Cooperative Parallel Tabu Search for Capacitated Network Design

We present a cooperative parallel tabu search method for the fixed charge, capacitated, multicommodity network design problem. Several communication strategies are analyzed and compared. The resulting parallel procedure displays excellent performances in terms of solution quality and solution times. The experiments show that parallel implementations find better solutions than sequential ones. They also show that, when properly designed and implemented, cooperative search outperforms independent search strategies, at least on the class of problems of interest here.     

A guide to vehicle routing heuristics

Several of the most important classical and modern heuristics for the vehicle routing problem are summarized and compared using four criteria: accuracy, speed, simplicity and flexibility. Computational results are reported.     

Scheduling a hot rolling mill

This paper presents a heuristic algorithm to schedule a hot rolling mill in the aluminum industry. One problematic issue is the tight coupling between the homogenizing furnaces and the mill, which needs to be integrated into the heuristic design. The latter also takes into account standard technological constraints like alloy hardness transitions, roll wear, homogenization code compatibilities and width transitions. The objective is to minimize the idle time on the mill and penalties for soft constraint violations related to production quality. The heuristic is divided into two phases. First, batches of ingots are constructed for the furnaces. These batches, called blocks, are then sequenced on the mill. Numerical results are reported on test instances derived from real-world data.     

Parallel asynchronous tabu search for multicommodity location-allocation with balancing requirements

We study and compare asynchronous parallelization strategies for tabu search, and evaluate the impact on performance and solution quality of some important algorithmic design parameters: number of processors, handling of exchanged information, etc. Parallelization approaches are implemented and compared by using a tabu search algorithm for multicommodity location-allocation problems with balancing requirements.     

Combinatorial auctions

Combinatorial auctions are an important class of market mechanisms in which participants are allowed to bid on bundles of multiple heterogeneous items. In this paper, we discuss several complex issues that are encountered in the design of combinatorial auctions. These issues are related to the formulation of the winner determination problem, the expression of combined bids, the design of progressive combinatorial auctions that require less information revelation, and the need for decision support tools to help participants make profitable bidding decisions. For each issue, we survey the existing literature and propose avenues for further research. An earlier version of this paper appeared in 4OR 2, 1–33, 2004.     

GENI Ants for the Traveling Salesman Problem

In this paper, the probabilistic nearest neighbor heuristic, which is at the core of classical ant colony systems for the Traveling Salesman Problem, is replaced by an alternative insertion procedure known as the GENI heuristic. The benefits provided by GENI-based ants are empirically demonstrated on a set of benchmark problems, through a comparison with the classical ant colony system and an iterated GENI heuristic.     

A hybrid constraint programming approach to?the?log-truck scheduling problem

Scheduling problems in the forest industry have received significant attention in the recent years and have contributed many challenging applications for optimization technologies. This paper proposes a solution method based on constraint programming and mathematical programming for a log-truck scheduling problem. The problem consists of scheduling the transportation of logs between forest areas and woodmills, as well as routing the fleet of vehicles to satisfy these transportation requests. The objective is to minimize the total cost of the non-productive activities such as the waiting time of trucks and forest log-loaders and the empty driven distance of vehicles. We propose a constraint programming model to address the combined scheduling and routing problem and an integer programming model to deal with the optimization of deadheads. Both of these models are combined through the exchange of global constraints. Finally the whole approach is validated on real industrial data.     

The orienteering problem with stochastic travel and service times

In this paper, we introduce a variant of the orienteering problem in which travel and service times are stochastic. If a delivery commitment is made to a customer and is completed by the end of the day, a reward is received, but if a commitment is made and not completed, a penalty is incurred. This problem reflects the challenges of a company who, on a given day, may have more customers than it can serve. In this paper, we discuss special cases of the problem that we can solve exactly and heuristics for general problem instances. We present computational results for a variety of parameter settings and discuss characteristics of the solution structure.     

A unified solution framework for multi-attribute vehicle routing problems

Vehicle routing attributes are extra characteristics and decisions that complement the academic problem formulations and aim to properly account for real-life application needs. Hundreds of methods have been introduced in recent years for specific attributes, but the development of a single, general-purpose algorithm, which is both efficient and applicable to a wide family of variants remains a considerable challenge. Yet, such a development is critical for understanding the proper impact of attributes on resolution approaches, and to answer the needs of actual applications. This paper contributes towards addressing these challenges with a component-based design for heuristics, targeting multi-attribute vehicle routing problems, and an efficient general-purpose solver. The proposed Unified Hybrid Genetic Search metaheuristic relies on problem-independent unified local search, genetic operators, and advanced diversity management methods. Problem specifics are confined to a limited part of the method and are addressed by means of assignment, sequencing, and route-evaluation components, which are automatically selected and adapted and provide the fundamental operators to manage attribute specificities. Extensive computational experiments on 29 prominent vehicle routing variants, 42 benchmark instance sets and overall 1099 instances, demonstrate the remarkable performance of the method which matches or outperforms the current state-of-the-art problem-tailored algorithms. Thus, generality does not necessarily go against efficiency for these problem classes.     

Branch-and-price algorithm for the Resilient Multi-level Hop-constrained Network Design

In this work, we investigate the Resilient Multi-level Hop-constrained Network Design (RMHND) problem, which consists of designing hierarchical telecommunication networks, assuring resilience against random failures and maximum delay guarantees in the communication. Three mathematical formulations are proposed and algorithms based on the proposed formulations are evaluated. A Branch-and-price algorithm, which is based on a delayed column generation approach within a Branch-and-bound framework, is proven to work well, finding optimal solutions for practical telecommunication scenarios within reasonable time. Computational results show that algorithms based on the compact formulations are able to prove optimality for instances of limited size in the scenarios of interest while the proposed Branch-and-price algorithm exhibits a much better performance.