Fire-related emergency vehicle scheduling has always been an important part of autonomous vehicle emergency management. To maintain the fire safety of buildings and reduce the transportation loss of emergency resource...
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Fire-related emergency vehicle scheduling has always been an important part of autonomous vehicle emergency management. To maintain the fire safety of buildings and reduce the transportation loss of emergency resources, this paper constructs a multi-objective integer planning model for fire emergency resource scheduling, which minimizes the time spent in the transportation of fire materials and considers the transportation needs of potential fire vehicles. The fixed-point iterative algorithm is realized and optimized, and distributed computing is adopted to improve the solving speed. In this paper, the comparison and simulation experiments with Branch and Bound, improved particle swarm optimization and other algorithms are carried out. The results show that the model and the algorithm can effectively deal with the fire emergency vehicle scheduling problem under different simulated fires and multiple fires occurring at the same time, and have certain advantages in numerical stability and convergence speed.
Nowadays, reaching a high level of employee satisfaction in efficient schedules is an important and difficult task faced by companies. We tackle a new variant of the personnel scheduling problem under unknown demand b...
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Nowadays, reaching a high level of employee satisfaction in efficient schedules is an important and difficult task faced by companies. We tackle a new variant of the personnel scheduling problem under unknown demand by considering employee satisfaction via endogenous uncertainty depending on the combination of their preferred and received schedules. We address this problem in the context of reserve staff scheduling, an unstudied operational problem from the transit industry. To handle the challenges brought by the two uncertainty sources, regular employee and reserve employee absences, we formulate this problem as a two-stage stochastic integer program with mixed-integer recourse. The first-stage decisions consist in finding the days off of the reserve employees. After the unknown regular employee absences are revealed, the second-stage decisions are to schedule the reserve staff duties. We incorporate reserve employees' days-off preferences into the model to examine how employee satisfaction may affect their own absence rates.
In organizational and academic settings, the strategic formation of teams is paramount, necessitating an approach that transcends conventional methodologies. This study introduces a novel application of multicriteria ...
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In organizational and academic settings, the strategic formation of teams is paramount, necessitating an approach that transcends conventional methodologies. This study introduces a novel application of multicriteria integer programming (MCIP), which simultaneously accommodates multiple criteria, thereby innovatively addressing the complex task of team formation. Unlike traditional single-objective optimization methods, our research designs a comprehensive framework capable of modeling a wide array of factors, including skill levels, backgrounds, and personality traits. The objective function of this framework is optimized to maximize within-team diversity while minimizing both conflict levels and variance in diversity between teams. Central to our approach is a two-stage optimization process. Initially, it segments the population into subgroups using a weighted heterogeneous multivariate K-means algorithm, allowing for a targeted and nuanced team assembly. This is followed by the application of a surrogate optimization technique within these subgroups, efficiently navigating the complexities of MCIP for large-scale applications. Our approach is further enhanced by the inclusion of explicit constraints such as potential interpersonal conflicts, a factor often overlooked in previous studies. The results from our study demonstrate the optimality and robustness of our model across simulation scenarios with different data heterogeneity levels. The contributions of this study are manifold, addressing critical gaps in the existing literature with a theory-backed, empirically validated framework for advanced team formation. Beyond theoretical implications, our work provides a practical guide for implementing conflict-aware, sophisticated team formation strategies in real-world scenarios. This advancement paves the way for future research to explore and enhance this model, providing more sophisticated and efficient team formation strategies.
One cannot make truly fair decisions using integer linear programs unless one controls the selection probabilities of the (possibly many) optimal solutions. For this purpose, we propose a unified framework when binary...
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One cannot make truly fair decisions using integer linear programs unless one controls the selection probabilities of the (possibly many) optimal solutions. For this purpose, we propose a unified framework when binary decision variables represent agents with dichotomous preferences, who only care about whether they are selected in the final solution. We develop several general-purpose algorithms to fairly select optimal solutions, for example, by maximizing the Nash product or the minimum selection probability, or by using a random ordering of the agents as a selection criterion (Random Serial Dictatorship). We also discuss in detail how to extend the proposed methods when agents have cardinal preferences. As such, we embed the "black-box"procedure of solving an integer linear program into a framework that is explainable from start to finish. Lastly, we evaluate the proposed methods on two specific applications, namely kidney exchange (dichotomous preferences), and the scheduling problem of minimizing total tardiness on a single machine (cardinal preferences). We find that while the methods maximizing the Nash product or the minimum selection probability outperform the other methods on the evaluated welfare criteria, methods such as Random Serial Dictatorship perform reasonably well in computation times that are similar to those of finding a single optimal solution.
During the last decades, the apprehension about environmental pollution and consuming of the global fossil resource reserves has raised the requisition to improve an environmental friendly and renewable fuel. Bio-base...
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During the last decades, the apprehension about environmental pollution and consuming of the global fossil resource reserves has raised the requisition to improve an environmental friendly and renewable fuel. Bio-based diesel fuel, which primarily occurs of methyl-esters in fatty acid is one of the finest replaces for fossil-based diesel fuel. Nowadays, plant oils are the primary source of bio-sourced diesel fuel. The fatty acid formation of manufactured bio-based diesel fuel from different plant-based sources and their features are disparate. The aim of the existent research is to choose the optimal plant oil as bio-based diesel through utilizing mathematical modeling technique. Amon plant-based oil kinds, the question of choosing the optimal plant oil is assessed, utilizing many criteria concerned with their features. This study spotlights a new perception into evaluating the optimal plant oil-derived bio-based diesels for the decision-makers like engine manufacturers and development and research engineers to strengthen the green reformation to provide the fuel economy and emission standards.
Traditionally, most of the prioritization models used by researchers and practitioners, rely on spatially dichotomous settings for threats, for species and for actions' benefit;i.e., threats and species are presen...
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Traditionally, most of the prioritization models used by researchers and practitioners, rely on spatially dichotomous settings for threats, for species and for actions' benefit;i.e., threats and species are present with equal intensity in some territorial units (while in the other units are not present at all), and actions have impact only on those units where they are applied. However, when dealing with ecological phenomena on large and complex territories, characterized by different areas (such as multiple realms or large river basins) and different spatial connectivity patterns among them, such a dichotomous setting does not capture the spatial (cumulative) diffusion of threats and thus actions' benefits. Hence, common conservation planning tools are likely to misestimate the benefits of actions and the impact of threats, yielding less effective solutions. In order to address this issue, we develop a framework for designing multi-action prioritization plans featuring threats and actions' benefit spatial diffusion. Our framework relies on a mathematical programming model that identifies priority areas for the implementation of management actions for multiple threats across a complex and large landscape. We consider the particular case an ecological setting characterized by different realms, multiple threats, and multiple species. We use the Tagus River (Iberian Peninsula) as a case study, including four realms (terrestrial, freshwater, estuary, and marine), where we integrate three different types of spatial connectivity: longitudinal along rivers, and multidimensional in the estuary and marine realms. We simulate the spatial diffusion of threats across the study area using four types of decay models (dispersal kernels): one exponential kernel, two negative triangular kernels (medium and high), and no dispersal. The results show how the MIP-based methodology offers a flexible and practical strategy for incorporating the cumulative effects of threats into action ma
integer programming (IP) is an extension of linear programming (LP) whereby the goal is to determine values for a set of decision variables (some or all of which have integer restrictions) so as to maximize or minimiz...
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It is natural to formulate sequencing problems as integer programming models. However, there are a number of possible formulations the practical value of which can be significantly different. In this paper, we first p...
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It is natural to formulate sequencing problems as integer programming models. However, there are a number of possible formulations the practical value of which can be significantly different. In this paper, we first propose a novel classification of integer programming formulations for single-machine sequencing. Next, we present associated mixed-integer linear programming models for total tardiness minimization. Finally, we conduct an extensive computational study on randomly generated instances. For the unweighted case, the position-indexed formulation with linearly many constraints outperforms others, whereas for the weighted case, it is best to use the sparse reformulation of the time-indexed formulation. integer programming turns out to be a viable option for many practical problem sizes.
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