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multi-objective linear fractional inventory problem under intuitionistic fuzzy environment

INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2019年第2期10卷 173-189页

作者： Ali, Irfan Gupta, Srikant Ahmed, Aquil Aligarh Muslim Univ Dept Stat & Operat Res Aligarh UP India

In this paper, we have assumed an inventory multi-objective optimization model under intuitionistic fuzziness. In modelling, we have considered the situations where triangular intuitionistic fuzzy numbers used to express some of the input information which associated with decision variables. Further, a ranking function approach by considering linear and the nonlinear degree of membership functions have been used to obtain the crisp form of the fuzzy parameters. Finally, the fuzzy goal programming approach has been used to solve the resultant model to obtain the optimal ordering quantity. Also, a comparative study of the formulated problem under intuitionistic fuzziness has been done with a deterministic model of inventory. The concept of the paper is explained through a numerical example.

关键词： Inventory problem multi-objective programming Linear fractional programming Intuitionistic fuzzy number Fuzzy goal programming

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multi-objective optimization of complex scheduling problems in low-volume low-variety production systems

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2019年 208卷 1-16页

作者： Russell, Arya Taghipour, Sharareh Ryerson Univ 350 Victoria St Toronto ON M5B 2K3 Canada

In this paper, a new approach for solving scheduling problems in low-volume low-variety production systems is proposed. Products assembled in such production systems follow a pre-defined processing order through a series of unique work centers, each budgeted with multiple classifications of resources, responsible to complete a pre-defined statement of work, over the span of an imposed takt-time. Aircraft, heavy aero-structures, and heavy mining and military equipment are examples of products assembled in such production systems. Despite prominent scholarly advancements in sequencing and scheduling optimization of a wide range of production systems, limited research has been reported on mathematical programming approaches for scheduling optimization of activities in low-volume low-variety production systems. This paper fills the gap in the current literature, through the formulation of a set of multi-objective mixed-integer linear mathematical programming models, developed for solving discrete-time work center scheduling problems in low-volume low-variety production systems. Three mathematical models are proposed in this paper, two of which are formulated for scheduling optimization of activities within a work center, differentiated by their objectives and underlying assumptions, reflective of two distinct industrial approaches to scheduling. Additionally, an alternative optimization model is proposed for evaluating a work center's maximum capacity given the complete saturation of resources, recommended for capacity studies and early detection of bottlenecks. The models proposed in this paper are validated and verified for compatibility and reliability through a real-world case study with a global leader in the aerospace industry.

关键词： Low-volume low-variety production systems Large-scale optimization Scheduling optimization multi-objective programming Mixed-integer programming Aerospace assembly line

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Deterministic approach for solving multi-objective non-smooth Environmental and Economic dispatch problem

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2019年第Jan.期104卷 880-897页

作者： Goncalves, Elis Balbo, Antonio Roberto da Silva, Diego Nunes Nepomuceno, Leonardo Baptista, Edrnea Cassia Soler, Edilaine Martins Unesp Univ Estadual Paulista Fac Engn FEB Dept Elect Engn BR-17033360 Bauru SP Brazil Unesp Univ Estadual Paulista FC Dept Math BR-17033360 Bauru SP Brazil Univ Sao Paulo Sao Carlos Sch Engn EESC Dept Elect Engn BR-13566590 Sao Carlos SP Brazil

The Environmental and Economic Dispatch Problem with Valve-Point loading effect representation (EEDP-VP) is a multi-objective, nonconvex and non-differentiable optimization problem. Due to these difficulties, it has been solved in the literature mainly by heuristic approaches, while deterministic approaches are scarce. Therefore, the main objectives of this paper are to propose a deterministic approach for solving this problem and compare its solutions with the ones obtained by some heuristic and deterministic approaches. The deterministic approach proposed has the following features: the multi-objective nature of the problem is handled by the Progressive Bounded Constraints (PBC) strategy, while the modified logarithmic barrier function method is used to solve the subproblems resulting from the PBC strategy;a smoothing technique is used to handle non-differentiability issues, while the inertia correction strategy is used so that only descent directions are generated. The methodology is applied to five generation systems and the results show that the Pareto-curve is obtained more efficiently when compared to other heuristic and deterministic optimization approaches.

关键词： Economic/environmental dispatch multi-objective programming Modified logarithmic barrier function Interior/exterior-point Smoothing functions methods

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Group fuzzy comprehensive evaluation method under ignorance

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 126卷 92-111页

作者： Du, Yuan-Wei Wang, Su-Su Wang, Ying-Ming Ocean Univ China Management Coll Qingdao 266100 Shandong Peoples R China Fuzhou Univ Decis Sci Inst Fuzhou 350116 Fujian Peoples R China Minist Educ Marine Dev Studies Inst OUC Key Res Inst Humanities & Social Sci Univ Qingdao 266100 Shandong Peoples R China

This paper aims at solving such a group fuzzy comprehensive evaluation (FCE) problem that the global or local ignorance may exist in judgments made by experts and the importance degrees of experts are different. The basic probability assignment (BPA) function is used to extract the expert's judgment information and the super fuzzy relationship matrices consisting of the individual type and the general type are constructed by Shafer's discounting and Dempster's rule. Then each type of super fuzzy relationship matrix is combined with factor weight set via a specified fuzzy operator and the comprehensive evaluation result that is a belief distribution on the power set of grade levels is obtained. A multi-objective programming model is established to compute the optimal belief distribution on each grade level and an algorithm is summarized to derive the final grade level that the evaluated alternative belongs to. Moreover, the numerical comparisons between the proposed method and relevant existing methods are given to clarify the advantages of the proposed method. Finally, an illustrative example is provided to demonstrate the applicability of the proposed method and algorithm. It is worth noting that the proposed method can be easily converted into a core algorithm, which is benefit for developing fuzzy expert system from the perspective of ignorance, and thus it has an important impact and significance on expert and intelligent systems. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Fuzzy comprehensive evaluation Group decision making Shafer's discounting Dempster's rule Super fuzzy relationship matrix multi-objective programming

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Mathematical analyses of ecological and economic tradeoffs in irrigated agriculture based on inexact optimization principles and hierarchical crop projections

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JOURNAL OF CLEANER PRODUCTION 2019年 235卷 69-84页

作者： Zhang, Shan Tan, Qian Cai, Yanpeng Zhang, Tong Song, Ge China Agr Univ Coll Water Resources & Civil Engn Beijing 100083 Peoples R China Guangdong Univ Technol Inst Environm & Ecol Engn Guangzhou 510006 Guangdong Peoples R China Beijing Normal Univ State Key Lab Water Environm Simulat Beijing 100875 Peoples R China

An entropy-based multi-objective interval stochastic programming (EMISP) approach based on interval hierarchical projection, interval information entropy, interval analysis and chance-constrained programming was developed for supporting agricultural water management. EMISP improved upon existing methods through measuring and balancing ecological and economic benefits of irrigated croplands under uncertainty. To demonstrate its applicability, the developed method has been applied to an arid Chinese watershed. The results suggested that grain crops would produce higher ecological benefits than economic crops. A series of benefit- and risk-explicit plans for agricultural water use were generated, which indicated that the relaxation of water availability constraints would generate higher comprehensive benefits, but increase the risks of system infeasibility. The results from the EMISP model were also compared to those from four potential alternative models. These comparisons revealed that EMISP could achieve higher water productivity and comprehensive benefits with controllable water-shortage risks. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： multi-objective programming Information entropy Uncertainty analysis Mathematical optimization Agricultural water management Ecological functions

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Solving an ammunition distribution network design problem using multi-objective mathematical modeling, combined AHP-TOPSIS, and GIS

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 129卷 512-528页

作者： Akgun, Ibrahim Erdal, Hamit Abdullah Gut Univ Dept Ind Engn Fac Engn TR-38080 Kayseri Turkey Ataturk Univ Inst Social Sci Erzurum Turkey

We study a strategic-level ammunution distribution network design problem (ADNDP) where the purpose is to determine the locations and the service assignments of main, regional, and local depots in order to meet the ammunition needs of military units considering several factors, e.g., stock levels at the depots, costs, and risk levels of depot locations. ADNDP is a real-world and large-scale problem for which scientific decision making methods do not exist. We propose a methodology that uses multi-objective mathematical modeling, Analytic Hierarchy Process (AHP), The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Geographic Information System (GIS) to solve the problem. The multi-objective mathematical model determines the locations and the service assignments of depots considering two objectives, namely, to minimize transportation costs and to minimize risk scores of main depot locations. The risk score of a depot location indicates how vulnerable the location is to disruptions and is determined by a combined AHP-TOPSIS analysis where TOPSIS is used to compute the risk scores and AHP is used to compute the weights needed by TOPSIS for the identified risk attributes. The GIS analysis is conducted to determine the potential depot locations using map layers based on spatial criteria. We have applied the proposed methodology in designing and evaluating a real ammunition distribution network under different scenarios in collaboration and cooperation with the area experts. We have employed the weighted-sum method to find non-dominated solutions for each scenario and discussed their tradeoffs with the area experts. The purpose of this paper is to present the proposed methodology, findings, and insights.

关键词： Distribution network design Military Ammunition Geographic information systems multi-criteria decision making multi-attribute decision making AHP TOPSIS multi-objective programming

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AN INTEGRATED FUZZY ANP-MOP APPROACH FOR PARTNER SELECTION PROBLEM AND ORDER ALLOCATION OPTIMIZATION: THE CASE OF VIRTUAL ENTERPRISE CONFIGURATION

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RAIRO-OPERATIONS RESEARCH 2019年第1期53卷 223-241页

作者： Ben Salah, Souhir Ben Yahia, Wafa Ayadi, Omar Masmoudi, Faouzi Sfax Univ Natl Engn Sch Sfax ENIS Mech Sfax 3038 Tunisia Univ Gabes Natl Engn Sch Gab ENIG Gabes 6072 Tunisia Sfax Univ Natl Engn Sch Sfax ENIS Mech Modelling & Prod Res Lab LA2MP BP 1173-3038 Sfax Tunisia

Virtual networked enterprises have become a necessity for organizations to integrate better with the highly competitive markets in the 21st century. Virtual Enterprise (VE) depends on the performance of its members. Hence, the development of an effective methodology for the evaluation and selection of the suitable partners is a crucial issue toward forming a successful VE. The aim of this paper is to address the partner selection problem and order allocation optimization for the VE creation from Virtual organizations Breeding Environments (VBE) to respond to a specific Business Opportunity (BO). In this context, an exhaustive list of evaluation criteria is identified. A two-stage methodic approach is also proposed to make an objective evaluation for selecting the suitable partners of a VE with considering the BO requirements. In the first stage, a fuzzy analytical network process is used to determine the weights of the partner evaluation criteria. In the second stage, the obtained criteria weights are incorporated into a multi-objective programming model, which is solved using Weighted Sum Method to identify the appropriate VE configuration. Two illustrative examples artificially designed as well as a real case study from a network in textile sector are presented to demonstrate the effectiveness of the developed approach.

关键词： Virtual Enterprise partner selection order allocation Fuzzy ANP multi-objective programming

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A stochastic multi-objective optimization model for renewable energy structure adjustment management - A case study for the city of Dalian, China

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ECOLOGICAL INDICATORS 2019年 97卷 476-485页

作者： Meng, Na Xu, Ye Huang, Guohe North China Elect Power Univ Coll Environm Sci & Engn MOE Key Lab Reg Energy & Environm Syst Optimizat Beijing 102206 Peoples R China Univ Regina Fac Engn Regina SK Canada

Due to the depletion of traditional resources and the deterioration of environmental quality, Dalian has always encouraged the explorations and utilizations of renewable energy sources. The research objective of this study is to develop a multi-objective stochastic chance constrained programming (MOSCCP) model for assisting local government to design and execute rational energy exploration and management strategies. The main advantage of this model is that it effectively broadens the decision maker's choice space and provides a more balanced optimization plan. A variety of renewable energy exploration schemes were identified under the reciprocal influence of different weight combinations and constraints-satisfaction levels. The calculation of power generation considering the variation in the meteorological factors was incorporated into the proposed optimization model, which effectively avoid the potential imbalance between electricity supply and demand under climate change. The obtained results verified that the abundant renewable energy sources in Dalian play an important complementary role to traditional energy, where the wind and solar energy always occupy a dominant position in the renewable energy utilization process with an optimal mix of around 83.6% of wind, 10.5% of solar, 5.8% of hydropower and 0.1% of biomass energy generation. In addition, the climate change scenario certainly altered the electricity demand and provision magnitudes, but did not markedly change the core role of wind and solar energy.

关键词： Renewable energy structure Stochastic optimization multi-objective programming Climate change Dalian

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Duality-based branch-bound computational algorithm for sum-of-linear-fractional multi-objective optimization problem

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SOFT COMPUTING 2019年第1期23卷 197-210页

作者： Agarwal, Deepika Singh, Pitam Bhati, Deepak Kumari, Saru Obaidat, Mohammad S. Motilal Nehru Natl Inst Technol Allahabad Dept Math Allahabad Uttar Pradesh India Univ Delhi Dept Math Shyama Prasad Mukharji Coll New Delhi India Chaudhary Charan Singh Univ Dept Math Meerut Uttar Pradesh India Nazarbayev Univ ECE Dept Astana Kazakhstan Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan

Optimizing the sum-of-fractional functions under the bounded feasible space is a very difficult optimization problem in the research area of nonlinear optimization. All the existing solution methods in the literature are developed to find the solution of single-objective sum-of-fractional optimization problems only. Sum-of-fractional multi-objective optimization problem is not attempted to solve much by the researchers even when the fractional functions are linear. In the present article, a duality-based branch and bound computational algorithm is proposed to find a global efficient (non-dominated) solution for the sum-of-linear-fractional multi-objective optimization (SOLF-MOP) problem. Charnes-Cooper transformation technique is applied to convert the original problem into non-fractional optimization problem, and equivalence is shown between the original SOLF-MOP and non-fractional MOP. After that, weighted sum method is applied to transform MOP into a single-objective problem. The Lagrange weak duality theorem is used to develop the proposed algorithm. This algorithm is programmed in MATLAB (2016b), and three numerical illustrations are done for the systematic implementation. The non-dominance of obtained solutions is shown by comparison with the existing algorithm and by taking some feasible solution points from the feasible space in the neighborhood of obtained global efficient solution. This shows the superiority of the developed method.

关键词： multi-objective programming Sum-of-ratio multi-objective linear fractional programming Duality Branch and bound

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A novel multi-objective model for green forward and reverse logistics network design

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JOURNAL OF CLEANER PRODUCTION 2019年 208卷 1304-1316页

作者： Zarbakhshnia, Navid Soleimani, Hamed Goh, Mark Razavi, Seyyedeh Sara IAU Qazvin Branch Young Researchers & Elites Club Qazvin Iran IAU Qazvin Branch Fac Ind & Mech Engn Dept Ind Engn Qazvin Iran Natl Univ Singapore NUS Business Sch Singapore Singapore Natl Univ Singapore Logist Inst Asia Pacific Singapore Singapore Islamic Azad Univ Qazvin Branch Dept Ind Management Fac Management & Accounting Qazvin Iran

With a greater awareness of responsibility for the environment and the need to sustain profitability in a competitive market, reverse logistics has become a key part of supply chain management. This paper therefore seeks to consider the designing and planning of a green forward and reverse logistics network, through a mixed integer linear programming model. The model is applied to a multi-stage, multiproduct, and multi-objective problem whereby the first objective is to minimize the cost of operations, processes, transportation, and fixed costs of the establishment. The second objective is to minimize the amount of CO2 emissions based on the gram unit, while the third is to optimize the number of machines in the production line. For validation, the model is applied to the home appliance industry through several test problems. In terms of the solution methodology, an epsilon-constraint method is developed as the area of optimization in order to obtain a set of Pareto solutions. Finally, sensitivity analysis is conducted to understand the effects of changes in the demand, cost, and rate of return of the used product, on the objective function values. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Supply chain management Green forward and reverse logistics multi-objective programming CO2 emissions Epsilon-constraint method

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