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Modeling and heuristics for production time crashing in supply chain network design

ANNALS OF OPERATIONS RESEARCH 2020年第1期288卷 331-361页

作者： Liao, Yi Diabat, Ali Alzaman, Chaher Zhang, Yiqiang Southwestern Univ Finance & Econ Sch Business Adm Chengdu 610074 Sichuan Peoples R China New York Univ Abu Dhabi Div Engn Abu Dhabi 129188 U Arab Emirates NYU Tandon Sch Engn Dept Civil & Urban Engn Brooklyn NY 11201 USA Concordia Univ Dept Supply Chain & Business Technol Management John Molson Sch Business Montreal PQ H3H 0A1 Canada Shanghai Lixin Univ Accounting & Finance Xulun Honor Sch Shanghai 201209 Peoples R China

Supply chains with shorter lead times can bring their constituents cost reductions, flexibility, and speed. Since manufacturing is a prominent operation within the supply chain, the reduction of its time duration can prove important in reducing the overall supply chain's lead-time. Some works in the area of supply chain network design (SCND) have looked at the crashing of supply chain's lead-time. However, the literature lacks works that explicitly model a crashing cost in SCND. The work formulates a cost model that integrates production, crashing, inventory, transportation, and plant selection. Given the complexity of these elements, the model emerging is a nonlinear and binary in both the objective function and the constraints. The paper introduces a gradient search method to solve the model coupled with efficient search heuristics. The work presents seven search heuristics along with variants to solve the difficult problem at hand. Further, the work looks at different parameters that affect the crashing cost, presents the cost avoidances that can result from crashing, and discusses the operational opportunities to be reaped.

关键词： Supply chain network design Manufacturing Resource allocation Gradient search nonlinear mixed integer programming Local search heuristic

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Ship's response to low-sulfur regulations: From the perspective of route, speed and refueling strategy

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 155卷 107140-107140页

作者： Ma, Dongfang Ma, Weihao Hao, Shunfeng Jin, Sheng Qu, Fengzhong Zhejiang Univ Ocean Coll Zhoushan Peoples R China Minist Educ Engn Res Ctr Ocean Sensing Technol & Equipment Zhoushan Peoples R China Zhejiang Univ Inst Intelligent Transportat Syst Hangzhou Peoples R China Key Lab Ocean Observat Imaging Testbed Zhejiang P Zhoushan Peoples R China

Optimizing speed, routing, and refueling strategy are three ways to reduce ship operating costs without modifying on ship. Most past studies have simultaneously optimized one or two variables, resulting in a local optimal solution. To limit sulfur emissions, some governments have established Emission Control Areas (ECAs) in recent years, where ships must use low sulphur fuels. The constraints created by ECAs makes the simultaneous optimization of the three variables even more challenging. This study developed a nonlinear mixed integer programming model to address the challenge of simultaneously optimizing the three variables. After transforming the nonlinear discrete mixed integer programming model into a convex nonlinear continuous programming model, the study proposed a solution algorithm for the proposed model. The new model in this study was applied to the Europe-United States (US) and Southeast Asia service route. The results show that the optimal refueling policy, route, and speed selection can be determined simultaneously using the proposed model and solution method. The study investigated the impact of fuel price on ship refueling strategy, route, and speed;and developed insights from the example analyses.

关键词： Route and speed planning Refueling strategy Simultaneously optimizing method nonlinear mixed integer programming Emission control areas

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A joint supplier selection and order allocation model with disruption risks in centralized supply chain

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 127卷 734-748页

作者： Esmaeili-Najafabadi, Elham Nezhad, Mohammad Saber Fallah Pourmohammadi, Hamid Honarvar, Mahboobeh Vandatzad, Mohammad Ali Yazd Univ Ind Engn Dept Yazd Iran Calif State Univ Dominguez Hills Dept ISOM Carson CA USA

Supplier's coordination plays a vital role in managing a supply chain. Organizations have learned that selecting the right suppliers and allocating right orders are the keys to success. The presence of disruption risks in the supply chain creates more complexity in the supplier selection and order allocation process. This paper presents a mixed-integer nonlinear programming model to optimize supplier selection and order allocation simultaneously in centralized supply chains considering disruption risks. In order to reduce disruption effects, two protective policies are taken into account. Protection policy for the selected suppliers and emergency inventory allocation policy for the protected suppliers are utilized in this study. The presented model proves to be non convex, indicating that, conventional methods are not capable of solving such a complex problem. The proposed method is exemplified by numerical instances. The results indicate that with an increase in the disruption probability the factors which effect the decisions related to supplier selection and order allocation are altered. At the low disruption probability, the disruption risk plays a key role in the decisions, while at the higher disruption probability, wholesale price and disruption risk are the dominant factors to form the decisions.

关键词： Supplier selection Disruption risk Pre-position inventory Protected supplier Supply chain coordination nonlinear mixed integer programming

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Operation optimizing for minimizing passenger travel time cost and operating cost with time-dependent demand and skip-stop patterns: nonlinear integer programming model with linear constraints

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TRANSPORTATION RESEARCH INTERDISCIPLINARY PERSPECTIVES 2021年 9卷

作者： Zhao, Junru Ye, Mao Yang, Zhiqiang Xing, Zongyi Zhang, ZiHan Nanjing Univ Sci & Technol Dept Transportat Engn Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Traff Informat Fus & Syst Control Lab Nanjing 210094 Peoples R China Guangzhou Metro Grp Co Ltd Guangzhou 510000 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

This paper focuses on how to minimize the total passenger travel time cost by computing and adjusting skip -sop patterns with given time-varying origin-to-destination passenger demand matrices. A bi-objective nonlin-ear integer programming model with linear constraints is proposed to precisely formulate the total travel time cost and operating cost under minute-dependent demand from the different origin-destination pairs. The pro-posed model is implemented by using the genetic algorithm with the idea point optimization solvers, and we show its effectiveness on the real world instance of Guangzhou Metro Line 8.

关键词： Urban rail transit Operation optimization Skip -stop pattern nonlinear mixed integer programming

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Supply chain network design with direct and indirect production costs: Hybrid gradient and local search based heuristics

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2018年 203卷 203-215页

作者： Alzaman, Chaher Zhang, Zhi-Hai Diabat, Ali Concordia Univ Dept Supply Chain & Business Technol Management John Molson Sch Business Montreal PQ H3H 0A1 Canada Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China New York Univ Abu Dhabi Div Engn Abu Dhabi 129188 U Arab Emirates NYU Tandon Sch Engn Dept Civil & Urban Engn Brooklyn NY 11201 USA

Many works in the area of Supply Chain Network Design (SCND) have integrated production costs as one lumped cost. However, production costs embody indirect (overhead) and direct costs. This work brings a model that integrates these two production costs along with inventory and transportation costs. In particular, the work looks at the tradeoff between the shortening of the production duration (Pd) and the consequent increase in direct production costs. The work implicitly incorporates direct production costs that are the result of applying additional production resources to shorten Pd. Integrating both costs results in a nonlinear mathematical model. The overall objective function also includes binary variables to govern the selection of production plants resulting in a nonlinear mixed integer mathematical model. In this work, we prove the convexity of the objective function and we introduce a combination of a gradient and a local search heuristic to solve the resulting model. For practical implications, the paper presents four different scenarios to cover a wide range of supply chain settings. Principally, our results demonstrate promising benefits of reduced overall production costs, reduced production lead time, freed-up production capacity, and improved supply chain throughput.

关键词： Supply chains Hybrid heuristics Gradient search nonlinear mixed integer programming

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On the Optimal Fixed-Up-To Pricing for Information Services

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JOURNAL OF THE ASSOCIATION FOR INFORMATION SYSTEMS 2019年第10期20卷 1447-1474页

作者： Wu, Shinyi Pavlou, Paul Arizona State Univ Informat Syst WP Carey Sch Business Tempe AZ 85287 USA Univ Houston CT Bauer Coll Business Houston TX 77004 USA

Fixed-up-to (FUT) pricing allows consumers to purchase a fixed usage amount of an information service for a certain fixed price chosen from a set of options. In this study, we derive an optimal analytical solution for FUT pricing without imposing the strong single-crossing assumption. Further, we illustrate the analytical solution by leveraging mixed integer nonlinear programming to derive an optimal FUT pricing scheme for information services and also investigate when and by how much FUT pricing improves upon commonly used "flat rate" pricing. Our numerical results show that FUT pricing improves the service provider's profits while enhancing social welfare when consumers face different maximum consumption-level bounds. Notably, in terms of optimal pricing, our numerical results show that the consumers' maximum consumption-level bounds are more important than their utility functions. Most importantly, our results show that FUT pricing performs better than flat rate pricing under conditions of incomplete information. Finally, we empirically show that it is not necessary to treat over-the-limit rates as a decision variable in terms of optimal FUT pricing since both FUT pricing and three-part tariffs are reasonable approximations of nonlinear pricing in terms of both firm profits and social welfare. We conclude with a discussion of theoretical and practical implications for the design of optimal FUT pricing in terms of enhancing firm profits, consumer surplus, and social welfare.

关键词： Pricing nonlinear mixed integer programming Information Services Fixed-Up-To (FUT) Pricing

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Optimization of Train Plan of Full-Length & Short-Turn Routing Mode Based on Data Analysis 3

Optimization of Train Plan of Full-Length & Short-Turn Routi...

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3rd IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Liu, Yansong Mao, Baohua Beijing Jiaotong Univ MOE Key Lab Urban Transportat Complex Syst Theory Beijing Peoples R China Beijing Jiaotong Univ Integrated Transportat Res Ctr China Beijing Peoples R China

ISBN: (纸本)9781538643013

According to the analysis of passenger travel behavior, the properties of the full-length & short-turn routing mode and huge amounts of AFC data processed by CLEMENTINE, a multi-objective nonlinear mixed integer programming optimization model of train plan for a single transit line is established. The objective functions are minimum passenger travel cost and enterprise operation cost. In order to validate the feasibility of the model, Matlab software can be used to figure out the solution to a case based on TOPSIS which can convert the above model into the single objective model. The validity of the model is verified by a case, and the sensitivities of upper limit of train load factor and positions of turn-back stations are also analyzed. The results indicate that compared with single routing mode, full-length & short-turn routing mode is better and the length of short-turn routing and train frequencies have a great impact.

关键词： full-length & short-turn routing mode AFC data nonlinear mixed integer programming

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A harmony search-based memetic optimization model for integrated production and transportation scheduling in MTO manufacturing

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2017年第PartB期66卷 327-343页

作者： Guo, Zhaoxia Shi, Leyuan Chen, Longchao Liang, Yong Sichuan Univ Sch Business Chengdu 610065 Peoples R China Univ Wisconsin Dept Ind & Syst Engn Madison WI 53706 USA Tsinghua Univ Sch Econ & Management Beijing 100084 Peoples R China

This paper investigates an integrated production and transportation scheduling problem in an MTO supply chain. A harmony search-based memetic optimization model is developed to handle this problem, in which certain heuristic procedures are proposed to convert the investigated problem into an order assignment problem. A novel improvisation process is also proposed to improve the optimum-seeking performance. The effectiveness of the proposed model is validated by numerical experiments. The experimental results show that (1) the proposed model can solve the investigated problem effectively and that (2) the proposed memetic optimization process exhibits better optimum-seeking performance than genetic algorithm-based and traditional memetic optimization processes. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Supply chain scheduling nonlinear mixed integer programming Harmony search Memetic algorithms

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rs-Sparse principal component analysis: A mixed integer nonlinear programming approach with VNS

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COMPUTERS & OPERATIONS RESEARCH 2014年第PartB期52卷 349-354页

作者： Carrizosa, Emilio Guerrero, Vanesa Univ Seville Inst Matemat Fac Matemat E-41012 Seville Spain

Principal component analysis is a popular data analysis dimensionality reduction technique, aiming to project with minimum error for a given dataset into a subspace of smaller number of dimensions. In order to improve interpretability, different variants of the method have been proposed in the literature, in which, besides error minimization, sparsity is sought. In this paper we formulate as a mixed integer nonlinear program the problem of finding a subspace with a sparse basis minimizing the sum of squares of distances between the points and their projections. Contrary to other attempts in the literature, with our model the user can fix the level of sparseness of the resulting basis vectors. Variable neighborhood search is proposed to solve the problem obtained this way. Our numerical experience on test sets shows that our procedure outperforms benchmark methods in the literature, both in terms of sparsity and errors. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Sparse principal component analysis Variable neighborhood search nonlinear mixed integer programming

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Treatment Planning for Volumetric-Modulated Arc Therapy: Model and Heuristic Algorithms

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2015年第1期12卷 116-126页

作者： Song, Jie Shi, Zhongshun Sun, Bofei Shi, Leyuan Peking Univ Dept Ind Engn & Management Beijing 100871 Peoples R China

In this paper, we study the radiation treatment planning optimization for Volumetric-Modulated Arc Therapy (VMAT). A nonlinear mixed integer programming model is formulated, then the linearization technique is used, and the resulting mixed integer programming model is solved by a heuristic approach based on the Nested-Partitions framework. The approach partitions the feasible region iteratively and constructs a feasible solution by solving the LP relaxation of the original problem. We design two partition strategies: partition by column and expansion from center of aperture. Numerical results with clinical cases show the efficiency of the proposed model and algorithm. Note to Practitioners-VMAT delivers a dose in a continuous manner which requires the dose intensity and aperture shape to be optimized simultaneously. The resulting treatment planning problem becomes very difficult to solve. In this paper, we develop a nonlinear mathematical model based on control points. The frequently used clinical setting such as dose-limit and dose volume constraints as well as multi-leaf collimator (MLC) constraints are considered in the model. We then use a linearization technique to transform the nonlinear treatment planning model to an equivalent mixed integer linear programming (MILP) model. The objective of the MILP model is a penalty function of target underdose and organ at risk (OAR) overdose. Based on the property of the MILP treatment planning problem, an effective heuristic method based on Nested-Partitions framework is developed to solve the MILP problem. The output of the model includes the dose intensity and aperture at each control point. As the prescription dose limit and feasibility of change between adjacent control points are ensured in the model, the output is deliverable through a planning system.

关键词： Heuristics nonlinear mixed integer programming radiation therapy planning volumetric-modulated arc therapy (VMAT)

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