Investments in Pinus elliottii Engelm. Plantations: Real Options Analysis in Discrete Time

Background: The commonly used methods for the financial evaluation of plantation forest investment projects do not incorporate uncertainties and ignore the value related to flexibility. The real options analysis makes it possible to capture these values in investment projects, increasing their value and return. Despite this, studies involving real options in forest investment projects are scarce, specifically those related to Pinus spp. Therefore, this study aimed to: (a) analyze whether the real options analysis adds value to investment projects of Pinus elliottii Engelm. plantations; and (b) make the real options analysis more accessible to forest managers and potentially increase its use in the investment projects of Pinus spp. plantations. Methods: We evaluated two investment projects in P. elliottii plantations in southern Brazil, which differed in the way of obtaining the land for planting: with lease or purchase of land on a planning horizon of 21 years. In the real options analysis, we used deferral, expansion, and abandonment. Results: Individually, the deferral, expansion, and abandonment options add value to investment projects in Pinus elliottii plantations. The option to expand the forested area is one that adds the most value to the investment project with land lease. In the investment project with land purchase, it is abandonment. Conclusions: Investment projects in Pinus elliotti plantations that contemplate the land purchase analyzed through the real options analysis present higher financial returns than those that consider land lease, inverting the result provided by the traditional analysis.

A Rich Vehicle Routing Problem for a City Logistics Problem

In this work, a Rich Vehicle Routing Problem (RVRP) is faced for solving city logistic problems. In particular, we deal with the problem of a logistic company that has to define the best distribution strategy for obtaining an efficient usage of vehicles and for reducing transportation costs while serving customers with different priority demands during a given planning horizon. Thus, we deal with a multi-period vehicle routing problem with a heterogeneous fleet of vehicles, with customers’ requirements and company restrictions to satisfy, in which the fleet composition has to be daily defined. In fact, the company has a fleet of owned vehicles and the possibility to select, day by day, a certain number of vehicles from the fleet of a third-party company. Routing costs must be minimized together with the number of vehicles used. A mixed integer programming model is proposed, and an experimental campaign is presented for validating it. Tests have been used for evaluating the quality of the solutions in terms of both model behavior and service level to grant to the customers. Moreover, the benefits that can be obtained by postponing deliveries are evaluated. Results are discussed, and some conclusions are highlighted, including the possibility of formulating this problem in such a way as to use the general solver proposed in the recent literature. This seems to be the most interesting challenge to permit companies to improve the distribution activities.

Planning Horizon in International Travel Decision-Making: The Role of Individual and Cultural Determinants

A solid understanding of when travel decisions are made in relation to travelers’ planning horizons is crucial for travel service providers. Despite its importance, there are very few empirical studies investigating the planning horizon and its antecedents in travel research literature. This study contributes to bridging this gap by conceptualizing a two-level model of antecedents of travelers’ planning horizons. In addition to individual traveler- and trip-related aspects, the model provides a cross-cultural perspective on international travelers’ planning horizons by including uncertainty avoidance, individualism, and long-term orientation as cultural-level antecedents. Drawing on a nested dataset of 4,074 international travelers from 17 countries worldwide, the results of a two-level hierarchical regression model show that, in addition to individual-level aspects, cultural antecedents play an important role in determining planning horizons. Based on the empirical results, the paper discusses implications for theory and travel service providers.

Optimising the Preventive Maintenance Interval Using a Semi-Markov Process, Z-Transform, and Finite Planning Horizon

This chapter uses a semi-Markov process and the z transform to find the optimal preventive maintenance interval when dealing with maintenance decision making for a finite time planning horizon. The result is a method that can be easily implemented to assets for which a Weibull reliability analysis exists. The suggested preventive interval formulation is simple and practical. The requirements to apply this simple formula are related to the existence of asset´s reliability data as well as cost/rewards that the assets have when remaining or transitioning to a given state. The application of this method can be very straightforward, and the tool can become a good decision support tool allowing “what if” analysis for different time horizon and maintenance policies.

Resource allocation procedures for unknown sales response functions with additive disturbances

We develop a modified exploration–exploitation algorithm which allocates a fixed resource (e.g., a fixed budget) to several units with the objective to attain maximum sales. This algorithm does not require knowledge of the form and the parameters of sales response functions and is able to cope with additive random disturbances. Note that additive random disturbances, as a rule, are a component of sales response functions estimated by econometric methods. We compare the developed algorithm to three rules of thumb which in practice are often used to solve this allocation problem. The comparison is based on a Monte Carlo simulation for 384 experimental constellations, which are obtained from four function types, four procedures (including our algorithm), similar/varied elasticities, similar/varied saturations, high/low budgets, and three disturbance levels. A statistical analysis of the simulation results shows that across a multi-period planning horizon the algorithm performs better than the rules of thumb considered with respect to two sales-related criteria.

Impact of planning horizon on water distribution network design

The continuous expansion of Water Distribution Network (WDN) makes its design a dynamic process performed within many planning horizons. An appropriate planning horizon is important to save costs and avoid over-design. Typically, a master plan is practiced around every 20 years. The complexity of WDN and computational demands have prevented a full network study of the impact of planning horizon on system cost and efficiency. In this paper, a dynamic network model was employed to simulate the growth of WDN under different growth patterns (exponential and linear) and planning horizons to explore the optimum planning horizon under different interest rates. It is found that the choice of the optimum (i.e. least costly) planning horizon is sensitive to the interest rate. For both growth patterns, a shorter planning horizon is favored with higher annual interest rates while a longer planning horizon is favored with lower rates. With the same interest rate, exponential growth pattern generally favors a shorter planning horizon than linear growth pattern due to more excess capacity provided at the beginning of the study period. The optimum planning horizon is longer than 20 years when interest rate is lower than 3.0% for linear growth or 2.0% for exponential growth.

Explicit Modelling of Multi-Period Setup Times in Proportional Lot-Sizing Problem with Constant Capacity

The planning horizon of small bucket models is often divided into many fictitious micro-periods, with non-zero demand only in the last micro-period of each real (macro-)period. On the one hand, such models ensure schedules with short cycle times and low work-in-process inventory in multilevel systems; on the other, they make setup times that are longer than a single period more likely. This paper presents a new mixed-integer programming model for the case with setup operations that overlap multiple periods. The new model assumes that the capacity is constant in the whole planning horizon and explicitly determines the entire schedule of each changeover. Moreover, a two-level MIP heuristic is presented that uses model-specific cuts to fix a priori some minor decisions. The results of the computational experiments show that the new model and MIP heuristic require a substantially smaller computational effort from a standard MIP solver than the known models.MSC Classification: 90B30 , 90C11

Kuzbass Strategy over 50-year Planning Horizon: Publications on Strategy of the Kuzbass Region

Over its 300-year history, the Kuzbass Region has become one of the strongest industrial and coal mining areas. However, new environmental requirements stipulated by the Paris Agreement and the EU Energy Strategy require a new diversified and innovative economy, i.e. comfortable conditions for people to live and do business. The Strategy for Socio-Economic Development of Kuzbass through 2035 was approved by Regional Law No. 163‑OS on December 23, 2020. The Strategy covers human capital, ecology, reclamation water resources, digitalization, economy, investment, tourism, exhibitions, etc. All these aspects are highlighted in the monographs of the Strategy of the Kuzbass Region. The publications prove that a long-term strategy should combine traditional and novel competitive advantages of the region, which include hydrogen cluster, transport infrastructure, digitalization of business and other spheres, better environmental conditions, forest and land reclamation, sustainable use of water resources, as well as the importance of rebranding the region on the domestic and international arena.

An Imperfect Production Model for Breakable Multi-Item with Dynamic Demand and Learning Effect on Rework over Random Planning Horizon

In recent times, in the literature of inventory management there exists a notorious interest in production-inventory models focused on imperfect production processes with a deterministic time horizon. Nevertheless, it is well-known that there is a high influence and impact caused by the learning effect on the production-inventory models in the random planning horizon. This research work formulates a mathematical model for a re-workable multi-item production-inventory system, in which the demand of the items depends on the accessible stock and selling revenue. The production-inventory model allows shortages and these are partial backlogged over a random planning horizon. Also, the learning effect on the rework policy, inflation, and the time value of money are considered. The main aim is to determine the optimum production rates that minimize the expected total cost of the multi-item production-inventory system. A numerical example is solved and a detailed sensitivity analysis is conducted in order to study the production-inventory model.

Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times

This study investigates replenishment planning in the case of discrete delivery time, where demand is seasonal. The study is motivated by a case study of a soft drinks company in Germany, where data concerning demand are obtained for a whole year. The investigation focused on one type of apple juice that experiences a peak in demand during the summer. The lot-sizing problem reduces the ordering and the total inventory holding costs using a mixed-integer programming (MIP) model. Both the lot size and cycle time are variable over the planning horizon. To obtain results faster, a dynamic programming (DP) model was developed, and run using R software. The model was run every week to update the plan according to the current inventory size. The DP model was run on a personal computer 35 times to represent dynamic planning. The CPU time was only a few seconds. Results showed that initial planning is difficult to follow, especially after week 30, and the service level was only 92%. Dynamic planning reached a higher service level of 100%. This study is the first to investigate discrete delivery times, opening the door for further investigations in the future in other industries.