دانلود رایگان مقاله انگلیسی مدیریت ریسک برای برنامه ریزی جنگلداری تحت عدم قطعیت در تقاضا و قیمت – الزویر ۲۰۱۸
|عنوان فارسی:||مدیریت ریسک برای برنامه ریزی جنگلداری تحت عدم قطعیت در تقاضا و قیمت|
|عنوان انگلیسی:||Risk management for forestry planning under uncertainty in demand and prices|
|تعداد صفحات مقاله انگلیسی : 24||تعداد صفحات ترجمه فارسی : ترجمه نشده|
|سال انتشار : 2018||نشریه : الزویر - Elsevier|
|فرمت مقاله انگلیسی : PDF||کد محصول : E8573|
|محتوای فایل : PDF||حجم فایل : mb 2|
|رشته های مرتبط با این مقاله: منابع طبیعی|
|گرایش های مرتبط با این مقاله: جنگلداری|
|مجله: مجله اروپایی تحقیقات عملیاتی - European Journal of Operational Research|
|دانشگاه: Estadística e Investigación Operativa - Universidad Rey Juan Carlos - Madrid - Spain|
|کلمات کلیدی: OR در منابع طبیعی، برنامه ریزی جنگلداری، بهینه سازی تحت عدم اطمینان، مدیریت ریسک، ثبات در اقدامات خطرناک|
The paper presents and compares approaches for controlling forest companies’ risk associated with advance planning under variable future timber prices and demand. Decisions to be made in advance are which stands to cut and which new access roads to build in each period, while maximizing profit under manageable risk. We first developed a tighter, improved formulation of our earlier deterministic mixed 0–1 model (see Andalaft et al. (2003)), and its stochastic counterpart for a set of representative scenarios, an extension of our simplified risk-neutral version (see Alonso-Ayuso, Escudero, Guignard, Quinteros, and Weintraub (2011)). Using the expected value of the stochastic parameters might produce poor or even infeasible solutions if some extreme scenarios are realized. A stochastic model, however, enables the planner to make more robust decisions. In particular, being able to control risk in early periods is important, as firms tend to emphasize short term financial results. We tested two risk measures that extend the classical Conditional Value-at-Risk (CVaR) by controlling the risk at a subset of intermediate periods (time-inconsistent TCVaR) or at a subset of scenario groups (time-consistent ECVaR), with time consistency as given in Homem-de Mello and Pagnoncelli (2016) and others. We also combined TCVaR and ECVaR into what we call MCVaR. We analyzed the planned and implementable policies of all above risk measures in a broad computational experiment, on a large size realistic instance. The results show that ECVaR, TCVaR and MCVaR outperform the classical CVaR approach. MCVAR usually provides better solutions for the first periods with overall profit distribution similar to the other measures for the planned policy, TCVaR gives the highest profit results for the implementable policy, while ECVaR gives the highest profit at the end of the time horizon in both policies.
5. Discussion and outline of future research plans
In this work, we have formulated and solved a multi-period stochastic mixed 0–1 model for planning forest harvesting and road building. We consider uncertainties in timber prices and demand along the time horizon, by analyzing a finite set of discrete scenarios, as already shown, contrary to the traditional approach that uses average (i.e., expected) values for the uncertain parameters. Contrary also to the most frequent approaches in the stochastic optimization literature, the model proposed in this work considers that the parameters’ uncertainty is not independent periodwise, but it is based on the probability distribution of the parameters’ realizations, which depend on the realization of the same or other uncertain parameters in the previous periods.
The main contribution of the work has been to extend the risk management on the solution of the risk neutral (RN) model for the forest harvesting problem, by considering two versions of the very popular CVaR risk averse measure in a stochastic model. It can be observed that the main advantage of the risk averse measures is that they advance the profit to early periods at the price of a very small deterioration (in the experiment, at least) in the expected profit at the end of the time horizon. That is, risk averse measures provide higher profits at early periods than EV and RN, in addition to reducing the variability of the profits for unwanted scenarios (i.e., low-probability scenarios with a profit in unwanted quantiles).