There are more than 30,000 km of cross-country skiing tracks in Norway, and to maintain these track networks municipalities and local ski clubs spend more than 250 million NOK every year. The primary cost driver is the daily grooming operations which are manually planned based on the experience of the snowcat operators. Large networks with many vehicles starting at different depots complicate the problem of finding effective routes. The result is unnecessary high costs due to suboptimal route choices, yielding a benefit of solving the route planning problem. Employees of the municipal enterprise responsible for the cross-country facilities in Trondheim, Trondheim Bydrift, explain that today’s planning of grooming activities is based on experience and old habits. As Trondheim is an area known for unstable weather conditions, long-term planning lacks robustness. Meetings are therefore conducted every morning to handle the variations. The multifaceted Snow Grooming Routing Problem (SGRP) is an arc routing problem with profits that involves multiple depots, time windows, a heterogeneous fleet of vehicles and track segments having numerous attributes. We formally define the SGRP and present a mathematical formulation of the problem. The formulation is then tested on a number of different networks taken from Bymarka, the largest cross-country area around Trondheim, Norway.

Forestry in coastal Norway has traditionally been a marginal activity with a low annual harvest rate. However, the region is now faced with large areas of spruce plantations that will reach harvest maturity within the next 25 years. Due to the poor infrastructure in the region, the current challenge is to harvest the maturing spruce plantations at an acceptable cost. Hence, there is considerable interest both from the forest sector and politicians to invest in infrastructure that can provide the basis for profitable forest sector development in coastal Norway. This paper presents a mathematical optimization model for timber transportation from stump to industry. The main decision variables are location of quays, upgrade of public road links, the length of new forest roads, and when the investments should happen. The main objective is to provide decision support for prioritization of infrastructure investments. The optimization model is combined with a dynamical forest resource model, providing details on available volumes and costs. A case study for coastal Norway is presented and solved to optimality. The instance includes 10 counties comprising more than 200 municipalities with forest resources, 53 possible new quays for timber export and 916 public road links that also can be upgraded. Compared with a no investment case, the optimal solution improved the objective by 23%. The study shows that consistent, informative and good analyses can be performed to evaluate trade-offs, prioritization, time and order of investment, and cost saving potentials of infrastructure investments in the forest industry. The solution seems reasonable based on present infrastructure and state of the forest.

Cost-efficient, environmental-friendly and socially sustainable biomass supply chains are urgently needed to achieve the 2020 targets of the Strategic Energy Technologies-Plan of the European Union. This paper investigated technical, social, economic, and environmental barriers to the development and innovation of supply chains, taking into account a large range of parameters influencing the performances of biomass systems at supply chain scale. An assessment framework was developed that combined economic optimization of a supply chain with a holistic and integrated sustainability assessment. The framework was applied to a case-study involving miscanthus biomass in the Burgundy region (Eastern France) to compare alternative biomass supply chain scenarios with different annual biomass demand, crop yield, harvest timing and densification technologies. These biomass supply chain scenarios were first economically optimized across the whole supply chain (from field to plant gate) by considering potential feedstock production (from a high-resolution map), costs, logistical constraints and product prices. Then sustainability assessment was conducted by combining recognized methodologies: economic analysis, multi-regional input-output analysis, emergy assessment, and life cycle assessment. The analysis of the case study scenarios found that expanding biomass supply from 6,000 to 30,000 tons of dry matter per year did not impact the profitability, which remained around 20 per ton of biomass procured. Regarding environmental impacts, the scenario with the lowest feedstock supply area had the lowest impact per ton due to low economies of scale. Mobile briquetting proved to be also a viable economic option, especially in situations with a considerable scattering of the crop production and expensive transportation logistics. By highlighting hot-spots in terms of economic, environmental and social impacts of biomass supply systems, this study provides guidance in the supply chain optimization and the design of technological solutions tailored to economic operators as well as other stakeholders, such as policy makers. (C) 2017 Elsevier Ltd. All rights reserved.

The European policies have been designed over the last decade to face the challenge of climate change. Several measures have been put in place to accelerate the development and deployment of cost-effective low carbon technologies. The domestic nature and its potential avaibility in Europe make biomass a relevant resource to be considered. The Logistics for Energy Crops Biomass (LogistEC) project aims to develop new or improve technologies of biomass logistics chain. The sustainability of different types of biomass is being analysed in terms of environmental, economic and social impacts, based on the supply chain of two existing plants. The objective of this paper is to present the main results obtained in the socio-economic analysis of the French case. The Input-Output Analysis has been seen as the most appropriate method to estimate these impacts using a Multiregional Input-Output Table from the World Input-Output Database project. Socio-economic effects have been estimated in terms of additional economic activity, added value and job creation. Additionally, the most stimulated sectors have been identified. Results highlight the importance of biomass at a national level.

Cost-efficient, environmental-friendly and socially sustainable biomass supply chains are urgently needed to achieve the 2020 targets of the Strategic Energy Technologies-Plan of the European Union, which are likely to be impeded by the potential scarcity of lignocellulosic biomass from agriculture. Innovative techniques for crop management, biomass harvesting and pre-treatment, storage and transport offer a prime avenue to increase biomass supply while keeping costs down and minimizing adverse environmental impacts. The LogistEC project aimed at developpingnew or improved technologies for all steps of the logistics chains, and to assess their sustainability at supply-area level for small to large-scale bio-based projects. It encompassed all types of lignocellulosic crops : annual and pluri-annual crops, perennial grasses, and short-rotation coppice, and included pilot- to industrial-scale demonstrations, in particular around 2 existing bioenergy and biomaterials value-chains in Europe (in Eastern France and Southern Spain). This paper reviews the main results abtained in the project on the main components of logistics chains, regarding feedstock production systems, harvesting and post-harvest handling, storage, densification and pre-treatment of biomass. The information and tools delevered by the project provides a first step to guide in incremental improvements as well as systemic changes in biomass feedstock supply chains from energy crops.

In this chapter, we present a new model for optimal strategic and tactical planning of the bioenergy supply chain under uncertainty. We discuss specific challenges, characteristics and issues related to this type of model. The technological details, variability in supply and demand, and uncertainty in virtually all aspects of the supply chain require advanced modeling techniques. Our model provides a broad modeling approach that addresses the entire supply chain using an integrated perspective. The broad applicability of the approach is illustrated by the two cases discussed at the end of the chapter. The first case presents a forest to bioenergy supply chain in a region of the Norwegian west coast. The second case presents the miscanthus supply chain to a transformation plant in Burgundy, France and takes into consideration uncertain final demand.

Cost-efficient, environmental-friendly and socially sustainable biomass supply chains are urgently needed to achieve the 2020 and 2030 targets of the European Union. Innovative techniques for crop management, biomass harvesting and pre-treatment, storage and transport offer a prime avenue to increase biomass supply while keeping costs down and minimizing adverse environmental impacts. The LogistEC project funded by the European Commission aimed at developing new or improved technologies for all steps of the logistics chains, and to assess their sustainability at supply-area level for small to large-scale biobased projects. It encompassed all types of lignocellulosic crops: annual and pluri-annual crops, perennial grasses, and short-rotation coppice, and included pilot- to industrial-scale demonstrations. This communication reviews the main results obtained during the project, along the different steps of biomass supply chains. Integration of these innovations into supply chains increased their environmental performance by up to 20%.

Scheduling the Norwegian football (soccer) league is a difficult process as there are many requirements to be considered. The schedule should be fair to all teams while fulfilling as many requests from the stakeholders as possible. For a schedule to be fair, the number of breaks (i.e., consecutive home or away games) should be as low as possible and a team should not be scheduled with many difficult games in a row. Further, the television companies want to have some attractive games in each round and there are several team-specific constraints or wishes to consider. This paper proposes a two-phase solution method that relies on a pattern-based model to provide good schedules. Phase 1 generates feasible patterns, while the pattern-based model in Phase 2 assigns a pattern to each team considering the various constraints and wishes. A specialized branching strategy that proves to reduce the solution time is also introduced. The computational study shows that the suggested method can handle the various requirements for the Norwegian football league, and is able to produce significantly better results than the schedules created manually by the Norwegian Football Association.

This paper considers a maritime inventory routing problem faced by a major cement producer. A heterogeneous fleet of bulk ships transport multiple non-mixable cement products from producing factories to regional silo stations along the coast of Norway. Inventory constraints are present both at the factories and the silos, and there are upper and lower limits for all inventories. The ship fleet capacity is limited, and in peak periods the demand for cement products at the silos exceeds the fleet capacity. In addition, constraints regarding the capacity of the ships’ cargo holds, the depth of the ports and the fact that different cement products cannot be mixed must be taken into consideration. A construction heuristic embedded in a genetic algorithmic framework is developed. The approach adopted is used to solve real instances of the problem within reasonable solution time and with good quality solutions.

0377-2217/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.ejor.2010.08.023 ⇑ Corresponding author. Tel.: +47 73593602; fax: + E-mail address: Marielle.Christiansen@iot.ntnu.no This paper considers a maritime inventory routing problem faced by a major cement producer. A heterogeneous fleet of bulk ships transport multiple non-mixable cement products from producing factories to regional silo stations along the coast of Norway. Inventory constraints are present both at the factories and the silos, and there are upper and lower limits for all inventories. The ship fleet capacity is limited, and in peak periods the demand for cement products at the silos exceeds the fleet capacity. In addition, constraints regarding the capacity of the ships’ cargo holds, the depth of the ports and the fact that different cement products cannot be mixed must be taken into consideration. A construction heuristic embedded in a genetic algorithmic framework is developed. The approach adopted is used to solve real instances of the problem within reasonable solution time and with good quality solutions. 2010 Elsevier B.V. All rights reserved.

The Node, Edge, and Arc Routing Problem (NEARP) was defined by Prins and Bouchenoua in 2004 along with the first benchmark called CBMix. The NEARP generalizes the classical Capacitated Vehicle Routing Problem (CVRP), the Capacitated Arc Routing Problem (CARP), and the General Routing Problem. It is also denoted the Mixed Capacitated General Routing Problem (MCGRP). The NEARP removes the strict and unwarranted dichotomy that previously existed in the literature between arc routing and node routing. In real applications, there are many cases where the pure node or arc routing models are not adequate. In fundamentally node-based routing applications such as newspaper delivery and communal waste management that have typically been modeled as arc routing problems in the literature, the number of points is often so large that demand aggregation is necessary. Aggregation heuristics will normally give a NEARP instance, possibly with side constraints. Hence, the NEARP is a scientifically challenging problem with high industrial relevance. In this report we present experiments with Spider, SINTEF’s industrial VRP solver, on the three NEARP benchmarks that have been published so far: CBMix, BHW, and DI-NEARP. Bach, Hasle, and Wøhlk have developed a combinatorial lower bound for the NEARP and defined the two latter benchmarks. Here, we present an experimental study with Spider on the three existing NEARP benchmarks. Upper and lower bounds are given for all instances. Three of the BHW instances have been solved to optimality. SINTEF has developed a web page for NEARP results on http://www.sintef.no/NEARP.

We study the problem of reconstructing (0,1)-matrices based on projections along a small number of directions. This discrete inverse problem is generally hard to solve for more than 3 projection directions. Building on previous work by the authors, we give a problem formulation with the objective of finding matrices with the maximal number of neighboring ones. A solution approach based on variable splitting and the use of subgradient optimization is given. Further, computational results are given for some structured instances. Optimal solutions are found for instances with up to 10,000 binary variables.