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An integration of algal biofuel production planning, scheduling, and order-based inventory distribution control systems

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BIOFUELS BIOPRODUCTS & BIOREFINING-BIOFPR 2019年第4期13卷 920-935页

作者： Nugroho, Yohanes K. Zhu, Liandong Wuhan Univ Sch Resource & Environm Sci 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China Wuhan Univ Hubei Biomass Resource Chem & Environm Biotechnol 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China

This paper studies an integration model of production planning, scheduling, and order- based inventory distribution control systems. It models supply-chain and production dynamics and employs process control to supply-chain operations and the production process and scheduling. It embeds generalized disjunctive programming (GDP) and branch-and-reduce algorithms to obtain an optimal solution. Generalized disjunctive programming reformulates the original mixed integer nonlinear programming (MINLP) model and solves the new model with a lower number of integer variables. Afterwards, the branch and reduce algorithm, which is strengthened with outer approximation (OA) cutting planes, is able to find optimal solutions with permissible optimality gaps. The results show that supply network size does not affect forecast accuracy;there is a forecast error rate of 12.9%. Similar figures are also shown for the inventory level, which is expected to be 32% of demand level, production changeover time at 0.58 days, inventory ramp-up period to meet the expected raw materials and final products inventory at 0.02 and 0.04 days respectively, and extra delivery time to meet a certain level of demand change at 1.1 days. The results show that the model is capable of controlling demand forecast, inventory level, and delivery quantity, so that the supply chain can avoid stock out at a minimal level of inventory, regardless of the size of supply-chain networks. (c) 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.

关键词： production planning scheduling process control branch and reduce generalized disjunctive programming mixed integer non-linear programming

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Solving a production optimization problem in a paper-converting mill with MILP

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COMPUTERS & CHEMICAL ENGINEERING 1998年第4-5期22卷 563-570页

作者： Westerlund, T Harjunkoski, I Isaksson, J Abo Akad Univ Proc Design Lab FIN-20500 Abo Finland Wisapak Corp FIN-66900 Pietarsaari Finland

The present paper deals with a production optimization problem connected with the paper-converting industry. The problem considered is to produce a set of product paper reels from larger raw paper reels such that a cost function is minimized. The problem is generally non-convex due to a bilinear objective function and some bilinear constraints, both of which give rise to certain problems. The problem can, however, be solved as a two-step optimization procedure, in which the latter step is a mixed integer linear programming problem. A numerical example is introduced to illustrate the proposed procedure. The example is taken from a real-life daily production optimization problem encountered at a Finnish paper-converting mill, Wisapak Oy, having an annual production of just over 100,000 tons of printed paper. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： optimization mixed integer non-linear programming trim loss problems scheduling problems production optimization

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New secure bilateral transaction matrix determination using AC distribution factors and impact of TCPAR and TCSC on its pattern

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2007年第8期35卷 921-943页

作者： Sharma, Ashwani Kumar NIT Kurukshetra Elect Engn Dept Haryana 136119 India

In the competitive electricity environment, a large number of transactions are expected to be negotiated, which can compromise the system security and reliabili ty. According to the Federal Energy Regulatory Commission (FERC) order 889, only those transactions that should be settled are within the permissible limits of a vaila ble transfer capability (ATC). Therefore, it has become essential to determine secure transactions occurring in the new environment for better planning and management of open access environment. This article proposes a new method of secure bilateral transaction determination using AC distribution factors. The bilateral transaction matrix pattern has been determined in the presence of thyristor-controlled phase angle regulator (TCPAR) and thyristor-controlled series compensator (TCSC). The optimal location of CPAR and TCSC has been determined using the mixed integer non- linear programming approach. The bilateral transaction matrix has been determined in a hybrid market model. The proposed technique has been applied on IEEE 24-bus reliability test system (RTS).

关键词： secure transaction matrix transaction matrix pattern mixed integer non-linear programming TCPAR and TCSC location distribution factors

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A cutting plane method for minimizing pseudo-convex functions in the mixed integer case

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COMPUTERS & CHEMICAL ENGINEERING 2000年第12期24卷 2655-2665页

作者： Pörn, R Westerlund, T Abo Akad Univ Proc Design Lab FIN-20500 Turku Finland Abo Akad Univ Dept Math FIN-20500 Turku Finland

In this paper, a mixed integer non linear programming (MINLP) algorithm for minimizing pseudo-convex functions under pseudo-convex constraints is proposed and illustrated. The solution procedure is iterative and relies on successive linear approximation of the objective function and on a line-search technique. The whole procedure is then embedded within the framework of a existing cutting plane method for mixed integer non-linear programs. This enables us to solve general MINLPs with pseudo-convex objective and pseudo-convex inequality constraints to global optimality. (C) 2000 Elsevier Science Ltd. All rights reserved.

关键词： mixed integer non-linear programming pseudo-convex functions cutting plane method

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Distributed energy resource short-term scheduling using Signaled Particle Swarm Optimization

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ENERGY 2012年第1期42卷 466-476页

作者： Soares, J. Silva, M. Sousa, T. Vale, Z. Morais, H. Polytech Porto IPP Knowledge Engn & Decis Support Res Ctr GECAD P-4200072 Oporto Portugal

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as mixed-integer non-linear programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology's performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Distributed energy resource scheduling mixed integer non-linear programming Particle swarm optimization Short-term scheduling

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Long- and Short-Run supply-chain optimization models for the allocation and congestion management of containerized imports from Asia to the United States

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2011年第5期47卷 593-608页

作者： Jula, Payman Leachman, Robert C. Simon Fraser Univ Fac Business Adm Burnaby BC V5A 1S6 Canada Univ Calif Berkeley Dept Ind Engn & Operat Res Berkeley CA 94720 USA

Analytical models are introduced for predicting the allocation to ports and transportation channels of containerized goods imported from Asia to the USA. Assuming fixed distributions for container flow-times, the Long-Run Model heuristically solves a mixed integer non-linear program to determine the least-cost supply-chain strategies for importers. The Short-Run Model uses estimates of the flow times as a function of traffic volumes on fixed infrastructure to iteratively develop the best near-term strategies. Minimum volume requirements and capacities for ports and landside channels are considered. The models are applied to predict the effects of container fees at the San Pedro Bay ports. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Supply-chain optimization intermodal freight transport Transportation planning Containerized imports mixed integer non-linear programming Queuing

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Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年 96卷 201-215页

作者： De, Arijit Mamanduru, Vamsee Krishna Reddy Gunasekaran, Angappa Subramanian, Nachiappan Tiwari, Manoj Kumar Indian Inst Technol Dept Ind & Syst Engn Kharagpur 721302 W Bengal India Univ Massachusetts Dartmouth Dept Decis & Informat Sci 285 Old Westport Rd N Dartmouth MA 02748 USA Univ Nottingham Ningbo China Nottingham Univ Business Sch China 199 Taikang East Rd Ningbo 315100 Zhejiang Peoples R China

Ship routing and scheduling problem is considered to meet the demand for various products in multiple ports within the planning horizon. The ports have restricted operating time, so multiple time windows are taken into account. The problem addresses the operational measures such as speed optimisation and slow steaming for reducing carbon emission. A mixed integer non-linear programming (MINLP) model is presented and it includes the issues pertaining to multiple time horizons, sustainability aspects and varying demand and supply at various ports. The formulation incorporates several real time constraints addressing the multiple time window, varying supply and demand, carbon emission, etc. that conceive a way to represent several complicating scenarios experienced in maritime transportation. Owing to the inherent complexity, such a problem is considered to be NP-Hard in nature and for solutions an effective meta-heuristics named Particle Swarm Optimization-Composite Particle (PSO-CP) is employed. Results obtained from PSO-CP are compared using PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) to prove its superiority. Addition of sustainability constraints leads to a 4-10% variation in the total cost. Results suggest that the carbon emission, fuel cost and fuel consumption constraints can be comfortably added to the mathematical model for encapsulating the sustainability dimensions. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Ship routing Carbon emission mixed integer non-linear programming Maritime transportation Particle Swarm Optimization-Composite Particle

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A hybrid artificial immune network for detecting communities in complex networks

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COMPUTING 2015年第5期97卷 483-507页

作者： Karimi-Majd, Amir-Mohsen Fathian, Mohammad Amiri, Babak Iran Univ Sci & Technol Dept Ind Engn Tehran Iran Univ Sydney Sydney NSW 2006 Australia

One of the challenging problems when studying complex networks is the detection of sub-structures, called communities. Network communities emerge as dense parts, while they may have a few relationships to each other. Indeed, communities are latent among a mass of nodes and edges in a sparse network. This characteristic makes the community detection process more difficult. Among community detection approaches, modularity maximization has attracted much attention in recent years. In this paper, modularity density (D value) has been employed to discover real community structures. Due to the inadequacy of previous mathematical models in finding the correct number of communities, this paper first formulates a mixed integer non-linear program to detect communities without any need of prior knowledge about their number. Moreover, the mathematical models often suffer from NP-Hardness. In order to overcome this limitation, a new hybrid artificial immune network (HAIN) has been proposed in this paper. HAIN aims to use a network's properties in an efficient way. To do so, this algorithm employs major components of the pure artificial immune network, hybridized with a well-known heuristic, to provide a powerful and parallel search mechanism. The combination of cloning and affinity maturation components, a strong local search routine, and the presence of network suppression and diversity are the main components. The experimental results on artificial and real-world complex networks illustrate that the proposed community detection algorithm provides a useful paradigm for robustly discovering community structures.

关键词： Complex network Community detection mixed integer non-linear programming Artificial immune network Modularity-based maximization

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Coupled optimization of electricity and natural gas systems using augmented Lagrangian and an alternating minimization method

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年第0期80卷 202-218页

作者： Biskas, P. N. Kanelakis, N. G. Papamatthaiou, A. Alexandridis, I. Aristotle Univ Thessaloniki Dept Elect Engn Thessaloniki 54124 Greece Hellen Gas Transmiss Syst Operator DESFA Patima Magoulas Attica Greece

A coupled optimization of the electricity and gas systems is presented in this paper. The electricity problem involves a unit commitment with co-optimization of energy and reserves under a power pool, considering all system operational and unit technical constraints. The gas problem involves a medium-scale highly non-convex and non-linear problem structure, which is modeled as a mixed integer non-linear programming model. The decomposition of the overall problem is based on the Augmented Lagrangian method. An iterative process is implemented, coordinating the two interdependent systems using an alternating minimization method, in which the Lagrange multipliers are updated using the subgradient method. The gas problem is solved in two phases in order to avoid numerical instabilities;first, the direction of flow is defined, and then the gas flow is derived in the second phase. The solution algorithm is evaluated using the Greek power and gas system, comprising thirteen gas-fired units and fifty-three gas network nodes. The test results indicate the strong interdependence of the two systems, and demonstrate the efficiency of the presented algorithm in coordinating them. (c) 2016 Elsevier Ltd. All rights reserved.

关键词： mixed integer non-linear programming Augmented Lagrangian method Alternating minimization method Day-ahead electricity market Unit commitment Natural gas network

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A new approach to generation scheduling in interconnected power systems using predictor-corrector proximal multiplier method

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ELECTRICAL ENGINEERING 2012年第3期94卷 177-186页

作者： Chung, K. H. Kim, B. H. Hur, D. Kwangwoon Univ Dept Elect Engn Seoul 139701 South Korea Korea Electrotechnol Res Inst Uiwang 437808 Gyeonggi South Korea Hongik Univ Sch Elect & Elect Engn Seoul 121791 South Korea

The purpose of this paper is to elaborate a new generation scheduling algorithm in the interconnected power systems. Typically, the generation scheduling problem as a mixed integer non-linear programming can be effectively solved by the generalized Benders decomposition technique which decouples an original problem into the master problem and subproblems to tremendously allow fast and accurate solutions of large-scale problems. In order to formulate efficient inter-temporal optimal power flow (OPF) subproblems, we will explore a regional decomposition framework based on predictor-corrector proximal multiplier method. In fact, this scheme can find the most economic generation schedules under the power transactions for a multi-utility system without the exchange of each utility's own private information and major disruption to existing economic dispatch or OPF adopted by individual utilities.

关键词： Benders decomposition Distributed optimal power flow Generation scheduling Interconnected power system mixed integer non-linear programming Predictor-corrector proximal multiplier method

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