Purpose - The rise of remote work increasingly requires organizations to coordinate a single large, consolidated talent pool into ad-hoc, short-term project teams on demand. This problem involves many simultaneous con...
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Purpose - The rise of remote work increasingly requires organizations to coordinate a single large, consolidated talent pool into ad-hoc, short-term project teams on demand. This problem involves many simultaneous considerations including project revenues and rejection costs, conflicting projects and roles, worker assignment costs, worker utilization preferences and limits, worker reassignment costs, and arbitrary role start and end times. Moreover, plans must be continuously updated in response to changing circumstances. This paper addresses the problem of dynamic virtual team planning and coordination. Design/methodology/approach - We show this problem is NP-hard and provide a dynamic mixed integer linear programming (MILP) formulation for both optimal initial plan generation as well as continuous plan adjustment and re-optimization. We utilized a factorial experiment design to generate benchmark problems spanning a wide range of characteristics and conducted extensive computational experimentation using a common MILP solver. Findings - Exactly optimal solutions to large, realistically sized problems were consistently obtained in short amounts of time. All observed solution times were sufficient to support the operational decision-making requirements of real-world virtual team coordination, demonstrating the viability of this approach. Practical implications - The approach developed in this research can enable organizations to optimally coordinate virtual teams on a large scale and continually adjust plans in response to changing circumstances, all in an automated manner. Originality/value - This paper addresses a new and complex problem of increasing importance to organizations due to the rise in remote work. We provide a problem formulation and exact approach for optimally solving both the planning and re-planning aspects of this problem.
In some applications, datasets may form several homogeneous clusters with respect to the relationship between the explanatory variables and the response variable. The presence of missing values in such datasets requir...
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In some applications, datasets may form several homogeneous clusters with respect to the relationship between the explanatory variables and the response variable. The presence of missing values in such datasets requires the use of two or more regression models subjected to a single objective function, which best summarizes the structure of the dataset, for imputing the missing values. This can be done using the cluster-wise linear regression model. The cluster-wise linear regression model is estimated through a mathematical programming approach based on the available data. Three imputation methods based on cluster-wise linear regression are used in this article. They are the largest cluster, the simple weighting, and the inverse distance weighting imputation methods. The cluster-wise linear regression is then integrated in the proposed imputation methods, to fill in the missing values in the response variable. The simulation study shows a decent performance for the proposed imputation methods based on cluster-wise linear regression.
Public bus transit service (PBTS) is recognized as a highly effective mode of transportation, offering accessibility, affordability, and adaptability that contribute to its critical role in transportation networks. Th...
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Public bus transit service (PBTS) is recognized as a highly effective mode of transportation, offering accessibility, affordability, and adaptability that contribute to its critical role in transportation networks. The extensive literature on PBTS encompasses various aspects, with mathematical programming emerging as a widely employed methodology to tackle the public bus transit network design and operations planning problem (PBTNDP&OPP). In this paper, first, we employ the critical path method (CPM) to visually map the development of existing literature on the application of mathematical programming in PBTND&OPP by focusing on manuscripts published in top-tier journals. The objective is to identify key sub-problems extensively studied in the literature and recently emerging topics. Then, we conduct a comprehensive review of recent applications of mathematical programming in PBTND&OPP, encompassing sustainable and green practices, as well as emerging transportation technologies and modes within PBTS. These two sub-problems have been identified as recently emerged and hot topics in the literature of mathematical programming and PBTND&OPP, based on the provided CPM in the first step. Selected papers for each sub-problem are examined, providing insights into problem formulation, objective functions, decision variables, demand patterns, network structures, and key findings. Based on the literature review, we systematically identify research gaps in each sub-problem and offer directions and suggestions for future studies. While there is a considerable body of literature that has applied mathematical programming to investigate these two emerging topics, our review highlights that the existing literature is still in the early stages of development. Hence, numerous problems relating to these topics remain ripe for exploration through mathematical programming. Examining the effects of sustainable development policies or the introduction of emerging technologies on the reliab
Wind energy is currently one of the most promising alternative energy sources. The optimization of the wind farm layout and the cable layout are two important elements in the design of wind farms. Since increasing the...
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Wind energy is currently one of the most promising alternative energy sources. The optimization of the wind farm layout and the cable layout are two important elements in the design of wind farms. Since increasing the distance between turbines can reduce wake loss but increase cable cost, these two optimizations are coupled and jointly affect the revenue of wind farms. In this paper, we propose a novel nonlinear mathematical programming model based on the 3D Gaussian wake model and use a mathematical programming approach to optimize the layout of the wind farm and the cable layout together, considering both power generation and cable cost. In this method, some of the constraints were linearized to facilitate the solution process. The optimization results show that profit increased by 9.07% when using annual economic efficiency as the objective function, compared with using energy production as the objective function.
Formation of energy-based industrial symbiosis networks (EISNs) is a measure by which industries can address their high energy consumption. EISNs are often designed through mathematical programming (MP) because this m...
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Formation of energy-based industrial symbiosis networks (EISNs) is a measure by which industries can address their high energy consumption. EISNs are often designed through mathematical programming (MP) because this method can represent the integration of numerous entities in a compact model while allowing tradeoff analysis of various EISN design objectives. In view thereof, this study presents a systematic review of MP models for EISN optimization. It addresses the research gap on the lack of studies which review the use of MP for optimizing EISNs involving waste heat as the shared resource. The models were analyzed based on five features: the typology of objective functions, the integrated entities in the EISN, the waste heat use options, the effects of considering distance between entities, and the method for modelling parameter uncertainty. This study has uncovered several gaps in EISN modelling. First, there is no consensus about the most relevant environmental and social impacts to include in EISN optimization. Second, novel approaches to simplify nonconvex models are scarce, thereby hindering the incorporation of more pertinent entities into the models due to the concomitant increase in solution time. Third, models analyzing the tradeoff among the various waste heat utilization pathways are limited. Fourth, most models do not include the implications of considering the physical layout of integrated entities in optimizing EISN design. Finally, the best method to incorporate parameter uncertainty in models is still unsettled. By addressing these gaps, more comprehensive MP models can be developed, thereby supporting better-informed decisions about EISN establishment.
This paper presents a review of mathematical programming models for supply chain production and transport planning. The purpose of this review is to identify current and future research in this field and to propose a ...
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This paper presents a review of mathematical programming models for supply chain production and transport planning. The purpose of this review is to identify current and future research in this field and to propose a taxonomy framework based on the following elements: supply chain structure, decision level, modeling approach, purpose, shared information, limitations, novelty and application. The research objective is to provide readers with a starting point for mathematical modeling problems in supply chain production and transport planning aimed at production management researchers. (C)2009 Elsevier B.V. All rights reserved.
Cellular manufacturing systems achieve the economies of scope and scale approaching that of flexible and high-volume production when the machine/part clusters are totally independent of each other. However, most real ...
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Cellular manufacturing systems achieve the economies of scope and scale approaching that of flexible and high-volume production when the machine/part clusters are totally independent of each other. However, most real systems contain bottleneck machines and exceptional parts (exceptional elements) that reduce these economies. Many grouping methods have been proposed for creating the initial machine/part cells where the presence of exceptional elements may greatly affect their performance. Furthermore, multiple alternative solutions are often possible for a given grouping algorithm. In this paper, the previous work dealing with exceptional elements is reviewed. A mathematical programming model used for comprehensively dealing with exceptional elements is investigated. The effect of alternative initial machine/part clusters on the total cost is evaluated. It is demonstrated that the mathematical programming model can provide useful information in making trade-off decisions when exceptional elements are present. (C) 1999 Elsevier Science B.V. All rights reserved.
mathematical programming is a branch of applied mathematics and has recently been used to derive new decoding approaches, challenging established but often heuristic algorithms based on iterative message passing. Conc...
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mathematical programming is a branch of applied mathematics and has recently been used to derive new decoding approaches, challenging established but often heuristic algorithms based on iterative message passing. Concepts from mathematical programming used in the context of decoding include linear, integer, and nonlinear programming, network flows, notions of duality as well as matroid and polyhedral theory. This paper reviews and categorizes decoding methods based on mathematical programming approaches for binary linear codes over binary-input memoryless symmetric channels.
mathematical programming (MP) discriminant analysis models are widely used to generate linear discriminant functions that can be adopted as classification models. Nonlinear classification models may have better classi...
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mathematical programming (MP) discriminant analysis models are widely used to generate linear discriminant functions that can be adopted as classification models. Nonlinear classification models may have better classification performance than linear classifiers, but although MP methods can be used to generate nonlinear discriminant functions, functions of specified form must be evaluated separately. Piecewise-linear functions can approximate nonlinear functions, and two new MP methods for generating piecewise-linear discriminant functions are developed in this paper. The first method uses maximization of classification accuracy (MCA) as the objective, while the second uses an approach based on minimization of the sum of deviations (MSD). The use of these new MP models is illustrated in an application to a test problem and the results are compared with those from standard MCA and MSD models.
This paper focuses on the resolution of the reachability problem in Petri nets, using the mathematical programming paradigm. The proposed approach is based on an implicit traversal of the Petri net reachability graph....
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This paper focuses on the resolution of the reachability problem in Petri nets, using the mathematical programming paradigm. The proposed approach is based on an implicit traversal of the Petri net reachability graph. This is done by constructing a unique sequence of Steps that represents exactly the total behaviour of the net. We propose several formulations based on integer and/or binary linear programming, and the corresponding sets of adjustments to the particular class of problem considered. Our models are validated on a set of benchmarks and compared with standard approaches from IA and Petri nets community. (c) 2006 Elsevier B.V. All rights reserved.
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