Review of recent developments in OR/MS research in disaster operations management

Potential consequences of disasters involve overwhelming economic losses, large affected populations and serious environmental damages. Given these devastating effects, there is an increasing interest in developing measures in order to diminish the possible impact of disasters, which gave rise to the field of disaster operations management (DOM). In this paper we review recent OR/MS research in DOM. Our work is a continuation of a previous review from Altay and Green (2006). Our purpose is to evaluate how OR/MS research in DOM has evolved in the last years and to what extent the gaps identified by Altay and Green (2006) have been covered. Our findings show no drastic changes or developments in the field of OR/MS in DOM since the publication of Altay and Green (2006). Additionally to our comparative analysis, we present an original evaluation about the most common assumptions in recent OR/MS literature in DOM. Based on our findings we provide future research directions in order to make improvements in the areas where lack of research is detected.     

中国运筹学发展研究报告

运筹学是自20世纪三四十年代发展起来的一门新兴交叉学科,主要研究如何应用数学和计算的理论与方法对社会系统和工程系统做出最优或满意的决策。为了更好地推动中国运筹学及相关领域的研究和应用,学会组织国内运筹学领域的部分专家,并参考了国内外运筹学多个分支代表性人物对运筹学的起源和发展的回忆与评述,共同完成了此报告。报告概述了运筹学的主要特征和方法,简述了运筹学的发展历程,剖析了运筹学研究中的成功经验,综述了运筹学几个主要分支的发展状况,介绍了运筹学中十几个有代表性的难题,展望了运筹学未来发展的方向。希望此报告能引起读者进一步思考运筹学的本质,运筹学是如何成长和将如何发展,并在一定程度上推动中国运筹学更好地发展。     

Globalization, social development, and operations research: an exploration of strategies for success

This paper argues that in today’s world order, policy-making takes precedence over analytical and technical models that have historically formed the mainstay of Operations Research. The structural and human dimensions of this transition is discussed in the form of six Policy Imperatives aimed at the creation of a world order that makes it more humane and less vulnerable to failure. It is proposed that though OR occupies a prominent role in developmental initiatives, in order for its successful implementation in meeting social goals, the context in which it is invoked must be conducive to social transformation. An earlier version of this paper was presented at the International Conference on Operations Research for Development (ICORD) held December 27–30, 2002 at Anna University, Chennai. The author would like to acknowledge the helpful comments of anonymous reviewers. He would also like to thank Professor K.S.P. Rao of Anna University for his gracious assistance.     

Issues in the use of ratings-based versus choice-based conjoint analysis in operations management research

Conjoint analysis has played an important role in helping make a number of operations management decisions including product and service design, supplier selection, and service operations capacity. Many recent advances in this area have raised questions about the most appropriate form of conjoint analysis for this research. We review recent developments in the literature and provide new evidence on how the choice between ratings- and choice-based conjoint models might affect the estimates of customer demand used in operations management models.     

Synergies between operations research and data mining: The emerging use of multi-objective approaches

Operations research and data mining already have a long-established common history. Indeed, with the growing size of databases and the amount of data available, data mining has become crucial in modern science and industry. Data mining problems raise interesting challenges for several research domains, and in particular for operations research, as very large search spaces of solutions need to be explored. Hence, many operations research methods have been proposed to deal with such challenging problems. But the relationships between these two domains are not limited to these natural applications of operations research approaches. The counterpart is also important to consider, since data mining approaches have also been applied to improve operations research techniques. The aim of this article is to highlight the interplay between these two research disciplines. A particular emphasis will be placed on the emerging theme of applying multi-objective approaches in this context.     

Centralized and decentralized management of groundwater with multiple users

In this work, we investigate two groundwater inventory management schemes with multiple users in a dynamic game-theoretic structure: (i) under the centralized management scheme, users are allowed to pump water from a common aquifer with the supervision of a social planner, and (ii) under the decentralized management scheme, each user is allowed to pump water from a common aquifer making usage decisions individually in a non-cooperative fashion. This work is motivated by the work of Saak and Peterson [14], which considers a model with two identical users sharing a common aquifer over a two-period planning horizon. In our work, the model and results of Saak and Peterson [14] are generalized in several directions. We first build on and extend their work to the case of n non-identical users distributed over a common aquifer region. Furthermore, we consider two different geometric configurations overlying the aquifer, namely, the strip and the ring configurations. In each configuration, general analytical results of the optimal groundwater usage are obtained and numerical examples are discussed for both centralized and decentralized problems.     

Cost/risk balanced management of scarce resources using stochastic programming

We consider the situation when a scarce renewable resource should be periodically distributed between different users by a Resource Management Authority (RMA). The replenishment of this resource as well as users demand is subject to considerable uncertainty. We develop cost optimization and risk management models that can assist the RMA in its decision about striking the balance between the level of target delivery to the users and the level of risk that this delivery will not be met. These models are based on utilization and further development of the general methodology of stochastic programming for scenario optimization, taking into account appropriate risk management approaches. By a scenario optimization model we obtain a target barycentric value with respect to selected decision variables. A successive reoptimization of deterministic model for the worst case scenarios allows the reduction of the risk of negative consequences derived from unmet resources demand. Our reference case study is the distribution of scarce water resources. We show results of some numerical experiments in real physical systems.     

Wildfire fuel management: Network-based models and optimization of prescribed burning

Wildfires are a common phenomenon on most continents. They have occurred for an estimated 60 million years and are part of a regular climatic cycle. Nevertheless, wildfires represent a real and continuing problem that can have a major impact on people, wildlife and the environment. The intensity and severity of wildfires can be reduced through fuel management activities. The most common and effective fuel management activity is prescribed burning. We propose a multi-period optimization framework based on mixed integer programming (MIP) techniques to determine the optimal spatial allocation of prescribed burning activities over a finite planning horizon. In contrast to the existing fuel management optimization literature, we model fuel accumulation with Olson’s equation. To capture potential fire spread along with irregular landscape connectivity considerations, we use a graph-theoretical approach that allows us to exploit graph connectivity measures (e.g., the number of connected components) as optimization objectives. The resulting mathematical programs can be tackled by general purpose MIP solvers, while for handling larger instances we propose a simple heuristic. Our computational experiments with test instances constructed based on real-life data reveal interesting insights and demonstrate the advantages and limitations of the proposed approaches.     

The sector design and assignment problem for snow disposal operations

Winter road maintenance operations involve a host of decision-making problems at the strategic, tactical, operational, and real-time levels. Those operations include spreading of chemicals and abrasives, snow plowing, loading snow into trucks, and hauling snow to disposal sites. In this paper, we present a model and two heuristic solution approaches based on mathematical optimization for the problem of partitioning a road network into sectors and allocating sectors to snow disposal sites for snow disposal operations. Given a road network and a set of planned disposal sites, the problem is to determine a set of non-overlapping subnetworks, called sectors, according to several criteria related to the operational effectiveness and the geographical layout, and to assign each sector to a single snow disposal site so as to respect the capacities of the disposal sites, while minimizing relevant variable and fixed costs. Our approach uses single street segments as the units of analysis and we consider sector contiguity, sector balance and sector shape constraints, hourly and annual disposal site capacities, as well as single assignment requirements. The resulting model is based on a multi-commodity network flow structure to impose the contiguity constraints in a linear form. The two solution approaches were tested on data from the city of Montreal in Canada.     

Strategic financial risk management and operations research

Risk management has become a vital topic for financial institutions in the 1990s. Strategically, asset/liability management systems are important tools for controlling a firm's financial risks. They manage these risks by dynamically balancing the firm's asset and liabilities to achieve the firm's objectives. We discuss such leading international firms as Towers Perrin, Frank Russell, and Falcon Asset Management, which apply asset/liability management for efficiently managing risk over extended time periods. Three components of asset/liability management are described: 1) a multi-stage stochastic program for coordinating the asset/liability decisions; 2) a scenario generation procedure for modeling the stochastic parameters; and 3) solution algorithms for solving the resulting large-scale optimization problem.     

A spatial optimisation model for multi-period landscape level fuel management to mitigate wildfire impacts

Elevated fuel loads are contributing to an increase in the occurrence of, and area burned by, severe wildfires in many regions across the globe. In an attempt to reverse this trend, fire and land management agencies are investing in extensive fuel management programs. However, the planning of fuel treatment activities poses complicated decision-making problems with spatial and temporal dimensions. Here, we present a mixed integer programming model for spatially explicit multi-period scheduling of fuel treatments. The model provides a flexible framework that allows for landscape heterogeneity and a range of ecological and operational considerations and constraints. The model’s functionality is demonstrated on a series of hypothetical test landscapes and a number of implementation issues are discussed.     

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.     

军事运筹学的过去、现在和未来

军事运筹学是从20世纪初发展起来的一门跨领域交叉学科,主要研究解决军事问题的定量分析和决策优化的理论和方法,以做出最优或满意的决策. 回顾军事运筹学的发展历程和国内军事运筹学的发展状况,简要介绍军事运筹学的主要理论方法和研究内容,展望军事运筹学未来发展趋势.     

两端固定资源连续分配问题的一种算法

用动态规划可解一端固定,一端自由的资源连续分配问题,对两端固定的此类问题,动态规划解法过程复杂．针对目标函数及约柬条件均为线性函数的此类问题,给出一个简化的一般算法及相应算例,本算法极大简化了计算的复杂性．     

两端固定资源连续分配问题的区间根式解算法证明

对两端固定资源连续分配问题,动态规划解法过程复杂.针对目标函数及约束条件均为线性函数的此类问题,给出一个根式解的算法证明,将针对点的根式解的算法推广到区间的根式解,使该根式解的算法真正具有实用性.     

Perspectives on operations research in data and knowledge management

A number of problems in the design and management of database systems and knowledge base systems (KBSs) can be addressed using techniques from operations research (OR). This article provides a perspective on these problems and the types of models that have been applied to them, and identifies some areas that pose interesting modeling and analysis questions for researchers working in areas such as mathematical programming, stochastic modeling, dynamic programming and simulation.     

PAST AND FUTURE MANAGEMENT OF A COLLAPSED FISHERY: THE BAY OF BISCAY ANCHOVY

Abstract We analyze the efficiency of the international management of the Bay of Biscay anchovy. While a sharing agreement between France and Spain has been in place since 1992, the fish stock collapsed in 2005 and the fishery closed from 2005 to spring 2010. We consider differences in production technologies between both countries and calibrate our model using data from 1987 to 2009. Our results suggest two sources of rent dissipation under the existing sharing agreement: inefficient quota allocation and production inefficiencies due to inflexible national regulations. We discuss several alternatives to improve management.     

Inexact joint-probabilistic chance-constrained programming with left-hand-side randomness: An application to solid waste management

In practical waste management systems, amounts of waste transported and treated are not always equal on a daily basis. To distinguish between these two kinds of amounts and reflect their random relationships effectively, an inexact joint-probabilistic left-hand-side chance-constrained programming (IJLCP) method was developed and applied to a municipal solid waste management problem under dual uncertainties. Dual uncertainties are defined as two kinds of uncertainties existing in the same programming model. Improving upon conventional right-hand-side chance-constrained programming, the IJLCP can not only reflect uncertainties presented in terms of interval parameters (unit transportation/treatment costs, capacities of waste treatment facilities, waste generation rates, waste transportation/treatment amounts and so on) and left-hand-side random variables (the relationship between waste transportation and treatment amounts), but also examine the reliability of satisfying (or risk of violating) the entire system constraints. A non-equivalent but sufficient linearization form of IJLCP for solving this type of problem was proposed and proved in a straightforward manner. The performance of IJLCP was analyzed under scenarios at joint and individual probabilities and compared with the corresponding internal-parameter programming model. The results indicated that the net system costs would both decrease with increasing joint probability levels and decrease slightly at different individual probabilities with the same joint probabilities. The two types of dual uncertainties were discussed as well.