The transportation problem with exclusionary side constraints.

We consider the so-called Transportation Problem with Exclusionary Side Con- straints (TPESC), which is a generalization of the ordinary transportation problem. We determine the complexity status for each of two special cases of this problem, by proving NP-completeness, and by exhibiting a pseudo-polynomial time algorithm. For the general problem, we show that it cannot be approximated with a constant perfor- mance ratio in polynomial time (unless P=NP). These results settle the complexity status of the TPESC.

Study on efficient planning for advanced logistics network model based on robust genetic algorithm

制度:新 ; 報告番号:甲3000号 ; 学位の種類:博士(工学) ; 授与年月日:2010/2/22 ; 早大学位記番号:新525

Relaxations and Cutting Planes for Linear Programs with Complementarity Constraints

We study relaxations for linear programs with complementarity constraints, especially instances whose complementary pairs of variables are not independent. Our formulation is based on identifying vertex covers of the conflict graph of the instance and generalizes the extended reformulation-linearization technique of Nguyen, Richard, and Tawarmalani to instances with general complementarity conditions between variables. We demonstrate how to obtain strong cutting planes for our formulation from both the stable set polytope and the boolean quadric polytope associated with a complete bipartite graph. Through an extensive computational study for three types of practical problems, we assess the performance of our proposed linear relaxation and new cutting-planes in terms of the optimality gap closed

Iterative algorithm for lane reservation problem on transportation network

International audienceIn this paper, we study an NP-hard lane reservation problem on transportation network. By selecting lanes to be reserved on the existing transportation network under some special situations, the transportation tasks can be accomplished on the reserved lanes with satisfying the condition of time or safety. Lane reservation strategy is a flexible and economic method for traffic management. However, reserving lanes has impact on the normal traffic because the reserved lanes can only be passed by the special tasks. It should be well considered choosing reserved lanes to minimize the total traffic impact when applying the lane reservation strategy for the transportation tasks. In this paper, an integer linear program model is formulated for the considered problem and an optimal algorithm based on the cut-and-solve method is proposed. Some new techniques are developed for the cut-and-solve method to accelerate the convergence of the proposed algorithm. Numerical computation results of 125 randomly generated instances show that the proposed algorithm is much faster than a MIP solver of commercial software CPLEX 12.1 to find optimal solutions on average computing time

Solving the nonlinear transportation problem by global optimization

The aim of this paper is to present the suitability of three different global optimization methods for specifically the exact optimum solution of the nonlinear transportation problem (NTP). The evaluated global optimization methods include the branch and reduce method, the branch and cut method and the combination of global and local search strategies. The considered global optimization methods were applied to solve NTPs with reference to literature. NTPs were formulated as nonlinear programming (NLP) optimization problems. The obtained optimal results were compared with those got from literature. A comparative evaluation of global optimization methods is presented at the end of the paper to show their suitability for solving NTPs. First published online: 10 Feb 201

A matheuristic for a customer assignment problem in direct marketing

In direct marketing, companies use sales campaigns to target their customers with personalized product offers. The effectiveness of direct marketing greatly depends on the assignment of customers to campaigns. In this paper, we consider a real-world planning problem of a major telecommunications company that assigns its customers to individual activities of its direct marketing campaigns. Various side constraints, such as budgets and sales targets, must be met. Conflict constraints ensure that individual customers are not assigned too frequently to similar activities. Related problems have been addressed in the literature; however, none of the existing approaches cover all the side constraints considered here. To close this gap, we develop a matheuristic that employs a new decomposition strategy to cope with the large number of conflict constraints in typical problem instances. In a computational experiment, we compare the performance of the proposed matheuristic to the performance of two mixed-binary linear programs on a test set that includes large-scale real-world instances. The matheuristic derives near-optimal solutions in short running times for small- to medium-sized instances and scales to instances of practical size comprising millions of customers and hundreds of activities. The deployment of the matheuristic at the company has considerably increased the overall effectiveness of its direct marketing campaigns

Selecting and scheduling of improvements in urban transportation networks using metaheuristics

Deciding which projects, alternatives and/or investments should be implemented is a complex and important topic not only in transportation engineering, but in management, operations research, and economics. If the project’s benefits or costs depend on which other project is realized, then the projects are interrelated. The evaluation method computes the costs of network flows determined with the Frank-Wolfe algorithm, which is modified to consider intersection flows and delays. Intersections are modelled with pseudo-links. The methods used for choosing the optimal schedule of project improvements are: Ant Colony Optimization, Simulated Annealing and Tabu Search. The heuristic that yields the best most quickly solution is Ant Colony Optimization and it is chosen for the sensitivity analysis. The results of the sensitivity analysis show how the changes in ACO parameters and the model parameters influence the behavior of the model and the algorithm