Multiple Objectives

This paper outlines the national approach to stormwater management in Malaysia, and evolving implementation practices by State and Local government authorities. The process of departing from the traditional, monodisciplinary approach is currently going on in Malaysia. Goals of stormwater management have already shifted from the traditional storm drainage and "end of pipe" approaches towards sustainable ecological solutions more in peace with nature. The new approach is based on a deeper understanding of the cyclicity of material and energy flows in nature. The research focused on new concepts for the urban stormwater runoff and the water quality management based on integrated stormwater solutions. The USM Engineering campus is a pilot project for urban stormwater management strategy as an example of an ecologically sustainable development based approach to urban stormwater management. The concept is based on integrating stormwater best management practices into urban plann...

Addressing forest sustainability requires negotiation and integration of individual forest management plans of multiple small non-industrial forest owners (NIPF). Recently, Portuguese forest policy prescribed the creation of Areas for Forest Intervention (AFI/ZIF)-forest areas encompassing at least 1 Â 10 3 ha and 50 NIPF-to address those requirements. Yet, the development of forest management plans for AFI is targeting multiple objectives in the framework of multiple-ownership. This is not trivial as conflict is prone to arise and negotiation is needed to satisfy individual and collective goals and constraints. This paper is prompted by the need to identify methods and tools that may be used to support forest management planning in the framework of an AFI. Emphasis is on the need of specific tools and methods that can support AFI management planning, in order to mitigate conflicts and achieve a consensual plan. This paper thus presents a review of methods and tools used to support group decision-making in forest management planning. It further discusses the potential of hybrid approaches for collaborative planning that may take advantage of the integrated functionality of both quantitative and qualitative decision support methods and tools. Published by Elsevier B.V.

Abstract. Data-centric synchronization groups fields of objects into atomic sets to indicate they must be updated atomically. Each atomic set has associated units of work, code fragments that preserve the consistency of that atomic set. We present a type system for data-centric synchronization that enables separate compilation and supports atomic sets that span multiple objects, thus allowing recursive data structures to be updated atomically. The type system supports full encapsulation for more efficient code generation. We evaluate our proposal using AJ, which extends the Java programming language with data-centric synchronization. We report on the implementation of a compiler and on refactoring classes from standard libraries and a multi-threaded benchmark to use atomic sets. Our results suggest that data-centric synchronization enjoys low annotation overhead while preventing high-level data races.

In this paper, we propose a new procedure based on linear mathematical programming formulation to find all efficient solutions of bi-objective 0-1 knapsack problem. We solve a limited number of single objective problems to find efficient solutions of bi-objective 0-1 knapsack problem. We show that the proposed method is able to generate efficient solutions with less constraints and variables than the general linear program found in the literature. An example is presented to illustrate the steps of the procedure.

The fast update rate and good performance of new generation electronic sector scanning sonars is now allowing practicable use of temporal information for signal processing tasks such as object classification and motion estimation. Problems remain, however, as objects change appearance, merge, maneuver, move in and out of the field of view, and split due to poor segmentation. This paper presents an approach to the segmentation, two-dimensional motion estimation, and subsequent tracking of multiple objects in sequences of sector scan sonar images. Applications such as ROV obstacle avoidance, visual servoing, and underwater surveillance are relevant. Initially, static and moving objects are distinguished in the sonar image sequence using frequency-domain filtering. Optical flow calculations are then performed on moving objects with significant size to obtain magnitude and direction motion estimates. Matches of these motion estimates, and the future positions they predict, are then used as a basis for identifying corresponding objects in adjacent scans. To enhance robustness, a tracking tree is constructed storing multiple possible correspondences and cumulative confidence values obtained from successive compatibility measures. Deferred decision making is then employed to enable best estimates of object tracks to be updated as subsequent scans produce new information. The method is shown to work well, with good tracking performance when objects merge, split, and change shape. The optical flow is demonstrated to give position prediction errors of between 10 and 50 cm (1%-5% of scan range), with no violation of smoothness assumptions using sample rates between 4 and 1 frames/s.

This paper concerns a design methodology for analog circuit problems, which is based on formulating the problem as a multi-objective geometric program (GP). Multiple objectives are combined into a scalar objective thru certain convex functions and thus can be solved using regular GP algorithms. In contrast to single-objective optimization, simultaneous consideration of all objectives yields a compromising solution among objectives.

The Biaffine Matrix Inequality (BMI) is a potentially very flexible new framework for approaching complex robust control system synthesis problems with multiple plants, multiple objectives and controller order constraints. The BMI problem may be viewed as the nondifferentiable biconvex programming problem of minimizing the maximum eigenvalue of a biaffine combination of symmetric matrices. The BMI problem is non-local-global in general, i.e. there may exist local minima which are not global minima. While local optimization techniques sometimes yield good results, global optimization procedures need to be considered for the complete solution of the BMI problem. In this paper, we present a global optimization algorithm for the BMI based on the branch and bound approach. A simple numerical example is included.

Video-frame-rate millimetre-wave imaging has recently been demonstrated with a quality similar to that of a low-quality uncooled thermal imager. In this paper we will discuss initial investigations into the transfer of image processing algorithms from more mature imaging modalities to ...

This paper presents a real-time auditory and visual tracking of multiple objects for humanoid under real-world environments. Real-time processing is crucial for sensorimotor tasks in tracking, and multiple-object tracking is crucial for real-world applications. Multiple sound source tracking needs perception of a mixture of sounds and cancellation of motor noises caused by body movements. However its real-time processing has not been reported yet. Real-time tracking is attained by fusing information obtained by sound source localization, multiple face recognition, speaker tracking, focus of attention control, and motor control. Auditory streams with sound source direction are extracted by active audition system with motor noise cancellation capability from 48 KHz sampling sounds. Visual streams with face ID and 3D-position are extracted by combining skincolor extraction, correlation-based matching, and multiple-scale image generation from a single camera. These auditory and visual streams are associated by comparing the spatial location, and associated streams are used to control focus of attention. Auditory, visual, and association processing are performed asynchronously on different PC's connected by TCP/IP network. The resulting system implemented on an upper-torso humanoid can track multiple objects with the delay of 200 msec, which is forced by visual tracking and network latency.

As corporations attempt to move toward environmental sustainability, management must extend their efforts to improve environmental practices across their supply chain. The literature characterizing environmental management within the supply chain has been slowly building but remains sparse. Using a survey of North American manufacturers, this paper examines the impact of collaborative green supply chain practices (GSCP) on manufacturing performance. Collaborative GSCP were defined to include the interactions between organizations in the supply chain, including such aspects as joint environmental goal setting, shared environmental planning, and working together to reduce pollution or other environmental impacts. These practices can be directed either upstream toward suppliers or downstream toward customers. The influence of collaboration in each direction was empirically assessed for multiple objective and perceptual measures of manufacturing performance using a sample of plants in the package printing industry. Generally, the benefits of collaborative green practices were broadest with suppliers. In contrast, collaboration with customers yielded mixed outcomes. Overall, evidence emerged that upstream practices were more closely linked with process-based performance, while downstream collaboration was associated with product-based performance.

Regrowing forests on cleared land is a key strategy to achieve both biodiversity conservation and climate change mitigation globally. Maximizing these co-benefits, however, remains theoretically and technically challenging because of the complex relationship between carbon sequestration and biodiversity in forests, the strong influence of climate variability and landscape position on forest development, the large number of restoration strategies possible, and long time-frames needed to declare success. Through the synthesis of three decades of knowledge on forest dynamics and plant functional traits combined with decision science, we demonstrate that we cannot always maximize carbon sequestration by simply increasing the functional trait diversity of trees planted. The relationships between plant functional diversity, carbon sequestration rates above ground and in the soil are dependent on climate and landscape positions. We show how to manage 'identities' and 'complementarities' between plant functional traits to achieve systematically maximal cobenefits in various climate and landscape contexts. We provide examples of optimal planting and thinning rules that satisfy this ecological strategy and guide the restoration of forests that are rich in both carbon and plant functional diversity. Our framework provides the first mechanistic approach for generating decisionmakingrules that can be used to manage forests for multiple objectives, and supports joined carbon credit and biodiversity conservation initiatives, such as Reducing Emissions from Deforestation and forest Degradation REDD+. The decision framework can also be linked to species distribution models and socio-economic models to find restoration solutions that maximize simultaneously biodiversity, carbon stocks, and other ecosystem services across landscapes. Our study provides the foundation for developing and testing cost-effective and adaptable forest management rules to achieve biodiversity, carbon sequestration, and other socio-economic co-benefits under global change.

a b s t r a c t As technologies evolve, there have been high demands for the three-dimensional (3D) shape measurement techniques to posses the following combined technical features: high accuracy, fast speed, easy implementation, capability of measuring multiple objects as well as measuring complex shapes. Generally, the existing techniques can satisfy some of the requirements, but not all of them. This paper presents four practical considerations in fringe projection profilometry (FPP) based 3D shape measurements, along with simple but robust solutions, including gamma correction of digital projection, arbitrary setup of system components, phase unwrapping with multi-frequency fringes, and system calibration with a least-squares inverse approach. The validity and practicability of the FPPbased 3D shape measurement technique using the four corresponding technical approaches have been verified by experiments. The presented technique is capable of satisfying the various critical demands in enormous scientific and engineering applications.

The decision on which strategy to use in the control of contagious animal diseases involves complex trade-offs between multiple objectives. This paper describes a Multi Criteria Decision Making (MCDM) application to illustrate its potential support to policy makers in choosing the control strategy that best meets all of the conflicting interests. The presented application focused on the evaluation of alternative strategies to control Classical Swine Fever (CSF) epidemics within the European Union (EU) according to the preferences of the European Chief Veterinary Officers (CVO). The performed analysis was centred on the three high-level objectives of epidemiology, economics and social ethics. The appraised control alternatives consisted of the EU compulsory control strategy, a pre-emptive slaughter strategy, a protective vaccination strategy and a suppressive vaccination strategy. Using averaged preference weights of the elicited CVOs, the preference ranking of the control alternatives was determined for six EU regions. The obtained results emphasized the need for EU region-specific control. Individual CVOs differed in their views on the relative importance of the various (sub)criteria by which the performance of the alternatives were judged. Nevertheless, the individual rankings of the control alternatives within a region appeared surprisingly similar. Based on the results of the described application it was concluded that the structuring feature of the MCDM technique provides a suitable tool in assisting the complex decision making process of controlling contagious animal diseases.

This paper addresses a real-life 1.5D cutting stock problem, which arises in a make-to-order plastic company. The problem is to choose a subset from the set of stock rectangles to be used for cutting into a number of smaller rectangular pieces so as to minimize total production cost and meet orders. The total production cost includes not only material wastage, as in traditional cutting stock problems, but also production time. A variety of factors are taken into account, like cutter knife changes, machine restrictions, due dates and other work in progress limitations. These restrictions make the combinatorial structure of the problem more complex. As a result, existing algorithms and mathematical models are no longer appropriate. Thus we developed a new 1.5D cutting stock model with multiple objectives and multi-constraints and solve this problem in an incomplete enumerative way. The computational results show that the solution procedure is easy to implement and works very well. Ó 2005 Published by Elsevier B.V.

Building optimization involving multiple objectives is generally an extremely time-consuming process. The GAINN approach presented in this study first uses a simulation-based Artificial Neural Network (ANN) to characterize building behaviour, and then combines this ANN with a multiobjective Genetic Algorithm (NSGA-II) for optimization. The methodology has been used in the current study for the optimization of thermal comfort and energy consumption in a residential house. Results of ANN training and validation are first discussed. Two optimizations were then conducted taking variables from HVAC system settings, thermostat programming, and passive solar design. By integrating ANN into optimization the total simulation time was considerably reduced compared to classical optimization methodology. Results of the optimizations showed significant reduction in terms of energy consumption as well as improvement in thermal comfort. Finally, thanks to the multiobjective approach, dozens of potential designs were revealed, with a wide range of trade-offs between thermal comfort and energy consumption.

This paper presents an integrated computer simulation, goal programming, and design of experiment (DOE) algorithm to solve the jobshop scheduling problem with multiple objectives. The proposed algorithm is applied to an actual textile dyeing, printing, and finishing workshop for minimization of tardiness and makespan. The simulation model enables managers to find schedule of jobs on machines given a defined combination of decision parameters which provide the smallest completion time of jobs. We then utilize DOE for determining the decision parameters to estimate the effect of both qualitative and qualitative factors through metamodeling. A goal programming model is used to find the optimum values of decision variables subject to a set of technical and managerial constraints. The proposed algorithm concentrates on two main managerial objectives simultaneously whereas previous studies are able to consider a single criterion. Finally, distinctive features of the proposed approach are compared with previous studies and models.

This paper presents an analysis of the requirements specification in electronic voting systems. In particular, it poses a specification that assumes a physical distributed architecture model with two networked intelligent units (Voting Terminal and Authorities Terminal). State Transition Diagrams and Use Cases are used in the modeling of the requirements. Finally, the model adaptation to two classes of different elections is analyzed: a national election of closed daily cycle and a university election with a cycle of several days, both with multiple objectives.

Indigenous chicken (IC) and their production systems were characterized to understand how the whole system operates for purposes of identifying threats and opportunities for holistic improvement. A survey involving 594 households was conducted in six counties with the highest population of IC in Kenya using structured questionnaires. Data on IC farmers' management practices were collected and analysed and inbreeding levels calculated based on the effective population size. Indigenous chicken were ranked highest as a source of livestock income by households in medium-to high-potential agricultural areas, but trailed goats in arid and semi-arid areas. The production system practised was mainly lowinput and small-scale free range, with mean flock size of 22.40 chickens per household. The mean effective population size was 16.02, translating to high levels of inbreeding (3.12%). Provision for food and cash income were the main reasons for raising IC, whilst high mortality due to diseases, poor nutrition, housing and marketing channels were the major constraints faced by farmers. Management strategies targeting improved healthcare, nutrition and housing require urgent mitigation measures, whilst rural access road network needs to be developed for ease of market accessibility. Sustainable genetic improvement programmes that account for farmers' multiple objectives, market requirements and the production circumstances should be developed for a full realization of IC productivity.

This study introduces new methods of quantifying and evaluating the human chewing cycle. These methods were validated on a sample of 26 young adults (11 women and 15 men) between 20±35 years of age. Movements of the mandibular central incisors were recorded (100 Hz) using an optoelectric computer system while the participants chewed gum. A subsample of 10 cycles was automatically selected, based on multiple objective criteria to ensure representative cycles for each individual. Once representative cycles had been identi®ed, multilevel statistical models were used to evaluate and describe the sample's kinematic patterns. The multilevel procedures allow for missing observations, they do not assume equal intervals, and variation can be partitioned hierarchically. Two-level models showed signi®cantly shorter cycle duration for males (835 msec) than females (973 msec). Inferior±superior (IS) cycle range was 2.6 mm larger and maximum IS velocity was 19.6 mm/sec faster in males than females. There were no signi®cant dierences in medial±lateral (ML) and anteroposterior (AP) excursive ranges or velocities. With the exception of cycle duration and ML ranges of motion, random variation was three to ®ve times larger between individuals than between cycles. The three-level models showed that eighth-order polynomials were necessary to describe IS, AP, and ML chewing movements of the entire cycle. The models identi®ed highly signi®cant sex dierences in cycle kinematics (excursions, velocities, accelerations, etc.) for each aspect of movement (AP, IS, and ML). It is concluded that this approach provides several important advantages over existing methods, including (a) its objectivity, (b) a more complete description of kinematic patterns, (c) a hierarchical description of variation, and (d) its ability to test hypotheses statistically. 7

The long-term performance of NPOs is based on their ability to link and maximize social value as defined in their mission. This involves legitimacy obtained from stakeholders influenced by and influencing NPO activities, and their operational capacity or economic efficiency. Thus, NPOs have to utilize multiple level accountability systems which should be compatible with their multiple objectives and stakeholders' claims. The accountability system of an NPO should focus on its operational capacity, because in order to maximize its efficiency, an NPO has to measure its resource use, cost structure, and financial structure. Legitimacy obtained from stakeholders is also integral. If an organization is considered a social contract between multiple stakeholders, it has to consider the social economic effects of its activities and it has the duty to account for them. However, since the mission of an NPO is to create and distribute social value to a certain specific group, its social value creation has the most important role and an NPO should measure the social value it has created. This article analyzes the accountability system of a specific type of Italian NPO called Centri di Servizio per il Volontariato (CSVs)-namely centers which provide services for voluntary associations. The aim of the research is to verify if the accountability system adopted by CSVs satisfies their need for multiple level information (operational, legitimacy, and social value) and accomplishes their stakeholder claims, and to determine its impact on the definition and implementation of their strategy and on their long-term performance. The research

Protected area zoning is a decision-making issue that inherently requires the evaluation of multiple land attributes according to multiple objectives. To promote transparency and facilitate communication with stakeholders, a clear step-by-step methodology needs to be adopted. This paper applies multicriteria decision analysis techniques in a spatial context to support zoning of the Paneveggio-Pale di S. Martino Natural Park (Italy). According to law provisions, the park is to be zoned into three protection levels, ranging from strict nature conservation to promotion of tourism and recreation. Firstly, spatial zoning elements were identified by partitioning the park into homogeneous land units. Subsequently, a GIS-based land suitability analysis was carried out for each protection level, and then aggregated into land units. Finally, land allocation was performed by assigning a protection level to each unit. For this purpose, a decision rule was applied based on an algorithm that optimizes choices by taking into account simultaneously the suitability of a unit for all protection levels. Different methods were tested and the stability of the results was assessed through sensitivity analysis. Through this study, we aimed at suggesting to park's management and other stakeholders an approach that is scientifically sound and practical.

Addressing forest sustainability requires negotiation and integration of individual forest management plans of multiple small non-industrial forest owners (NIPF). Recently, Portuguese forest policy prescribed the creation of Areas for Forest Intervention (AFI/ZIF)-forest areas encompassing at least 1 Â 10 3 ha and 50 NIPF-to address those requirements. Yet, the development of forest management plans for AFI is targeting multiple objectives in the framework of multiple-ownership. This is not trivial as conflict is prone to arise and negotiation is needed to satisfy individual and collective goals and constraints. This paper is prompted by the need to identify methods and tools that may be used to support forest management planning in the framework of an AFI. Emphasis is on the need of specific tools and methods that can support AFI management planning, in order to mitigate conflicts and achieve a consensual plan. This paper thus presents a review of methods and tools used to support group decision-making in forest management planning. It further discusses the potential of hybrid approaches for collaborative planning that may take advantage of the integrated functionality of both quantitative and qualitative decision support methods and tools. Published by Elsevier B.V.

In this paper, we propose a framework of genetic algorithms to search for Pareto optimal solutions (i.e., non-dominated solutions) of multi-objective optimization problems. Our approach differs from single-objective genetic algorithms in its selection procedure and elite presence strategy. The selection procedure in our genetic algorithms selects individuals for a crossover operation based on a weighted sum of multiple objective functions.

In the context of modern power systems the multi objective feeder reconfiguration problem has assumed significant importance. This paper presents a fuzzy set theory based approach for the multi objective network reconfiguration of distribution system. The multiple objectives which are met are feeder balancing, minimize power losses, feeder-current constraints and deviation of node voltage. The method has been applied on

In this paper, we consider the problem of permutation flowshop scheduling with the objectives of minimizing the makespan and total flowtime of jobs, and present a Multi-Objective Simulated-annealing Algorithm (MOSA). Two initial sequences are obtained by using simple and fast existing heuristics, supplemented by the implementation of three improvement schemes. Each of the two resultant sequences corresponds to a possible non-dominated solution containing the minimum value of one objective function. These sequences, taken one at a time, are given as the starting sequences to the MOSA. The MOSA seeks to obtain non-dominated solutions through the implementation of a simple probability function that attempts to generate solutions on the Pareto-optimal front. The probability function selects probabilistically a particular objective function, considering which the algorithm uncovers non-dominated solutions. Moreover, the probability function is varied in such a way that the entire objective-function space is covered uniformly so as to obtain as many non-dominated and well-dispersed solutions as possible. The parameters in the proposed MOSA are determined after conducting a pilot study. Two variants of the proposed algorithm, called MOSA-I and MOSA-II, with different parameter settings with respect to the temperature and epoch length, are considered in the performance evaluation of algorithms. In order to evaluate MOSA-I and MOSA-II, we have made use of 90 benchmark problems provided by [Eur. J. Operation. Res. 64 (1993) 278]. After an extensive literature survey, the following flowshop multi-objective scheduling algorithms have been identified as benchmark procedures: (a) MOGLS (Multi-Objective Genetic Local Search) by Ishibuchi and Murata [IEEE Trans. Syst., Man, Cybernet. C: Appl. Rev. 28 (1998) 392]; (b) Elitist Non-dominated sorting Genetic Algorithm (ENGA) by Bagchi [Multi-Objective Scheduling by Genetic Algorithms, Kluwer Academic Publishers, 1999]; (c) GPW (Gradual Priority Weighting) approach by Chang, Hsieh and Lin [Int. J. Prod. Econ. 79 (2002) 171]; and (d) a posteriori approach based heuristic by Framinan, Leisten and Ruiz-Usano [Eur. J. Operation. Res. 141 (2002) 559]. The non-dominated sets obtained from each of the existing bench-0377-2217/$ -see front matter Ó

This paper describes a new algorithm, called MDQL, for the solution of multiple objective optimization problems. MDQL is based on a new distributed Q-learning algorithm, called DQL, which is also introduced in this paper. In DQL a family of independent agents, explo- ring different options, finds a common policy in a common environment. Information about action goodness is transmitted using traces over state- action pairs. MDQL extends this idea to multiple objectives, assigning a family of agents for each objective involved. A non-dominant criterion is used to construct Pareto fronts and by delaying adjustments on the rewards MDQL achieves better distributions of solutions. Furthermore, an extension for applying reinforcement learning to continuous functions is also given. Successful results of MDQL on several test-bed problems suggested in the literature are described.

Marine protected areas (MPA) have been widely suggested as a tool for both fisheries management and conservation goals. These multiple objectives are hard to achieve simultaneously as sustainable development implies the balance between the specific interests of conservation and economical activities. MPA success, namely for fisheries management, entails the implementation of restrictive measures that in a short-time frame may have negative effects on local fishermen communities. It is extremely important to evaluate their performance and effectiveness aiming at a quasi-optimal management, minimizing potential impacts on the social domain. In this study, a methodology for assessing the effectiveness of MPA as a small-scale fisheries management tool was developed, based on a set of indicators grouped in four dimensions (ecological, economic, social and management and governance). The indicators were scored individually according to an original score scale of five values and a median score was calculated for every dimension. Finally, the median overall score was calculated including the scores of the four dimensions. The scores were attributed for two distinct periods: before and after MPA implementation, in order to evaluate the performance of MPA. This methodology can be applied even with few scientific data available and taking into account experts and stakeholders' judgements. The Arrábida MPA (Portugal) was used as a case study and it was found that with the implementation of the MPA social and economical aspects were impaired (median scores decreased near one value for the period after MPA implementation), while the other two dimensions showed an improvement trend. Thus, the overall score was the same before and after MPA implementation, an intermediate scale score. Results from the application of this method can give important indications about the state of an MPA and evaluate if the initial goals are being achieved through the implemented measures. The method is of easy communication and can be a useful tool for decision making and fisheries management processes.

Multi-objective genetic local search Self organizing maps Variable Neighborhood Search (VNS) Multi-objective evolutionary algorithm Learning Multi-reservoir operation management a b s t r a c t Genetic Algorithms (GAs) are population based global search methods that can escape from local optima traps and find the global optima regions. However, near the optimum set their intensification process is often inaccurate. This is because the search strategy of GAs is completely probabilistic. With a random search near the optimum sets, there is a small probability to improve current solution. Another drawback of the GAs is genetic drift. The GAs search process is a black box process and no one knows that which region is being searched by the algorithm and it is possible that GAs search only a small region in the feasible space. On the other hand, GAs usually do not use the existing information about the optimality regions in past iterations.

A review of existing analysis codes for nuclear fuel cycle systems was performed to determine if any existing codes meet technical and functional requirements defined for a U.S. national program supporting the global and domestic assessment, development, and deployment of nuclear energy systems. The program would be implemented using an interconnected architecture of different codes ranging from the fuel cycle analysis code, which is the subject of the review, to fundamental physical and mechanistic codes. Four main functions are defined for the code. Function 1 is the ability to characterize and deploy individual fuel cycle facilities and reactors in a simulation while discretely tracking material movements. Function 2 is the capability to perform an uncertainty analysis for each element of the fuel cycle and an aggregate uncertainty analysis. Function 3 is the inclusion of an optimization engine able to optimize simultaneously across multiple objective functions. Function 4 is open and accessible code software and documentation to aid in collaboration between multiple entities and to facilitate software updates.

This paper describes a new algorithm, called MDQL, for the solution of multiple objective optimization problems. MDQL is based on a new distributed Q-learning algorithm, called DQL, which is also introduced in this paper. In DQL a family of independent agents, explo- ring different options, finds a common policy in a common environment. Information about action goodness is transmitted using traces over state- action pairs. MDQL extends this idea to multiple objectives, assigning a family of agents for each objective involved. A non-dominant criterion is used to construct Pareto fronts and by delaying adjustments on the rewards MDQL achieves better distributions of solutions. Furthermore, an extension for applying reinforcement learning to continuous functions is also given. Successful results of MDQL on several test-bed problems suggested in the literature are described.

Active contours and active shape models (ASM) have been widely employed in image segmentation. A major limitation of active contours, however, is in their 1) inability to resolve boundaries of intersecting objects and to 2) handle occlusion. Multiple overlapping objects are typically segmented out as a single object. On the other hand, ASMs are limited by point correspondence issues since object landmarks need to be identified across multiple objects for initial object alignment. ASMs are also are constrained in that they can usually only segment a single object in an image. In this paper, we present a novel synergistic boundary and region-based active contour model that incorporates shape priors in a level set formulation with automated initialization based on watershed. We demonstrate an application of these synergistic active contour models using multiple level sets to segment nuclear and glandular structures on digitized histopathology images of breast and prostate biopsy specimens. Unlike previous related approaches, our model is able to resolve object overlap and separate occluded boundaries of multiple objects simultaneously. The energy functional of the active contour is comprised of three terms. The first term is the prior shape term, modeled on the object of interest, thereby constraining the deformation achievable by the active contour. The second term, a boundary-based term detects object boundaries from image gradients. The third term drives the shape prior and the contour towards the object boundary based on region statistics. The results of qualitative and quantitative evaluation on 100 prostate and 14 breast cancer histology images for the task of detecting and segmenting nuclei and lymphocytes reveals that the model easily outperforms two state of the art segmentation schemes (geodesic active contour and Rousson shape-based model) and on average is able to resolve up to 91% of overlapping/occluded structures in the images.

This paper proposes to use Muther's systematic layout planning procedure as the infrastructure to solve a fab layout design problem. A multiple objective decision making tool, analytic hierarchy process, is then proposed to evaluate the design alternatives. The proposed procedure is illustrated to be a viable approach for solving a fab layout design problem through a real-world case study. It features both the simplicity of the design process and the objectivity of the multiple-criteria evaluation process as opposed to existing solution methodologies.

As corporations attempt to move toward environmental sustainability, management must extend their efforts to improve environmental practices across their supply chain. The literature characterizing environmental management within the supply chain has been slowly building but remains sparse. Using a survey of North American manufacturers, this paper examines the impact of collaborative green supply chain practices (GSCP) on manufacturing performance. Collaborative GSCP were defined to include the interactions between organizations in the supply chain, including such aspects as joint environmental goal setting, shared environmental planning, and working together to reduce pollution or other environmental impacts. These practices can be directed either upstream toward suppliers or downstream toward customers. The influence of collaboration in each direction was empirically assessed for multiple objective and perceptual measures of manufacturing performance using a sample of plants in the package printing industry. Generally, the benefits of collaborative green practices were broadest with suppliers. In contrast, collaboration with customers yielded mixed outcomes. Overall, evidence emerged that upstream practices were more closely linked with process-based performance, while downstream collaboration was associated with product-based performance.

The classical vehicle routing problem involves designing a set of routes for a fleet of vehicles based at one central depot that is required to serve a number of geographically dispersed customers, while minimizing the total travel distance or the total distribution cost. Each route originates and terminates at the central depot and customers demands are known. In many practical distribution problems, besides a hard time window associated with each customer, defining a time interval in which the customer should be served, managers establish multiple objectives to be considered, like avoiding underutilization of labor and vehicle capacity, while meeting the preferences of customers regarding the time of the day in which they would like to be served (soft time windows). This work investigates the use of goal programming to model these problems. To solve the model, an enumeration-followed-by-optimization approach is proposed which first computes feasible routes and then selects the set of best ones. Computational results show that this approach is adequate for medium-sized delivery problems.

This paper presents an analysis of the requirements specification in electronic voting systems. In particular, it poses a specification that assumes a physical distributed architecture model with two networked intelligent units (Voting Terminal and Authorities Terminal). State Transition Diagrams and Use Cases are used in the modeling of the requirements. Finally, the model adaptation to two classes of different elections

In classic pattern recognition problems, classes are mutually exclusive by deÿnition. Classiÿcation errors occur when the classes overlap in the feature space. We examine a di erent situation, occurring when the classes are, by deÿnition, not mutually exclusive. Such problems arise in semantic scene and document classiÿcation and in medical diagnosis. We present a framework to handle such problems and apply it to the problem of semantic scene classiÿcation, where a natural scene may contain multiple objects such that the scene can be described by multiple class labels (e.g., a ÿeld scene with a mountain in the background). Such a problem poses challenges to the classic pattern recognition paradigm and demands a di erent treatment. We discuss approaches for training and testing in this scenario and introduce new metrics for evaluating individual examples, class recall and precision, and overall accuracy. Experiments show that our methods are suitable for scene classiÿcation; furthermore, our work appears to generalize to other classiÿcation problems of the same nature.

This paper develops a balanced scorecard for supply chain management (SCM) that measures and evaluates day-to-day business operations from following four perspectives: finance, customer, internal business process, and learning and growth. Balanced scorecard has been developed based on extensive review of literature on SCM performance measures, supported by three case studies, each illustrating ways in which BSC was developed and applied in small and medium sized enterprises (SMEs) in India. The paper further suggests that a balanced SCM scorecard can be the foundation for a strategic SCM system provided that certain development guidelines are properly followed, appropriate metrics are evaluated, and key implementation obstacles are overcome. The balanced scorecard developed in this paper provides a useful guidance for the practical managers in evaluation and measuring of SCM in a balanced way and proposes a balanced performance measurement system to map and analyze supply chains. While suggesting balanced scorecard, different SCM performance metrics have been reviewed and distributed into four perspectives. This helps managers to evaluate SCM performance in a much-balanced way from all angles of business.

Purpose -The objective of this paper is to collate and debate the main issues driving the stakeholder theory academic debate. Design/methodology/approach -First, a discussion of the stakeholder concept is set out before moving on to the history and nature of stakeholder theory. The work proceeds with an attempt to bring together systematically the points of divergence among researchers interested in stakeholder theory, and, finally, there is a brief discussion of these theoretical loopholes in conjunction with a proposed research agenda for the field. Findings -Based on the unification of the theoretically problematic issues, research agenda are put forward with the objective of clarifying doubts and resolving the controversies ongoing among academics. As regards the formulation of stakeholder theory, one question requiring resolution is that of the stakeholder concept itself. Additionally, further research should focus on the boundaries as to what constitutes a stakeholder group as well as defining the criteria for attributing individual membership of one or another group. In practical theoretical application, it is correspondingly necessary to target research on aspects such as conflicts of interest between stakeholders and management difficulties in coping with multiple objectives. Finally, there is a need for research that systematizes the knowledge produced with the objective of attaining the theoretical convergence necessary for the development of stakeholder theory. Originality/value -The main contribution of this paper derives from the systematization of the various shortcomings that need overcoming within the framework of stakeholder theory and the identification of research agendas.

In the tropics, numerous organizations have promoted multipurpose fodder trees (MPFT) with an emphasis on exotic species. These species have generally been selected and recommended by the research system through the conventional nutritional and agronomic experimentation for use as animal feed and soil conservation. In Ethiopia, the introduction of exotic MPFT started in the 1970s. However, despite its apparent benefits, the adoption of exotic MPFT by smallholder farmers has been slow and in some cases farmers ceased using exotic MPFT in their farming systems. The objectives of the present study were to assess farmers' preference criteria, compare their preference between exotic and local MPFT, and evaluate the relationship of farmers' knowledge of feed value assessment with laboratory indicators. Focus group discussions and preference ranking and scoring by a total of 40 farmers were conducted in two districts representing two production systems (cereal and coffee-based livestock production systems) in the Ethiopian highlands. The comparison between exotic and local MPFT for their benefits and desired tree characteristics showed that farmers preferred local MPFT to exotics for biomass production, multi-functionality, life span, and compatibility to the cropping system. In terms of feed value, ease of propagation, and growth potential local MPFT were ranked lower than or comparable to exotics. There was also a strong correlation between farmers' feed value score and laboratory results. Farmers were able to discriminate effectively MPFT species that had high and low protein and fibre content using their indigenous feed value indicator system for all pairwise comparisons. We concluded from this study that farmers' preference criteria encompass multiple objectives beyond feed value and soil rehabilitation. The different merits that farmers associate with exotic and local MPFT could provide the opportunity to use both MPFT types and to improve farm bio-diversity. Hence, incorporating locally available MPFT, farmers' indigenous knowledge and preference criteria at the research inception process is vital to maximize the likelihood of farmers' adopting and maintaining these technologies.

A model for problem decomposition in Genetic Programming based classification is proposed consisting of four basic components: competitive coevolution, local Gaussian wrapper operators, evolutionary multiobjective (EMO) fitness evaluation, and an explicitly cooperative objective. The framework specifically emphasizes the relations between different components of the model. Thus, both the local wrapper operator and cooperative objective components work together to establish exemplar subsets against which performance is evaluated and the decomposition of the problem domain is achieved. Moreover, the cost of estimating fitness over multiple objectives is mitigated by the ability to associate specific subsets of exemplars with each classifier. The competitive coevolutionary model, in combination with a balanced sampling heuristic guides the identification of relevant exemplars and retention of the best classifiers; whereas the EMO component drives the application of selection and variation operators to maintain the diversity of the classifier population. Empirical evaluation is conducted over twelve data sets representative of different application domains, class distributions and feature counts. A benchmarking methodology is developed in order to provide comparison between the proposed model and the deterministic classifier paradigm in general, and the SVM method in particular. Moreover, by adopting performance metrics for classification, model complexity and training scalability, we are able to provide a more informative assessment of the relative merits of each model.

Certified market-driven organic agriculture in Africa needs to build upon and strengthen internal natural and social resources in order to bring greater benefit to smallholders. The organic sector in Tanzania is predominantly export-oriented, supported by development funding and aimed at improving incomes. This study is designed to assess and provide a foundation for enhancing organic agriculture's contribution to sustainable livelihoods and food security amongst Tanzanian smallholder farmers. Holistic and multi-perspective analysis revealed that organic agriculture is making efficient use of resources in the current institutional context and building upon existing livelihood strategies. Certified organic agriculture can address issues of sustainable food production and improved farmer livelihoods. However, findings suggest that the international trade focus behind recent efforts by the private sector, donors and the government to commercialize smallholder organic agriculture carries significant risks. Local-and national-level strategies need to incorporate multiple objectives, not only production of high quality products for the market, but also farmer empowerment, household nutrition and sustainable management of natural resources.

Important decisions are often taken by groups of decision makers who need to make choices among several alternatives, based on an appraisal of how the alternatives are likely to perform with regard to multiple objectives. Such decision processes can be supported by the methods of multi-criteria decision analysis (MCDA) which help generate decision recommendations and offer process benefits in terms of enhanced decision quality, improved communication, and enhanced commitment to decision implementation. In this Chapter, we outline widely used MCDA methods and consider their uses in group decision making. We also review selected case studies and offer guidelines for the design of MCDA-assisted group decision processes. We conclude with thoughts on promising application domains and future research topics.

This paper presents the application of path planning in construction sites according to multiple objectives. It quantitatively evaluates the performance of three optimisation algorithms namely: Dijkstra, A p , and Genetic algorithms that are used to find multi-criteria paths in construction sites based on transportation and safety-related cost. During a construction project, site planners need to select paths for site operatives and vehicles, which are characterised by short distance, low risks and high visibility. These path evaluation criteria are combined using a multi-objective approach. The criteria can be optimised to present site planners with the shortest path, the safest path, the most visible path or a path that reflects a combination of short distance, low risk and high visibility. The accuracy of the path solutions and the time complexities of the optimisation algorithms are compared and critically analysed. q