Introducing cost-plus-loss analysis into a hierarchical forestry planning environment

Cost-plus-loss analysis of data for forestry planning has often been carried out for highly simplified planning situations. In this study, we suggest an advance in the cost-plus-loss methodology that aims to capture the hierarchical structure and iterative nature of planning by the large forest owner. The simulation system that is developed to simulate the planning process of the forest owner includes the tactical and operational levels of a continuous planning process. The system is characterized by annual re-planning of the tactical plan with a planning horizon of ten year and with the option to reassess data for selected stands before operational planning. Operational planning is done with a planning horizon of two years and the first year of the plan is considered to have been executed before moving the planning process one year forward. The annual cycle is repeated 10 times, simulating decisions made over a ten-year time horizon. The optimizing planning models of the system consider wood flow requirements, available harvest resources, seasonal variation of ground conditions and spatiality. The data used are evaluated according to standard procedures in cost-plus-loss analysis. Results from a test case indicate high decision losses when planning at both levels is based on the type of data prevalent in the stand databases of Swedish companies. The losses can be reduced substantially if higher-quality data are introduced before operational planning. In summary, the results indicate that the method makes it possible to analyze where in the planning process one needs better data and their value.     

Cost allocation in collaborative forest transportation

Transportation planning is an important part of the supply chain or wood flow chain in forestry. There are often several forest companies operating in the same region and collaboration between two or more companies is rare. However, there is an increasing interest in collaborative planning as the potential savings are large, often in the range 5–15%. There are several issues to agree on before such collaborative planning can be used in practice. A key question is how the total cost or savings should be distributed among the participants. In this paper, we study a large application in southern Sweden with eight forest companies involved in a collaboration. We investigate a number of sharing mechanisms based on economic models including Shapley value, the nucleolus, separable and non-separable costs, shadow prices and volume weights. We also propose a new allocation method, with the aim that the participants relative profits are as equal as possible. We use two planning models, the first is based on direct flows between supply and demand points and the second includes backhauling. We also study how several time periods and geographical distribution of the supply and demand nodes affect the solutions. Better planning within each company can save about 5% and collaboration can increase this about another 9% to a total of 14%. The proposed allocation method is shown to be a practical approach to share the overall cost/savings.     

Generalized M/G/C/C state dependent queueing models and pedestrian traffic flows

The generality and usefulness ofM/G/C/C state dependent queueing models for modelling pedestrian traffic flows is explored in this paper. We demonstrate that the departure process and the reversed process of these generalizedM/G/C/C queues is a Poisson process and that the limiting distribution of the number of customers in the queue depends onG only through its mean. Consequently, the models developed in this paper are useful not only for the analysis of pedestrian traffic flows, but also for the design of the physical systems accommodating these flows. We demonstrate how theM/G/C/C state dependent model is incorporated into the modelling of large scale facilities where the blocking probabilities in the links of the network can be controlled. Finally, extensions of this work to queueing network applications where blocking cannot be controlled are also presented, and we examine an approximation technique based on the expansion method for incorporating theseM/G/C/C queues in series, merge, and splitting topologies of these networks.     

Application of a simulation-based dynamic traffic assignment model

The evaluation of on-line intelligent transportation system (ITS) measures, such as adaptive route-guidance and traffic management systems, depends heavily on the use of faster than real time traffic simulation models. Off-line applications, such as the testing of ITS strategies and planning studies, are also best served by fast-running traffic models due to the repetitive or iterative nature of such investigations. This paper describes a simulation-based, iterative dynamic equilibrium traffic assignment model. The determination of time-dependent path flows is modeled as a master problem that is solved using the method of successive averages (MSA). The determination of path travel times for a given set of path flows is the network-loading sub-problem, which is solved using the space-time queuing approach of Mahut. This loading method has been shown to provide reasonably accurate results with very little computational effort. The model was applied to the Stockholm road network, which consists of 2100 links, 1191 nodes, 228 zones, representing and 4964 turns. The results show that this model is applicable to medium-size networks with a very reasonable computation time.     

Review of combinatorial problems induced by spatial forest harvesting planning

In the last two decades forest planning and management has become very much focused on spatially oriented decisions, due to the introduction of spatial details, such as road building, and environmental concerns, like wildlife protection, biodiversity, reducing erosion and improving water quality. This has led to modeling transformations and extensions in planning forest activities, as mixed integer decision variables are necessary to account for spatial restrictions. These new models are much harder to solve due to the added combinatorial complexity. In this paper, we discuss issues of modeling spatial criteria, as well as algorithms that have been developed to solve them, both heuristic and exact. We present these results as they have evolved in a state-of-the-art structure.     

Scheduling of road vehicles in sugarcane transport: A case study at an Australian sugar mill

Pressure to remain internationally competitive has forced Australian sugar mills to reduce capital and operational costs. Improved scheduling of road transport vehicles provides one such opportunity, as it would reduce vehicle queue and mill idle times and hence the number of vehicles needed. It is difficult for mill traffic officers to produce good transport schedules manually due to the need to service a large number of harvesters in different locations. To address this issue, research was undertaken participatively with a sugar milling company in Australia to produce and implement a mixed integer programming model that represents the road transport operations. Two meta-heuristics were applied to find a solution to the model, leading to potential cost savings of AU$240,000 per year versus schedules produced manually by the mill traffic officer. The model was also applied to explore regional planning options for a more integrated harvesting and transport system.     

Estimation of time-varying origin-destination flows from traffic counts: A neural network approach

A dynamic model based on the error back-propagation learning principle in neural network theory is proposed for estimating origin-destination flows from the road entering and exiting counts in a transportation network. The origin-destination flows in each short time interval are estimated through minimization of the squared errors between the predicted and observed exiting counts which are normalized using a logistic function. Two numerical experiments are conducted to evaluate the performance of the proposed model; one uses a typical four-way intersection, and the other one uses a real freeway section. Numerical results show that the back-propagation based model is capable of tracking the time variations of the origin-destination flows with a high stability.     

Scheduling farm operations

Scheduling operations on a farm is considered depending on the available men and machinery and on the influence of the weather on materials (moisture content). A simulation model with a heuristic strategy for selecting operations at each moment of decision based on the state of the system and a linear programming model are used in the grain harvest to demonstrate the influence of the models on the resulting variable costs (overtime, drying of wet grain and timeliness losses of wheat and straw) and the influence of input data (weather, attributes of material and number of workable hours) on those costs. The lower costs found with simplified input (hourly, daily, weekly data) in simulation is continued with the linear programming model due to its deviation from the real workable time constraints and decision variables. Such a tendency suggests that LP-models usual in agricultural planning are too simple.     

FuelOpt: a decision support system for forest fuel logistics

The use of forest fuel is increasing at heating plants in Sweden. Heating plants provide energy in the form of hot water for heating houses and apartments in local municipalities. Forest fuel are products obtained from harvesting in forests that cannot be used for further processing at sawmills and pulp and paper mills. Examples of such products are tree branches, tree tops and low quality logs. The optimization of the supply chain for round-wood (logs to sawmills, pulp and paper mills) and for forest fuel is similar but involves two main differences. First, forest fuel has to be converted into chips before delivery to the customer, and second, the demand for forest fuel varies over the year due to the temperature. To balance the chipping and transportation capacities over time, it is important to manage inventory levels at terminals. The optimization model developed provides decision support for questions regarding the choice of technology for chipping, where to perform the chipping operations, and the allocation of different assortments to heating plants. The system has been tested on a large case study from a Swedish forest energy company. The results show large savings and that the system is very useful for both planning and business development.     

Supply chain design considering economies of scale and transport frequencies

In this paper we consider a 3-echelon, multi-product supply chain design model with economies of scale in transport and warehousing that explicitly takes transport frequencies into consideration. Our model simultaneously optimizes locations and sizes of tank farms, material flows, and transport frequencies within the network. We consider all relevant costs: product cost, transport cost, tank rental cost, tank throughput cost, and inventory cost. The problem is based on a real-life example from a chemical company. We show that considering economies of scale and transport frequencies in the design stage is crucial and failing to do so can lead to substantially higher costs than optimal. We solve a wide variety of problems with branch-and-bound and with the efficient solution heuristics based on iterative linearization techniques we develop. We show that the heuristics are superior to the standard branch-and-bound technique for large problems like the one of the chemical company that motivated our research.     

不确定条件下的乡村中小学布局优化

学校的合理规划布局是实现教育资源优化配置、提高办学效益和推动教育均衡发展的重要途径。已有许多学者研究了学校的布局问题,但基本上都忽略了交通网络条件以及不确定因素对学校布局的影响。本研究将在前人研究基础上,重点考虑交通网络对乡村中小学选址的影响,并假设旅行时间具有不确定性,从而以最小化学生旅行成本、学校建设、道路修建和道路升级成本为目标,构建不确定条件下的设施区位设计模型。在算法求解方面提出混合模拟退火算法,用于确定新建学校的最佳位置,以及新道路的修建和原有道路的升级情况。最后,将提出的模型和算法应用到实际案例中。     

基于双层规划的高速公路收费费率优化模型

在全国联网收费的背景下,从动态收费的角度考虑,建立了双层规划模型,上层规划中将路网管理者作为领导者,以高速公路收费效益最大化为目标函数,同时考虑道路运营管理方的合理收益和养护成本支出情况,下层规划则以用户出行效用最大化为目标,充分考虑了道路使用者的道路选择差异性及道路拥堵对交通分布的影响,建立随机用户均衡模型.最后结合...     

Long-term optimization model of tree water networks

This paper describes an optimization method to design, over a period of time, a radial water network consisting of pipes, pumps and pressure reducing valves. The network structure can be modified during the planning period by the addition or removal of certain nodes and elements. The evolution of the consumption at the nodes is known. The hydraulic constraints of pressure and flow velocity are respected throughout the studied period.The investment decisions are determined in such a manner as to minimize the sum of the present worth values of the investment costs and operation costs over the planning period.The choice of pipe lengths to be invested in each branch as variables allows one to formulate the dynamic investment problem as a multi-stage linear program. Each stage corresponds to the state of the network at a time of the planning period. Such a formulation of a combinatorial problem of investments allows one to design networks of large dimensions in the long term whilst maintaining acceptable times of computation.An application of the model to a real problem is presented.     

Maximum likelihood estimation for a road traffic network model

The use of the maximum likelihood method for parameter estimation in a probabilistic path choice model used for road traffic networks is described. The method requires observation of mean travel costs in a given network rather than the traffic flows throughout the network, and thus data collection efforts may be reduced. A test network, for which many sets of artificial data were generated, is examined so that the usefulness of the parameter estimation procedure may be assessed. It is shown that the procedure is both convenient and quick, and that accurate estimates of model parameters may be obtained.     

Integration of timetable planning and rolling stock in rapid transit networks

The aim of this paper is to propose an integrated planning model to adequate the offered capacity and system frequencies to attend the increased passenger demand and traffic congestion around urban and suburban areas. The railway capacity is studied in line planning, however, these planned frequencies were obtained without accounting for rolling stock flows through the rapid transit network. In order to provide the problem more freedom to decide rolling stock flows and therefore better adjusting these flows to passenger demand, a new integrated model is proposed, where frequencies are readjusted. Then, the railway timetable and rolling stock assignment are also calculated, where shunting operations are taken into account. These operations may sometimes malfunction, causing localized incidents that could propagate throughout the entire network due to cascading effects. This type of operations will be penalized with the goal of selectively avoiding them and ameliorating their high malfunction probabilities. Swapping operations will also be ensured using homogeneous rolling stock material and ensuring parkings in strategic stations. We illustrate our model using computational experiments drawn from RENFE (the main Spanish operator of suburban passenger trains) in Madrid, Spain. The results show that through this integrated approach a greater robustness degree can be obtained.     

Designing a two-echelon distribution network under demand uncertainty

This paper proposes a comprehensive methodology for the stochastic multi-period two-echelon distribution network design problem (2E-DDP) where product flows to ship-to-points are directed from an upper layer of primary warehouses to distribution platforms (DPs) before being transported to the ship-to-points. A temporal hierarchy characterizes the design level dealing with DP location and capacity decisions, as well as the operational level involving transportation decisions as origin-destination flows. These design decisions must be calibrated to minimize the expected distribution cost associated with the two-echelon transportation schema on this network under stochastic demand. We consider a multi-period planning horizon where demand varies dynamically from one planning period to the next. Thus, the design of the two-echelon distribution network under uncertain customer demand gives rise to a complex multi-stage decisional problem. Given the strategic structure of the problem, we introduce alternative modeling approaches based on two-stage stochastic programming with recourse. We solve the resulting models using a Benders decomposition approach. The size of the scenario set is tuned using the sample average approximation (SAA) approach. Then, a scenario-based evaluation procedure is introduced to post-evaluate the design solutions obtained. We conduct extensive computational experiments based on several types of instances to validate the proposed models and assess the efficiency of the solution approaches. The evaluation of the quality of the stochastic solution underlines the impact of uncertainty in the two-echelon distribution network design problem (2E-DDP).     

Supply chain management in forestry––case studies at Södra Cell AB

The use of supply chain management and optimisation is of increasing importance in the forest industry. The overall wood-flow starts with standing trees in forests and continues with harvesting, bucking, sorting, transportation to terminals, sawmills, pulp mills, paper mills and heating plants, conversion into products such as pulp, paper, lumber, and ends at different customers. Many planning problems arise along the chain and these cover different time horizons. Co-ordinating the wood-flow is a vital concern for many companies. We study Södra, one of the larger Swedish forest companies, which is involved in all stages of the wood-flow. We focus in particular on Södra Cell AB, a company within Södra, which is responsible for pulp production. We describe the operations at Södra Cell and the decision support tools used for supply chain planning. We describe five major projects or cases which focus on improving their supply chain management and optimisation. These cases include the introduction of new technologies for sales and orders, new distribution structures using terminals, and the development of integrated optimisation models and methods.     

GAMBIT-A Linear Programming Model to Balance Transmission Investment and Market Options<Superscript>†</Superscript>

Since the discovery of large-scale reserves of natural gas in the North Sea, the Gas Council has been given extended powers to purchase gas and to transmit it in bulk to the twelve Area Gas Boards. One of the functions of the Gas Council's Economic Planning Division is to determine the economic balance of the revenue obtainable from the large industrial markets and the costs of purchasing gas and constructing a national transmission system to supply the load. The paper describes the formulation of one of the mathematical models being used for planning studies and discusses the reasons for the choice of a linear programming package and the selection of objective function. The paper also describes a series of studies undertaken and the results obtained, together with some conclusions as to the effectiveness of the model as a planning tool.     

Models and algorithms to improve earthwork operations in road design using mixed integer linear programming

In road construction, earthwork operations account for about 25% of the construction costs. Existing linear programming models for earthwork optimization are designed to minimize the hauling costs and to balance the earth across the construction site. However, these models do not consider the removal of physical blocks that may influence the earthwork process. As such, current models may result in inaccurate estimates of optimal earthwork costs, leading to poor choices in road design. In this research, we extend the classical linear program model of earthwork operations to a mixed integer linear program model that accounts for blocks. We examine the economic impact of incorporating blocks via mixed integer linear programming, and find significant savings for most road designs in our test-set. However, the resulting model is considerably harder to solve than the original linear program. Based on structural observations, we introduce a set of algorithms that theoretically reduce the solving time of the model. We confirm this reduction in solve time with numerical experiments.     

Using fuzzy integral for evaluating subjectively perceived travel costs in a traffic assignment model

A good traffic assignment model can be a powerful tool to describe the characteristics of traffic behavior in a road network. The traffic assignment results often play an important role in transportation planning, e.g., an optimal and economical network design. Many traditional traffic assignment models rely heavily on the travel cost function established by Wardrop’s principles; however, the Wardrop’s travel cost function has been proven to be weak for explaining the uncertainty and interactivity of traffic among links. This study tries to construct a traffic assignment model that is different from Wardrop’s in many aspects. First, it considers the cross-effect among the links. Second, a fuzzy travel cost function is established based on the possibility concept instead of precise calculation of traffic volumes. Third, the techniques of fuzzy measure and fuzzy integral are applied to calculate the subjectively perceived travel costs during traffic assignment. Furthermore, in order to validate our model, a detailed network with 22 nodes and 36 links is used to illustrate it. Study results show that our model explains more interactivity and uncertainty of traffic among links when compared with the traditional model of Wardrop’s.