A mixed-integer program is presented for scheduling canal irrigation among a group of users where the duration of flow of each outlet and a target start time is specified by the users. Two models are developed. The fi...
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A mixed-integer program is presented for scheduling canal irrigation among a group of users where the duration of flow of each outlet and a target start time is specified by the users. Two models are developed. The first is a single-period model which uses as input a minimized demand at the head of the canal. This will allow the discharge at the head of the canal to be set once at the beginning of the irrigation period. Within this constraint, the single-period model minimizes the sum over all outlets of the difference between the scheduled start time of flow to each user and the requested target start times. The second model is a multiperiod model. This model favors those users who had been disadvantaged in the previous irrigation periods by giving them priority in scheduling their actual start times with target start times for the subsequent irrigation period. The proposed models can be used to support decisions in irrigation schemes operating on an arranged demand.
Semi-Markov decision processes on Borel spaces with deterministic kernels have many practical applications, particularly in inventory theory. Most of the results from general semi-Markov decision processes do not carr...
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Semi-Markov decision processes on Borel spaces with deterministic kernels have many practical applications, particularly in inventory theory. Most of the results from general semi-Markov decision processes do not carry over to a deterministic kernel since such a kernel does not provide "smoothness." We develop infinite dimensional linear programming theory for a general stochastic semi-Markov decision process. We give conditions, general enough to allow deterministic kernels, for solvability and strong duality of the resulting linear programs. By using the developed linear programming theory we give conditions for the existence of a stationary deterministic policy for deterministic kernels, which is optimal among all possible policies.
High mix-low volume operations face difficulties determining a specific order of processing, due to constant-changing market requirements and variability in processing times. Having an optimal and effective process fl...
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ISBN:
(纸本)9780983762447
High mix-low volume operations face difficulties determining a specific order of processing, due to constant-changing market requirements and variability in processing times. Having an optimal and effective process flow is essential for any manufacturing industry to meet customer requirements and revenue goals. linear programming and job sequencing techniques have been applied to determine the best sequence for processing group products. The constraints have been defined based in machine availability, production lot size, market demand, monthly revenue goals, and machines' capacity per type of product. The program selects the unit that enters the station comparing processing times and revenue, so that the waiting time between stations is minimized while simultaneously guaranteeing the revenue goal is fulfilled. The program have significantly reduced the cycle time and established effective sequences for every component to enter the production cycle. The total benefits have been maximized, the market demand has been attend, and the business goal in terms of revenue on deadline has been covered.
Static (lower bound) and kinematic (upper bound) theorems for shakedown analysis (and limit analysis as special case) are presented for two-phase, fully saturated poroplastic solids in a context characterized by the f...
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Static (lower bound) and kinematic (upper bound) theorems for shakedown analysis (and limit analysis as special case) are presented for two-phase, fully saturated poroplastic solids in a context characterized by the following features: quasi-static, non-associative, non-softening (perfect or hardening) poroplasticity;piecewise linearized yield loci and hardening;constant permeability (linear fluid transport law);small deformations;non-traditional Galerkin finite element discretization. With reference to cyclic (periodic) external actions, numerical examples centred on linear programming evidence the computational potentialities and limitations of 'direct' (non-evolutive) methods of integrity assessment according to limit-state design concepts in poroplasticity. Copyright (C) 2000 John Wiley & Sons, Ltd.
Ranking circular Pythagorean fuzzy sets using distance-based techniques involves calculating the distance between a circular Pythagorean fuzzy set and a reference point that represents either maximum (positive ideal s...
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Ranking circular Pythagorean fuzzy sets using distance-based techniques involves calculating the distance between a circular Pythagorean fuzzy set and a reference point that represents either maximum (positive ideal solution) or minimum (negative ideal solution) values. Theoretical design is a major link in the procedure of complex product design, and it is most valuable and dominant to choose the appropriate design scheme, however, there are various kinds and inaccuracies of the evaluation information, and there is a problem of mutual influence among the evaluation criteria, which leads to unreliable decision-making of the optimal solution. In order to evaluate these problems, we concentrate on designing the model of power average/geometric operators based on Schweizer-Sklar operational laws based on the technique of circular Pythagorean fuzzy values. For this, first, we compute the model of Schweizer-Sklar operational laws for circular Pythagorean fuzzy valuable, and then we derive the model of circular Pythagorean fuzzy Schweizer-Sklar power averaging operator, circular Pythagorean fuzzy Schweizer-Sklar power weighted averaging operator, circular Pythagorean fuzzy Schweizer-Sklar power geometric operator, and circular Pythagorean fuzzy Schweizer-Sklar power weighted geometric operator for both t-norm and t-conorm. For the above operators, we also simplify the model of idempotency, monotonicity, and boundedness. Further, we construct the technique of Evaluation Based on the Distance from the Average Solution method based on initiated operators. Additionally, we develop three different procedures for evaluating the problem of transportation for linear programming with the help of a multi-attribute decision-making problem based on the Evaluation Based on Distance from Average Solution method, based on averaging operators, and based on geometric operators. In a sensitive analysis, we compare the proposed techniques with various extant
This article proposes a linear programming model based on an improved score function to solve interval-valued intuitionistic fuzzy multicriteria decision-making problems. In this decision-making process, the score mat...
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This article proposes a linear programming model based on an improved score function to solve interval-valued intuitionistic fuzzy multicriteria decision-making problems. In this decision-making process, the score matrix of the interval-valued intuitionistic fuzzy decision matrix can be constructed by the score function. We solve the linear programming model to determine the optimal criteria weights and then utilize the obtained criteria weights and the weighted score function as the suitability function of each alternative to rank the alternatives and select the most desirable one(s). Finally, an illustrative example for engineering investment alternatives demonstrates the effectiveness of the decision-making method.
We study a class of mixed-integer programs for solving linear programs with joint probabilistic constraints from random right-hand side vectors with finite distributions. We present greedy and dual heuristic algorithm...
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We study a class of mixed-integer programs for solving linear programs with joint probabilistic constraints from random right-hand side vectors with finite distributions. We present greedy and dual heuristic algorithms that construct and solve a sequence of linear programs. We provide optimality gaps for our heuristic solutions via the linear programming relaxation of the extended mixed-integer formulation of Luedtke et al. (2010) [13] as well as via lower bounds produced by their cutting plane method. While we demonstrate through an extensive computational study the effectiveness and scalability of our heuristics, we also prove that the theoretical worst-case solution quality for these algorithms is arbitrarily far from optimal. Our computational study compares our heuristics against both the extended mixed-integer programming formulation and the cutting plane method of Luedtke et al. (2010) [13]. Our heuristics efficiently and consistently produce solutions with small optimality gaps, while for larger instances the extended formulation becomes intractable and the optimality gaps from the cutting plane method increase to over 5%. (C) 2013 Elsevier B.V. All rights reserved.
epsilon-Sensitivity analysis (epsilon-SA) is a kind of method to perform sensitivity analysis for linear programming. Its main advantage is that it can be directly applied for interior-point methods with a little comp...
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epsilon-Sensitivity analysis (epsilon-SA) is a kind of method to perform sensitivity analysis for linear programming. Its main advantage is that it can be directly applied for interior-point methods with a little computation. In this paper, we discuss the property of epsilon SA analysis and its relationship with other sensitivity analysis methods. First, we present a new property of epsilon-SA, from which we derive a simplified formula for finding the characteristic region of epsilon-SA. Next, based on the simplified formula, we show that the characteristic region of epsilon-SA includes the characteristic region of Yildirim and Todd's method. Finally, we show that the characteristic region of epsilon-SA asymptotically becomes a subset of the characteristic region of sensitivity analysis using optimal partition. Our results imply that epsilon-SA can be used as a practical heuristic method for approximating the characteristic region of sensitivity analysis using optimal partition.
Resource management for large-scale high performance computing systems poses difficult challenges to system administrators. The extreme scale of these modern systems require task scheduling algorithms that are capable...
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Resource management for large-scale high performance computing systems poses difficult challenges to system administrators. The extreme scale of these modern systems require task scheduling algorithms that are capable of handling at least millions of tasks and thousands of machines. Highly scalable algorithms are necessary to efficiently schedule tasks to maintain the highest level of performance from the system. In this study, we design a novel linear programming based resource allocation algorithm for heterogeneous computing systems to efficiently compute high quality solutions for minimizing makespan. The novel algorithm tightly bounds the optimal makespan from below with an infeasible schedule and from above with a fully feasible schedule. The new algorithms are highly scalable in terms of solution quality and computation time as the problem size increases because they leverage similarity in tasks and machines. This novel algorithm is compared to existing algorithms via simulation on a few example systems. (C) 2015 Elsevier Inc. All rights reserved.
This work proposes a novel linear programming approach for the joint detection and decoding of LDPC-based space-time (ST) coded signals in multi-antenna orthogonal frequency division multiplexing (OFDM) systems. While...
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This work proposes a novel linear programming approach for the joint detection and decoding of LDPC-based space-time (ST) coded signals in multi-antenna orthogonal frequency division multiplexing (OFDM) systems. While traditional receivers typically decouple the detection and decoding processes as two disjunctive blocks or require iterative turbo exchange of extrinsic information between the soft detector and decoder, we formulate a joint linear program (LP) by exploiting the constraints imposed on the data symbols, training symbols, noise subspace as well as channel code. In consideration of the vast amount of LDPC parity check inequalities, we further present an adaptive procedure to significantly reduce the complexity of the joint LP receiver. Our LP-based receivers outperform existing receivers with substantial performance gains. Moreover, the proposed joint LP receiver demonstrates strong robustness when pilot symbols are sparsely arranged on subcarriers.
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