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A novel exact solution algorithm for a robust product portfolio problem under return uncertainty

SCIENTIA IRANICA 2022年第3期29卷 1638-1645页

作者： Goli, A. Zare, H. Khademi Tavakkoli-Moghaddam, R. Sadegheih, A. Yazd Univ Dept Ind Engn Saffayieh Yazd Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Univ Isfahan Fac Engn Dept Ind Engn & Future Studies Esfahan Iran

This research is aimed to address the optimization of a product portfolio problem under uncertainty using the principles of financial portfolios theory. Since the success of a product portfolio is dependent on strategic decision making as well as on future changes of return, the return is best considered when it is deemed an uncertain parameter. The specific innovation of this research is the use of a robust optimization approach and providing an exact solution algorithm based on the model of Bertsimas and Sim. Given the uncertainty of the returns, the product portfolio model was developed based on the robust counterpart formulation of Bertsimas and Sim. An exact solution algorithm was also formulated to reduce the solution time. The results obtained by applying the model to a real case study of the dairy industry in Iran showed that increasing the confidence level would decrease total returns of the portfolio and increase its total risk. A comparison between the proposed algorithm and similar methods showed that, on average, it would make 3% improvement in the solution time. (C) 2022 Sharif University of Technology. All rights reserved.

关键词： Robust optimization Product portfolio selection exact solution algorithm Return Uncertainty

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An exact solution algorithm for maximizing the fleet availability of a unit of aircraft subject to flight and maintenance requirements

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2015年第2期242卷 631-643页

作者： Gavranis, Andreas Kozanidis, George Hellen Air Force Combat Wing 111 N Aghialos Greece Univ Thessaly Dept Mech Engn Syst Optimizat Lab Pedion Areas 38334 Volos Greece

We address the Flight and Maintenance Planning (FMP) problem, i.e., the problem of deciding which available aircraft to fly and for how long, and which grounded aircraft to perform maintenance operations on in a group of aircraft that comprise a unit. The aim is to maximize the unit fleet availability over a multi-period planning horizon, while also ensuring that certain flight and maintenance requirements are satisfied. Heuristic approaches that are used in practice to solve the FMP problem often perform poorly, generating solutions that are far from the optimum. On the other hand, the exact optimization models that have been developed to tackle the problem handle small problems effectively, but tend to be computationally inefficient for larger problems, such as the ones that arise in practice. With these in mind, we develop an exact solution algorithm for the FMP problem, which is capable of identifying the optimal solution of considerably large realistic problems in reasonable computational times. The algorithm solves suitable relaxations of the original problem, utilizing valid cuts that guide the search towards the optimal solution. We present extensive experimental results, which demonstrate that the algorithm's performance on realistic problems is superior to that of two popular commercial optimization software packages, whereas the opposite is true for a class of problems with special characteristics that deviate considerably from those of realistic problems. The important conclusion of this research is that the proposed algorithm, complemented by generic optimization software, can handle effectively a large variety of FMP problem instances. (C) 2014 Published by Elsevier B.V.

关键词： Mixed integer programming Fleet availability Flight and Maintenance Planning exact solution algorithm Valid inequalities

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Data-Driven Optimization with Distributionally Robust Second Order Stochastic Dominance Constraints

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OPERATIONS RESEARCH 2024年第3期72卷 1298-1316页

作者： Peng, Chun Delage, Erick Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China HEC Montreal GERAD Montreal PQ H3T 2A7 Canada HEC Montreal Dept Decis Sci Montreal PQ H3T 2A7 Canada

Optimization with stochastic dominance constraints has recently received an increasing amount of attention in the quantitative risk management literature. Instead of requiring that the probabilistic description of the uncertain parameters be exactly known, this paper presents a comprehensive study of a data-driven formulation of the distributionally robust second order stochastic dominance constrained problem (DRSSDCP) that hinges on using a type-1 Wasserstein ambiguity set. This formulation allows us to identify solutions with finite sample guarantees and solutions that are asymptotically consistent when observations are independent and identically distributed. It is, furthermore, shown to be axiomatically motivated in an environment with distribution ambiguity. Leveraging recent results in the field of robust optimization, we further formulate the DRSSDCP as a multistage robust optimization problem and further propose a tractable conservative approximation that exploits finite adaptability and a scenario-based lower bounding problem, both of which can reduce to linear programs under mild conditions. We then propose, to the best of our knowledge, the first exact optimization algorithm for this DRSSDCP, the efficiency of which is confirmed by our numerical results. Finally, we illustrate how the data-driven DRSSDCP can be applied in practice on resource-allocation problems with both synthetic and real data. Our empirical results show that, with a proper adjustment of the size of the Wasserstein ball, DRSSDCP can reach "acceptable" out-of-sample feasibility yet still generating strictly better performance than what is achieved by the reference strategy.

关键词： robust stochastic dominance distributionally robust optimization Wasserstein ambiguity set affine decision rule exact solution algorithm resource allocation out-of-sample SSD feasibility

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A multi-commodity two-Echelon capacitated vehicle routing problem with time windows: Model formulations and solution approach

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COMPUTERS & OPERATIONS RESEARCH 2021年 127卷 105154-105154页

作者： Dellaert, Nico Woensel, Tom Van Crainic, Teodor Gabriel Saridarq, Fardin Dashty Eindhoven Univ Technol Sch Ind Engn NL-5600 MB Eindhoven Netherlands Univ Quebec Montreal Interuniv Res Ctr Enterprise Networks Logist & Tr POB 8888Stn Ctr Ville Montreal PQ H3C 3P8 Canada Univ Quebec Montreal Dept Management & Technol POB 8888Stn Ctr Ville Montreal PQ H3C 3P8 Canada

This paper studies the multi-commodity two-echelon capacitated vehicle routing problem with time windows. Specifically, we take customer-specific origin to destination, non-substitutable demands into account. The main components are: (i) first echelon tours, (ii) second echelon tours and (iii) movement of commodities from first echelon to second echelon, considering route connection and synchronization. Exploiting the structure of the problem, we propose a decomposition scheme which decouples the first and second echelon routing problems, by deleting the third component, and re-couples them by using constraints ensuring movement of commodities between the two echelons. Based on this decomposition, we present (i) a number of model formulations for the MC-2E-VRPTW, and (ii) develop an exact solution approach for this real-life problem. A computational study on a comprehensive set of instances shows the effectiveness in terms of computational effort and solution quality. (C) 2020 The Author(s). Published by Elsevier Ltd.

关键词： Distribution Multi-commodity Two-echelon Capacitated vehicle routing problem Model formulations exact solution algorithm

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A fast branch, bound and remember algorithm for disassembly line balancing problem

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2020年第11期58卷 3220-3234页

作者： Li, Zixiang Cil, Zeynel Abidin Mete, Suleyman Kucukkoc, Ibrahim Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Wuhan Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan Hubei Peoples R China Izmir Democracy Univ Dept Ind Engn Izmir Turkey Munzur Univ Dept Ind Engn Tunceli Turkey Balikesir Univ Ind Engn Dept Balikesir Turkey

In recent years, the interests of disassembly line have increased owing to economic reasons and the increase of environmental awareness. Effective line can provide many advantages in terms of economic aspect and it facilitates competition the companies with others. This study contributes to the relevant literature by a branch, bound and remember algorithm for disassembly line balancing problem with AND/OR precedence. The proposed exact solution method employs the memory-based dominance rule to eliminate the reduplicated sub-problems by storing all the searched sub-problems and to utilise cyclic best-first search strategy to obtain high-quality complete solutions fast. In this paper, minimising the number of stations is taken as the performance measure. The proposed methodology is tested on a set of 260 instances and compared with the mathematical model using CPLEX solver and five well-known metaheuristics. Computational results show that the proposed method is capable of obtaining the optimal solutions for all the tested instances with less than 0.1 seconds on average. Additionally, comparative study demonstrates that the proposed method is the state-of-the-art algorithm and outperforms the CPLEX solver and metaheuristics in terms of both solution quality and search speed aspects.

关键词： combinatorial optimisation branch and bound remember disassembly line balancing exact solution algorithm

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Optimal decision-making for a single-stage manufacturing system with rework options

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS 2020年第1期7卷 90-104页

作者： Nobil, Amir Hossein Nobil, Erfan Sarker, Bhaba R. Islamic Azad Univ Fac Ind & Mech Engn Dept Ind Engn Qazvin Branch Qazvin Iran Islamic Azad Univ Ruddehn Branch Fac Engn Tehran Iran Louisiana State Univ Dept Mech & Ind Engn Baton Rouge LA 70803 USA

This study investigates a manufacturing system with a single-stage production system that sometimes produces imperfect items. In this system, we assume that the rework process can start after the regular production process (immediate rework) or when the inventory of perfect goods is equal to zero (delayed rework). Also, we assumed non-zero set-up time for rework. Two models are presented for manufacturing systems with re-workable goods. The first model deals with non-zero set-up time for immediate rework and the second model deals with non-zero set-up times for delayed rework. We also investigate special cases associated with these models wherein the set-up times are zero. A general and aggregated model that included those two models is considered. The novelty of this research lies in selecting for a decision-maker the best approach based on system conditions and solutions. This paper provides a framework for rework policies to minimise the total cost of the system, which includes the cost of manufacturing cost, set-up cost for rework and regular production, holding cost for perfect and imperfect items, and the cost of rework. Finally, we solve the aggregated model by using an exact solution algorithm, and the optimal value of production in each period is obtained.

关键词： Lot-sizing imperfect production rework process set-up time exact solution algorithm

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An extensive operations and maintenance planning problem with an efficient solution method

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COMPUTERS & OPERATIONS RESEARCH 2018年第Jul.期95卷 151-162页

作者： Seif, Javad Yu, Andrew J. Univ Tennessee Dept Ind & Syst Engn Knoxville TN 37996 USA

In this paper, we extend the formulation and solution algorithm of a Flight and Maintenance Planning (FMP) problem to cover a wider range of applications. We consider a general Operations and Maintenance Planning (OMP) case where machines are shared between multiple operation schedules as well as have multiple preventive maintenance activities. In this case, machines have different usage-based intervals and the maintenance duration varies for each machine. We formulate and optimize the OMP problem to accommodate the maintenance requirements and physical characteristics of multiple stations. We evaluate the performance of the generalized solution methodology and show its effectiveness and efficiency through computational experiments. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Mixed integer programming Operations and maintenance planning Availability exact solution algorithm Cutting planes

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Service location grouping and pricing in transportation: Application in air cargo

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2018年第3期267卷 933-943页

作者： Azadian, Farshid Murat, Alper Embry Riddle Aeronaut Univ Coll Business 600 S Clyde Morris Blvd Daytona Beach FL 32114 USA Wayne State Univ Dept Ind & Syst Engn 4815 Fourth St Detroit MI 48202 USA

It is a common practice for transportation firms to group together a set of locations that they serve and price their services based on the group that the origin and destination of a service belong to rather than point-to-point. Proper grouping of service locations and correct pricing under this policy is essential to the financial success of firms. In this research, we develop a novel model for transportation firms to simultaneously group service locations and determine group-to-group pricing by considering the price elasticity of customers' demand. We formulate the problem as a mixed-integer nonlinear programming and propose two exact solution algorithms based on decomposition principles. The performances of our algorithms are evaluated using computational experiments and results show that proposed methods are effective. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Transportation Pricing Mixed-integer nonlinear programming exact solution algorithm

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Hybrid artificial intelligence and robust optimization for a multi-objective product portfolio problem Case study: The dairy products industry

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 137卷 106090-000页

作者： Goli, Alireza Zare, Hasan Khademi Tavakkoli-Moghaddam, Reza Sadeghieh, Ahmad Yazd Univ Dept Ind Engn Saffayieh Yazd Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

The optimization of the product portfolio problem under return uncertainty is addressed here. The contribution of this study is based on the application of a hybrid improved artificial intelligence and robust optimization and presenting a new method for calculating the risk of a product portfolio. By applying an improved neural network with runner root algorithm (RRA), the future demand of each product is predicted and the risk index of each product is calculated based on its predicted future demand. A two-objective (minimizing risk and maximizing return) mathematical model is proposed where, the effect of investments, reliability and allowable lost sales on the designed product portfolio are of concern. Due to the return uncertainty, two robust counterpart models based on the Bertsimas and Sim and Ben-Tal and Nemirovski approaches are developed. Then, an exact solution method is proposed to reduce the solving time of robust model. The results of the implementation in the dairy industry of Iran indicate that an increase in the confidence level, increase the investment risk and decrease the total return. The obtained results by the statistical tests indicate that the two newly proposed robust models are of similar performance in the finding the maximum return solutions, while, here the least risky solutions, the Bertsimas model outperforms its counterparts. Moreover, the results of the proposed exact solution method indicate that this method reduces the execution time by an average of 3%, indicative of proposed method effectiveness.

关键词： Artificial intelligence Runner root algorithm Multi-objective product portfolio problem Robust optimization exact solution algorithm

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Stronger K-tree relaxations for the vehicle routing problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2004年第1期158卷 56-71页

作者： Martinhon, C Lucena, A Maculan, N Univ Fed Fluminense Inst Computacao Dept Ciencia Computacao BR-24210240 Niteroi RJ Brazil Univ Fed Rio de Janeiro Dept Adm BR-22290240 Rio De Janeiro Brazil Univ Fed Rio de Janeiro COPPE Programa Engn Sistemas & Computacao BR-21945970 Rio De Janeiro Brazil

A Lagrangian based exact solution algorithm for the vehicle routing problem (VRP), defined on an undirected graph, is introduced in this paper. Lower bounds are obtained by allowing exponentially many inequalities as candidates to Lagrangian dualization. Three different families of strong valid inequalities (each one with exponentially many elements) are used within VRP formulations. For each of them, separation procedures are proposed for points that define incidence vectors of K-trees. Violated inequalities identified in this way are then dualized in a relax and cut framework. Upper bounds are generated through a Lagrangian Clarke and Wright heuristic. A variable fixation test based on (approximating) linear programming reduced costs, is also implemented. Computational results are presented for the proposed algorithm. (C) 2003 Elsevier B.V. All rights reserved.

关键词： integer programming exact solution algorithm vehicle routing problem relax and cut

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