Showing papers in "Computers & Operations Research in 2010"

TL;DR: This paper describes industrial aspects of combined inventory management and routing in maritime and road-based transportation, and gives a classification and comprehensive literature review of the current state of the research.

TL;DR: This paper proposes a model for integrated logistics network design to avoid the sub-optimality caused by a separate, sequential design of forward and reverse logistics networks, and develops a bi-objective mixed integer programming formulation to minimize the total costs and maximize the responsiveness of a logistics network.

TL;DR: An extensive review of recently published papers on hybrid flow shop (HFS) scheduling problems is presented and the papers are classified first according to the HFS characteristics and production limitations considered in the respective papers and then according toThe solution approach proposed.

TL;DR: The DPFSP is characterized and six different alternative mixed integer linear programming (MILP) models that are carefully and statistically analyzed for performance are proposed.

TL;DR: The purpose of this paper is to describe industrial aspects of combined fleet composition and routing in maritime and road-based transportation, and to present the current status of research in the form of a comprehensive literature review.

TL;DR: This paper provides a thorough review of the current state-of-the-art within airline disruption management of resources, including aircraft, crew, passenger and integrated recovery.

TL;DR: A generalized closed-loop model for the logistics planning was proposed by formulating a cyclic logistics network problem into an integer linear programming model and a Genetic Algorithm, which is based on spanning tree structure was developed.

TL;DR: The proposed algorithm not only improved several of the known solutions, but also presented a very satisfying scalability.

TL;DR: This paper provides an annotated bibliography for sports scheduling articles, noticeable that the number of papers has risen in recent years, demonstrating that scientific interest is increasing in this area.

TL;DR: A heuristic based on a k-shortest path algorithm is developed by using a surrogate distance to denote the effectiveness of each member in the supply chain and a ''good'' solution with a small gap relative to the lower bound is obtained in a short computational time.

TL;DR: Experimental results on Solomon's and Gehring and Homberger benchmarks demonstrate that the algorithm outperforms previous approaches and is able to improve 184 best-known solutions out of 356 instances.

TL;DR: A novel hybrid discrete differential evolution (HDDE) algorithm for solving blocking flow shop scheduling problems to minimize the maximum completion time (i.e. makespan) and a local search algorithm based on insert neighborhood structure is embedded in the algorithm to balance the exploration and exploitation by enhancing the local searching ability.

TL;DR: A pre-disaster planning problem that seeks to strengthen a highway network whose links are subject to random failures due to a disaster is addressed and it is shown that using the first order terms of this function leads to a knapsack problem whose solution is a local optimum to the original problem.

TL;DR: The portfolio selection is formulated as a tri-objective optimization problem so as to find tradeoffs between risk, return and the number of securities in the portfolio and quantity and class constraints are introduced into the model.

TL;DR: This paper proposes a competitive variable neighborhood search-based heuristic, using three classes of neighborhoods, based on the ejection chain idea, which exploits the existence of arcs where the vehicle load is zero, giving rise to natural sequences of requests.

TL;DR: This paper presents the first upper and lower bounding procedures for this new problem, the CCVRP, by a memetic algorithm using non-trivial evaluations of cost variations in the local search and good results are obtained.

TL;DR: A multiobjective binary programming model that facilitates both obtaining efficient portfolios in line with the set of objectives pursued by the organization, as well as their scheduling regarding the optimum time to launch each project within the portfolio without the need for a priori information on the decision-maker's preferences is proposed.

TL;DR: This work presents the multi-period orienteering problem with multiple time windows (MuPOPTW), a new routing problem combining objective and constraints of the orienteering Problem (OP) and teamorienteering problem (TOP), constraints from standard vehicle routing problems, and original constraints from a real-world application.

TL;DR: The use of evolutionary algorithms specializing in intensification and diversification for building hybrid metaheuristics becomes a prospective line of research for obtaining effective search algorithms.

TL;DR: An algorithm based on Ant Colony Optimization paradigm to solve the joint production and maintenance scheduling problem and outperforms two well-known Multi-Objective Genetic Algorithms (MOGAs): SPEA 2 and NSGA II.

TL;DR: The proposed solution method is a greedy randomized adaptive search procedure (GRASP), calling an evolutionary local search (ELS) and searching within two solution spaces: giant tours without trip delimiters and true CLRP solutions.

TL;DR: An overview of the following classes of models: Gradual cover models, which seek to relax the ''all or nothing'' assumption by replacing it with a general coverage function which represents the proportion of demand covered at a certain distance from the facility.

TL;DR: In this paper, two metaheuristic algorithms using tabu search and variable neighbourhood search that are based around the neighbour sets are presented for publicly available waste collection problems involving up to 2092 customers and 19 waste disposal facilities.

TL;DR: A new hybrid algorithmic nature inspired approach based on Particle Swarm Optimization (PSO), Greedy Randomized Adaptive Search Procedure (GRASP) and Expanding Neighborhood Search (ENS) Strategy is proposed for the solution of the PTSP.

TL;DR: A heuristic method-based on the GRASP and path relinking methodologies-for finding approximate solutions to the max-min diversity problem and results indicate that the proposed hybrid implementations compare favorably to previous metaheuristics, such as tabu search and simulated annealing.

TL;DR: A hybrid methodology that combines exact and heuristic procedures within a branch-and-price framework is developed to solve the underlying MIP, which takes advantage of the efficiency of heuristics and the precision of branch and price.

TL;DR: This work treats a special truck scheduling problem arising in the (zero-inventory) cross docks of the food industry, where strict cooling requirements forbid an intermediate storage inside the terminal, so that all products are to be instantaneously loaded onto refrigerated outbound trucks.

TL;DR: The first computational study which aims at computing delay resistant periodic timetables is provided and results suggest that a significant decrease of passenger delays can be obtained at a relatively small price of robustness, i.e. by increasing the nominal travel times of the passengers.

TL;DR: This work considers the problems aircraft routing and crew pairing: aircraft and crew must be allocated to flights in a schedule in a minimal cost way and generates solutions that incur small costs and are also robust to typical stochastic variability in airline operations.

TL;DR: The proposed memetic algorithm couples genetic operators and local search procedures proven to be effective on deterministic routing problems with a novel individual evaluation and reparation strategy that accounts for the stochastic nature of the problem.