Martin Josef Geiger

On operators and search space topology in multi-objective flow shop scheduling

Abstract Multi-objective optimization using evolutionary algorithms identifies Pareto-optimal alternatives or their close approximation by means of a sequence of successive local improvement moves. While several successful applications to combinatorial optimization problems are known, studies of underlying problem structures are still scarce. The paper presents a study of the problem structure of multi-objective permutation flow shop scheduling problems and investigates the effectiveness of local search neighborhoods within an evolutionary search framework. First, small problem instances with up to six objective functions for which the optimal alternatives are known are studied. Second, benchmark instances taken from literature are investigated. It turns out for the investigated data sets that the Pareto-optimal alternatives are found relatively concentrated in alternative space. Also, it can be shown that no single neighborhood operator is able to equally identify all Pareto-optimal alternatives. Taking this into consideration, significant improvements have been obtained by combining different neighborhood structures into a multi-operator search framework.

Decision support for multi-objective flow shop scheduling by the Pareto Iterated Local Search methodology

The article describes the proposition and application of a local search metaheuristic for multi-objective optimization problems. It is based on two main principles of heuristic search, intensification through variable neighborhoods, and diversification through perturbations and successive iterations in favorable regions of the search space. The concept is successfully tested on permutation flow shop scheduling problems under multiple objectives and compared to other local search approaches. While the obtained results are encouraging in terms of their quality, another positive attribute of the approach is its simplicity as it does require the setting of only very few parameters. The metaheuristic is a key element of the Multi-Objective Optimization and Production Planning Solver MOOPPS. The software has been awarded the European Academic Software Award in Ronneby, Sweden (http://www.bth.se/llab/easa_2002.nsf), and has since been used for research and higher education in the mentioned problem domain (Geiger, 2006).

On heuristic search for the single machine total weighted tardiness problem - Some theoretical insights and their empirical verification

The article presents theoretical and experimental investigations of computational intelligence techniques for machine sequencing problems. Contrary to other approaches, which are experimentally driven only, our work is motivated by gaining insights in the underlying principles of heuristic search for this particular problem. We therefore first theoretically analyze local search neighborhoods, deriving expectations about their relative performance. An empirical study on benchmark data follows, verifying the initial propositions. In result, we may conclude theoretically and empirically on the relative performance of neighborhood search operators for the single machine total weighted tardiness problem. The results are useful for the proposition of heuristic search procedures based on local search, as they lead to an order of neighborhood structures with respect to their relative performance. The obtained insights are verified by investigating the effectiveness of a (multi-operator) Variable Neighborhood Search approach for the problem at hand. We are able to show that most known benchmark instances are reliably solved to optimality, leaving an overall average gap of around 1% above the optimum.

Metaheuristics in the service industry

A Bicriteria Traveling Salesman Problem with Sequence Priorities.- Metaheuristics for Tourist Trip Planning.- Solving Fuzzy Multi-Item Economic Order Quantity Problems via Fuzzy Ranking Functions and Particle Swarm Optimization.- Fixed and Variable Toll Pricing in Road Networks with Direct Search Meta-Heuristics.- Scatter Search for locating a treatment plant and the necessary transfer centers in a reverse network.- Variable Neighbourhood Descent for Planning Crane Operations in a Train Terminal.- Design and Analysis of Evolutionary Algorithms for the No-wait Flow-shop Scheduling Problem.- Metaheuristics for the Index Tracking Problem.- A Hybrid Algorithm for Vehicle Routing of Less-Than-Truckload Carriers.

A multi-threaded local search algorithm and computer implementation for the multi-mode, resource-constrained multi-project scheduling problem

The article describes a solution approach for the multi-mode, resource-constrained multi-project scheduling problem. Our key ideas are based on the concepts of Variable Neighborhood Search, together with Iterated Local Search. A particular focus of this work is devoted towards the parallel implementation of such local search solution techniques. With the increasing availability of parallel (multi-core) computer hardware, we believe that such techniques are about to gain attractiveness in the future.

Applying the threshold accepting metaheuristic to curriculum based course timetabling

The article presents a study of local search algorithms for timetabling problems, with the particular goal of providing a contribution to competition track 3 of the International Timetabling Competition 2007 (ITC 2007). In this track, a formulation of a curriculum based course timetabling has been published, and novel benchmark instances have been presented that allow the comparison of optimization approaches.Our heuristic local search procedure is based on the principles of Threshold Accepting, overcoming local optima by a deterministic acceptance of inferior solutions throughout the search runs. A stochastic neighborhood is proposed and implemented, randomly removing and reassigning events from the current solution.The overall concept has been incrementally obtained from a series of experiments, which we describe in each (sub)section of the paper. In conclusions, we successfully derived a potential candidate solution approach for the finals of track 3 of the ITC 2007, held in August 2008 in Montréal, Canada.

The biobjective inventory routing problem: problem solution and decision support

The article presents a study on a biobjective generalization of the inventory routing problem, an important optimization problem found in logistical networks in which aspects of inventory management and vehicle routing intersect. A compact solution representation and a heuristic optimization approach are presented, and experimental investigations involving novel benchmark instances are carried out. As the investigated problem is computationally very demanding, only a small subset of solutions can be computed within reasonable time. Decision support is nevertheless obtained by means of a set of reference points, which guide the search towards the Pareto-front.

Swap Body Vehicle Routing Problem: A Heuristic Solution Approach

A variant of the Vehicle Routing Problem, namely the Vehicle Routing Problem with Swap Bodies (SB-VRP) is investigated. This type of problem involves swap locations where different actions can take place. For example, the swap body can be parked in order to visit customers where only a truck configuration can be used. For supporting the planning of a SB-VRP, an Iterated Variable Neighborhood Search is formulated. Computational results are provided and the solvability of the model under different types of test instances, which are released by the VeRoLog Solver Challenge 2014, is assessed.

On the interactive resolution of multi-objective vehicle routing problems

The article presents a framework for the resolution of rich vehicle routing problems which are difficult to address with standard optimization techniques. We use local search on the basis on variable neighborhood search for the construction of the solutions, but embed the techniques in a flexible framework that allows the consideration of complex side constraints of the problem such as time windows, multiple depots, heterogeneous fleets, and, in particular, multiple optimization criteria. In order to identify a compromise alternative that meets the requirements of the decision maker, an interactive procedure is integrated in the resolution of the problem, allowing the modification of the preference information articulated by the decision maker. The framework is implemented in a computer system. Results of test runs on multiple depot multi-objective vehicle routing problems with time windows are reported.

Multi-criteria Curriculum-Based Course Timetabling--A Comparison of a Weighted Sum and a Reference Point Based Approach

The article presents a solution approach for a curriculum-based timetabling problem, a complex planning problem found in many universities. With regard to the true nature of the problem, we treat it as multi-objective optimization problem, trying to balance several aspects that simultaneous have to be taken into consideration. A solution framework based on local search heuristics is presented, allowing the planner to identify compromise solutions. Two different aggregation techniques are used and studied. First, a weighted sum aggregation, and second, a reference point based approach. Experimental investigations are carried out for benchmark instances taken from track 3 of the International Timetabling Competition ITC 2007 . After having been invited to the finals of the competition, held in August 2008 in Montreal, and thus ranking among the best five approaches world-wide, we here extend our work towards the use of reference points.