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Integrated retail decisions with multiple selling periods and customer segments: optimization and insights

OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2015年 55卷 38-52页

作者： Ghoniem, Ahmed Maddah, Bacel Univ Massachusetts Isenberg Sch Management Dept Operat & Informat Management Amherst MA 01003 USA Amer Univ Beirut Engn Management Program Beirut 11072020 Lebanon

Integrating retail decisions on such aspects as assortment, pricing, and inventory greatly improves profitability. We examine a multi-period selling horizon where a retailer jointly optimizes assortment planning, pricing, and inventory decisions for a product line of substitutable products, in a market with multiple customer segments. Focusing on fast-moving retail products, the problem is modeled as a mixed-integer nonlinear program where demand is driven by exogenous consumer reservation prices and endogenous assortment and pricing decisions. A mixed-integer linear reformulation is developed, which enables an exact solution to large problem instances (with up to a hundred products) in manageable times. Empirical evidence is provided in support of a classical deterministic maximum-surplus consumer choice model. Computational results and managerial insights are discussed. We find that the optimal assortment and pricing decisions do not exhibit a simple, intuitive structure that could be analytically characterized, which reflects the usefulness of optimization approaches to numerically identify attractive trade-offs for the decision-maker. We also observe that suboptimal inventory policies significantly decrease profitability, which highlights the importance of integrated decision-making. Finally, we find that the seasonality of consumer preferences and supply costs present an opportunity for boosting the profit via higher inventory levels and wider assortments. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Assortment planning Pricing and inventory Dynamic lot-sizing optimization modeling Mathematical programming

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Data-driven optimization model customization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第2期287卷 438-451页

作者： Hewitt, Mike Frejinger, Emma Loyola Univ Quinlan Sch Business Chicago IL 60611 USA Univ Montreal CIRRELT Montreal PQ Canada Univ Montreal Dept Comp Sci & Operat Res Montreal PQ Canada

When embedded in software-based decision support systems, optimization models can greatly improve organizational planning. In many industries, there are classical models that capture the fundamentals of general planning decisions (e.g., designing a delivery route). However, these models are generic and often require customization to truly reflect the realities of specific operational settings. Yet, such customization can be an expensive and time-consuming process. At the same time, popular cloud computing software platforms such as Software as a Service (SaaS) are not amenable to customized software applications. We present a framework that has the potential to autonomously customize optimization models by learning mathematical representations of customer-specific business rules from historical data derived from model solutions and implemented plans. Because of the wide-spread use in practice of mixed integer linear programs (MILP) and the power of MILP solvers, the framework is designed for MILP models. It uses a common mathematical representation for different optimization models and business rules, which it encodes in a standard data structure. As a result, a software provider employing this framework can develop and maintain a single code-base while meeting the needs of different customers. We assess the effectiveness of this framework on multiple classical MILPs used in the planning of logistics and supply chain operations and with different business rules that must be observed by implementable plans. Computational experiments based on synthetic data indicate that solutions to the customized optimization models produced by the framework are regularly of high-quality. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Decision support systems Statistical learning Mixed integer linear programming optimization modeling

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optimization decomposition approach for layered QoS scheduling in grid computing

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JOURNAL OF SYSTEMS ARCHITECTURE 2007年第11期53卷 816-832页

作者： Chunlin, Li Layuan, Li Wuhan Univ Technol Dept Comp Sci Wuhan 430063 Peoples R China

The paper presents optimization decomposition based layered Quality of Service (QoS) scheduling for computational grid. Cross layer joint QoS scheduling is studied by considering the global problem as decomposed into three sub-problems: resource allocation at the fabric layer, service composing at the collective layer, and user satisfaction degree at the application layer. The paper proposes a complete solution from optimization modeling, Lagrange relaxation based decomposition, to solutions for each sub-problem Lagrange relaxation based decomposition. These aspects match the vertical organization of the considered grid system: each layer trade with adjacent layers to find a global optimum of the whole grid system. Through multi-layer QoS joint optimization approach, grid global QoS optimization can be achieved. The cross layer policy produces an optimal set of grid resources, service compositions, and user's payments at the fabric layer, collective layer and application layer, respectively, to maximize global grid QoS. The owner of each layer obtains inputs from other layers, tries to maximize its own utility and provides outputs back to other layers. This iterative process lasts until presumably all layers arrive at the same solution. (c) 2007 Elsevier B.V. All rights reserved.

关键词： optimization decomposition layered QoS scheduling grid computing optimization modeling

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optimization of calcined eggshell as an effective coagulant for treating colloidal particles in complex effluents in the CPCP industry: mechanistic insights, scale-up design, and comparative analysis with alum

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CHEMICAL ENGINEERING COMMUNICATIONS 2024年第12期211卷 1842-1863页

作者： Ugonabo, Victor Ifeanyi Ovuoraye, Prosper Eguono Igwegbe, Chinenye Adaobi Balogun, Philip A. Egwuatu, Christopher A. Nnamdi Azikiwe Univ Dept Chem Engn Awka 5025 Nigeria Fed Univ Petr Resources Dept Chem Engn Effurun 1221 Nigeria Wroclaw Univ Environm & Life Sci Dept Appl Bioecon Wroclaw Poland Ahmadu Bello Univ Zaira Dept Civil Engn Zaria Nigeria

Highly turbid industrial effluent poses significant challenges to environmental sustainability. In this study, we investigated the optimal aggregation kinetics for the removal of colloidal and turbid particles in Cosmetics and Personal Care Products (CPCP) industrial effluent using calcined eggshell (CES) and industrial-grade aluminum sulfate as flocculants. The effluent characteristics were determined following ASTM specifications, and process optimization was carried out using a response surface design approach. The optimal operating conditions at a settling time of 3 min were found to be pH 8, 0.2 g/L dosage of CES, pH 10, and 0.3 g/L of alum dosage. The flocculation efficiencies of CES and alum were found to be 80% and 78%, respectively, demonstrating the effectiveness of both flocculants in removing colloidal particles and reducing turbidity in CPCP effluent. Mechanistic studies revealed that CES achieved particle removal through enmeshment and inter-particle bridging with a significant aggregation rate (1.3 x 10-7) and a lower floc breakup tendency (2.8 x 10-14) compared to alum-sweep flocculation. The hydrodynamics at a velocity gradient of 269s-1 <= G <= 29s-1 indicated spontaneous agglomeration within a flocculation period of <= 10 min, indicating rapid and efficient particle removal. The findings of this study justify the use of CES as a sustainable and effective option for the removal of colloid/turbid particles from CPCP effluent. The optimized operating conditions and mechanistic insights into the flocculation process contribute to establishing an optimized sedimentation geometry, with specified aspect ratio, detention time, hypothetical settling, and sludge depth for CES, ensuring the favorable stability of the finished CPCP effluent before discharge.

关键词： optimization modeling flocculation treatment time evolution kinetics industrial effluent sedimentation geometry

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OPTIMAL ALLOCATION OF RENEWABLE ENERGY PARKS: A TWO-STAGE optimization MODEL

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RAIRO-OPERATIONS RESEARCH 2013年第2期47卷 125-150页

作者： Gervet, Carmen Atef, Mohammad German Univ Cairo New Cairo City 11835 Egypt

Applied research into Renewable Energies raises complex challenges of a technological, economical or political nature. In this paper, we address the techno-economical optimization problem of selecting locations of wind and solar Parks to be built in Egypt, such that the electricity demand is satisfied at minimal costs. Ultimately, our goal is to build a decision support tool that will provide private and governmental investors into renewable energy systems, valuable insights to make informed short and longer term decisions with respect to park creation and placements. Existing approaches have essentially focused on past data to tackle variations of this problem. In this paper, we introduce a novel approach that considers both past and forecast data, and show the impact for accounting for both sets of data and constraints in a two-stage optimization model. We first show that integer linear programming is best suited to solve the past data model compared to Dynamic Programming and Constrained Local Search. We then introduce our two-stage model that accounts for forecast data as well, adding new constraints to the initial model. Our empirical results show that the two-stage model improves solution quality and overall costs, and can be solved effectively to optimality using Integer Linear Programming.

关键词： optimization modeling constraint-based reasoning park placement problem renewable energy economics

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An integrated supply-demand model for the optimization of energy flow in the urban system

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JOURNAL OF CLEANER PRODUCTION 2016年 114卷 269-285页

作者： Farzaneh, Hooman Doll, Christopher N. H. Puppim de Oliveira, Jose A. United Nations Univ Inst Adv Study Sustainabil Tokyo Japan Natl Inst Environm Studies Tsukuba Ibaraki Japan Univ Fed Rio de Janeiro Inst COPPEAD Adm COPPEAD BR-21941 Rio De Janeiro Brazil FGV Sao Paulo Sch Business Adm FGV EAESP Sao Paulo Brazil Brazilian Sch Publ & Business Adm FGV EBAPE Sao Paulo Brazil MIT UTM Malaysia Sustainable Cities Program Johor Baharu Malaysia

This research aims to develop a bottom-up integrated supply-demand model to assess the optimal performance of urban energy systems. To this end, an optimization model founded on the principles of microeconomics was developed and deployed using mathematical programming. In this model, the urban energy system is treated as an economic actor in the market seeking to establish an effective energy system to improve its overall resource efficiency with minimum total cost of the system. The model leads the system to achieve supply and demand energy commitments which include alternative energy and energy efficiency targets. In this paper, we apply the model to address the electricity deficiency in Delhi, India, as a case study. The results suggest that the saving at the end-user level could reach about 220 GWh per annum in the near future through improving energy end-use efficiency in the domestic sector. Besides this, the installation of about 40 MW from waste-to-electricity plants and generating approximately 210 GWh electricity from the rooftop solar PV by 2030 could enable a sufficient surplus for the power supply sector to meet the city's electricity demand in the near future. Even though the system has some inherent limitations stemming from the assumptions of microeconomics and challenges related to data needs, the model can help the local actors, from governments to property owners, to find the best solutions for their energy needs. Such a modeling framework could address an organization's sustainable performance at the urban level through the resource use optimization, minimization of waste, cleaner technologies and pollution limits which are used in achieving co-benefits and a broad range of Eco-Industrial Development (EID) goals. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Urban energy system optimization modeling Cost-effective Electricity deficiency Co-benefits Delhi

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optimization Model of Raw Material Selection Process for Complex Industry Based on Improved Sequential Quadratic Programming Algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2022年第1期15卷 1-13页

作者： He, Xiuli Zhang, Yang Hong, Mengna Li, Jigeng South China Univ Technol State Key Lab Pulp & Paper Engn Guangzhou 510640 Peoples R China

Raw materials for industrial production mainly come from nature with wide sources and uneven quality, while production requires strictly with the materials' quality and their quality indicators are very mutual coupling. In actual production, unreasonable combination and formula often exist in the process of raw material selection, impeding the improvement of production efficiency, quality, energy-saving, and emission reduction. Unfortunately, research on this actual issue is currently vacant, so the main object of this paper is to address the selection of raw materials and formulations in actual production. Based on the designed logical combination algorithm and improved SQP algorithm, this study established the batch combination model and formula calculation model from the perspective of optimal combination and formula. Finally, a practical example of actual production is given to demonstrate the utility and application of this research.

关键词： Industrial production Raw material optimization modeling Improved SQP algorithm Glass industry Carbon emission

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optimization of non-catalytic transesterification of tobacco (Nicotiana tabacum) seed oil using supercritical methanol to biodiesel production

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ENERGY CONVERSION AND MANAGEMENT 2017年 131卷 99-108页

作者： Garcia-Martinez, Nuria Andreo-Martinez, Pedro Quesada-Medina, Joaquin Perez de los Rios, Antonia Chica, Antonio Beneito-Ruiz, Ruben Carratala-Abril, Juan Univ Murcia Fac Chem Dept Chem Engn Campus Espinardo E-30071 Murcia Spain Univ Politecn Valencia CSIC Inst Tecnol Quim Ave Naranjos S-N E-46022 Valencia Spain Asociac Invest Ind Juguete Conexas & Afines Ave Ind 23 Alicante 03440 Spain

The biodiesel production from non-edible oils has high potential as renewable and ecological fuel. Few researches have been conducted to date on the production of biodiesel from tobacco (Nicotiana tabacum) seed oil. The aim of this study was to optimize the biodiesel production from this crude oil by non catalytic supercritical methanolysis using response surface methodology (RSM). Triglyceride conversion, total and individual FAME yield, monoglyceride and diglyceride yield, and thermal decomposition degree of biodiesel were determined in the temperature and reaction time ranges of 250-350 degrees C (12-43 MPa) and 15-90 min, respectively, at a fixed methanol-to-oil molar ratio of 43:1. According to the RSM, the optimal conditions were 303.4 degrees C and 90 min, reaching a predicted maximum FAME yield of 91.1 +/- 3.2 mol%. This maximum was very close to that obtained experimentally (92.8 +/- 2.1 mol%) at 300 degrees C and 90 min. Decomposition of biodiesel became evident at 325 degrees C and 60 min of reaction due to the thermal instability of unsaturated methyl esters (methyl linoleate and oleate). The biodiesel obtained in the best experimental reaction conditions (300 degrees C and 90 min), where no thermal decomposition of FAMEs was observed, contained most of the byproduct glycerol generated, which was degraded and incorporated to the product. This biodiesel basically failed to meet the content of FAMEs as required by the standard EN 14214, the content of monoglycerides and total glycerol, and the acid value, being a little above the required upper limits. However, this biodiesel can be blended with other biodiesels of complementary features to produce a biofuel meeting the requirements of the standard EN 14214. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Supercritical methanol Tobacco seed oil Biodiesel production optimization modeling Thermal decomposition

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Multi-product multi-period fixed charge transportation problem: An ant colony optimization approach

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IFAC-PapersOnLine 2019年第13期52卷 1937-1942页

作者： Panicker, Vinay V. Sarin, I.V. Department of Mechanical Engineering National Institute of Technology Calicut Kerala673601 India

Most of the practical applications of a transportation network, in addition to the variable cost, there incurs a fixed charge. This work formulates a Multi-Stage Multi-Period Fixed Charge Transportation Problem for a multi-product scenario. The problem is modeled using an optimization modeling tool, ‘A Mathematical Programming Language' and its solution is obtained in BONMIN solver. The exact algorithms mostly require longer computational time to find an optimal solution for large problem size that are in practice. In these operational problems, in which process speed is as important as the solution quality, an Ant Colony optimization based heuristic is proposed. Finally, the solution obtained from proposed heuristic is compared with that of exact methods using randomly generated data sets. The comparative analysis shows the competitiveness of the proposed heuristic. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Heuristic methods Ant colony optimization Mathematical programming modeling languages Models Problem oriented languages Comparative analysis Computational time Fixed charge transportation Multi period Operational problems optimization modeling Transportation network Transportation logistics

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Simulation-based optimization approach with scenario-based product sequence in a reconfigurable manufacturing system (RMS): A case study

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IFAC-PapersOnLine 2019年第13期52卷 2638-2643页

作者： Petroodi, S. Ehsan Hashemi Dit Eynaud, Amélie Beauville Klement, Nathalie Tavakkoli-Moghaddam, Reza IMT Atlantique LS2N - UMR CNRS 6004 La Chantrerie 4 rue Alfred Kastler Nantes Cedex 3F-44307 France LISPEN Arts et Métiers HeSam. Ecole Nationale Supérieure d'Arts et Métiers. 8 boulevard Louis XIV Lille59046 France Groupe PSA Route de Gisy Vélizy-Villacoublay Cedex78943 France School of Industrial Engineering College of Engineering University of Tehran Tehran Iran

In this study, we consider a production planning and resource allocation problem of a Reconfigurable Manufacturing System (RMS). Four general scenarios are considered for the product arrival sequence. The objective function aims to minimize total completion time of jobs. For a given set of input parameters defined by the market, we want to find the best configuration for the production line with respect to the number of resources and their allocation on workstations. In order to solve the problem, a hybridization approach based on simulation and optimization (Sim-Opt) is proposed. In the simulation phase, a Discrete Event Simulation (DES) model is developed. On the other hand, a simulated annealing (SA) algorithm is developed in Python to optimize the solution. In this approach, the results of the optimization feed the simulation model. On the other side, performance of these solutions are copied from simulation model to the optimization model. The best solution with the best performance can be achieved by this manually cyclic approach. The proposed approach is applied on a real case study from the automotive industry. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Manufacture Automotive industry Computer aided manufacturing Computer software Discrete event simulation Production control Simulated annealing optimization modeling Reconfigurability Reconfigurable manufacturing system Resource allocation problem Simulated annealing algorithms Simulation and optimization Simulation based optimizations Total completion time

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