bring the following conclusions. First, the survivable variant is shown to be much harder than the original Ring Star Problem, and the extra cost induced by survivability is significant. Second, the ILP formulation tends to produce tighter lower bounds but memory issues are frequent for large instances. Finally, the Branch‐and‐Benders‐cut algorithm returns feasible solutions that are often of better quality than those produced by ILP, and is less frequently subjected to memory issues on the considered set of instances.","PeriodicalId":54734,"journal":{"name":"Networks","volume":" 943","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135186737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yun He, Mike Hewitt, Fabien Lehuédé, Juliette Medina, Olivier Péton
Abstract This paper considers the integrated planning of goods transportation through a multi‐echelon supply chain consisting of a nationwide network and regional distribution system. The previously studied Service Network Design and Routing Problem considered similar planning decisions, albeit with multiple restrictions regarding the transportation of goods that can eliminate the opportunities for transportation savings. It also does not explicitly model the opportunity to increase vehicle utilization by having vehicles serve multiple purposes within the supply chain. We propose a mathematical model of the problem we consider that is inspired by the operations of an industrial partner. We present an adaptation of the Dynamic Discretization Discovery algorithm to solve this problem and illustrate its computational effectiveness on instances derived from the operations of a retail distribution network in France. Finally, we illustrate the potential savings enabled by solving the proposed model.
{"title":"A continuous‐time service network design and vehicle routing problem","authors":"Yun He, Mike Hewitt, Fabien Lehuédé, Juliette Medina, Olivier Péton","doi":"10.1002/net.22194","DOIUrl":"https://doi.org/10.1002/net.22194","url":null,"abstract":"Abstract This paper considers the integrated planning of goods transportation through a multi‐echelon supply chain consisting of a nationwide network and regional distribution system. The previously studied Service Network Design and Routing Problem considered similar planning decisions, albeit with multiple restrictions regarding the transportation of goods that can eliminate the opportunities for transportation savings. It also does not explicitly model the opportunity to increase vehicle utilization by having vehicles serve multiple purposes within the supply chain. We propose a mathematical model of the problem we consider that is inspired by the operations of an industrial partner. We present an adaptation of the Dynamic Discretization Discovery algorithm to solve this problem and illustrate its computational effectiveness on instances derived from the operations of a retail distribution network in France. Finally, we illustrate the potential savings enabled by solving the proposed model.","PeriodicalId":54734,"journal":{"name":"Networks","volume":"48 211","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135540317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nadine Friesen, Tim Sander, Christina Büsing, Karl Nachtigall, Nils Nießen
Abstract Because of the long planning periods and their long life cycle, railway infrastructure has to be outlined long ahead. At the present, the infrastructure is designed while only little about the intended operation is known. Hence, the timetable and the operation are adjusted to the infrastructure. Since space, time and money for extension measures of railway infrastructure are limited, each modification has to be done carefully and long lasting and should be appropriate for the future unknown demand. To take this into account, we present the robust network design problem for railway infrastructure under capacity constraints and uncertain timetables. Here, we plan the required expansion measures for an uncertain long‐term timetable. We show that this problem is NP‐hard even when restricted to bipartite graphs and very simple timetables and present easier solvable special cases. This problem corresponds to the fixed‐charge network design problem where the expansion costs are minimized such that the timetable is conductible. We model this problem by an integer linear program using time expanded networks. To incorporate the uncertainty of the future timetable, we use a scenario‐based approach. We define scenarios with individual departure and arrival times and optional trains. The network is then optimized such that a given percentage of the scenarios can be operated while minimizing the expansion costs and potential penalty costs for not scheduled optional trains.
{"title":"The complexity of the timetable‐based railway network design problem","authors":"Nadine Friesen, Tim Sander, Christina Büsing, Karl Nachtigall, Nils Nießen","doi":"10.1002/net.22192","DOIUrl":"https://doi.org/10.1002/net.22192","url":null,"abstract":"Abstract Because of the long planning periods and their long life cycle, railway infrastructure has to be outlined long ahead. At the present, the infrastructure is designed while only little about the intended operation is known. Hence, the timetable and the operation are adjusted to the infrastructure. Since space, time and money for extension measures of railway infrastructure are limited, each modification has to be done carefully and long lasting and should be appropriate for the future unknown demand. To take this into account, we present the robust network design problem for railway infrastructure under capacity constraints and uncertain timetables. Here, we plan the required expansion measures for an uncertain long‐term timetable. We show that this problem is NP‐hard even when restricted to bipartite graphs and very simple timetables and present easier solvable special cases. This problem corresponds to the fixed‐charge network design problem where the expansion costs are minimized such that the timetable is conductible. We model this problem by an integer linear program using time expanded networks. To incorporate the uncertainty of the future timetable, we use a scenario‐based approach. We define scenarios with individual departure and arrival times and optional trains. The network is then optimized such that a given percentage of the scenarios can be operated while minimizing the expansion costs and potential penalty costs for not scheduled optional trains.","PeriodicalId":54734,"journal":{"name":"Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135967776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bernard Fortz, Mariusz Mycek, Michał Pióro, Artur Tomaszewski
Abstract This article considers resilience of service networks that are composed of service and control nodes to node‐targeted attacks. Two complementary problems of selecting attacked nodes and placing control nodes reflect the interaction between the network operator and the network attacker. This interaction can be analyzed within the framework of game theory. Considering the limited performance of the previously introduced iterative solution algorithms based on non‐compact problem models, new compact integer programming formulations of the node attack optimization problem are proposed, which are based on the notion of pseudo‐components and on a bilevel model. The efficiency of the new formulations is illustrated by the numerical study that uses two reference networks (medium‐size and large‐size), and a wide range of the sizes of attacks and controllers placements.
{"title":"Min–max optimization of node‐targeted attacks in service networks","authors":"Bernard Fortz, Mariusz Mycek, Michał Pióro, Artur Tomaszewski","doi":"10.1002/net.22191","DOIUrl":"https://doi.org/10.1002/net.22191","url":null,"abstract":"Abstract This article considers resilience of service networks that are composed of service and control nodes to node‐targeted attacks. Two complementary problems of selecting attacked nodes and placing control nodes reflect the interaction between the network operator and the network attacker. This interaction can be analyzed within the framework of game theory. Considering the limited performance of the previously introduced iterative solution algorithms based on non‐compact problem models, new compact integer programming formulations of the node attack optimization problem are proposed, which are based on the notion of pseudo‐components and on a bilevel model. The efficiency of the new formulations is illustrated by the numerical study that uses two reference networks (medium‐size and large‐size), and a wide range of the sizes of attacks and controllers placements.","PeriodicalId":54734,"journal":{"name":"Networks","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136352914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Georg E. A. Fröhlich, Margaretha Gansterer, Karl F. Doerner
Abstract As a major real‐world problem, snow plowing has been studied extensively. However, most studies focus on deterministic settings with little urgency yet enough time to plan. In contrast, we assume a severe snowstorm with little known data and little time to plan. We introduce a novel time‐dependent multi‐visit dynamic safe street snow plowing problem and formulate it on a rolling‐horizon‐basis. To solve this problem, we develop an adaptive large neighborhood search as the underlying method and validate its efficacy on team orienteering arc routing problem benchmark instances. We create real‐world‐based instances for the city of Vienna and examine the effect of (i) different snowstorm movements, (ii) having perfect information, and (iii) different information‐updating intervals and look‐aheads for the rolling horizon method. Our findings show that different snowstorm movements have no significant effect on the choice of rolling horizon settings. They also indicate that (i) larger updating intervals are beneficial, if prediction errors are low, and (ii) larger look‐aheads are better suited for larger updating intervals and vice versa. However, we observe that less look‐ahead is needed when prediction errors are low.
{"title":"A rolling horizon framework for the time‐dependent multi‐visit dynamic safe street snow plowing problem","authors":"Georg E. A. Fröhlich, Margaretha Gansterer, Karl F. Doerner","doi":"10.1002/net.22189","DOIUrl":"https://doi.org/10.1002/net.22189","url":null,"abstract":"Abstract As a major real‐world problem, snow plowing has been studied extensively. However, most studies focus on deterministic settings with little urgency yet enough time to plan. In contrast, we assume a severe snowstorm with little known data and little time to plan. We introduce a novel time‐dependent multi‐visit dynamic safe street snow plowing problem and formulate it on a rolling‐horizon‐basis. To solve this problem, we develop an adaptive large neighborhood search as the underlying method and validate its efficacy on team orienteering arc routing problem benchmark instances. We create real‐world‐based instances for the city of Vienna and examine the effect of (i) different snowstorm movements, (ii) having perfect information, and (iii) different information‐updating intervals and look‐aheads for the rolling horizon method. Our findings show that different snowstorm movements have no significant effect on the choice of rolling horizon settings. They also indicate that (i) larger updating intervals are beneficial, if prediction errors are low, and (ii) larger look‐aheads are better suited for larger updating intervals and vice versa. However, we observe that less look‐ahead is needed when prediction errors are low.","PeriodicalId":54734,"journal":{"name":"Networks","volume":"299 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135347298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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