A framework for sustainable forest resource allocation: A Canadian case study

This paper addresses the problem of public-owned forest resource allocation observed in Canada. An integrated framework based on mill abilities to create value (expressed as economic, environmental and social benefits) is proposed. It encompasses three phases: (1) election of sustainable allocation criteria, (2) evaluation of mill performance with regard to the allocation criteria and (3) allocation of wood volumes according to mill performance. The framework is applied to a case study proposed by Ministry of Forests, Fauna and Parks (MFFP) in Québec. It is implemented as follows: first, the international standards, Forest Stewardship Council (FSC) and Global Reporting Initiative (GRI) are used to identify relevant allocation criteria covering the three sustainability dimensions (economy, environment and society). Second, the Group-Analytic Hierarchy Process (Group-AHP) is used to weight the allocation criteria and evaluate mill sustainability performance. Finally, optimization models are formulated to allocate the wood following two strategies: (1) maximizing the total created value by all mills and (2) promoting inter-firm fairness. The numerical results show that integrating sustainability concerns in the evaluation process has a significant impact on the allocation decisions. Moreover, adopting a wood allocation strategy seeking to guarantee fairness between forest companies is a win–win strategy as it also leads to maximizing the created value with a minimum deviation from the optimal targeted value. Lessons learned from this collaboration with MFFP are presented in order to help other interested researchers and public organizations develop their own roadmap to sustainable public resource allocation.     

效率与公平视角下企业及政府的资源配置决策

考虑企业及政府资源配置中效率与公平目标的不同内涵,采用"客户嫉妒"和"个体变权"刻画不同主体的决策行为,分别引入公平参数建立多目标决策模型,并提出基于客户和决策者偏好的公平参数选择方法,实现效率与公平的权衡。将模型转化为变分不等式,设计修正投影算法求解。通过算例进一步分析了不同主体的决策行为差异、公平参数的选择及影响。研究表明：企业与政府的效率最优决策相近,但公平最优决策差异明显;客户公平偏好越高,企业资源配置的分化特征反而越显著;政府决策者公平偏好越高,所有个体间资源配置趋于均等。     

A multi-criteria approach to fair and efficient bandwidth allocation

In systems which serve many users there is a need to respect some fairness rules while looking for the overall efficiency. This applies among others to network design where a central issue is how to allocate bandwidth to flows efficiently and fairly. The so-called max–min fairness is widely used to meet these goals. However, allocating the bandwidth to optimize the worst performance may cause a large worsening of the overall throughput of the network. In this paper we show how the concepts of mult-criteria equitable optimization can effectively be used to generate various fair and efficient allocation schemes. We introduce a multi-criteria model equivalent to equitable optimization and we develop a corresponding reference point procedure to generate fair and efficient bandwidth allocations. Our analysis is focused on the nominal network design for elastic traffic that is currently the most significant traffic of IP networks. The procedure is tested on a sample network dimensioning problem for elastic traffic and its abilities to model various preferences are demonstrated.     

The impact of stochastic lead times on the bullwhip effect under correlated demand and moving average forecasts

We quantify the bullwhip effect (which measures how the variance of replenishment orders is amplified as the orders move up the supply chain) when both random demands and random lead times are estimated using the industrially popular moving average forecasting method. We assume that the lead times constitute a sequence of independent identically distributed random variables and the correlated demands are described by a first-order autoregressive process. We obtain an expression that reveals the impact of demand and lead time forecasting on the bullwhip effect. We draw a number of conclusions on the bullwhip behaviour with respect to the demand auto-correlation and the number of past lead times and demands used in the forecasts. We find maxima and minima in the bullwhip measure as a function of the demand auto-correlation.     

Importance of fairness in humanitarian relief distribution

Abstract

This article is inspired by some of the challenges faced by non-governmental organizations in the distribution of humanitarian relief to vulnerable rural communities. A major concern of these organizations is to warrant that relief is distributed in an impartial and transparent way, which gives rise to the notion of fairness. This article thus discusses the importance of fairness in relief distribution and how it can be defined, especially in a context where delivery of vital items must be ensured periodically. We also propose some performance indicators to measure fairness, which can be useful to organizations that are held accountable for the impartiality of their decisions. Finally, an empirical study of an academic case, inspired by a rural aid distribution problem, is used to analyse how different mathematical formulations may contribute in helping crisis managers integrate fairness or equity in their decisions.     

多目标条件下企业碳配额分配和政府公平——基于(p,α)比例公平的视角

本文在企业和政府主从博弈框架下，研究政府兼顾社会福利和减排成本的多目标条件下企业最优碳配额分配方式，并采用（p，α）比例公平建立了最优分配与政府公平态度之间的对应关系，进一步分析企业间减排效率差异对碳配额最优分配以及政府所持公平态度的影响。采用遗传算法模拟结果显示，在政府最大化社会福利和最小化减排总成本的多目标下，政府始终给予高效率减排企业更多的配额，在企业间减排效率差异较小和较大的行业，减排效率对最优配额分配和政府所持公平态度的影响是完全不同的。当行业内企业减排效率差异较小时，随减排效率差异的增加，低效率企业所得配额逐渐减少，政府公平性逐渐降低；当企业减排效率差异较大时，情况刚好相反，低效率企业的配额逐渐增加，政府变得越来越注重公平。除此之外，政府公平性并不意味着低效率企业得到更多的碳配额，当企业间减排效率差异增加到某一区间，随减排效率差异的增加，低效率企业所得配额逐渐减小，而政府公平逐渐增加。我们的结论为相关部门多目标下的最优碳配额分配决策以及分析政府的公平性提供了有益参考。     

A TUTORIAL ON BOTTLENECK DYNAMICS: A HEURISTIC SCHEDULING METHODOLOGY

We give a tutorial on bottleneck dynamics. Bottleneck dynamics is a scheduling framework that uses approximate dual resource prices to make decentralized decisions. The basic idea is to establish a price for a resource as a function of the set of jobs that need to be processed by the resource. Tasks are then sequenced according to a cost/benefit ratio. Starting with one resource sequencing problems, we describe how priorities for jobs can be developed and how they translate into resource prices. We then describe how resource prices can be approximated in a multiresource situation and how lead times which are critical for these approximations can be accurately computed. We also describe a number of studies that have shown bottleneck dynamics to be an effective approach in several different problem areas.     

Marriage,Labor Supply,and Home Production

We develop a search model of marriage where men and women draw utility from private consumption and leisure, and from a non‐market good that is produced in the home using time resources. We condition individual decisions on wages, education, and an index of family attitudes. A match‐specific, stochastic bliss shock induces variation in matching given wages, education, and family values, and triggers renegotiation and divorce. Using BHPS (1991–2008) data, we take as given changes in wages, education, and family values by gender, and study their impact on marriage decisions and intrahousehold resource allocation. The model allows to evaluate how much of the observed gender differences in labor supply results from wages, education, and family attitudes. We find that family attitudes are a strong determinant of comparative advantages in home production of men and women, whereas education complementarities induce assortative mating through preferences.     

Do the contingencies of external monitoring, ownership incentives, or free cash flow explain opposing firm performance expectations?

Neoclassical and strategy frameworks stipulate that managers promote corporate performance and shareholder interests in their resource allocation decisions. Agency related works anticipate that executives seek their own personal interests at a cost to performance and shareholder wealth in their resource allocation choices. In this study, an attempt is made to resolve these conflicting anticipations. We propose that changes in levels of resource allocations (advertising expenditure, R&D spending, capital intensity) may be more positively associated with changes in levels of subsequent corporate performance for firms with greater external monitoring or with higher CEO ownership incentives. We also propose that changes in levels of resource allocations may directly (inversely) affect changes in levels of subsequent performance of the actively (passively) monitored enterprises, lacking (possessing) free cash flow. Additionally, we propose that changes in levels of resource allocations may directly (inversely) affect changes in levels of subsequent performance of firms with high (low) CEO ownership incentives in the absence (presence) of free cash flow. Regression models are utilized to test our proposals on a longitudinal sample obtained from the Compustat database. The empirical findings are generally supportive of our proposals.

Neighbors in Time: Development and Use of Some Values for Intergenerational Justice from the Jewish and Christian Religious Traditions

The overlapping understandings of Jewish and Christian traditions (the Traditions), both influencing and reincorporating Western culture from the Greeks to the present, underpin many of the ethical constructs of present society, in ways both obvious and subtle. The concept of justice is central to the ethical outlooks of both religious traditions. This article systematically develops values related to justice from within the Traditions and extends them to the question of intergenerational equity as proposed precepts for assessment and action. Many of the results seem familiar because they are deeply embedded in our culture. They are assertions that transcend time-the value of human life; an obligation to support the necessary interests of others, especially the powerless; the concepts of brotherhood, the common good, and stewardship of resources; and the transmission of knowledge, resources, and values through the vehicles of families and communities. Other ideas from within the Traditions, such as giving beyond the requirements of equity and the connection of human life with God whose abilities are unbounded by time, continue to draw people beyond the limits of culture. These outlooks are organized into 17 statements or proposals constitutive of this view of justice. These summary statements are used as the basis for comparing a system of principles proposed by a panel of the National Academy of Public Administration for intergenerational decision making with the views represented by the Traditions. This broaches a broader question of fairness in resource distribution. The article concludes with reference to some developed resources of the Traditions from which further insight can be drawn, and another illustration related to resource allocation. It is hoped that the article helps stimulate wider analysis of the values on which our decisions are made.     

The Effects of Learning,Forgetting, and Relearning on Decision Rule Performance in Multiproject Scheduling

Product development occurs in multiproject environments where preemption is often allowed so that critical projects can be addressed immediately. Because product development is characterized by time-based competition, there is pressure to make decisions quickly using heuristics methods that yield fast project completion. Preemption heuristics are needed both to choose activities for preemption and then to determine which resources to use to restart preempted activities. Past research involving preemption has ignored any completion time penalty due to the forgetting experienced by project personnel during preemption and the resulting relearning time required to regain lost proficiency. The purpose of this research is to determine the impact of learning, forgetting, and relearning (LFR) on project completion time when preemption is allowed. We present a model for the LFR cycle in multiproject development environments. We test a number of priority rules for activity scheduling, activity preemption, and resource assignment subsequent to preemption, subject to the existence of the LFR cycle, for which a single type of knowledge worker resource is assigned among multiple projects. The results of the simulation experiments clearly demonstrate that LFR effects are significant. The tests of different scheduling, preemption, and resource reassignment rules show that the choice of rule is crucial in mitigating the completion time penalty effects of the LFR cycle, while maintaining high levels of resource utilization. Specifically, the worst performing rules tested for each performance measure are those that attempt to maintain high resource utilization. The best performing rules are based on activity criticality and resource learning.     

Fairness in Selling to the Newsvendor

This paper studies the impact of fairness concerns on supply chain performance (SCP) in the two‐party newsvendor setting. We extend prior fairness analysis to a wide range of demand distributions, and also allow the degree and definition of fairness to assume a broader range of preferences than those in prior literature. Contrary to prior literature, we find that if the retailer's ideal allocation to the supplier is not sufficiently large, regardless of demand variability, a fair‐minded retailer makes no difference to system efficiency when facing a traditional profit‐maximizing supplier. Only when the retailer's ideal allocation to the supplier is above a threshold can the retailer's fairness concern improve the system efficiency for sufficiently high demand uncertainty. In order for the retailer's fairness concern to improve expected profits of both parties compared to the traditional supply chain case (win–win), the demand uncertainty cannot be too low, the retailer is not very averse to disadvantageous inequity, and his ideal allocation to the supplier is within a specific range. If only the supplier is concerned for fairness, the results range from worsening to improving (but not coordinating) the system and a win–win situation is impossible. Finally, when both the supplier and retailer are fair‐minded, SCP is improved unless both parties prefer to allocate small portions of system profit to the other. Again, win–win will be achieved only when the demand uncertainty is sufficiently high, the retailer's ideal allocation is within a certain range, and he is not very averse to disadvantageous inequity.     

The value of board monitoring in promoting R&D: a test of agency-theory in the US context

Prior agency-theory research has presented conflicting findings regarding the importance of board monitoring in motivating R&D. We reinvestigate this literature by examining the value monitoring exerts in abating both the agency costs of underinvestment and overinvestment in R&D. We argue that monitoring that relies on board independence has both benefits and costs associated with promoting R&D. While we assert that intense monitoring by the board heightens underinvestment in the US context, it can also provide discipline over a firms free cash flows. We test our theory using a longitudinal panel data set consisting of a cross-section of S&P 1500 US-firms between 1997 and 2007. On average our study finds inside directors increase overinvestment in R&D, but facilitate better resource allocation when a firm has rich growth opportunities. Also, while too much emphasis on outside directors heightens underinvestment in R&D, a more independent board encourages better resource allocation when firms have high free cash flows that need to be paid back to owners. Thus, our results suggest a more inclusive perspective of agency-theory can help managers make better R&D investment decisions.     

Fair task allocation in transportation

Task allocation problems have traditionally focused on cost optimization. However, more and more attention is being given to cases in which cost should not always be the sole or major consideration. In this paper we study a fair task allocation problem in transportation where an optimal allocation not only has low cost but more importantly, it distributes tasks as even as possible among heterogeneous participants who have different capacities and costs to execute tasks. To tackle this fair minimum cost allocation problem we analyze and solve it in two parts using two novel polynomial-time algorithms. We show that despite the new fairness criterion, the proposed algorithms can solve the fair minimum cost allocation problem optimally in polynomial-time. In addition, we conduct an extensive set of experiments to investigate the trade-off between cost minimization and fairness. Our experimental results demonstrate the benefit of factoring fairness into task allocation. Among the majority of test instances, fairness comes with a very small price in terms of cost.     

Managing Risk at the Tucson Sector of the U.S. Border Patrol

This article describes a risk analysis used to inform resource allocation at the Tucson Sector of the U.S. Border Patrol, the busiest sector for alien and drug trafficking along the Southwest land border with Mexico. The model and methodology that underlie this analysis are generally applicable to many resource allocation decisions regarding the management of frequently occurring hazards, decisions regularly made by officials at all levels of the homeland security enterprise. The analysis was executed by agents without previous risk expertise working under a short time frame, and the findings from the analysis were used to inform several resource allocation decisions.     

Modeling External Risks in Project Management

To ascertain the viability of a project, undertake resource allocation, take part in bidding processes, and other related decisions, modern project management requires forecasting techniques for cost, duration, and performance of a project, not only under normal circumstances, but also under external events that might abruptly change the status quo. We provide a Bayesian framework that provides a global forecast of a project's performance. We aim at predicting the probabilities and impacts of a set of potential scenarios caused by combinations of disruptive events, and using this information to deal with project management issues. To introduce the methodology, we focus on a project's cost, but the ideas equally apply to project duration or performance forecasting. We illustrate our approach with an example based on a real case study involving estimation of the uncertainty in project cost while bidding for a contract.     

Managing a Portfolio of Product Development Projects under Resource Constraints

Managers of product development (PD) project portfolios face difficult decisions in allocating limited resources to minimize project or portfolio delay. Although PD projects are highly iterative (cyclical), almost all of the vast literature on project scheduling assumes that projects are acyclical. This article addresses this gap with a comprehensive analysis of 31 priority rules (PRs) on 18,480 portfolios containing 55,440 iterative projects. We find that the best PRs for iterative project portfolios differ significantly from those for acyclical ones, and that the best PRs at the project level differ from those at the portfolio level. The best PR depends on project and portfolio characteristics such as network density, iteration intensity, resource loading profile, and amount of resource contention. In particular, by amplifying the effects of iteration, high‐density networks hold dramatically different implications for iterative projects. Moreover, the best PR also differs depending on whether the objective is to minimize the average delay to all projects or to minimize delay to the overall portfolio. Thus, a project or portfolio manager who uses the same PR on all occasions will exhibit unnecessarily poor performance in most cases.     

Partially Flexible Operating Rooms for Elective and Emergency Surgeries

In hospitals, the management of operating rooms faces a trade‐off between the need to be responsive to emergency surgeries and to conduct scheduled elective surgeries efficiently. Operating rooms can be configured as flexible and handle both electives and emergencies, or as dedicated to focus on either electives or emergencies. With flexible rooms, the prioritization of emergencies over scheduled electives can lead to schedule disruptions. Focused rooms can lead to imbalances between capacity and surgery workload. Whereas hospital administrators typically handle this trade‐off by employing either flexible rooms (complete flexibility) or dedicated rooms (complete focus), we investigate whether a combination of flexible and dedicated rooms (partial flexibility) could be a preferable alternative. The ensuing question is what is the right combination of flexible and dedicated rooms? A versatile simulation model is developed to evaluate different resource allocation policies under various environmental parameters and performance metrics, including patient wait time, staff overtime, and operating room utilization. The main result is that partial flexibility configurations outperform both complete flexibility and complete focus policies by providing solutions with improved values of expected wait time for both emergency and elective patients.     

Optimal Resource Allocation for Defense of Targets Based on Differing Measures of Attractiveness

This article describes the results of applying a rigorous computational model to the problem of the optimal defensive resource allocation among potential terrorist targets. In particular, our study explores how the optimal budget allocation depends on the cost effectiveness of security investments, the defender's valuations of the various targets, and the extent of the defender's uncertainty about the attacker's target valuations. We use expected property damage, expected fatalities, and two metrics of critical infrastructure (airports and bridges) as our measures of target attractiveness. Our results show that the cost effectiveness of security investment has a large impact on the optimal budget allocation. Also, different measures of target attractiveness yield different optimal budget allocations, emphasizing the importance of developing more realistic terrorist objective functions for use in budget allocation decisions for homeland security.