A column generation algorithm for tactical timber transportation planning

We present a tactical wood flow model that appears in the context of the Canadian forestry industry, and describe the implementation of a decision support system created for use by an industrial partner. In this problem, mill demands and harvested volumes of a heterogeneous set of log types are given over a multi-period planning horizon. Wood can be stored at the forest roadside before delivery at a financial cost. Rather than solve this as a network linear programme on the basis of out-and-back deliveries, we choose to model this problem as a generalization of a log-truck scheduling problem. By routing and scheduling the trucks in the resolution, this allows us to both anticipate potential backhaul opportunities for cost and fuel savings, and also minimize queuing times at log-loaders, management of which is a major concern in the industry. We model this problem as a mixed integer linear programme and solve it via column generation. The methodology is tested on several case studies.     

Balancing of agricultural census data by using discrete optimization

In the case of large-scale surveys, such as a Census, data may contain errors or missing values. An automatic error correction procedure is therefore needed. We focus on the problem of restoring the consistency of agricultural data concerning cultivation areas and number of livestock, and we propose here an approach to this balancing problem based on optimization. Possible alternative models, either linear, quadratic or mixed integer, are presented. The mixed integer linear one has been preferred and used for the treatment of possibly unbalanced data records. Results on real-world Agricultural Census data show the effectiveness of the proposed approach.     

Static target search path planning optimization with heterogeneous agents

Disruptions in airline operations can result in infeasibilities in aircraft and passenger schedules. Airlines typically recover aircraft schedules and disruptions in passenger itineraries sequentially. However, passengers are severely affected by disruptions and recovery decisions. In this paper, we present a mathematical formulation for the integrated aircraft and passenger recovery problem that considers aircraft and passenger related costs simultaneously. Using the superimposition of aircraft and passenger itinerary networks, passengers are explicitly modeled in order to use realistic passenger related costs. In addition to the common routing recovery actions, we integrate several passenger recovery actions and cruise speed control in our solution approach. Cruise speed control is a very beneficial action for mitigating delays. On the other hand, it adds complexity to the problem due to the nonlinearity in fuel cost function. The problem is formulated as a mixed integer nonlinear programming (MINLP) model. We show that the problem can be reformulated as conic quadratic mixed integer programming (CQMIP) problem which can be solved with commercial optimization software such as IBM ILOG CPLEX. Our computational experiments have shown that we could handle several simultaneous disruptions optimally on a four-hub network of a major U.S. airline within less than a minute on the average. We conclude that proposed approach is able to find optimal tradeoff between operating and passenger-related costs in real time.     

Revised multi-choice goal programming for multi-period,multi-stage inventory controlled supply chain model with popup stores in Guerrilla marketing

In this paper, we consider a supply chain network design problem with popup stores which can be opened for a few weeks or months before closing seasonally in a marketplace. The proposed model is multi-period and multi-stage with multi-choice goals under inventory management constraints and formulated by 0–1 mixed integer linear programming. The design tasks of the problem involve the choice of the popup stores to be opened and the distribution network design to satisfy the demand with three multi-choice goals. The first goal is minimization of the sum of transportation costs in all stages; the second is to minimization of set up costs of popup stores; and the third goal is minimization of inventory holding and backordering costs. Revised multi-choice goal programming approach is applied to solve this mixed integer linear programming model. Also, we provide a real-world industrial case to demonstrate how the proposed model works.     

Automatic identification of embedded network rows in large-scale optimization models

The solution of a large-scale linear, integer, or mixed integer programming problem is often facilitated by the exploitation of special structure in the model. This paper presents heuristic algorithms for identifying embedded network rows within the coefficient matrix of such models. The problem of identifying a maximum-size embedded pure network is shown to be among the class of NP-hard problems. The polynomially-bounded, efficient algorithms presented here do not guarantee network sets of maximum size. However, upper bounds on the size of the maximum network set are developed and used to show that our algorithms identify embedded networks of close to maximum size. Computational tests with large-scale, real-world models are presented.     

对带有盒约束的二次整数规划的一种线性化方法

本文主要讨论了二次整数规划问题的线性化方法.在目标函数为二次函数的情况下,我们讨论了带有二次约束的整数规划问题的线性化方法,并将文献中对二次0-1问题的研究拓展为对带有盒约束的二次整数规划问题的研究.最终将带有盒约束的二次整数规划问题转化为线性混合本文主要讨论了二次整数规划问题的线性化方法.在目标函数为二次函数的情况下,我们讨论了带有二次约束的整数规划问题的线性化方法,并将文献中对二次0-1问题的研究拓展为对带有盒约束的二次整数规划问题的研究.最终将带有盒约束的二次整数规划问题转化为线性混合0-1整数规划问题,然后利用Ilog-cplex或Excel软件中的规划求解工具进行求解,从而解决原二次整数规划.     

A cutting plane algorithm for the Capacitated Connected Facility Location Problem

We consider a network design problem that arises in the cost-optimal design of last mile telecommunication networks. It extends the Connected Facility Location problem by introducing capacities on the facilities and links of the networks. It combines aspects of the capacitated network design problem and the single-source capacitated facility location problem. We refer to it as the Capacitated Connected Facility Location Problem. We develop a basic integer programming model based on single-commodity flows. Based on valid inequalities for the capacitated network design problem and the single-source capacitated facility location problem we derive several (new) classes of valid inequalities for the Capacitated Connected Facility Location Problem including cut set inequalities, cover inequalities and combinations thereof. We use them in a branch-and-cut framework and show their applicability and efficacy on a set of real-world instances.     

Description of 2-integer continuous knapsack polyhedra

In this paper we discuss the polyhedral structure of several mixed integer sets involving two integer variables. We show that the number of the corresponding facet-defining inequalities is polynomial on the size of the input data and their coefficients can also be computed in polynomial time using a known algorithm [D. Hirschberg, C. Wong, A polynomial-time algorithm for the knapsack problem with two variables, Journal of the Association for Computing Machinery 23 (1) (1976) 147–154] for the two integer knapsack problem. These mixed integer sets may arise as substructures of more complex mixed integer sets that model the feasible solutions of real application problems.     

On relaxations of the max k-cut problem formulations

A tight continuous relaxation is a crucial factor in solving mixed integer formulations of many NP-hard combinatorial optimization problems. The (weighted) max k-cut problem is a fundamental combinatorial optimization problem with multiple notorious mixed integer optimization formulations. In this paper, we explore four existing mixed integer optimization formulations of the max k-cut problem. Specifically, we show that the continuous relaxation of a binary quadratic optimization formulation of the problem is: (i) stronger than the continuous relaxation of two mixed integer linear optimization formulations and (ii) at least as strong as the continuous relaxation of a mixed integer semidefinite optimization formulation. We also conduct a set of experiments on multiple sets of instances of the max k-cut problem using state-of-the-art solvers that empirically confirm the theoretical results in item (i). Furthermore, these numerical results illustrate the advances in the efficiency of global non-convex quadratic optimization solvers and more general mixed integer nonlinear optimization solvers. As a result, these solvers provide a promising option to solve combinatorial optimization problems. Our codes and data are available on GitHub.     

Managing pharmaceuticals delivery service using a hybrid particle swarm intelligence approach

Medicines or drugs have unique characteristics of short life cycle, small size, light weight, restrictive distribution time and the need of temperature and humidity control (selected items only). Thus, logistics companies often use different types of vehicles with different carrying capacities, and considering fixed and variable costs in service delivery, which make the vehicle assignment and route optimization more complicated. In this study, we formulate the problem to a multi-type vehicle assignment and mixed integer programming route optimization model with fixed fleet size under the constraints of distribution time and carrying capacity. Given non-deterministic polynomial hard and optimal algorithm can only be used to solve small-size problem, a hybrid particle swarm intelligence (PSI) heuristic approach, which adopts the crossover and mutation operators from genetic algorithm and 2-opt local search strategy, is proposed to solve the problem. We also adapt a principle based on cost network and Dijkstra’s algorithm for vehicle scheduling to balance the distribution time limit and the high loading rate. We verify the relative performance of the proposed method against several known optimal or heuristic solutions using a standard data set for heterogeneous fleet vehicle routing problem. Additionally, we compare the relative performance of our proposed Hybrid PSI algorithm with two intelligent-based algorithms, Hybrid Population Heuristic algorithm and Improved Genetic Algorithm, using a real-world data set to illustrate the practical and validity of the model and algorithm.

     

On the maximum small-world subgraph problem

A clique (or a complete subgraph) is a popular model for an “ideal” cluster in a network. However, in many practical applications this notion turns out to be overly restrictive as it requires the existence of all pairwise links within the cluster. Thus, the researchers and practitioners often rely on various clique relaxation ideas for more flexible models of highly connected clusters. In this paper, we propose a new clique relaxation model referred to as a small-world subgraph, which represents a network cluster with “small-world” properties: low average distance and high clustering coefficient. In particular, we demonstrate that the proposed small-world subgraph model has better “cohesiveness” characteristics than other existing clique relaxation models in some worst-case scenarios. The main focus of the paper is on the problem of finding a small-world subgraph of maximum cardinality in a given graph. We describe a mixed integer programming (MIP) formulation of the problem along with several algorithmic enhancements. For solving large-scale instances of the problem we propose a greedy-type heuristic referred to as the iterative depth-first search (IDF) algorithm. Furthermore, we show that the small-world subgraphs identified by the IDF algorithm have an additional property that may be attractive from the practical perspective, namely, 2-connectivity. Finally, we perform extensive computational experiments on real-world and randomly generated networks to demonstrate the performance of the developed computational approaches that also reveal interesting insights about the proposed clique relaxation model.     

Designing new European rail freight services

We present an optimization model for design of transportation services, with a particular emphasis on how attention to service quality affects the design. The model is applied to a real-world case study where opportunities for new rail-based freight transportation services are explored. Fleet sizing is introduced to the service network design, because cost of acquiring locomotives is high, and inefficient resource utilization could result if fleet sizing is carried out a posteriori. The problem is a cross-border planning problem, introducing need for change of locomotives at borders, and separate fleets of locomotives thus have to be modelled. Demand in the system is characterized by various commodity groups with different service quality requirements. We propose a mixed integer programming model with a nonlinear objective function. The model finds the most profitable operations for the train operator when important service quality factors are accounted for.     

Parametric global optimisation for bilevel programming

We propose a global optimisation approach for the solution of various classes of bilevel programming problems (BLPP) based on recently developed parametric programming algorithms. We first describe how we can recast and solve the inner (follower’s) problem of the bilevel formulation as a multi-parametric programming problem, with parameters being the (unknown) variables of the outer (leader’s) problem. By inserting the obtained rational reaction sets in the upper level problem the overall problem is transformed into a set of independent quadratic, linear or mixed integer linear programming problems, which can be solved to global optimality. In particular, we solve bilevel quadratic and bilevel mixed integer linear problems, with or without right-hand-side uncertainty. A number of examples are presented to illustrate the steps and details of the proposed global optimisation strategy.     

Simple solution methods for separable mixed linear and quadratic knapsack problem

Quadratic knapsack problem has a central role in integer and nonlinear optimization, which has been intensively studied due to its immediate applications in many fields and theoretical reasons. Although quadratic knapsack problem can be solved using traditional nonlinear optimization methods, specialized algorithms are much faster and more reliable than the nonlinear programming solvers. In this paper, we study a mixed linear and quadratic knapsack with a convex separable objective function subject to a single linear constraint and box constraints. We investigate the structural properties of the studied problem, and develop a simple method for solving the continuous version of the problem based on bi-section search, and then we present heuristics for solving the integer version of the problem. Numerical experiments are conducted to show the effectiveness of the proposed solution methods by comparing our methods with some state of the art linear and quadratic convex solvers.     

Strategic design of competing supply chain networks for inelastic demand

A model for designing the network of a new entrant supply chain under inelastic demand and in the presence of pre-existing competing chains is proposed. These supply chains provide an identical product for a market area. The model considers the location of distribution centres and retail outlets on a discrete set of potential locations. The assumptions of the model are: (1) static competition between the new and pre-existing chains and (2) a probabilistic customer behaviour based on an attraction function depending on both the location and the quality of the retailers. This model also incorporates the impact of the facilities’ location decisions on the operational inventory and shipment decisions. The resulting model is formulated as a mixed integer non-linear programme (MINLP). To solve the MINLP it is transformed to a linear one. We illustrate the model, discuss the results of a real-world case, and investigate the effectiveness of the proposed algorithm using randomly generated examples.     

Mixed Integer Models for the Stationary Case of Gas Network Optimization

A gas network basically consists of a set of compressors and valves that are connected by pipes. The problem of gas network optimization deals with the question of how to optimize the flow of the gas and to use the compressors cost-efficiently such that all demands of the gas network are satisfied. This problem leads to a complex mixed integer nonlinear optimization problem. We describe techniques for a piece-wise linear approximation of the nonlinearities in this model resulting in a large mixed integer linear program. We study sub-polyhedra linking these piece-wise linear approximations and show that the number of vertices is computationally tractable yielding exact separation algorithms. Suitable branching strategies complementing the separation algorithms are also presented. Our computational results demonstrate the success of this approach. Received: April, 2004     

γ-Robust facility relocation problem

In this paper, we consider relocating facilities, where we have demand changes in the network. Relocations are performed by closing some of the existing facilities from low demand areas and opening new ones in newly emerging areas. However, the actual changes of demand are not known in advance. Therefore, different scenarios with known probabilities are used to capture such demand changes. We develop a mixed integer programming model for facility relocation that minimizes the expected weighted distance while making sure that relative regret for each scenario is no greater than γ. We analyzed the problem structure and developed a Lagrangian Decomposition Algorithm (LDA) to expedite the solution process. Numerical experiments are carried out to show the performance of LDA against the exact solution method.     

Some models for water distribution systems

In this paper the results of our investigation of methods and algorithms for operative and dynamical control of water distribution systems are presented. Our investigation was directed to the distribution system equipped with a variety of components—pipes, consumers, valves and the inner water store (a reservoir) as the most important one. The final model of the optimization problem containing all necessary restrictions is so complex that it is impossible to solve it without some special techniques. In the presented study we propose a multilevel approach based on the idea of aggregation of the pipelines network. In the paper the method of determination of aggregated model of the network is presented. The usefulness of this method in the optimization of water supply systems is examined. Based on an aggregated network model one determines optimal strategies for control of pumping stations. The optimization model consists of a linear objective function and quadratic constraints. A nonlinear mixed integer programming problem which is solved by modified branch and bound method is obtained.     

New mixed integer-programming model for the pickup-and-delivery problem with transshipment

In recent years, many important real-world applications are studied as “rich” vehicle routing problems that are variants and generalizations of the well-known vehicle routing problem. In this paper we address the pickup-and-delivery version of this problem and consider further generalization by allowing transshipment in the network. Moreover, we allow heterogenous vehicles and flexible fleet size. We describe mixed integer-programming formulations for the problem with and without time windows for services. The number of constraints and variables in the models are bounded by polynomial size of the problem. We discuss several problem variants that are either captured by our models or can be easily captured through simple modifications. Computational work gave promising results and confirms that transshipment in network can indeed enhance optimization.