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A new multi-objective mathematical model for the high-level synthesis of integrated circuits

APPLIED MATHEMATICAL MODELLING 2016年第3期40卷 2274-2290页

作者： Aras, Necati Yurdakul, Arda Bogazici Univ Dept Ind Engn Istanbul Turkey Bogazici Univ Dept Comp Engn Istanbul Turkey

An integrated circuit contains millions of components, all of which have to fit in the reserved silicon area and fulfill a defined functionality within a specified amount of execution time. Therefore, the design of an effective integrated circuit is a nontrivial task. Actually, it can be considered as a multi-objective optimization problem with two conflicting objectives: minimizing the total execution time called latency and the total silicon area of the integrated circuit. The overall problem is composed of tightly-coupled subproblems, i.e., determining the allocation of operators that execute the operations, the assignment of operations to operators, and scheduling of the operations. We formulate a multi-objective mixed-integer linear programming model (MOMILP) to solve this complex problem. It is novel since it incorporates decisions about the so-called multiplexers, which are essential components of an integrated circuit. The proposed MOMILP model is solved exactly using an augmented epsilon-constrained method. This enables us to find all the Pareto optimal solutions and hence the Pareto frontier for a given problem instance within a reasonable amount of computation time. The minimum latency and minimum area solutions of our model are 13.20 and 7.24% better on the average than the model that ignores multiplexers. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Integrated circuits Multiobjective optimization mixed-integer linear programming Pareto frontier

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Discrete optimization methods to fit piecewise affine models to data points

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COMPUTERS & OPERATIONS RESEARCH 2016年第Nov.期75卷 214-230页

作者： Amaldi, E. Coniglio, S. Taccari, L. Politecn Milan Dipartimento Elettron Informaz & Bioingn Piazza Leonardo Vinci 32 I-20133 Milan Italy Univ Southampton Dept Math Sci Univ Rd Southampton SO17 1BJ Hants England

Fitting piecewise affine models to data points is a pervasive task in many scientific disciplines. In this work, we address the k-Piecewise Affine Model Fitting with Piecewise linear Separability problem (k-PAMF-PLS) where, given a set of m points {a(1),...,a(m)} subset of R-n and the corresponding observations {b(1),...,b(m)} subset of R, we have to partition the domain R-n into k piecewise linearly (or affinely) separable subdomains and to determine an affine submodel (i.e., an affine function) for each of them so as to minimize the total linear fitting error w.r.t. the observations b(i). To solve k-PAMF-PLS to optimality, we propose a mixed-integer linear programming (MILP) formulation where symmetries are broken by separating shifted column inequalities. For medium-to-large scale instances, we develop a four-step heuristic involving, at each iteration, a point reassignment step based on the identification of critical points and a domain partition step based on multicategory linear classification. Differently from traditional approaches proposed in the literature for similar fitting problems, in both our exact and heuristic methods the domain partitioning and submodel fitting aspects are taken into account simultaneously. Computational experiments on real-world and structured randomly generated instances show that, with our MILP formulation with symmetry breaking constraints, we can solve to proven optimality many small-size instances. Our four-step heuristic turns out to provide close-to-optimal solutions for the small size instances, while allowing to tackle instances of much larger size. The experiments also show that the combined impact of the main features of our heuristic is quite substantial when compared to standard variants not including them. We conclude the paper with an application to the identification of dynamical, piecewise affine systems, for which we obtain promising results of comparable quality with those achieved with state-of-the-art methods

关键词： Data mining Piecewise affine model fitting mixed-integer linear programming Heuristics

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A shortcut model for energy efficient water network synthesis

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APPLIED THERMAL ENGINEERING 2016年第0期96卷 88-91页

作者： Liang, Yingzong Hui, Chi-Wai Hong Kong Univ Sci & Technol Dept Chem & Biomol Engn Kowloon Hong Kong Peoples R China

This paper presents a shortcut model for energy efficient water network synthesis with single contaminant. The proposed model is based on the idea of reducing repeated heating and cooling proposed by Feng et al. [9]. To avoid sub-optimum that can be generated from Feng's model, the proposed model only minimizes the number of temperature 'valleys' instead of the total number of 'peaks and valleys' of the water network. With the new formulation, the proposed model not only guarantees global optimum but also becomes much easier to be solved. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Water allocation network Heat integration mixed-integer linear programming

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A novel two-model based approach for optimal scheduling in CSP plants

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SOLAR ENERGY 2016年 126卷 73-92页

作者： Jesus Vasallo, Manuel Manuel Bravo, Jose Univ Huelva Dept Elect Engn Comp Syst & Automat Carretera Palos de La Frontera S-N Palos De La Frontera 21819 Huelva Spain

Thermal energy storage (TES) systems allow concentrated solar power (CSP) producers to participate in a day-ahead market. Then, the optimal power scheduling problem can be posed, whose objective is the maximisation of profits derived from electricity sales. Most papers in literature use a mixed-integer linear programming (MILP) approach to solve this type of problems. This paper proposes a novel approach based on the use of two models: a detailed model and a MILP model. This approach combines MILP capabilities and the accuracy of a detailed model. The proposed approach is applied to a 50 MW parabolic-trough-collector based CSP plant with molten-salt-based TES. A detailed model available in literature and validated against operating plant data is used, but some improvements are included for its use in optimal scheduling problems. Moreover, the MILP model was developed to adjust as much as possible to the features of the detailed model. The improvements regarding other scheduling strategies for a specific example are shown. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Concentrating solar power plant Thermal energy storage Electricity market Optimized operation strategy Parabolic trough collector mixed-integer linear programming

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linear approximated formulation of AC optimal power flow using binary discretisation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第5期10卷 1117-1123页

作者： Akbari, Tohid Bina, Mohammad Tavakoli KN Toosi Univ Technol Fac Elect Engn Tehran Iran

This study presents a novel linear approximated methodology for full alternating current-optimal power flow (AC-OPF). The AC-OPF can provide more precise and real picture of full active and reactive power flow modelling, along with the voltage profile of buses compared to the commonly used direct current-optimal power flow. While the AC-OPF is a non-linear programming problem, this can be transformed into a mixed-integer linear programming environment by the proposed model without loss of accuracy. The global optimality of the solution for the approximated model can be guaranteed by existing algorithms and software. The numerical results and simulations which represent the effectiveness and applicability of the proposed model are given and completely discussed in this study.

关键词： load flow nonlinear programming integer programming linear programming linear approximated formulation AC optimal power flow binary discretisation linear approximated methodology full alternating current-optimal power flow AC-OPF reactive power flow modelling voltage profile direct current-optimal power flow nonlinear programming problem mixed-integer linear programming

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Multi-objective metaheuristics for solving a type II robotic mixed-model assembly line balancing problem

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2016年第7期33卷 472-484页

作者： Rabbani, Masoud Mousavi, Zahra Farrokhi-Asl, Hamed Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Iran Univ Sci & Technol Sch Ind Engn Tehran Iran

Nowadays, robots are used extensively in robotic assembly line balancing system because of the capabilities of the robots. Robotic assembly lines are used to manufacture high volume product in customization and specialization production. In this paper, type II robotic mixedmodel assembly line balancing is considered. The goals are to minimize robot purchasing costs, robot setup costs, sequence dependent setup costs, and cycle time. The proposed model tries to determine an optimal or near-optimal configuration of tasks, workstations in U-shaped assembly line balancing. In this model, two types of tasks including the special task for one product model and the common task for several products models exist. The problem with the aforementioned conditions is NP-hard problem. So, we used two different multi-objective evolutionary algorithms (MOEAs) to solve the problem. First algorithm is non-dominated sorting genetic algorithm (NSGA-II) and the second one is multi-objective particle swarm optimization (MOPSO). Also, we used GAMS software to solve the problem in small size problem to validate our proposed model. Then, some numerical examples are presented and the experimental results and the performance of the algorithms are compared with each other.

关键词： Robotic mixed-model assembly line balancing mixed-integer linear programming U-shaped assembly line multiobjective optimization

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A computational framework and solution algorithms for two-stage adaptive robust scheduling of batch manufacturing processes under uncertainty

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AICHE JOURNAL 2016年第3期62卷 687-703页

作者： Shi, Hanyu You, Fengqi Northwestern Univ Dept Chem & Biol Engn Evanston IL 60208 USA

A novel two-stage adaptive robust optimization (ARO) approach to production scheduling of batch processes under uncertainty is proposed. We first reformulate the deterministic mixed-integer linear programming model of batch scheduling into a two-stage optimization problem. Symmetric uncertainty sets are then introduced to confine the uncertain parameters, and budgets of uncertainty are used to adjust the degree of conservatism. We then apply both the Benders decomposition algorithm and the column-and-constraint generation (C&CG) algorithm to efficiently solve the resulting two-stage ARO problem, which cannot be tackled directly by any existing optimization solvers. Two case studies are considered to demonstrate the applicability of the proposed modeling framework and solution algorithms. The results show that the C&CG algorithm is more computationally efficient than the Benders decomposition algorithm, and the proposed two-stage ARO approach returns 9% higher profits than the conventional robust optimization approach for batch scheduling. (c) 2015 American Institute of Chemical Engineers AIChE J, 62: 687-703, 2016

关键词： two-stage adaptive robust optimization batch manufacturing processes short-term scheduling problem Benders decomposition algorithm column-and-constraint generation algorithm mixed-integer linear programming

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Robust Optimization for Strategic Energy Planning

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INFORMATICA 2016年第3期27卷 625-648页

作者： Moret, Stefano Bierlaire, Michel Marechal, Francois Ecole Polytech Fed Lausanne Ind Proc & Energy Syst Engn Grp IPESE CH-1015 Lausanne Switzerland Ecole Polytech Fed Lausanne Transport & Mobil Lab CH-1015 Lausanne Switzerland

Long-term planning for energy systems is often based on deterministic economic optimization and forecasts of fuel prices. When fuel price evolution is underestimated, the consequence is a low penetration of renewables and more efficient technologies in favour of fossil alternatives. This work aims at overcoming this issue by assessing the impact of uncertainty on energy planning decisions. A characterization of uncertainty in energy systems decision-making is performed. Robust optimization is then applied to a mixed-integer linear programming problem, representing the typical trade-offs in energy planning. It is shown that in the uncertain domain investing in more efficient and cleaner technologies can be economically optimal.

关键词： energy planning robust optimization uncertainty characterization mixed-integer linear programming

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Optimal location-multi-allocation-routing in capacitated transportation networks under population-dependent travel times

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2016年第6期29卷 652-676页

作者： Shiripoura, Saber Mahdavi-Amiri, Nezam Mahdavi, Iraj Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Sharif Univ Technol Fac Math Sci Tehran Iran

A capacitated location-multi-allocation-routing model is presented for a transportation network with travel times between the nodes represented by links on the network. The concept of multi-allocation arises from the possibility of allocating the population in a demand node to more than one server node. In normal conditions, travel time between two nodes is a fixed value. However, since the flow of population in a link can affect the travel time, here the impact of the population flow on link time is considered to be simultaneous. This way, distribution of the population over the network has a direct influence on the travel link times. It is assumed that all links are two-way and capacities of the server nodes and arcs for accepting population are limited. Our aim is to ?nd optimal locations of server node(s), optimal allocation of the population in demand nodes to the server(s) and optimal allocation of the population of the nodes to different routes to reach the assigned servers so that total transportation time is minimised. First, the proposed problem is formulated as a mixed-integer non-linear programming model, followed by its suitable transformation into a mixed-integer linear programming problem. Then, a standard genetic algorithm (GA) and a heuristic algorithm combining genetic algorithm and local search (GALS) are presented to solve large instances of the problem. Finally, three sets of numerical experiments are made to compare the results obtained by CPLEX, standard GA and GALS. Numerical results show outperformance of GALS over CPLEX and the standard GA.

关键词： genetic algorithm mixed-integer linear programming local search population-dependent travel times capacitated location-multi-allocation-routing problem

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A joint chance-constrained programming approach for call center workforce scheduling under uncertain call arrival forecasts

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年 96卷 16-30页

作者： Excoffier, M. Gicquel, C. Jouini, O. Univ Paris 11 Rech Informat Lab Campus OrsayBatiment 650 F-91405 Orsay France Ecole Cent Paris Lab Genie Ind F-92290 Chatenay Malabry France

We consider a workforce management problem arising in call centers, namely the shift-scheduling problem. It consists in determining the number of agents to be assigned to a set of predefined shifts so as to optimize the trade-off between manpower cost and customer quality of service. We focus on explicitly taking into account in the shift-scheduling problem the uncertainties in the future call arrival rates forecasts. We model them as independent random variables following a continuous probability distribution. The resulting stochastic optimization problem is handled as a joint chance-constrained program and is reformulated as an equivalent large-size mixed-integer linear program. One key point of the proposed solution approach is that this reformulation is achieved without resorting to a scenario generation procedure to discretize the continuous probability distributions. Our computational results show that the proposed approach can efficiently solve real-size instances of the problem, enabling us to draw some useful managerial insights on the underlying risk-cost trade-off. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Personnel planning Call center shift scheduling Customer abandonment Stochastic programming Probabilistic constraints mixed-integer linear programming

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