Showing papers in "Computers & Operations Research in 2013"

TL;DR: A new cuckoo search for multiobjective optimization is formulated and applied to solve structural design problems such as beam design and disc brake design.

TL;DR: The paper presents an efficient Hybrid Genetic Search with Advanced Diversity Control for a large class of time-constrained vehicle routing problems, introducing several new features to manage the temporal dimension.

TL;DR: In the study, the IDEA shows its effectiveness to optimize task scheduling and resource allocation compared with both the DEA and the NSGA-II and is confirmed to find the better Pareto-optimal solutions.

TL;DR: A sequence of Set Partitioning (SP) models, with columns corresponding to routes found by a metaheuristic approach, are solved, not necessarily to optimality, using a Mixed Integer Programming (MIP) solver that may interact with theMetaheuristic during its execution.

TL;DR: This paper proposes a branch-and-cut algorithm for the exact solution of several classes of Inventory-Routing Problems (IRPs), including the multi-vehicle IRP with a homogeneous and a heterogeneous fleet, theIRP with transshipment options, and the IRPs with added consistency features.

TL;DR: This paper studies a vehicle routing problem with soft time windows and stochastic travel times, and proposes a Tabu Search method to solve this model that considers both transportation costs and service costs.

TL;DR: This paper addresses the robust vehicle routing problem with time windows by proposing two new formulations for the robust problem, each based on a different robust approach, and develops a new cutting plane technique for robust combinatorial optimization problems with complicated constraints.

TL;DR: The numerical experiments show that SO-MI reaches significantly better results than the other algorithms when the number of function evaluations is very restricted (200-300 evaluations), and the algorithm converges to the global optimum almost surely.

TL;DR: This paper develops Lagrangian relaxation-based (LR) solution algorithms and demonstrates their computational efficiency, and compares the effectiveness of the LR-based solutions to that of the solutions obtained by a myopic policy which aims to fortify most reliable facilities regardless of the demand topology.

TL;DR: This paper proposes a new version of MOEA/D with uniform design, named the uniform design multiobjective evolutionary algorithm based on decomposition (UMOEA/ D), and compares the proposed algorithm with MOEA /D and NSGA-II on some scalable test problems with three to five objectives.

TL;DR: In ERABC, an efficient and robust artificial bee colony (ERABC) algorithm is proposed, a combinatorial solution search equation is introduced to accelerate the search process and chaotic initialization is used to produce initial population.

TL;DR: It is shown that many different areas of mathematical optimization play a central role in off-the-shelf supervised classification methods, and mathematical optimization turns out to be extremely useful to address important issues in classification.

TL;DR: A flexible modeling framework for IRP, which can accommodate various practical features and a simple algorithmic framework of an optimization based heuristic method is also proposed.

TL;DR: In this article, a two-phase hybrid heuristic algorithm is proposed to solve the capacitated location-routing problem (CLRP), which combines depot location and routing decisions, and the objective is to minimize the sum of the costs of the open depots, of the fixed cost associated with the used vehicles, and of the variable traveling costs related to the performed routes.

TL;DR: A new scenario reduction heuristic named forward selection in wait-and-see clusters (FSWC) is test in the context of long-term power generation expansion planning to mitigate the computational complexity of the widely-used forward selection heuristic for scenario reduction.

TL;DR: A new mathematical model for multiple echelon, multiple commodity Supply Chain Network Design (SCND), based on a Lagrangian Relaxation method, is presented and considers different time resolutions for tactical and strategic decisions.

TL;DR: This paper proposes various neighborhood search heuristics (VNS) for solving the location routing problem with multiple capacitated depots and one uncapacitated vehicle per depot, and proposes five neighborhood structures which are either of routing or location type.

TL;DR: This paper proposes a two-phase optimal supplier selection scheme in which phase one filters the inferior suppliers and phase two helps to select the best supplier among the set of non-inferior suppliers by multi-stage stochastic dynamic programming.

TL;DR: A hybrid column generation and large neighborhood search algorithm is proposed and different hybridization strategies are compared on a set of benchmark instances from the literature.

TL;DR: A novel two-phase based scheduling method with the consideration of task clustering for solving SOSP and utilizes a hybrid ant colony optimization coming with a mechanism of local search, called ACO-LS to produce optimal or near optimal schedules.

TL;DR: This work proposes an exact solution method for the constrained shortest path capable of handling large-scale networks in a reasonable amount of time and obtained significant speedups against alternative column generation schemes that solve the auxiliary problem with state-of-the-art commercial (linear) optimizers.

TL;DR: The comparison results identify the best existing methods and show that the five newly presented heuristics are competitive or better than the best performing ones in the literature for the permutation flowshop problem with the total completion time criterion.

TL;DR: A simple and flexible hybrid approach which is based on local search and large neighborhood search as well as standard metaheuristic control strategies is presented which can compete with complex state-of-the-art approaches with respect to speed and accuracy on the TTRP.

TL;DR: This work model the tourist planning problem, integrating public transportation, as the time-dependent team orienteering problem with time windows (TD-TOPTW) in order to allow PETs to create personalised tourist routes in real-time.

TL;DR: Experimental results show that the proposed iterative genetic algorithm (IGA) is very effective and robust for a large set of benchmark problems and compared with heuristic and metaheuristic approaches on benchmark problem instances.

TL;DR: It is shown that the PSS meta-heuristic can rapidly generate a large number of alternative train timetables and then described how the technique is generalized to construct an integrated timetable which includes track maintenance.

TL;DR: This paper presents Breakout Local Search (BLS) which can be applied to both MC and MWC problems without any particular adaptation and reports for the first time a detailed landscape analysis, which has been missing in the literature.

TL;DR: The proposed ACS-SPSP algorithm, an ant colony optimization (ACO) approach, is compared with a genetic algorithm to solve the SPSP on 30 random instances and can obtain higher hit rates with more accuracy compared to the previous genetic algorithm solution.

TL;DR: Heuristic algorithms for the deviation-flow refueling location model (DFRLM) that overcome the difficulty of the large number of possible deviations from each path and of combinations of facilities that can cover each path are developed.

TL;DR: A variable iterated greedy algorithm (IG) with differential evolution (vIG_DE) designed to solve the no-idle permutation flowshop scheduling problem and computational results show that all three IG variants represent state-of-art methods for the NIPFS problem.