University of British Columbia Co director s(s ) name(s) : John Nelson Student s name

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1 Research Project Title : Truck scheduling and dispatching for woodchips delivery from multiple sawmills to a pulp mill Research Project Start Date : September/2011 Estimated Completion Date: September/2014 Director s name : Institution : Gary Bull Co director s(s ) name(s) : Institution : John Nelson Student s name Institution Fattane Nadimi Degree (Master s, Ph.D., PDF) : Student PhD fattane.nadimi@gmail.com Executive Summary Background Transportation is an essential part of every supply chain. Truck transportation is the main mode of commercial transportation in Canada and most parts of the world. Truck transportation problems in a supply chain models can be divided into three main categories: strategic, tactical and operational problems. Scheduling and dispatching of a truck network are operational decisions. Scheduling means determining schedules some time in advance, for example one day earlier and dispatching means determining destinations in real time. In most cases, scheduling problems consider a time interval of one day to one week and operational problems address a time interval of a few minutes to one day. In this research the scheduling and dispatching problem for a truck network transporting woodchips from multiple sawmills to a pulp will be addressed. The advantages of using truck transportation are its flexibility and capability to transport goods with different specifications, in different volumes and between various destinations. The main limitations are complexity of the problem, difficulties in finding an efficient work schedule for available machinery, equipment and drivers, high transportation costs, limitations in availability of trucks and driver shortages. The key issues in pulp mill transportation activities are: (1) supplying enough woodchips for the pulp mill, (2) utilizing available machinery, equipment and human resources efficiently and (3) dealing with uncertainties. Research objectives The main purpose of this research is to develop a decision support system (DSS) that determines oneweek-schedule for a truck network transporting woodchips from multiple sawmills to a pulp mill. Given the inherent uncertainties in scheduling, a solution would require real-time (actually a-fewminutes) updating abilities. The DSS then has dispatching abilities. The DSS considers key limitations and constraints in the system such as: Only one dumper is available, dumper may be down from time to time (for operator shift change, preventive maintenance or because of unexpected breakdowns), trucks have different configurations, drivers have different constraints for visiting sites, certain regulations for 1

2 working hours should be regarded, drivers can change shift at certain switch points, sawmills are in different locations and each of them has specific production plans and limitations. The decision support tool will be developed for two cases of full truck loads and volume-based transportation and it will evaluate the effects of changing the number of trucks and drivers, adding more switch points for drivers shift changing, considering drivers favorite routes, using a portable dumper at the pulp mill to relieve congestion, investing in a new dumper and planning for winter seasonality. To solve the problem, I will develop two optimization algorithms based on simulated annealing and tabu search. The results of these methods will be compared to the schedules developed manually by the dispatcher. For sensitivity analysis purposes and winter condition, the results of the two metaheuristics will be compared to find out if one of them is producing better results in general or in some special conditions. The specific objectives of the research are as follows: The first objective is to evaluate the efficiency of a truck network transporting woodchips from several sawmills to a pulp mill and develop a DSS to improve the work schedule for the trucks and the dumper. Two models will be built to produce work schedules that maximize number of loads transported, minimize truck queue length and minimize dumper idle time with the ability to update the solutions in a few minutes. Each of the two models will be developed for considering transportation based on full truck loads and based on volume of woodchips and capacity of different trucks. The second objective is to investigate the effects of changing critical transportation parameters. These parameters include: the number of trucks and drivers, adding more drivers switch points, eliminating drivers favorite routes, using a portable dumper as a backup and investing in a new dumper. The third objective is to evaluate the effect of seasonal conditions in winter. In winter the conditions change and more parameters need to be assessed such as: more road closures, longer travel times because of road conditions, more frequent truck breakdowns, longer maintenance time, shorter woodchips storage time (because woodchips freezes after being stored for a few hours in a bin or trailer). Scope The DSS will be designed for special conditions of the pulp mill under study. It can be applied to problems with similar conditions. However, depending on circumstances of the new case, it will need modification. The DSS will investigate the effects of parameters determined in strategic and tactical planning, such as number of trucks or investing in a new dumper. However, the scope of this study includes operational level planning and it will not involve in details of strategic or tactical decisions. The DSS will consider all regulations and safety issues but it will not involve environmental issues such as climate change, conservation and fuel consumption reduction. It will mainly interpret things based on benefits of the pulp mill. However, some issues in favor of the pulp mill may also be beneficial to the environment. For example reducing working hours of trucks also results in less greenhouse gas emissions. 2

3 Methodology Writing a mathematical model for woodchips truck scheduling and dispatching problem introduced here is difficult. On the dumper side, we have a G/D/1/ queuing system which cannot be investigated easily. On the other side, there are other parts of the system that includes sawmills, their production and other constraints. This makes truck arrival times dependent and makes the queuing system even more complicated. Moreover, the study must optimize the arrival rate for the system after including constraints related to the dumper, operators, drivers, working hours, shift changing, sawmills and their locations, different production rates and different priorities. One approach for writing the mathematical model of woodchips transportation problem is to write the objective function and key constraints as a mixed integer program and apply other constraints when building feasible routes. Because of the size of the mathematical model, it will be computationally hard to solve and usually a method like column generation should be applied. In producing columns other constraints related to simulation aspects that were previously ignored should be included. A formulation like this is dealing with a mixed integer program and it is very time consuming to be solved. For a scheduling and dispatching model that should be able to run in a few minutes (approximately real time) a metaheuristic method seems more promising. Modern metaheuristic techniques such as tabu search, simulated annealing, genetic algorithm, ant colony, particle swarm optimization can be applied to find a good solution for complicated problems in a reasonable time. Metaheuristic methods use available information from some base solutions to direct the search procedure towards finding the global optimum; the advantage of metaheuristics is that they usually converge to a good solution very quickly. However, there is no guarantee that a metaheuristic can find the global optimum solution. In this study we use two metaheuristic methods to solve the problem: simulated annealing and tabu search. The main advantages of a metaheuristic are: they can find a good quality solution in a reasonable time. Simulated annealing Simulated annealing is one of the most popular metaheuristics to deal with complicated optimization problems. It is inspired from the process of heating and cooling materials in metallurgy in order to form good crystallization in metals and it is good at finding an approximation of the global optimum in a large feasible region. The move selection rule in simulated annealing determines how to the algorithm should move from one solution to another solution in the search procedure. If the potential solution results in improving the objective value, it will be accepted. If not, it will be accepted by a probability that depends on algorithm s parameters. The probability of acceptance of a non-improving solution is large at first which makes it possible to search the feasible region in a process similar to random search. When the search proceeds, the probability of acceptance decrease gradually to give higher priority to improving solutions to enter the search. 3

4 Tabu search Tabu search is another widely used metaheuristic method. All tabu search methods use a local search procedure and they have a tabu list to escape from local optimum solutions. Tabu search can also include two advanced search rules: (1) intensification and (2) diversification. Intensification is directing the algorithm to search one part of feasible region more thoroughly if the region shows to have more promising solutions. Diversification is intentionally making the search process to look at parts of feasible region that have not been searched. Case study To show that the model can be used in the real world, it will be applied to a case study: Tembec s Pulp Mill in Skookumchuck. British Columbia. Tembec pulp mill in Skookumchuck was built in It produces northern bleached softwood kraft (NBSK). Woodchips needed by Tembec s pulp mill in Skookumchuck is provided by truck transportation from several sawmills. Some of these sawmills are located in Canada and some are located in US. Woodchips loads transported to the pulp mill are dumped and kept in inventory before being used to produce NBSK. At the end of each week, sawmills announce their approximate production rate of woodchips for the following week. Sawmills are ranked according to their importance for the manager and it is more important to serve a high priority sawmill before a low priority sawmill. In many cases transportation of woodchips from a low priority sawmill is delayed to the following week or weekends. When a load of woodchips is produced, it is stored in a bin before a truck picks it up. The capacity of the storage bin is different at different sawmills. Some sawmills also allow storage of woodchips in a stockpile. In that case transportation of the loads can be postponed for a longer time. Woodchips can be stored in a stockpile for up to a few months. The transportation system uses different truck-trailer combinations with different drivers. Most trucks are company trucks with two drivers and there are a few lease trucks with one driver. For trucks with two drivers there are a few switch points to meet and take over a shift but lease drivers go home at the end of each daily shift. There are regulations for the number of working hours in US and Canada that must be considered for shift assignments. For the convenience of drivers, the pulp mill tries to give drives nearly the same number of working hours every working day and every week. However, usually a few drivers are willing to work on Saturdays. When a truck comes back from one of the sawmills, it should be unloaded by the dumper at the pulp mill. The dumper can serve one truck at a time and the service time is about fifteen minutes. If the dumper is busy at the time of arrival of a truck, the truck should wait in a line. In many occasions, trucks have to wait in the unloading line-up before being ready to go to the next trip. Currently, the dispatchers work mainly based on try and error and their experience. The dispatcher at the pulp mill knows when and where a new load is ready to be picked up and he knows different constraints, regulations and limitations in the system. The dispatchers are mainly determining schedules manually and they have felt that they need a decision support system to evaluate their decisions and improve them. 4

5 Anticipated results In case of truck scheduling and dispatching for woodchips transportation from sawmills to a pulp mill, there is no research focusing on providing an optimized real time schedule. Even in studies on log transportation and studies in other industries, the problem with so many parameters, uncertainties and constraints have not been studied. In the first step this research aims to develop two models to provide improved schedules and dispatch plans for the whole network of trucks for full truck load transportation and volume-based transportation. The models will be tested using sample data and they will be tested using real case results and experts in the pulp mill. In the second step, different ways to improve the system will be explored by introducing flexibility to different parameters such as number of trucks and drivers, number and location of truckers switch points, effects of drivers favorite routes, benefits of a portable dumper and costs and benefits of purchasing a new dumper. In the third step, the effect of seasonality in winter will be explored. The contribution of this research will be developing a decision support tool that suggests a schedule and dispatch for a truck transportation network that involves one pulp mill and several sawmill providing woodchips for it and applying it to a real case study. Of course, all these developments will be in the context of safety. The study will develop a general model for woodchips transportation problem which can be applied to similar situations after necessary modifications. Developing such a decision support tool can help decision makers to answer important question such as: Is it possible to increase utilization of the equipment? Is it possible to reduce working hours? Is it possible to increase the performance and efficiency of the system? Is the number of involved trucks optimum? Is it profitable to increase or decrease number of trucks? What is the cost of assigning drivers only to their favorite routes? Is it profitable to increase number of switch points? Is it profitable to invest in the available portable dumper or a new dumper? Which options or combination of options is more attractive from financial point of view? Is it possible to manage winter condition better? Fit within Network Research Theme(s) VCO projects try to cover the three planning levels in the forestry supply chain: strategic planning, tactical planning and operational planning. My research addresses a type of truck scheduling and dispatching which are in operational level planning. I think my research fits within theme IV : value optimizing, scheduling and control. It focuses on flow of woodchips from multiple sawmills to a pulp mill with regard to various real world constraints and commitments and tries a develope a one week schedule with the ability to update in real-time (actually near real-time, within a few minutes). The schedule considers the whole time span of one week to avoid achiving a good schedule early in a week at the cost of shifting delays and unefficiency to later times in the week. Every time that an update is needed, again the models update the schedule for the rest of the week. On approach could be considering shorter time intervals for planning, for example one-hour intervals. A short scheduling interval makes the problem smaller and easier to solve, maybe even with exact methods but actually it can produce good schedules for intervals early in the week at the cost of producing bad schedules for intervals in the middle and end of a week. As mentioned in theme IV description: «the sum of all these local short-term optimal decisions is unlikely to be even feasible for the overall problem». The quote is about necessity of integration in decisions about wood supply-demand allocation but the same thing is true in decisions about transportation activities. 5