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Sustainable supplier selection and order allocation: A novel multi-objective programming model with a hybrid solution approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 129卷 192-209页

作者： Moheb-Alizadeh, Hadi Handfield, Robert North Carolina State Univ Grad Program Operat Res Raleigh NC 27695 USA North Carolina State Univ Dept Business Management Coll Management Raleigh NC 27695 USA

Sustainable supplier selection is the process of identifying the appropriate supply partners of an organization with the most beneficial monetary value, while diminishing the various effects of its operations on society and environment. Therefore, it plays a significant role in moving an organization toward sustainable development. Alongside sustainable supplier selection, assigning proper quantity of orders to suppliers satisfying sustainability criteria is another activity that should be simultaneously carried out in order to develop a comprehensive tool in sustainable supplier management. Meanwhile, the issue of sustainable supplier selection and order allocation has attracted limited attention in the literature. The present paper aims at developing an inclusive multi-objective mixed integer linear programming model, which accounts for multiple periods, multiple products and multimodal transportation, to evaluate suppliers and allocate order quantities. Furthermore, the developed model that includes all sustainability aspects also takes both shortage and discount conditions into account. A hybrid three-step solution methodology is then presented, using which the original multi-objective problem is firstly converted to a single objective model by epsilon-constraint method. In the second step, the current single objective programming model is solved using the benders decomposition algorithm that in turn is accelerated by a variety of algorithmic enhancements. Finally, among all the Pareto optimal solutions of the original multi-objective programming problem, the preferable solution is intelligently selected based on the DEA super efficiency score of all purchasing firms as a decision support tool. The applicability of the proposed approach is illustrated by a real-world case study in automotive industry.

关键词： Supplier selection Order allocation Sustainability epsilon-constraint method benders decomposition algorithm Data envelopment analysis

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Adjustable robust balanced hub location problem with uncertain transportation cost

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COMPUTATIONAL & APPLIED MATHEMATICS 2021年第1期40卷 1-28页

作者： Rahmati, Reza Neghabi, Hossein Ferdowsi Univ Mashhad Dept Ind Engn Mashhad Razavi Khorasan Iran

In this paper, an adjustable robust optimization with a polyhedral uncertainty set is used to deal with uncertain transportation cost in an uncapacitated multiple allocation balanced hub location problem. Adjustable robust optimization is modeled as two-stage or multi-stage problems in which decisions are determined in two or multi-separated stages. In two-stage robust optimization, first, the location of hubs is determined in the absence of uncertain parameters;then, the second-stage decision determined flows path in the presence of uncertainty. Two new mathematical models are proposed for this problem with mixed-integer linear and non-linear structures. benders decomposition algorithm with stronger cut (Pareto-optimal cut) is used to solve proposed models. Adjustable robust models and accelerated benders decomposition algorithms are analyzed using well-known AP data set with different levels of uncertainty. Also, a size reduction method is introduced to solve medium and large instances with good solution quality and shorter computation time. The numerical experiment shows the superiority of the Pareto-optimal cut benders decomposition algorithm comparing with a classic one. Also, the mixed-integer non-linear model has better results in CPU time and the gap in comparison with the linear integer one. Flow balancing affects hub configuration with a decreasing number of hub facilities. Also by increasing the uncertainty budget, more hubs are established and with increasing discount factor, number of hub facilities are decreased.

关键词： Balanced hub location benders decomposition algorithm Adjustable robust optimization Uncertain transportation cost Pareto-optimal cut

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A multi-echelon multi-product stochastic model to supply chain of small-and-medium enterprises in industrial clusters

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 115卷 69-79页

作者： Kayvanfar, Vahid Husseini, S. M. Moattar Sajadieh, Mohsen S. Karimi, B. Amirkabir Univ Technol Dept Ind Engn 424 Hafez Ave Tehran 158754413 Iran

An Industrial Cluster (IC) is a set of similar and interrelated firms in a specific field situated in a geographic concentration to share joint resources. To date, the interrelations between ICs and Supply Chain Management (SCM) have been improperly mathematically studied, despite their inherent relationship. In this paper, a Supply Demand Hub in Industrial Clusters (SDHIC) as a specific common provider of warehousing and logistics activities managed by a third-party logistics provider (3PL) is proposed to minimize the total cost of the considered supply chain. Activities of businesses in IC is modeled thorough a two-stage stochastic programming model followed by an acceleration techniques for the benders decomposition. The numerical experiments comprising sensitivity analysis are conducted over a case study to show the attractiveness of the proposed model. Some managerial insights are presented based on the obtained results.

关键词： Stochastic supply chain benders decomposition algorithm Industrial Clusters (IC) Small and Medium Enterprises (SMEs)

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A computational framework and solution algorithms for two-stage adaptive robust scheduling of batch manufacturing processes under uncertainty

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AICHE JOURNAL 2016年第3期62卷 687-703页

作者： Shi, Hanyu You, Fengqi Northwestern Univ Dept Chem & Biol Engn Evanston IL 60208 USA

A novel two-stage adaptive robust optimization (ARO) approach to production scheduling of batch processes under uncertainty is proposed. We first reformulate the deterministic mixed-integer linear programming model of batch scheduling into a two-stage optimization problem. Symmetric uncertainty sets are then introduced to confine the uncertain parameters, and budgets of uncertainty are used to adjust the degree of conservatism. We then apply both the benders decomposition algorithm and the column-and-constraint generation (C&CG) algorithm to efficiently solve the resulting two-stage ARO problem, which cannot be tackled directly by any existing optimization solvers. Two case studies are considered to demonstrate the applicability of the proposed modeling framework and solution algorithms. The results show that the C&CG algorithm is more computationally efficient than the benders decomposition algorithm, and the proposed two-stage ARO approach returns 9% higher profits than the conventional robust optimization approach for batch scheduling. (c) 2015 American Institute of Chemical Engineers AIChE J, 62: 687-703, 2016

关键词： two-stage adaptive robust optimization batch manufacturing processes short-term scheduling problem benders decomposition algorithm column-and-constraint generation algorithm mixed-integer linear programming

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A production and distribution planning of perishable products with a fixed lifetime under vertical competition in the seller-buyer systems: A real-world application

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JOURNAL OF MANUFACTURING SYSTEMS 2021年第PartA期58卷 223-247页

作者： Aazami, Adel Saidi-Mehrabad, Mohammad Iran Univ Sci & Technol Dept Ind Engn Tehran Iran

Motivated by a real problem, in this paper, a new multi-period production-distribution planning (PDP) for perishable products with a fixed lifetime in a seller-buyer system is developed. The objective is to maximize the seller's profit conditional on the optimality of the buyer in a three-level supply chain (SC), including the factories, distribution centers, and retailers. The factories and distribution centers act cooperatively at the leader level and, as the seller, make the location, production, inventory, and distribution decisions. While the retailers operate as the follower;therefore, a vertical competition is held in this SC. In addition to price, freshness owing to the perishability of the product is considered as an important competitive factor. Also, to encourage the retailers, three strategies in the development of a new demand function, including the return, discount policies, and credit period, are employed. Since the problem is NP-hard, a hierarchical heuristic approach based on benders decomposition algorithm (BDA) and Genetic algorithm (GA) is proposed. Finally, to demonstrate the applicability and efficiency of the developed model, it is applied to a real industrial case. The computational results of the problems in different sizes confirm the efficient performance of the proposed model and solution method.

关键词： Production distribution planning Seller-buyer problem Perishable product Bi-level programming benders decomposition algorithm Genetic algorithm

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THERMAL ENERGY CONTROL IN BUILDING ENERGY SYSTEM

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THERMAL SCIENCE 2021年第4期25卷 3123-3131页

作者： Chen, Yuan Hetao Coll Bayaner City Inner Mongolia Peoples R China

There is usually a waste of energy consumption in building systems. To help buildings reduce energy waste, the article established a building-sharing heat and power energy sharing system to achieve optimal energy allocation. Furthermore, the report determined the dual operation strategy model of using heat energy to determine power supply and electricity to determine heat energy. At the same time, we use stochastic programming and multi-objective optimization of the heating model and propose a two-level optimization model solution method based on the benders decomposition algorithm. At the end of the thesis, the process was applied to actual cases to verify the method's effectiveness.

关键词： combined cooling thermal energy storage stochastic planning building energy system benders decomposition algorithm Pareto optimal solution set heating and power system

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A performance guarantee heuristic for electronic components placement problems including thermal effects

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COMPUTERS & OPERATIONS RESEARCH 2005年第11期32卷 2937-2957页

作者： Miranda, G Luna, HPL Mateus, GR Ferreira, RPM Univ Fed Minas Gerais Dept Ciencia Computac BR-31710970 Belo Horizonte MG Brazil

In this work, benders decomposition algorithm is used to deal with a computer motherboard design problem. Amongst all the possible fionnulations for the component placement problem, the chosen one creates an instance of the extensively studied Quadratic Ass cjninent Problem (QAP). This problem arises as a great challenge for engineers and computer scientists. The QAP inherent combinatorial structure makes the most efficient optimization algorithms to exhibit low performance for real size instances. It is also considered here the important addition of linear costs. This approach is directly responsible for the performance gain presented by our decomposition method. Coupled with the placement problem, it is under investigation the maximum temperature rising on the board surface. In order to solve the Energy Conduction Equation the Finite Volume Method is implemented, becoming possible to derive a secondary quality solution criterion. A set of test instances is then solved and the corresponding results are reported. (c) 2004 Elsevier Ltd. All rights reserved.

关键词： placement problems quadratic assignment problems combinatorial optimization benders decomposition algorithm electronics cooling

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Design of a reliable hierarchical location-allocation model under disruptions for health service networks: A two-stage robust approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 109卷 130-150页

作者： Zarrinpoor, Naeme Fallahnezhad, Mohammad Saber Pishvaee, Mir Saman Shiraz Univ Technol Dept Ind Engn Shiraz Iran Yazd Univ Dept Ind Engn Yazd Iran Iran Univ Sci & Technol Sch Ind Engn Tehran Iran

This study proposes a novel reliable hierarchical location-allocation model for health service network designs where facilities are subject to the risk of disruptions. To characterize the relationship between various levels of the concerned health network, a two-level multi-flow nested hierarchy with service referral is taken into account. The queuing system is considered in the model which handles uncertainty associated with demand as well as service. To ensure the service quality, a minimum limit reflected in the probability of patients' waiting time is defined. The proposed model is formulated based on a two-stage robust optimization approach in which decisions are defined in two stages such that a first-stage solution should be robust against the possible realizations of the disruption that can only be revealed in a second stage. A solution procedure based on benders decomposition type algorithm is implemented to solve the model. To illustrate the applicability of the proposed model, a practical case study is presented. The results show that the proposed model can be applied as a flexible decision support tool for decision makers to adopt appropriate strategies and policies in designing a reliable and robust health service network. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Hierarchical location-allocation model Health service network design Disruption Queuing theory Two-stage robust optimization benders decomposition algorithm

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Flexible two-stage robust model for moving the transmission and reactive power infrastructures expansion planning towards power system integration of renewables

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IET RENEWABLE POWER GENERATION 2020年第11期14卷 1921-1932页

作者： Olfatinezhad, Nader Vahidinasab, Vahid Ahmadian, Mohammad Arasteh, Hamidreza Aghaei, Jamshid Mehran, Kamyar Shahid Beheshti Univ Abbaspour Sch Engn Dept Elect Engn Tehran *** Iran Niroo Res Inst Power Syst Planning & Operat Grp Tehran Iran Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Queen Mary Univ London Sch Elect Engn & Comp Sci London England

This study proposes a two-stage robust optimisation framework to model uncertainties in construction cost, load and renewable power generations (RPGs) in the coordinated transmission and reactive power infrastructure expansion planning problem. The proposed planning model, based on the network AC model and its voltage constraints, is represented as a mixed-integer linear programing problem with the aim of minimising: (i) the construction costs of lines, (ii) installation cost of reactive power resources and (iii) active load shedding as RPGs curtailment. Using the suggested robust optimisation approach, the best expansion plan is obtained even in the worst case of the defined uncertainty sets and the plan robustness level is controlled via parameter definition for the individual uncertainty sets. The benders decomposition algorithm is used to solve the two-stage problem where the plan security level is also taken into consideration using theN-1 contingency criterion. The standard Garver 6-bus system and the IEEE 24-bus reliability test system are used to validate the proposed robust framework.

关键词： load shedding nonlinear programming power system security power transmission planning integer programming reactive power power generation planning power generation reliability power transmission reliability power system integration renewable power generations coordinated transmission network AC model mixed-integer nonlinear programing problem installation cost robust optimisation approach plan security level uncertainty sets reactive power infrastructure expansion planning benders decomposition algorithm load shedding RPG curtailment Garver 6-bus system

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A scenario-based stochastic programming approach for the product configuration problem under uncertainties and carbon emission regulations

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2018年 115卷 126-146页

作者： Li, Xiaohong Yang, Dong Hu, Mengqi Donghua Univ Sch Business & Management 1882 Yan An Rd Shanghai 200051 Peoples R China Univ Illinois Dept Mech & Ind Engn 842 W Taylor St Chicago IL 60607 USA

To handle the product configuration problem with uncertain supply and demand, stochastic programming approach is applied to formulate the problem as a stochastic mixed-integer programming model. Carbon emission is further integrated into the deployed stochastic model under four different carbon emission regulations. benders decomposition algorithm is utilized to solve the stochastic model. Computational studies show that the benders decomposition method can solve large-scale stochastic programming problems with faster convergence rate than commercial solver CPLEX does. The results from the numerical experimental analysis demonstrate the impacts of carbon emission regulations on product configuration decisions.

关键词： Product configuration Stochastic programming benders decomposition algorithm Carbon emission

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