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Expanding scope and computational challenges in process scheduling

COMPUTERS & CHEMICAL ENGINEERING 2018年 114卷 14-42页

作者： Castro, Pedro M. Grossmann, Ignacio E. Zhang, Qi Univ Lisbon Fac Ciencias Ctr Matemat Aplicacoes Fundamentals Invest Operat P-1749016 Lisbon Portugal Carnegie Mellon Univ Ctr Adv Proc Decis Making Dept Chem Engn Pittsburgh PA 15213 USA BASF SE D-67063 Ludwigshafen Germany

In this paper, we present a brief overview of enterprise-wide optimization and challenges in multiscale temporal modeling and integration of different models for the levels of planning, scheduling and control. Next, we review Generalized Disjunctive programming (GDP), as a new modeling paradigm for scheduling problems that are illustrated with the STN and RTN models. We then address scheduling problems that expand the scope of the area: simultaneous scheduling and heat integration, pipeline scheduling, crude oil and refined products blending, and demand side management. We illustrate the advantage of the GDP modeling framework, describe effective strategies for global optimization, and describe multistage affinely adjustable robust optimization for uncertain interruptible load. We address integration of planning and scheduling, for which several approaches are reviewed, including use of traveling salesman constraints for multiperiod refinery planning, and multisite planning and scheduling of multiproduct batch plants. We report computational results to highlight the challenges. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Planning Scheduling Demand side management mixed-integer programming Generalized disjunctive programming

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Online Scheduling: Understanding the Impact of Uncertainty ⁎

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IFAC-PapersOnLine 2019年第1期52卷 727-732页

作者： Dhruv Gupta Christos T. Maravelias University of Wisconsin-Madison Madison WI 53706 USA

We present a framework to study quality of schedules obtained iteratively and online (real-time) in the presence of demand uncertainty Using this framework, we carry out a computational study, and make interesting observations. First, we find that uncertainty plays a less important role as a manufacturing facility is operated close to capacity. Second, the choice of the horizon for the online iterations, is dependent on the mean load, but independent of the accuracy of the forecasts. Finally, feedback, in the form of re-optimization, plays a very important role in mitigating the impact of uncertainty. Thus, through the analysis presented in this work, we gain insights that are applicable to all general rescheduling approaches.

关键词： Uncertainty Rescheduling Operations MPC mixed-integer programming

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A mixed-integer formulation of OLTC dynamics to minimize distribution and subtransmission tap changes induced by wind and solar power variations

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2018年第3期9卷 793-829页

作者： Baghsorkhi, Sina Sadeghi Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

Transformers equipped with an on-load tap-changer (OLTC) are the primary voltage regulators in sub-transmission and distribution networks. As the size of variable generation grows in respect to local feeder demand, the varying bidirectional flow of active power in the feeders interferes with the operation of the OLTC transformers. Currently most grid codes require utility-scale wind and PV plants to strictly regulate the voltage at the point of interconnection. This is typically achieved by installing dynamic reactive support in the form of STATCOM or SVC with instantaneously controllable setpoints and no switching cost. An effective and robust voltage coordination scheme would rely on voltage setpoints of these devices as the primary control input. Thus it is crucial to coordinate these new Volt/VAr controllers with the OLTC transformers to eliminate unnecessary tap changes and consequently reduce substation failure risk and maintenance cost. This paper introduces a centrally coordinated voltage control scheme that determines the optimal voltage setpoint based on variation of wind and solar generation in a given time window with the objective of reducing tap-change operations and resistive losses. Later the paper introduces an equivalent mixed-integer programming formulation that finds the same optimal control output but at a higher computational efficiency. Finally the paper discusses the results of the implementation of the MIP-formulation of the voltage control scheme on the DTE/ITC system serving Eastern Michigan.

关键词： Sub-transmission networks Voltage control OLTC transformers Hybrid dynamics mixed-integer programming Variable generation Wind power Solar power

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Lot sizing and supplier selection with multiple items, multiple periods, quantity discounts, and backordering

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 116卷 59-71页

作者： Alfares, Hesham K. Turnadi, Rio KPUPM Dhahran 31261 Saudi Arabia Univ Pertamina Jakarta 12220 Indonesia

A general model is presented for a realistic multi-item lot-sizing problem with multiple suppliers, multiple time periods, quantity discounts, and backordering of shortages. mixed integer programming (MIP) is used to formulate the problem and obtain the optimum solution for smaller problems. Due to the large number of variables and constraints, the model is too hard to solve optimally for practical problems. In order to tackle larger problem sizes, two heuristic solution methods are proposed. The first method is developed by modifying the Silver-Meal heuristic, and the second one by developing a problem-specific Genetic Algorithm (GA). Both heuristic methods are shown to be effective in solving the lot-sizing problem, but the GA is generally superior.

关键词： Lot sizing Supplier selection Quantity discounts Production and inventory control mixed-integer programming Genetic algorithms

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Vehicle Routing Problem with Drones for Last Mile Delivery

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Procedia Manufacturing 2019年 39卷 314-324页

作者： Patchara Kitjacharoenchai Seokcheon Lee School of Industrial Engineering 315 N Grant Street West Lafayette IN 47907-2023 USA

E-commerce and retail companies are seeking ways to cut delivery times and costs by exploring opportunities to use drones for making last mile delivery deliveries. In recent years, drone routing and scheduling has become a highly active area of research. This research addresses the delivery concept of a truck-drone combination along with the idea of allowing autonomous drones to fly from delivery trucks, make deliveries, and fly to delivery trucks nearby. The proposed model considers the synchronized truck drone routing model by allowing multiple drones to fly from a truck, serve customers and immediately return to the same truck for the battery swap and package retrieval. The model also takes into account both trucks and drones capacities to ensure that the amount of loads carried by each drone must not exceed its capacity and the total amount of loads in each delivery route must be less than truck’s capacity. The goal is to find the optimal routes of both trucks and drones which minimize the total arrival time of both trucks and drones at the depot after completing the deliveries. The problem can be solved by the formulated mixed integer programming (MIP) for the small size problem. Numerical results in the case study and benchmark problems are presented to show the delivery time improvement over the delivery time from other delivery types.

关键词： Drone delivery Vehicle Routing Problem Optimization Last Mile Delivery Drones mixed-integer programming

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Embedding Equality Constraints of Optimization Problems into a Quantum Annealer

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ALGORITHMS 2019年第4期12卷

作者： Vyskocil, Tomas Djidjev, Hristo Rutgers State Univ Dept Comp Sci 110 Frelinghuysen Rd Piscataway NJ 08854 USA Los Alamos Natl Lab Los Alamos NM 87545 USA

Quantum annealers such as D-Wave machines are designed to propose solutions for quadratic unconstrained binary optimization (QUBO) problems by mapping them onto the quantum processing unit, which tries to find a solution by measuring the parameters of a minimum-energy state of the quantum system. While many NP-hard problems can be easily formulated as binary quadratic optimization problems, such formulations almost always contain one or more constraints, which are not allowed in a QUBO. Embedding such constraints as quadratic penalties is the standard approach for addressing this issue, but it has drawbacks such as the introduction of large coefficients and using too many additional qubits. In this paper, we propose an alternative approach for implementing constraints based on a combinatorial design and solving mixed-integer linear programming (MILP) problems in order to find better embeddings of constraints of the type Sigma xi=k for binary variables xi. Our approach is scalable to any number of variables and uses a linear number of ancillary variables for a fixed k.

关键词： quantum annealing D-Wave QUBO constrained optimization mixed-integer programming

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Stochastic Planning of Distributed PV Generation

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ENERGIES 2019年第3期12卷

作者： Bazrafshan, Mohammadhafez Yalamanchili, Likhitha Gatsis, Nikolaos Gomez, Juan Univ Texas San Antonio Dept Elect & Comp Engn San Antonio TX 78249 USA E TRADE Arlington VA 22203 USA San AntonioWater Syst San Antonio TX 78212 USA

Recent studies by electric utility companies indicate that maximum benefits of distributed solar photovoltaic (PV) units can be reaped when siting and sizing of PV systems is optimized. This paper develops a two-stage stochastic program that serves as a tool for optimally determining the placing and sizing of PV units in distribution systems. The PV model incorporates the mapping from solar irradiance to AC power injection. By modeling the uncertainty of solar irradiance and loads by a finite set of scenarios, the goal is to achieve minimum installation and network operation costs while satisfying necessary operational constraints. First-stage decisions are scenario-independent and include binary variables that represent the existence of PV units, the area of the PV panel, and the apparent power capability of the inverter. Second-stage decisions are scenario-dependent and entail reactive power support from PV inverters, real and reactive power flows, and nodal voltages. Optimization constraints account for inverter's capacity, PV module area limits, the power flow equations, as well as voltage regulation. A comparison between two designs, one where the DC:AC ratio is pre-specified, and the other where the maximum DC:AC ratio is specified based on historical data, is carried out. It turns out that the latter design reduces costs and allows further reduction of the panel area. The applicability and efficiency of the proposed formulation are numerically demonstrated on the IEEE 34-node feeder, while the output power of PV systems is modeled using the publicly available PVWatts software developed by the National Renewable Energy Laboratory. The overall framework developed in this paper can guide electric utility companies in identifying optimal locations for PV placement and sizing, assist with targeting customers with appropriate incentives, and encourage solar adoption.

关键词： distributed photovoltaic generation mixed-integer programming power distribution planning inverter

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A Hierarchical Hybrid Heuristic-Optimization Approach for Multi-Product Assembly Line Design Problem

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Procedia Manufacturing 2019年 39卷 1067-1075页

作者： Gürsel A Süer Roohollah Younes Sinaki Azadeh Sadeghi Department of Industrial and System Engineering Ohio University Athens 45701 OH United States

In this research, an alternative parallel assembly line design strategy for multi products is proposed. Assigning operators to the parallel assembly stations optimally is an essential task specifically when the production volume is considerably high and the numbers of required operators are more than the number of assembly operations. The primary objective in the parallel assembly line problem is to balance more than one assembly line together. To deal with this problem a three phase hierarchy methodology is developed: (1) based on the assembly network for each product, operations are grouped into stations for various configuration. (2) In order to determine how many operators should be assigned to each station, a mathematical model is used and parallel stations are designed. (3) The number of assembly lines is determined with the objective of minimizing total number of operators required in the system. This approach guarantees that minimum number of workers is obtained thus maximizing overall system efficiency. This study is the extension of Suer’s (1998) study in which only one product was considered.

关键词： Assembly line balancing mixed-integer programming Parallel stations Parallel Assembly line Multi products Heuristic

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Robust auction design under multiple priors by linear and integer programming

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ANNALS OF OPERATIONS RESEARCH 2018年第1-2期260卷 233-253页

作者： Kocyigit, Cagil Bayrak, Halil I. Pinar, Mustafa C. Bilkent Univ Dept Ind Engn TR-06800 Ankara Turkey Ecole Polytech Fed Lausanne Lausanne Switzerland

It is commonly assumed in the optimal auction design literature that valuations of buyers are independently drawn from a unique distribution. In this paper we study auctions under ambiguity, that is, in an environment where valuation distribution is uncertain itself, and present a linear programming approach to robust auction design problem with a discrete type space. We develop an algorithm that gives the optimal solution to the problem under certain assumptions when the seller is ambiguity averse with a finite prior set and the buyers are ambiguity neutral with a prior . We also consider the case where all parties, the buyers and the seller, are ambiguity averse, and formulate this problem as a mixed integer programming problem. Then, we propose a hybrid algorithm that enables to compute an optimal solution for the problem in reduced time.

关键词： Optimal auction design Robustness Multiple priors Ambiguity Linear programming mixed-integer programming

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A Stochastic-Goal mixed-integer programming approach for integrated stock and bond portfolio optimization

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COMPUTERS & INDUSTRIAL ENGINEERING 2011年第4期61卷 1285-1295页

作者： Stoyan, Stephen J. Kwon, Roy H. Univ So Calif Daniel J Epstein Dept Ind & Syst Engn Los Angeles CA 90089 USA Univ Toronto Dept Mech & Ind Engn Toronto ON Canada

We consider a Stochastic-Goal mixed-integer programming (SGMIP) approach for an integrated stock and bond portfolio problem. The portfolio model integrates uncertainty in asset prices as well as several important real-world trading constraints. The resulting formulation is a structured large-scale problem that is solved using a model specific algorithm that consists of a decomposition, warm-start, and iterative procedure to minimize constraint violations. We present computational results and portfolio return values in comparison to a market performance measure. For many of the test cases the algorithm produces optimal solutions, where CPU time is improved greatly. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Stochastic programming Goal programming mixed-integer programming Geographical decomposition

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