The location-routing problem with simultaneous pickup and delivery: Formulations and a heuristic approach

In this paper, we consider a variant of the Location-Routing Problem (LRP), namely the LRP with simultaneous pickup and delivery (LRPSPD). The LRPSPD seeks to minimize total cost by simultaneously locating the depots and designing the vehicle routes that satisfy pickup and delivery demand of each customer at the same time. We propose two polynomial-size mixed integer linear programming formulations for the problem and a family of valid inequalities to strengthen the formulations. While the first formulation is a node-based formulation, the second one is a flow-based formulation. Furthermore, we propose a two-phase heuristic approach based on simulated annealing, tp_SA, to solve the large-size LRPSPD and two initialization heuristics to generate an initial solution for the tp_SA. We then empirically evaluate the strengths of the proposed formulations with respect to their ability to find optimal solutions or strong lower bounds, and investigate the performance of the proposed heuristic approach. Computational results show that the flow-based formulation performs better than the node-based formulation in terms of the solution quality and the computation time on small-size problems. However, the node-based formulation can yield competitive lower bounds in a reasonable amount of time on medium-size problems. Meantime, the proposed heuristic approach is computationally efficient in finding good quality solutions for the LRPSPD.     

A General Model of Some Inverse Combinatorial Optimization Problems and Its Solution Method Under l ∞ Norm

This paper proposes an optimization model and shows that most inverse combinatorial optimization problems so far discussed can be fit into this model as special cases. We propose a Newton-type algorithm for this model under l norm. This algorithm can solve the model in strongly polynomial time if the subproblem involved is solvable in strongly polynomial time for any fixed value of the parameter appearing in the subproblem, and it is shown that most particular inverse optimization problems encountered are this kind. Therefore, through this paper we show that a large group of inverse optimization problems can be handled in a uniform way and solved in strongly polynomial time.     

More-for-less algorithm for fixed-charge transportation problems

The more-for-less (MFL) phenomenon in distribution problems occurs when it is possible to ship more total goods for less (or equal) total cost, while shipping the same quantity or more from each origin and to each destination. This paradox occurs often in fixed-charge transportation problems (FCTPs), and further analysis could bring significant reduction in costs. The MFL phenomenon for FCTPs has received minimal attention in the literature despite the fact that existing analytical algorithms, such as branch and bound, are limited to small problems due to excessive computational effort. In this paper, we develop a simple heuristic algorithm to identify the demand destinations and the supply points to ship MFL in FCTPs. The proposed method builds upon any existing basic feasible solution. It is easy to implement and can serve as an effective tool for managers for solving the more-for-less paradox for large distribution problems.     

An Arborescent Network Formulation and Dual Ascent Based Procedure for the Two-Stage Multi-Item Dynamic Demand Lotsize Problem

Traditional approaches for modeling and solving dynamic demand lotsize problems are based on Zangwill's single-source network and dynamic programming algorithms. In this paper, we propose an arborescent fixed-charge network (ARBNET) programming model and dual ascent based branch-and-bound procedure for the two-stage multi-item dynamic demand lotsize problem. Computational results show that the new approach is significantly more efficient than earlier solution strategies. The largest set of problems that could be solved using dynamic programming contained 4 end items and 12 time periods, and required 475.38 CPU seconds per problem. The dual ascent algorithms averaged .06 CPU seconds for this problem set, and problems with 30 end items and 24 time periods were solved in 85.65 CPU seconds. Similar results verify the superiority of the new approach for handling backlogged demand. An additional advantage of the algorithm is the availability of a feasible solution, with a known worst-case optimality gap, throughout the problem-solving process.     

Partitioning 2-edge-colored complete multipartite graphs into monochromatic cycles, paths and trees

In this paper we consider the problem of partitioning complete multipartite graphs with edges colored by 2 colors into the minimum number of vertex disjoint monochromatic cycles, paths and trees, respectively. For general graphs we simply address the decision version of these three problems the 2-PGMC, 2-PGMP and 2-PGMT problems, respectively. We show that both 2-PGMC and 2-PGMP problems are NP-complete for complete multipartite graphs and the 2-PGMT problem is NP-complete for bipartite graphs. This also implies that all these three problems are NP-complete for general graphs, which solves a question proposed by the authors in a previous paper. Nevertheless, we show that the 2-PGMT problem can be solved in polynomial time for complete multipartite graphs. Research supported by NSFC.     

On step fixed-charge transportation problem

The fixed-charge problem is a nonlinear programming problem of practical interest in business and industry. One of its variations is the fixed-charge transportation problem (FCTP) where fixed cost is incurred for every route that is used in the solution, along with the variable cost that is proportional to the amount shipped. That cost structure causes the value of the objective function Z to also behave like a step function. Each time we open or close a route the objective function jumps a step. The step fixed-charge transportation problem (SFCTP) is a variation of the FCTP where the fixed cost is in the form of a step function dependent on the load in a given route. While the value of the objective function Z in the FCTP is a step function, the introduction of the step fixed cost in the SFCTP results in the objective function Z being itself a step function with many more steps. Fixed-charge problems are usually solved using sophisticated analytical or computer software. This paper discusses the theory of SFCTP and presents a computationally simple heuristic algorithm for solving small SFCTPs.     

Transversal Graphs for Partially Ordered Sets: Sequencing, Merging and Scheduling Problems

This paper introduces an approach to solving combinatorial optimization problems on partially ordered sets by the reduction to searching source-sink paths in the related transversal graphs. Different techniques are demonstrated in application to finding consistent supersequences, merging partially ordered sets, and machine scheduling with precedence constraints. Extending the approach to labeled partially ordered sets we also propose a solution for the smallest superplan problem and show its equivalence to the well studied coarsest regular refinement problem. For partially ordered sets of a fixed width the number of vertices in their transversal graphs is polynomial, so the reduction allows us easily to establish that many related problems are solvable in polynomial or pseudopolynomial time. For example, we establish that the longest consistent supersequence problem with a fixed number of given strings can be solved in polynomial time, and that the precedence-constrained release-date maximum- or total-cost preemptive or nonpreemptive job-shop scheduling problem with a fixed number of jobs can be solved in pseudopolynomial time. We also show that transversal graphs can be used to generalize and strengthen similar results obtained earlier by dynamic programming.     

The Center Location Improvement Problem Under the Hamming Distance

In this paper, we consider the center location improvement problems under the sum-type and bottleneck-type Hamming distance. For the sum-type problem, we show that achieving an algorithm with a worst-case ratio of O(log |V|) is NP-hard, and for the bottleneck-type problem, we present a strongly polynomial algorithm.     

Approximation algorithms for the makespan minimization with positive tails on a single machine with a fixed non-availability interval

In this article, we consider the non-resumable case of the single machine scheduling problem with a fixed non-availability interval. We aim to minimize the makespan when every job has a positive tail. We propose a polynomial approximation algorithm with a worst-case performance ratio of 3/2 for this problem. We show that this bound is tight. We present a dynamic programming algorithm and we show that the problem has an FPTAS (Fully Polynomial Time Approximation Algorithm) by exploiting the well-known approach of Ibarra and Kim (J. ACM 22:463–468, 1975). Such an FPTAS is strongly polynomial. The obtained results outperform the previous polynomial approximation algorithms for this problem.     

Optimum turn-restricted paths,nested compatibility,and optimum convex polygons

We consider two apparently unrelated classes of combinatorial and geometric optimization problems. First, we give compact extended formulations, i.e., polynomial-size linear programming formulations with integer optima, for optimum path problems with turn restrictions satisfying a nested compatibility condition in acyclic digraphs. We then apply these results to optimum convex polygon problems in the plane, by interpreting certain dynamic programming algorithms as sequences of optimum turn-restricted path problems with nested compatibility in acyclic digraphs. As a result, we derive compact extended formulations for these geometric problems as well.     

Single-machine scheduling with production and rejection costs to minimize the maximum earliness

In this paper, we consider the single-machine scheduling problem with production and rejection costs to minimize the maximum earliness. If a job is accepted, then this job must be processed on the machine and a corresponding production cost needs be paid. If the job is rejected, then a corresponding rejection cost has to be paid. The objective is to minimize the sum of the maximum earliness of the accepted jobs, the total production cost of the accepted jobs and the total rejection cost of the rejected jobs. We show that this problem is equivalent to a single-machine scheduling problem to minimize the maximum earliness with two distinct rejection modes. In the latter problem, rejection cost might be negative in the rejection-award mode which is different from the traditional rejection-penalty mode in the previous literatures. We show that both of two problems are NP-hard in the ordinary sense and then provide two pseudo-polynomial-time algorithms to solve them. Finally, we also show that three special cases can be solved in polynomial time.     

Improved Linear Programming Models for Discriminant Analysis*

Discriminant analysis is an important tool for practical problem solving. Classical statistical applications have been joined recently by applications in the fields of management science and artificial intelligence. In a departure from the methodology of statistics, a series of proposals have appeared for capturing the goals of discriminant analysis in a collection of linear programming formulations. The evolution of these formulations has brought advances that have removed a number of initial shortcomings and deepened our understanding of how these models differ in essential ways from other familiar classes of LP formulations. We will demonstrate, however, that the full power of the LP discriminant analysis models has not been achieved, due to a previously undetected distortion that inhibits the quality of solutions generated. The purpose of this paper is to show how to eliminate this distortion and thereby increase the scope and flexibility of these models. We additionally show how these outcomes open the door to special model manipulations and simplifications, including the use of a successive goal method for establishing a series of conditional objectives to achieve improved discrimination.     

Weighted Inverse Minimum Spanning Tree Problems Under Hamming Distance

In this paper, we consider weighted inverse minimum spanning tree problems under Hamming distance. Three models are considered: unbounded case, unbounded case with forbidden edges, and general bounded case. In terms of the method for minimum-weight node cover problem on bipartite graph, we present their respective strongly polynomial algorithms.This research is supported by TRAPOYT, National Natural Science Foundation of China (10271110, 60021201)     

Combining Linear and Non-Linear Objectives in Spanning Tree Problems

A classical approach to multicriteria problems asks for the optimization of a suitable linear combination of the objectives. In this work we address such problems when one of the objectives is the linear function, the other is a non-linear one and we seek for a spanning tree of a given graph which optimizes the combination of the two functions. We consider both maximization and minimization problems and present the complexity status of 56 such problems, giving, whenever possible, polynomial solution algorithms.     

Probabilistic graph-coloring in bipartite and split graphs

We revisit in this paper the stochastic model for minimum graph-coloring introduced in (Murat and Paschos in Discrete Appl. Math. 154:564–586, 2006), and study the underlying combinatorial optimization problem (called probabilistic coloring) in bipartite and split graphs. We show that the obvious 2-coloring of any connected bipartite graph achieves standard-approximation ratio 2, that when vertex-probabilities are constant probabilistic coloring is polynomial and, finally, we propose a polynomial algorithm achieving standard-approximation ratio 8/7. We also handle the case of split graphs. We show that probabilistic coloring is NP-hard, even under identical vertex-probabilities, that it is approximable by a polynomial time standard-approximation schema but existence of a fully a polynomial time standard-approximation schema is impossible, even for identical vertex-probabilities, unless P=NP. We finally study differential-approximation of probabilistic coloring in both bipartite and split graphs. Part of this research has been performed while the second author was with the LAMSADE on a research position funded by the CNRS.     

Adaptive Local Polynomial Whittle Estimation of Long‐range Dependence

The local Whittle (or Gaussian semiparametric) estimator of long range dependence, proposed by Künsch (1987) and analyzed by Robinson (1995a), has a relatively slow rate of convergence and a finite sample bias that can be large. In this paper, we generalize the local Whittle estimator to circumvent these problems. Instead of approximating the short‐run component of the spectrum, ϕ(λ) , by a constant in a shrinking neighborhood of frequency zero, we approximate its logarithm by a polynomial. This leads to a “local polynomial Whittle” (LPW) estimator. We specify a data‐dependent adaptive procedure that adjusts the degree of the polynomial to the smoothness of ϕ(λ) at zero and selects the bandwidth. The resulting “adaptive LPW” estimator is shown to achieve the optimal rate of convergence, which depends on the smoothness of ϕ(λ) at zero, up to a logarithmic factor.     

求解带时间窗的装-卸载问题的概率式禁忌搜索算法

本文介绍一个求解有时间窗的装载—卸载问题的概率式禁忌搜索算法。论文首先回顾了求解VRP、PDP问题的主要方法,介绍了搜索算法的基本概念,给出了基于概率式禁忌搜索的详细算法。初步实验表明新算法优于传统禁忌搜索算法。     

Robotic-Cell Scheduling: Special Polynomially Solvable Cases of the Traveling Salesman Problem on Permuted Monge Matrices

In this paper, we introduce the 1 − K robotic-cell scheduling problem, whose solution can be reduced to solving a TSP on specially structured permuted Monge matrices, we call b-decomposable matrices. We also review a number of other scheduling problems which all reduce to solving TSP-s on permuted Monge matrices. We present the important insight that the TSP on b-decomposable matrices can be solved in polynomial time by a special adaptation of the well-known subtour-patching technique. We discuss efficient implementations of this algorithm on newly defined subclasses of permuted Monge matrices.     

Separating online scheduling algorithms with the relative worst order ratio

The relative worst order ratio is a measure for the quality of online algorithms. Unlike the competitive ratio, it compares algorithms directly without involving an optimal offline algorithm. The measure has been successfully applied to problems like paging and bin packing. In this paper, we apply it to machine scheduling. We show that for preemptive scheduling, the measure separates multiple pairs of algorithms which have the same competitive ratios; with the relative worst order ratio, the algorithm which is “intuitively better” is also provably better. Moreover, we show one such example for non-preemptive scheduling.     

Change-making problems revisited: a parameterized point of view

The change-making problem is the problem of representing a given amount of money with the fewest number of coins possible from a given set of coin denominations. In the general version of the problem, an upper bound for the availability of every coin value is given. Even the special case, where for each value an unlimited number of coins is available, is NP-hard. Since in the original problem some amounts can not be represented, especially if no coin of value one exists, we introduce generalized problems that look for approximations of the given amount such that a cost function is minimized. We recall algorithms for the change-making problem and present new algorithms for the generalized version of the problem. Motivated by the NP-hardness we study fixed-parameter tractability of all these problems. We show that some of these problems are fixed-parameter tractable and that some are \(\hbox {W}[1]\)-hard. In order to show the existence of polynomial and constant-size kernels we prove some general results and apply them to several parameterizations of the change-making problems.