This work aimed to develop a personalized multi-objective recommender system for food diets, which seeks to suggest to the user a diverse list of four meals a day (breakfast, lunch, snack, dinner). The efficient solut...
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ISBN:
(数字)9781665467087
ISBN:
(纸本)9781665467087
This work aimed to develop a personalized multi-objective recommender system for food diets, which seeks to suggest to the user a diverse list of four meals a day (breakfast, lunch, snack, dinner). The efficient solutions are capable of simultaneously meeting a list of nutritional specifications and also minimizing both the concentration in a specific meal or food item and the total cost of acquisition and preparation. Efficient solutions are sought from the joint use of the NSGA-II and Gurobi optimization packages, after formulating the diet problem as a bilevel optimization: a combinatorial and multi-objective problem at the upper level - which food items from each category (the available food categories and the categories allocated to each of the four meals are defined in advance) should make up the diet - and a mathematical programming problem at the lower level - what is the optimal amount of the selected food items to compose the diet, given nutritional constraints. As Gurobi does not operate directly with a multi-objective optimization perspective, its lower-level objective function involves maximizing the total energy of the daily diet. Experimental results, considering fictitious food costs, show that NSGA-II and Gurobi operate in synergy, providing a diverse list of menus for the four daily meals, thus making a valuable approximation of the Pareto frontier. The distinctive aspect of this multi-objective bilevel solution to the diet problem, then, resides in the supply of diverse and, at the same time, efficient candidate solutions, in the sense of achieving the Pareto frontier and being scattered along its extension.
The problem of optimal placement of phasor measurement units (PMUs) was first studied using graph theoretic and mathematical programming methods. However, due to the increase of renewable generations, PMUs may be requ...
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ISBN:
(纸本)9781467348966;9781467348942
The problem of optimal placement of phasor measurement units (PMUs) was first studied using graph theoretic and mathematical programming methods. However, due to the increase of renewable generations, PMUs may be required not only in transmission networks, but also in sub-transmission, or even distribution networks. Hence, the size of this PMU placement problem will become too big for mathematical programming approaches. As a result, in this paper, we investigated solving this problem using a novel metaheuristic technique, called chemical reaction optimization (CRO). CRO loosely mimics the interactions between molecules in a chemical reaction process. Based on the canonical CRO, we propose a simplified version of CRO (SCRO) for the optimal PMU placement (OPP) problem. Both canonical CRO and SCRO are tested on the full observability of OPP in two scenarios, i.e., considering and not considering zero injections. To investigate the effectiveness of the proposed methods, we evaluate their performances in the IEEE 14-bus, 30-bus, 57-bus, 118-bus, and 300-bus standard systems as well as a large-scale system with 1180 buses. Our simulation results show that, compared with other deterministic and metaheuristc algorithms, SCRO can find the optimal solutions in a shorter time for small-scale systems, and a near-optimal solution within a reasonable time even for a large-scale system.
In this paper a new method to find optimal strategies for scheduling of pumped hydro storage reservoirs is presented. Allowing start-up costs and non-linear head variations to be handled within a formal optimization a...
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ISBN:
(纸本)9781467356688
In this paper a new method to find optimal strategies for scheduling of pumped hydro storage reservoirs is presented. Allowing start-up costs and non-linear head variations to be handled within a formal optimization algorithm shows increased value for hydro storage operators. Two cases are presented and the proposed solutions are compared to state of the art tools within hydro scheduling.
This paper investigates optimal lot-splitting policies in a multiprocess flow shop environment with the objective of minimizing either mean flow time or makespan. Using a quadratic programming approach to the mean flo...
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This paper investigates optimal lot-splitting policies in a multiprocess flow shop environment with the objective of minimizing either mean flow time or makespan. Using a quadratic programming approach to the mean flow time problem, we determine the optimal way of splitting a job into smaller sublots under various setup times to run time ratios, number of machines in the flow shop, and number of allowed sublots. Our results come from a deterministic flow shop environment, but also provide insights into the repetitive lots scheme using equal lot splits for job shop scheduling in a stochastic environment. We indicate those conditions in which managers should implement the repetitive lots scheme and where other lot-splitting schemes should work better. [ABSTRACT FROM AUTHOR]
Resource-constrained scheduling problems are commonly found in various areas, such as project management, manufacturing, transportation, software engineering, computer networks, and supply chain management. Its proble...
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ISBN:
(纸本)9783319396309;9783319396293
Resource-constrained scheduling problems are commonly found in various areas, such as project management, manufacturing, transportation, software engineering, computer networks, and supply chain management. Its problem models involve a large number of constraints and discrete decision variables, including binary and integer. In effect, the representation of resource allocation, for instance, is often expressed using binary or integer decision variables to form several constraints according to the respective scheduling problem. It significantly increases the number of decision variables and constraints as the problem scales;such kind of traditional approaches based on operations research is insufficient. Therefore, a hybrid approach to decision support for resource-constrained scheduling problems which combines operation research (OR) and constraint logic programming (CLP) is proposed. Unlike OR-based approaches, declarative CLP provides a natural representation of different types of constraints. This approach provides: (a) decision support through the answers to the general and specific questions, (b) specification of the problem based on a set of facts and constraints, (c) reduction to the combinatorial solution space. To evaluate efficiency and applicability of the proposed hybrid approach and implementation platform, implementation examples of job-shop scheduling problem are presented separately for the three environments, i.e., mathematical programming (MP), CLP, and hybrid implementation platform.
Generalized Multiobjective Multitree model (GMMmodel) studied for the first time multitree-multicast load balancing with splitting in a multiobjective context. To solve the GMM-model, a multiobjective evolutionary alg...
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ISBN:
(纸本)0780389387
Generalized Multiobjective Multitree model (GMMmodel) studied for the first time multitree-multicast load balancing with splitting in a multiobjective context. To solve the GMM-model, a multiobjective evolutionary algorithm (MOEA) inspired by the Strength Pareto Evolutionary Algorithm (SPEA) was already proposed. In this paper, we extend the GMM-model to dynamic multicast groups (i.e., egress nodes can change during the connection's lifetime), given that, if recomputed from scratch, it may consume a considerable amount of CPU time. To alleviate this drawback we propose a Dynamic Generalized Multiobjective Multitree model (Dynamic-GMM-model) that in order to add new egress nodes makes use of a multicast tree previously computed with the GMM-model. To solve the Dynamic-GMM-model, a new MASPA (multiobjective approximation using shortest path algorithm) heuristic is proposed. Experimental results considering up to 11 different objectives are presented for the well-known NSF network. We compare the performance of the GMM-model using MOEA with the proposed Dynamic-GMM-model using MASPA, showing that reasonable good solutions may be found using fewre resources (as memory and time). The main contributions of this paper are the optimization model for dynamic multicast routing;and the proposed heuristic algorithm.
We consider a cognitive radio (CR) network consisting of a secondary user transmitter (SU-Tx) equipped with multiple antennas and a secondary user receiver (SU-Rx) that share spectrum with multiple primary user transm...
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ISBN:
(纸本)9781467346757;9781467346733
We consider a cognitive radio (CR) network consisting of a secondary user transmitter (SU-Tx) equipped with multiple antennas and a secondary user receiver (SU-Rx) that share spectrum with multiple primary user transmitter (PU-Tx) and receiver (PU-Rx) pairs. We assume that the CR has a loose cooperation with the primary network and therefore, only partial channel state information of each of the PU-Tx to PU-Rx and SU-Tx to each PU-Rx links is available. Furthermore, we assume that the SU-Tx to SU-Rx link CSI is imperfect, with the channel error modelled as additive Gaussian noise. Under these assumptions, we propose a new statistically robust CR beamformer where the total SU-Tx transmit power is minimised subject to PU-Rx and SU-Rx outage probability constraints. We present expressions for PU-Rx and SU-Rx outage probabilities and formulate the robust beamformer optimisation problem as a convex semidefinite program (SDP). SU-Tx transmit power, PU-Rx signal-to-interference-and-noise ratio (SINR) and SU-Rx signal-to-noise (SNR) cumulative distribution functions (CDFs) are obtained through solution of our optimisation problem.
This article presents an optimization model for the economic analysis and strategic planning of port-hinterland container logistics systems. The model was employed to investigate the inland multimodal distribution of ...
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This article presents an optimization model for the economic analysis and strategic planning of port-hinterland container logistics systems. The model was employed to investigate the inland multimodal distribution of import/export containers handled at the seaports located in the Campania region of Southern Italy. The loading units can transit through the regional off-dock intermodal and logistic facilities called 'interports', as well as through extra-regional locations which have a railway terminal, before reaching the final inland destinations or the seaports. The model mainly aims at highlighting and measuring possible advantages arising both from shifting the seaport exit/entry of containers to regional interports, and from employing intermodal solutions for inland distribution. The programming problem minimizes the sum of all container-related generalized logistic costs throughout the entire multimodal port-hinterland network. The logistic costs include transportation costs (by road and railway), terminal handling and storage costs, customs control costs, in-transit inventory holding costs and container leasing costs. A numerical prototype has been formulated and solved using a high-level programming language for large-scale mathematical optimization problems. The results demonstrate how the competitiveness of the regional container seaport cluster can be boosted by an interport-based extended gateway system with adequate customs facilities and improved railway connections.
programming assignments in my discrete mathematics course have changed recently due to an influx of non-computer science students with little or no programming experience. programming problems are now assigned in a si...
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ISBN:
(纸本)0897914686
programming assignments in my discrete mathematics course have changed recently due to an influx of non-computer science students with little or no programming experience. programming problems are now assigned in a simple to learn, easy to write, mathematical-like functional programming language that requires no previous programming experience. In theory, all students begin on the same basis. Exposure to the concepts of functional programming is an essential part of computer science and mathematics curricula. For most students this is the only exposure to functional programming. Functional programming and discrete mathematics are a natural combination. One week of lectures and perhaps a small monetary investment is all that is required. An instructor totally unfamiliar with functional programming can easily learn enough in a week or so to present a simple introduction to the topic. Introducing functional programming concepts in discrete mathematics was very successful. Students found the exposure to functional programming to be an insight they had never experienced before and enthusiastically recommended an introduction to functional programming be a permanent part of the course.
An algebraic approach is applied for solving the problem of nonconvex quadratic programming that is based on the concept of the Groebner bases in combination with the necessary optimality conditions of the first order...
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An algebraic approach is applied for solving the problem of nonconvex quadratic programming that is based on the concept of the Groebner bases in combination with the necessary optimality conditions of the first order. It is shown that a problem of nonconvex quadratic programming may be reduced to finding all real roots of an one-variable polynomial. The numerical examples are presented.
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