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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted by number of followers
Review of Economics and Statistics     Hybrid Journal   (Followers: 160)
Statistics in Medicine     Hybrid Journal   (Followers: 152)
Journal of Econometrics     Hybrid Journal   (Followers: 84)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 73, SJR: 3.746, CiteScore: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Biometrics     Hybrid Journal   (Followers: 52)
Sociological Methods & Research     Hybrid Journal   (Followers: 45)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 40, SJR: 3.664, CiteScore: 2)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 40)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 37)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 36)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 34)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 33)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 30)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 28)
The American Statistician     Full-text available via subscription   (Followers: 26)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 26)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 24)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Forecasting     Hybrid Journal   (Followers: 20)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 18)
Statistical Modelling     Hybrid Journal   (Followers: 18)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
Journal of Statistical Software     Open Access   (Followers: 16, SJR: 13.802, CiteScore: 16)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 16)
Risk Management     Hybrid Journal   (Followers: 16)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 15)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 15)
Computational Statistics     Hybrid Journal   (Followers: 15)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Demographic Research     Open Access   (Followers: 14)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 13)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
International Statistical Review     Hybrid Journal   (Followers: 12)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 12)
Journal of Statistical Physics     Hybrid Journal   (Followers: 12)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 11)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Journal of Probability and Statistics     Open Access   (Followers: 10)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Biometrical Journal     Hybrid Journal   (Followers: 9)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
Argumentation et analyse du discours     Open Access   (Followers: 8)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Current Research in Biostatistics     Open Access   (Followers: 8)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Stata Journal     Full-text available via subscription   (Followers: 8)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 8)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 7)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
Lifetime Data Analysis     Hybrid Journal   (Followers: 7)
Significance     Hybrid Journal   (Followers: 7)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 7)
Queueing Systems     Hybrid Journal   (Followers: 7)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Statistical Methods and Applications     Hybrid Journal   (Followers: 6)
Law, Probability and Risk     Hybrid Journal   (Followers: 6)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
Journal of Global Optimization     Hybrid Journal   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Engineering With Computers     Hybrid Journal   (Followers: 5)
CHANCE     Hybrid Journal   (Followers: 5)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 4)
Metrika     Hybrid Journal   (Followers: 4)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Statistical Papers     Hybrid Journal   (Followers: 4)
Sankhya A     Hybrid Journal   (Followers: 3)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 3)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
Building Simulation     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Stochastic Models     Hybrid Journal   (Followers: 2)
Optimization Letters     Hybrid Journal   (Followers: 2)
TEST     Hybrid Journal   (Followers: 2)
Extremes     Hybrid Journal   (Followers: 2)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Statistics and Economics     Open Access  
Review of Socionetwork Strategies     Hybrid Journal  
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Journal of the Korean Statistical Society     Hybrid Journal  
Sequential Analysis: Design Methods and Applications     Hybrid Journal  

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Similar Journals
Journal Cover
Optimization Letters
Journal Prestige (SJR): 0.721
Citation Impact (citeScore): 1
Number of Followers: 2  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1862-4480 - ISSN (Online) 1862-4472
Published by Springer-Verlag Homepage  [2467 journals]
  • A note on study on proportionate flowshop scheduling with due-date
           assignment and position-dependent weights

    • Free pre-print version: Loading...

      Abstract: Abstract In a recent paper (Lv and Wang, Study on proportionate flowshop scheduling with due-date assignment and position-dependent weights. Optim. Lett., 2021. https://doi.org/10.1007/s11590-020-01670-4), Lv and Wang studied due date proportionate flowshop scheduling problems with position-dependent weights. For common due date and slack due date assignments, they proved that these two problems can be solved in \(O(n\log n)\) time respectively, where n is the number of jobs. For slack due date assignment, there is an error. We will show the error by a counter example and explain why it is incorrect.
      PubDate: 2022-12-01
       
  • On properties of univariate max functions at local maximizers

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      Abstract: Abstract More than three decades ago, Boyd and Balakrishnan established a regularity result for the two-norm of a transfer function at maximizers. Their result extends easily to the statement that the maximum eigenvalue of a univariate real analytic Hermitian matrix family is twice continuously differentiable, with Lipschitz second derivative, at all local maximizers, a property that is useful in several applications that we describe. We also investigate whether this smoothness property extends to max functions more generally. We show that the pointwise maximum of a finite set of q-times continuously differentiable univariate functions must have zero derivative at a maximizer for \(q=1\) , but arbitrarily close to the maximizer, the derivative may not be defined, even when \(q=3\) and the maximizer is isolated.
      PubDate: 2022-12-01
       
  • On a conservative partition refinement (CPR) method for a class of
           two-stage stochastic programming problems

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      Abstract: Abstract Two-stage stochastic programming is a mathematical framework widely used in real-life applications such as power system operation planning, supply chains, logistics, inventory management, and financial planning. Since most of these problems cannot be solved analytically, decision makers make use of numerical methods to obtain a near-optimal solution. Some applications rely on the implementation of non-converged and therefore sub-optimal solutions because of computational time or power limitations. In this context, the existing partition-refinement methods provide an optimistic solution whenever convergence is not attained. Optimistic solutions often generate high disappointment levels because they consistently underestimate the actual costs in the approximate objective function. To address this issue, we developed a conservative convergent partition-refinement method for two-stage stochastic linear programming problems with a convex recourse function of the uncertainty. Given a partition of the uncertainty support, a conservative decision can be obtained by means of a distributionally robust problem whose complexity grows exponentially with the uncertainty dimensionality. We prove the convergence of the method given a refining partition sequence and propose algorithmic schemes to address the problem of dimensionality. For problems with low-dimensional uncertainty, we developed a deterministic equivalent linear programming model; whereas, for medium-sized uncertainty dimensionality, we propose a column and constraint generation algorithm. To handle high dimensional uncertainty, we propose a simplex-based heuristic method whose complexity grows linearly with the uncertainty dimension—size of the random vector. In the presence of monotone recourse functions with regard to an uncertain parameter, we prove convergence of the proposed simplex-based heuristic method. Computational experiments are presented for a farmer’s problem, an aircraft allocation problem, and a Unit Commitment problem.
      PubDate: 2022-12-01
       
  • Robust inventory problem with budgeted cumulative demand uncertainty

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      Abstract: Abstract In this paper, a robust inventory problem with uncertain cumulative demands is considered. Interval-budgeted uncertainty sets are used to model possible cumulative demand scenarios. It is shown that under discrete budgeted uncertainty the robust min–max problem can be solved in polynomial time. On the other hand, for continuous budgeted uncertainty, the problem is weakly NP-hard. It can be solved in pseudopolynomial time and admits an FPTAS for nonoverlapping cumulative demand intervals.
      PubDate: 2022-12-01
       
  • Individual and cooperative portfolio optimization as linear program

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      Abstract: Abstract We derive a linear program for minimization, subject to a linear constraint, of an arbitrary positively homogeneous convex functional, whose dual set is given by linear inequalities, possibly involving auxiliary variables. This allows to reduce to linear programming individual and cooperative portfolio optimization problems with arbitrary deviation measures whose risk envelopes are given by a finite number of linear constraints. Earlier, such linear programs were known only for individual porfolio optimization problems with special examples of deviation measures, such as mean absolute deviation or CVaR deviation.
      PubDate: 2022-12-01
       
  • Convergence analysis of the stochastic reflected forward–backward
           splitting algorithm

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      Abstract: Abstract We propose and analyze the convergence of a novel stochastic algorithm for solving monotone inclusions that are the sum of a maximal monotone operator and a monotone, Lipschitzian operator. The propose algorithm requires only unbiased estimations of the Lipschitzian operator. We obtain the rate \({\mathcal {O}}(log(n)/n)\) in expectation for the strongly monotone case, as well as almost sure convergence for the general case. Furthermore, in the context of application to convex–concave saddle point problems, we derive the rate of the primal–dual gap. In particular, we also obtain \({\mathcal {O}}(1/n)\) rate convergence of the primal–dual gap in the deterministic setting.
      PubDate: 2022-12-01
       
  • On lower and upper bounds for single machine parallel batch scheduling

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      Abstract: Abstract In this paper, we consider a single machine parallel batch scheduling problem subject to chains of jobs. We present polynomial lower and upper bounds as well as their experimental comparison and relative errors. According to the results of the numerical experiment, the relative difference between upper and lower bounds does not exceed 1.5.
      PubDate: 2022-12-01
       
  • Cooperative congestion games: existence of a Nash-stable coalition
           structure

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      Abstract: Abstract This paper studies a model for cooperative congestion games. There is an array of cooperative games V and a player’s strategy is to choose a subset of the set V. The player gets a certain payoff from each chosen game. The paper demonstrates that if a payoff is the Shapley or the Banzhaf value, then the corresponding cooperative congestion game has a Nash equilibrium in pure strategies. The case is examined where each game in V has a coalition partition. The stability of the vector of coalition structures is determined, taking into account the transitions of players within a game and their migrations to other games. The potential function is defined for coalition partitions, and is used as a means of proving the existence of a stable vector of coalition structures for a certain class of cooperative game values.
      PubDate: 2022-12-01
       
  • Statistical robustness of two-stage stochastic variational inequalities

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      Abstract: Abstract The two-stage stochastic variational inequality (TSVI) is a popular modeling paradigm recently with a wide range of applications in stochastic programming, game problems, equilibrium, etc. To numerically solve a TSVI, samples drawn from the true probability distribution are employed to discretely approximate the original problem with continuous probability distribution. However, this key assumption can hardly be fulfilled in practice because the perceived data may usually contain noise. This leads to the so-called statistically robust analysis. This paper considers the statistical robustness of TSVIs. We first give some preliminaries on both TSVIs and statistical robustness, which lay the foundation for the subsequent discussion. Then both qualitative and quantitative statistical robustness of TSVIs are studied.
      PubDate: 2022-12-01
       
  • QN-tensor and tensor complementarity problem

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      Abstract: Abstract In this paper, we introduce three classes of structured tensor: QN-tensor, S-QN tensor and generalized S-QN tensor, which are proved to be nonsingular \({\mathcal {H}}\) -tensors. Moreover, we present the upper bound for the norm of the solution of TCP \(({{\mathcal {A}}},q)\) defined by a nonsingular \({\mathcal {H}}\) -tensor with positive diagonal entries. The estimation of upper bound requires a diagonal scaling matrix D such that \({{\mathcal {A}}}\cdot D\) is strictly diagonally dominant. When \({{\mathcal {A}}}\) belongs to the set of QN-tensor or S-QN tensor, a choice for the diagonal scaling matrix is given.
      PubDate: 2022-12-01
       
  • Convergence rates of damped inerial dynamics from multi-degree-of-freedom
           system

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      Abstract: Abstract In this article, we investigate the convergence rate of the following dynamic system in \({\mathbb {R}}^{n}\) $$\begin{aligned} \ddot{x}(t)+\frac{A}{t^\theta }{\dot{x}}(t)+\nabla F(x(t))=0,\quad t>0, \end{aligned}$$ where A denotes the constant positive definite matrix and the potential function \(F:{\mathbb {R}}^n\rightarrow {\mathbb {R}}\) is continuous differentiable. This system is of vital importance, especially in optimization and engineering. This article presents new convergence rates of the above dynamics when F(x) satisfies some local geometrical properties by constructing a proper Lyapunov function. Finally, some numerical experiments were performed to explain the convergence results.
      PubDate: 2022-12-01
       
  • The multi-depot k-traveling repairman problem

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      Abstract: Abstract In this paper, we study the multi-depot k-traveling repairman problem. This problem extends the traditional traveling repairman problem to the multi-depot case. Its objective, similar to the single depot variant, is the minimization of the sum of the arrival times to customers. We propose two distinct formulations to model the problem, obtained on layered graphs. In order to find feasible solutions for the largest instances, we propose a hybrid genetic algorithm where initial solutions are built using a splitting heuristic and a local search is embedded into the genetic algorithm. The efficiency of the mathematical formulations and of the solution approach are investigated through computational experiments. The proposed models are scalable enough to solve instances up to 240 customers.
      PubDate: 2022-12-01
       
  • Guillotine cutting is asymptotically optimal for packing consecutive
           squares

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      Abstract: Abstract More than half a century ago Martin Gardner popularized a question leading to the benchmark problem of determining the minimum side length of a square into which the squares of sizes \(1,2,\dots ,n\) can be packed without overlap. Constructions are known for a certain range of n, and summing up the areas yields that a packing in a square of size smaller than \(N:= \!\sqrt{n(n+1)(2n+1)/6)} \) is not possible. Here we prove that an asymptotically minimal packing exists in a square of size \(N+cn+O(\!\sqrt{n})\) with \(c<1\) , and such a packing is achievable with guillotine-cuts. An improved construction is also given for the case where the constraint of guillotine cutting is dropped.
      PubDate: 2022-12-01
       
  • Robustness of solutions to the capacitated facility location problem with
           uncertain demand

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      Abstract: Abstract We investigate the properties of robust solutions of the Capacitated Facility Location Problem with uncertain demand. We show that the monotonic behavior of the price of robustness is not guaranteed, and therefore that one cannot discriminate among alternative robust solutions by simply relying on the trade-off price-vs-robustness. Furthermore, we report a computational study on benchmark instances from the literature and on instances derived from a real-world application, which demonstrates the validity in practice of our findings.
      PubDate: 2022-12-01
       
  • On ambiguity-averse market equilibrium

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      Abstract: Abstract We develop a Nash equilibrium problem representing a perfectly competitive market wherein all players are subject to the same source of uncertainty with an unknown probability distribution. Each player—depending on her individual access to and confidence over empirical data—builds an ambiguity set containing a family of potential probability distributions describing the uncertain event. The ambiguity set of different players is not necessarily identical, yielding a market with potentially heterogeneous ambiguity aversion. Built upon recent developments in the field of Wasserstein distributionally robust chance-constrained optimization, each ambiguity-averse player maximizes her own expected payoff under the worst-case probability distribution within her ambiguity set. Using an affine policy and a conditional value-at-risk approximation of chance constraints, we define a tractable Nash game. We prove that under certain conditions a unique Nash equilibrium point exists, which coincides with the solution of a single optimization problem. Numerical results indicate that players with comparatively lower consumption utility are highly exposed to rival ambiguity aversion.
      PubDate: 2022-11-19
       
  • Modeling approaches for addressing unrelaxable bound constraints with
           unconstrained optimization methods

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      Abstract: Abstract We explore novel approaches for solving nonlinear optimization problems with unrelaxable bound constraints, which must be satisfied before the objective function can be evaluated. Our method reformulates the unrelaxable bound-constrained problem as an unconstrained optimization problem that is amenable to existing unconstrained optimization methods. The reformulation relies on a domain warping to form a merit function; the choice of the warping determines the level of exactness with which the unconstrained problem can be used to find solutions to the bound-constrained problem, as well as key properties of the unconstrained formulation such as smoothness. We develop theory when the domain warping is a multioutput sigmoidal warping, and we explore the practical elements of applying unconstrained optimization methods to the formulation. We develop an algorithm that exploits the structure of the sigmoidal warping to guarantee that unconstrained optimization algorithms applied to the merit function will find a stationary point to the desired tolerance.
      PubDate: 2022-11-13
       
  • Polyhedral results and stronger Lagrangean bounds for stable spanning
           trees

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      Abstract: Abstract Given a graph \(G=(V,E)\) and a set C of unordered pairs of edges regarded as being in conflict, a stable spanning tree in G is a set of edges T inducing a spanning tree in G, such that for each \(\left\{ e_i, e_j \right\} \in C\) , at most one of the edges \(e_i\) and \(e_j\) is in T. The existing work on Lagrangean algorithms to the NP-hard problem of finding minimum weight stable spanning trees is limited to relaxations with the integrality property. We exploit a new relaxation of this problem: fixed cardinality stable sets in the underlying conflict graph \(H =(E,C)\) . We find interesting properties of the corresponding polytope, and determine stronger dual bounds in a Lagrangean decomposition framework, optimizing over the spanning tree polytope of G and the fixed cardinality stable set polytope of H in the subproblems. This is equivalent to dualizing exponentially many subtour elimination constraints, while limiting the number of multipliers in the dual problem to E . It is also a proof of concept for combining Lagrangean relaxation with the power of integer programming solvers over strongly NP-hard subproblems. We present encouraging computational results using a dual method that comprises the Volume Algorithm, initialized with multipliers determined by Lagrangean dual-ascent. In particular, the bound is within 5.5% of the optimum in 146 out of 200 benchmark instances; it actually matches the optimum in 75 cases. All of the implementation is made available in a free, open-source repository.
      PubDate: 2022-11-11
       
  • A generalized Frank–Wolfe method with “dual averaging” for strongly
           convex composite optimization

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      Abstract: Abstract We propose a simple variant of the generalized Frank–Wolfe method for solving strongly convex composite optimization problems, by introducing an additional averaging step on the dual variables. We show that in this variant, one can choose a simple constant step-size and obtain a linear convergence rate on the duality gaps. By leveraging the convergence analysis of this variant, we then analyze the local convergence rate of the logistic fictitious play algorithm, which is well-established in game theory but lacks any form of convergence rate guarantees. We show that, with high probability, this algorithm converges locally at rate O(1/t), in terms of certain expected duality gap.
      PubDate: 2022-11-07
       
  • A note on the hierarchical multi-switch multi-echelon vehicle routing
           problem

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      Abstract: Abstract This paper introduces the Hierarchical Multi-Switch Multi-Echelon Vehicle Routing Problem, a new variant of the well-known Vehicle Routing Problem. It is a real-world problem originating from the policies of a Nordic distribution company. The problem includes a single depot, a non-predetermined hierarchy of intermediate facilities, and two different fleets, consisting of homogeneous original and homogeneous local vehicles, which are pulling swap-bodies. Original vehicles with attached swap-bodies depart from the central depot. They can either visit customers directly if only one swap-body is attached or visit one or two consecutive switch points in order to transfer one or two loaded swap-bodies to a corresponding number of local vehicles, which are subsequently routed to customers while the original vehicle itself proceeds to serve customers with the remaining loaded swap-body. A mixed-integer formulation of the problem is proposed. A short bibliographic review, relations, shared characteristics, and differences of the proposed variant and several known VRP variants are analyzed and discussed. The solution of an illustrative instance is presented in order to demonstrate the solution concept for the problem as well as to compare with solution concepts for previously stated VRP variants. Computational experiments on small instances that could be solved within one hour are also presented. The problem is computationally hard to solve. Thus, the development of heuristics and metaheuristics is an important future task in order to enable solution of real case instances or instances of realistic sizes.
      PubDate: 2022-11-05
       
  • Solving SDP relaxations of Max-Cut problem with large number of
           hypermetric inequalities by L-BFGS-B

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      Abstract: Abstract We present a computational study of SDP-based bounds for Max-Cut that use a subset of hypermetric inequalities as cutting planes to strengthen the basic relaxation. Solving these relaxations is computationally challenging due to the potentially large number of violated constraints. To overcome these difficulties, we describe a heuristic separation algorithm for hypermetric inequalities and propose to use the augmented Lagrangian method as a bounding routine. Computational experiments show that the resulting relaxations provide very tight bounds for the Max-Cut.
      PubDate: 2022-11-04
       
 
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