Martin Romauch

Heuristics for the multi-period orienteering problem with multiple time windows

We present the multi-period orienteering problem with multiple time windows (MuPOPTW), a new routing problem combining objective and constraints of the orienteering problem (OP) and team orienteering problem (TOP), constraints from standard vehicle routing problems, and original constraints from a real-world application. The problem itself comes from a real industrial case. Specific route duration constraints result in a route feasibility subproblem. We propose an exact algorithm for this subproblem, and we embed it in a variable neighborhood search method to solve the whole routing problem. We then provide experimental results for this method. We compare them to a commercial solver. We also adapt our method to standard benchmark OP and TOP instances, and provide comparative tables with state-of-the-art algorithms.

Dynamic facility location with stochastic demands

In this paper, a Stochastic Dynamic Facility Location Problem (SDFLP) is formulated. In the first part, an exact solution method based on stochastic dynamic programming is given. It is only usable for small instances. In the second part a Monte Carlo based method for solving larger instances is applied, which is derived from the Sample Average Approximation (SAA) method.

Addendum: Addendum to Heuristics for the multi-period orienteering problem with multiple time windows

This is an addendum to the paper by Tricoire et al. on various orienteering problems [1]. We present experimental results on the orienteering problem with time windows (OPTW) and team orienteering problem with time windows (TOPTW) test instances introduced by Montemanni et al. [2].

A mathematical program to refine gene regulatory networks

In this paper, we propose a methodology for making sense of large, multiple time-series data sets arising in expression analysis. Specifically, we present a mathematical model to release a reduced and coherent regulatory system given a putative regulatory network. We give two equivalent formulations of the problem and prove that the problem is NP-complete. For solving large scale instances we implemented an Ant Colony Optimization procedure. A computational analysis on randomly generated test instances validates the proposed algorithm and the computations on real data concerning Saccharomyces cerevisiae show the practicability of the proposed methodology.

Product mix optimization for a semiconductor fab

For optimizing a semiconductor fab we are aiming to match the production capabilities and capacities with the demand in the most profitable way. In this paper we address a linear programming model of the product mix problem considering product dependent demand limits (obligations and demand forecast) and profits while respecting the capacity bounds of the production facility. Since the capacity consumption is highly depended on choosing from different production alternatives we are implicitly solving a static capacity planning problem for each product mix. This kind of planning approach is supported by the fluid flow concept of complete resource pooling in high traffic. We propose a general model that considers a wide range of objectives and we introduce a decomposition heuristic. The computational study of the approaches is based on real world data and on randomly generated instances.

The Influence of Routing on Lateral Transhipment

We propose a model for lateral transhipments in a supply chain [6] that considers to use one vehicle to combine the transhipments in a tours to improve the overall costs. The corresponding problem is an extension of the One-Commodity Pickup and Delivery Traveling Salesman and the Pickup and Delivery Vehicle Routing Problem. In general we have to consider a maximum tour length and capacity limits, hence the problem also has aspects of an orienteering problem. The main contribution is the discussion of the tour planning aspects for lateral transhipments which may be valuable for an in-house planning but also for price negotiations with external contractors. We will introduce a mixed integer mathematical model for the single route and single commodity version and a LNS heuristic to solve the problem.

Product mix optimization for a semiconductor fab: modeling approaches and decomposition techniques

For optimizing a semiconductor fab we are aiming to match the production capabilities and capacities with the demand in the most profitable way. In this paper we address a linear programming model of the product mix problem considering product dependent demand limits (obligations and demand forecast) and profits while respecting the capacity bounds of the production facility. Since the capacity consumption is highly depended on choosing from different production alternatives we are implicitly solving a static capacity planning problem for each product mix. This kind of planning approach is supported by the fluid flow concept of complete resource pooling in high traffic. We propose a general model that considers a wide range of objectives and we introduce a decomposition heuristic. The computational study of the approaches is based on real world data and on randomly generated instances.