4.7 Article

Exact solution of the soft-clustered vehicle-routing problem

期刊

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 280, 期 1, 页码 164-178

出版社

ELSEVIER
DOI: 10.1016/j.ejor.2019.07.019

关键词

Routing; Branch-and-price; Shortest-path problem with resource constraints; Dynamic-programming labeling; Branch-and-cut

资金

  1. Deutsche Forschungsgemeinschaft (DFG) [IR 122/10-1]

向作者/读者索取更多资源

The soft-clustered vehicle-routing problem (SoftCluVRP) extends the classical capacitated vehicle-routing problem by one additional constraint: The customers are partitioned into clusters and feasible routes must respect the soft-cluster constraint, that is, all customers of the same cluster must be served by the same vehicle. In this article, we design and analyze different branch-and-price algorithms for the exact solution of the SoftCluVRP. The algorithms differ in the way the column-generation subproblem, a variant of the shortest-path problem with resource constraints (SPPRC), is solved. The standard approach for SPPRCs is based on dynamic-programming labeling algorithms. We show that even with all the recent acceleration techniques (e.g., partial pricing, bidirectional labeling, decremental state space relaxation) available for SPPRC labeling algorithms, the solution of the subproblem remains extremely difficult. The main contribution is the modeling and solution of the subproblem using a branch-and-cut algorithm. The conducted computational experiments prove that branch-and-price equipped with this integer programming-based approach outperforms sophisticated labeling-based algorithms by one order of magnitude. The largest SoftCluVRP instances solved to optimality have more than 400 customers or more than 50 clusters. (C) 2019 Elsevier B.V. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Computer Science, Hardware & Architecture

Routing electric vehicles with a single recharge per route

Maximilian Loeffler, Guy Desaulniers, Stefan Irnich, Michael Schneider

NETWORKS (2020)

Article Operations Research & Management Science

Variable Fixing for Two-Arc Sequences in Branch-Price-and-Cut Algorithms on Path-Based Models

Guy Desaulniers, Timo Gschwind, Stefan Irnich

TRANSPORTATION SCIENCE (2020)

Article Mathematics, Applied

A branch-price-and-cut algorithm for the capacitated multiple vehicle traveling purchaser problem with unitary demand

Nicola Bianchessi, Stefan Irnich, Christian Tilk

Summary: This study discusses variants of the Multiple Vehicle Traveling Purchaser Problem (MVTPP), introduces a branch-price-and-cut algorithm, proposes a new branching rule and valid inequalities, and demonstrates how to handle product incompatibilities. Experimental results show that the new approach is highly competitive.

DISCRETE APPLIED MATHEMATICS (2021)

Article Mathematics, Applied

A branch-and-cut algorithm for the soft-clustered vehicle-routing problem

Katrin Hessler, Stefan Irnich

Summary: The soft-clustered vehicle-routing problem is a variant of the classical capacitated vehicle-routing problem, with a novel symmetric formulation and an asymmetric sub-model for clustering. A branch-and-cut algorithm is used to solve the new model, with problem-specific cutting planes and separation procedures introduced. Computational results show that the algorithm can now solve several previously open instances to proven optimality.

DISCRETE APPLIED MATHEMATICS (2021)

Article Computer Science, Interdisciplinary Applications

Large multiple neighborhood search for the soft-clustered vehicle-routing problem

Timo Hintsch

Summary: The paper introduces a large multiple neighborhood search algorithm for the SoftCluVRP, utilizing various cluster destroy and repair operators and two post optimization components. Computational experiments demonstrate that the algorithm outperforms existing heuristic approaches and provides 130 new best solutions for medium-sized instances.

COMPUTERS & OPERATIONS RESEARCH (2021)

Article Operations Research & Management Science

Branch-Price-and-Cut for the Soft-Clustered Capacitated Arc-Routing Problem

Timo Hintsch, Stefan Irnich, Lone Kiilerich

Summary: SoftCluCARP is a variant of the capacitated arc-routing problem that involves partitioning streets into clusters. The branch-price-and-cut algorithm is designed to solve SoftCluCARP by using metaheuristic and branch-and-cut-based solvers for the column-generation subproblem, resulting in an effective and accurate solution approach.

TRANSPORTATION SCIENCE (2021)

Article Operations Research & Management Science

Bin packing with lexicographic objectives for loading weight- and volume-constrained trucks in a direct-shipping system

Katrin Hessler, Stefan Irnich, Tobias Kreiter, Ulrich Pferschy

Summary: This study tackles a packing problem in the food and beverage industry direct-shipping system, focusing on optimizing truck utilization while considering different product categories and constraints. The authors propose a heuristic and an exact solution approach, demonstrating the applicability through computational results on real-world and difficult instances.

OR SPECTRUM (2022)

Article Operations Research & Management Science

The last-mile vehicle routing problem with delivery options

Christian Tilk, Katharina Olkis, Stefan Irnich

Summary: The ongoing rise in e-commerce has increased the number of first-time delivery failures, resulting in rework and impacting carriers' delivery costs. A new vehicle routing problem with delivery options (VRPDO) is introduced to minimize costs and reach a specified service level based on customer preferences, with a focus on respecting capacities when assigning shipments. A new branch-price-and-cut algorithm is presented for solving the VRPDO and optimal solutions are provided for instances with up to 100 delivery options.

OR SPECTRUM (2021)

Article Operations Research & Management Science

A note on the linearity of Ratliff and Rosenthal's algorithm for optimal picker routing

Katrin Hessler, Stefan Irnich

Summary: This article presents a study on the dynamic programming algorithm for optimal picker routing, which has linear complexity in the number of aisles. The algorithm is linear in solving the dynamic programming problem, but computing the cost coefficients requires considering all picking positions.

OPERATIONS RESEARCH LETTERS (2022)

Article Operations Research & Management Science

New neighborhoods and an iterated local search algorithm for the generalized traveling salesman problem

Jeanette Schmidt, Stefan Irnich

Summary: The generalized traveling salesman problem (GTSP) aims to find a minimum-cost cycle in a given graph that contains exactly one vertex from each cluster. This study introduces three new GTSP neighborhoods that allow for simultaneous permutation of cluster sequence and vertex selection. These neighborhoods, along with existing ones from literature, are combined to form an effective iterated local search (ILS) algorithm for GTSP. Computational experiments on standard GTSP libraries demonstrate that the ILS algorithm, with some refinements, can compete with state-of-the-art GTSP algorithms.

EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION (2022)

Article Mathematics, Applied

Solving the skiving stock problem by a combination of stabilized column generation and the Reflect Arc-Flow model

Laura Korbacher, Stefan Irnich, John Martinovic, Nico Strasdat

Summary: The skiving stock problem is a counterpart of the cutting stock problem and has become an independent branch of research in the operations research (OR) community. A graph-theoretic approach called the reflect arc-flow model has shown promising results in solving specific skiving stock instances. However, there are still challenging benchmark instances that current formulations cannot solve. In this study, a new approach based on sparse graphs and a stabilized column-generation approach is proposed, which demonstrates optimal solutions for previously unsolved benchmark instances.

DISCRETE APPLIED MATHEMATICS (2023)

Article Computer Science, Interdisciplinary Applications

Partial Dominance in Branch-Price-and-Cut for the Basic Multicompartment Vehicle-Routing Problem

Katrin Hessler, Stefan Irnich

Summary: This study considers the exact solution to the basic version of the multiple-compartment vehicle-routing problem. It focuses on clustering customers, routing vehicles, and packing customer demand into compartments. The objective is to minimize the total length of all vehicle routes. The study compares different labeling approaches for solving the shortest-path subproblem, finding that partial dominance performs better than standard labeling.

INFORMS JOURNAL ON COMPUTING (2023)

Article Computer Science, Interdisciplinary Applications

Resource-Window Reduction by Reduced Costs in Path-Based Formulations for Routing and Scheduling Problems

Nicola Bianchessi, Timo Gschwind, Stefan Irnichb

Summary: Many routing and scheduling problems are solved using branch-price-and-cut (BPC) algorithms, which involve variable fixing techniques to improve efficiency. Reduction of resource windows based on traversal direction can further enhance the solution process.

INFORMS JOURNAL ON COMPUTING (2023)

Article Computer Science, Interdisciplinary Applications

A Branch-and-Price Framework for Decomposing Graphs into Relaxed Cliques

Timo Gschwind, Stefan Irnich, Fabio Furini, Roberto Wolfler Calvo

Summary: The article explores the family of problems related to partitioning and covering graphs with a minimum number of relaxed cliques, with applications in various fields. A unified framework based on branch-and-price techniques is proposed, with new pricing algorithms and branching schemes developed. Comparative studies demonstrate the effectiveness of the framework and the validity of the approach in social network instances.

INFORMS JOURNAL ON COMPUTING (2021)

Review Management

Survey of optimization models for power system operation and expansion planning with demand response

Vinicius N. Motta, Miguel F. Anjos, Michel Gendreau

Summary: This survey presents a review of optimization approaches for the integration of demand response in power systems planning and highlights important future research directions.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2024)

Article Management

R-SALSA: A branch, bound, and remember algorithm for the workload smoothing problem on simple assembly lines

Philipp Schulze, Armin Scholl, Rico Walter

Summary: This paper proposes an improved branch-and-bound algorithm, R-SALSA, for solving the simple assembly line balancing problem, which performs well in balancing workloads and providing initial solutions.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2024)

Article Management

Adaptive scheduling in service systems: A Dynamic programming approach

Roshan Mahes, Michel Mandjes, Marko Boon, Peter Taylor

Summary: This paper discusses appointment scheduling and presents a phase-type-based approach to handle variations in service times. Numerical experiments with dynamic scheduling demonstrate the benefits of rescheduling.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2024)

Article Management

Discrete scheduling and critical utilization

Oleg S. Pianykh, Sebastian Perez, Chengzhao Richard Zhang

Summary: Efficient scheduling is crucial for optimizing resource allocation and system performance. This study focuses on critical utilization and efficient scheduling in discrete scheduling systems, and compares the results with classical queueing theory.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2024)

Review Management

Supply chain network design with financial considerations: A comprehensive review

Hamed Jahani, Babak Abbasi, Jiuh-Biing Sheu, Walid Klibi

Summary: Supply chain network design is a large and growing area of research. This study comprehensively surveys and analyzes articles published from 2008 to 2021 to detect and report financial perspectives in SCND models. The study also identifies research gaps and offers future research directions.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2024)

Article Management

A branch-and-cut algorithm for the connected max- k-cut problem

Patrick Healy, Nicolas Jozefowiez, Pierre Laroche, Franc Marchetti, Sebastien Martin, Zsuzsanna Roka

Summary: The Connected Max-k-Cut Problem is an extension of the well-known Max-Cut Problem, where the objective is to partition a graph into k connected subgraphs by maximizing the cost of inter-partition edges. The researchers propose a new integer linear program and a branch-and-cut algorithm for this problem, and also use graph isomorphism to structure the instances and facilitate their resolution. Extensive computational experiments show that, if k > 2, their approach outperforms existing algorithms in terms of quality.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2024)

Article Management

Estimating production functions through additive models based on regression splines

Victor J. Espana, Juan Aparicio, Xavier Barber, Miriam Esteve

Summary: This paper introduces a new methodology based on the machine learning technique MARS for estimating production functions that satisfy classical production theory axioms. The new approach overcomes the overfitting problem of DEA through generalized cross-validation and demonstrates better performance in reducing mean squared error and bias compared to DEA and C2NLS methods.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2024)

Article Management

Time-flexible min completion time variance in a single machine by quadratic programming

Stefano Nasini, Rabia Nessah

Summary: In this paper, the authors investigate the impact of time flexibility in job scheduling, showing that it can significantly affect operators' ability to solve the problem efficiently. They propose a new methodology based on convex quadratic programming approaches that allows for optimal solutions in large-scale instances.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2024)

Article Management

Convex support vector regression

Zhiqiang Liao, Sheng Dai, Timo Kuosmanen

Summary: Nonparametric regression subject to convexity or concavity constraints is gaining popularity in various fields. The conventional convex regression method often suffers from overfitting and outliers. This paper proposes the convex support vector regression method to address these issues and demonstrates its advantages in prediction accuracy and robustness through numerical experiments.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2024)

Article Management

A simulation evacuation framework for effective disaster preparedness strategies and response decision making

Kuo-Hao Chang, Ying-Zheng Wu, Wen-Ray Su, Lee-Yaw Lin

Summary: The damage and destruction caused by earthquakes necessitates the evacuation of affected populations. Simulation models, such as the Stochastic Pedestrian Cell Transmission Model (SPCTM), can be utilized to enhance disaster and evacuation management. The analysis of SPCTM provides insights for government officials to formulate effective evacuation strategies.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2024)

Article Management

An effective hybrid evolutionary algorithm for the clustered orienteering problem

Qinghua Wu, Mu He, Jin-Kao Hao, Yongliang Lu

Summary: This paper studies a variant of the orienteering problem known as the clustered orienteering problem. In this problem, customers are grouped into clusters and a profit is associated with each cluster, collected only when all customers in the cluster are served. The proposed evolutionary algorithm, incorporating a backbone-based crossover operator and a destroy-and-repair mutation operator, outperforms existing algorithms on benchmark instances and sets new records on some instances. It also demonstrates scalability on large instances and has shown superiority over three state-of-the-art COP algorithms. The algorithm is also successfully applied to a dynamic version of the COP considering stochastic travel time.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2024)

Article Management

Improving uplift model evaluation on randomized controlled trial data

Bjorn Bokelmann, Stefan Lessmann

Summary: Estimating treatment effects is an important task for data analysts, and uplift models provide support for efficient allocation of treatments. However, evaluating uplift models is challenging due to variance issues. This paper theoretically analyzes the variance of uplift evaluation metrics, proposes variance reduction methods based on statistical adjustment, and demonstrates their benefits on simulated and real-world data.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2024)

Article Management

Newsvendor conditional value-at-risk minimisation: A feature-based approach under adaptive data selection

Congzheng Liu, Wenqi Zhu

Summary: This paper proposes a feature-based non-parametric approach to minimizing the conditional value-at-risk in the newsvendor problem. The method is able to handle both linear and nonlinear profits without prior knowledge of the demand distribution. Results from numerical and real-life experiments demonstrate the robustness and effectiveness of the approach.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2024)

Article Management

Right-left asymmetry of the eigenvector method: A simulation study

Laszlo Csato

Summary: This paper compares the performance of the eigenvalue method and the row geometric mean as two weighting procedures. Through numerical experiments, it is found that the priorities derived from the two eigenvectors in the eigenvalue method do not always agree, while the row geometric mean serves as a compromise between them.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2024)

Article Management

Compete or cooperate? Effects of channel relationships on government policies for sustainability

Guowei Dou, Tsan-Ming Choi

Summary: This study investigates the impact of channel relationships between manufacturers on government policies and explores the effectiveness of positive incentives versus taxes in increasing social welfare. The findings suggest that competition may be more effective in improving sustainability and social welfare. Additionally, government incentives for green technology may not necessarily enhance sustainability.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2024)