Oddvar Kloster

Industrial Vehicle Routing

Solving the Vehicle Routing Problem (VRP) is a key to efficiency in transportation and supply chain management. The VRP is an NP-hard problem that comes in many guises. The VRP literature contains thousands of papers, and VRP research is regarded as one of the great successes of OR. Vehicle routing decision support tools provide substantial savings in society every day, and an industry of routing tool vendors has emerged. Exact methods of today cannot consistently solve VRP instances with more than 50–100 customers in reasonable time, which is generally a small number in real-life applications. For industrial problem sizes, and if one aims at solving a variety of VRP variants, approximation methods is the only viable approach. There is still a need for VRP research, particularly for large-scale instances and complex, rich VRP variants. In this chapter, we give a brief general introduction to the VRP. We then describe how industrial requirements motivate extensions to the basic, rather idealized VRP models that have received most attention in the research community, and how such extensions can be made. At SINTEF Applied Mathematics, industrial variants of the VRP have been studied since 1995. Our efforts have led to the development of a generic VRP solver that has been commercialized through a spin-off company. We give a description of the underlying, rich VRP model and the selected uniform algorithmic approach, which is based on metaheuristics. Finally, results from computational experiments are presented. In conclusion, we point to important issues in further VRP research.

Dynamic And Stochastic Vehicle Routing In Practice

The VRP is a key to efficient transportation logistics. It is a computationally very hard problem. Whereas classical OR models are static and deterministic, these assumptions are rarely warranted in an industrial setting. Lately, there has been an increased focus on dynamic and stochastic vehicle routing in the research community. However, very few generic routing tools based on stochastic or dynamic models are available. We illustrate the need for dynamics and stochastic models in industrial routing, describe the Dynamic and Stochastic VRP, and how we have extended a generic VRP solver to cope with dynamics and uncertainty

A solution to the Angel Problem

We solve the Angel Problem, by describing a strategy that guarantees the win of an Angel of power 2 or greater. Basically, the Angel should move north as quickly as possible. However, he should detour around eaten squares, as long as the extra distance does not exceed twice the number of eaten squares evaded. We show that an Angel following this strategy will always spot a trap early enough to avoid it.

Interactive planning for sustainable forest management

The long-term planning of sustainable forest treatment at the landscape level is an increasingly more complex task. Local treatment schedules, pertaining to homogeneous sub-areas called stands, must be developed over a time horizon of a few centuries. Thousands of local schedules must be coordinated to satisfy hard constraints, and balance soft constraints and optimization criteria. Constraints and objectives are defined in terms of economical, recreational, and environmental effect.The aim of the forest treatment schedule is twofold. Over the near time horizon, it must provide clear instructions for forest treatment. In addition, sustainability over the full horizon must be demonstrated. In this context, sustainability means balancing growth and yield in the long term, the preservation of bio-diversity, and catering for human recreational and cultural value. Conventional OR based approaches have failed to give satisfactory results for this type of problem. We describe a method built on explicit constraint descriptions and a memory-based local search procedure for solving rich models of the long-term forest treatment scheduling problem. We also describe a configurable decision support system, called Ecoplan, where the scheduling kernel is based on our method.The system relies heavily on close interaction with a stand simulator, which must provide forestry knowledge necessary to guide the scheduling process, including the definition of abstract forest treatment actions. Ecoplan also provides facilities for user interaction in the planning process, functionality for locking specific parts of a plan, and flexibility to alter key factors in the plan such as active constraints and objective criteria. In this way, the system supports the definition and exploration of “what-if” scenarios.The Ecoplan system has been built on the initiative of the major Norwegian forest owners, addressing a problem area that is becoming increasingly more complex to handle and more critical to society.

Solving the Long-Term Forest Treatment Scheduling Problem

The Long-Term Forest Treatment Scheduling Problem (LTFTSP) is the task of allocating treatments in a forest such that both sustainability and economic outcome is maximized. Solving such problems is demanded in more and more countries and the task is increasingly more complex because one must adhere to local legislation, environmental issues, and public interests. To be able to handle most aspects of the LTFTSP with adjacency constraints (which is the problem we solve), a rich, spatial model which is parameterized, is required. We present a model defined on discrete land units and time points, where the treatments to perform are parameterized. Many of the most commonly used criteria in the form of constraints and objective components in long-term forestry scheduling are included. Such criteria may be defined for the complete forest region in question, or for specific sub-regions.