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Decision-maker's Preferences Modelling within the Goal-programming Model: A New Typology

JOURNAL OF multi-CRITERIA DECISION ANALYSIS 2009年第5-6期16卷 163-178页

作者： Aouni, Belaid Hassaine, Amal Martel, Jean-Marc Laurentian Univ Sch Commerce & Adm Decis Aid Res Grp Fac Management Sudbury ON P3E 2C6 Canada Univ Tlemcen Fac Sci Econ & Gest Tilimsen Algeria Univ Laval Fac Sci Adm Ste Foy PQ Canada

Several classifications of the multiple objectives programming (MOP) models have been proposed in the literature. In general, these classifications are based on the timing of introducing the decision-maker's (DM) preferences and the type of the required information about the parameters of the decision-making situation. The DM's preference information can take different forms such as: weights, priority levels, thresholds or trade-offs among the objectives. The Goal programming (GP) is one of the well-known MOP models. The different GP formulations deal differently with the DM's preferences. The aim of this paper is to propose a new typology of the GP variants based on the way that the DM's preferences are considered. Copyright (C) 2010 John Wiley & Sons, Ltd.

关键词： goal programming multi-objective programming decision-maker's preferences typology

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objective programming Approach to Food Supply-chain Theory

Objective Programming Approach to Food Supply-chain Theory

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International Conference on Environmental Biotechnology and Materials Engineering

作者： Wang-Qiang Zhang-Yu Harbin Univ Commerce Sch Management Harbin Peoples R China Harbin Univ Commerce Sch Basic Sci Harbin Peoples R China

ISBN: (纸本)9783037850220

Contemporary food supply chains are generating externalities with high economic and social costs, notably in public health terms through the rise in diet-related non-communicable disease. A decision making model of multi-product multi-stage food supply-chain consisted of single manufacturer and single supplier is proposed in this article. The model is proved to be superior and efficient through application example. Reasonable decisions are suggested by analyzing the solution of dual problem. The decisions could help members of food supply-chain get more profits.

关键词： food supply-chain management multi-objective programming dual problem

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Improving Fairness in Staff Assignment: An Approach for Lexicographic Goal programming

Improving Fairness in Staff Assignment: An Approach for Lexi...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Rihm, Tom Baumann, Philipp Univ Bern Dept Business Adm Bern Switzerland Univ Calif Berkeley Dept Ind Engn & Operat Res Berkeley CA USA

ISBN: (纸本)9781467380669

In many staff-assignment problems, a large variety of requirements has to be considered when assigning employees to work shifts. As the importance of the requirements is often described in a hierarchical manner, lexicographic goal programming has been used to minimize the number of requirement violations. The resulting schedules are in general of high quality with respect to requirement violations but may lack acceptance by employees because of an unfair distribution of the violations. We introduce a novel approach for lexicographic goal programming that allows to improve an existing schedule in terms of fairness without deteriorating its quality with regard to requirement violations. The effectiveness of the proposed approach is demonstrated for a test set derived from real-world data.

关键词： Staff Assignment Fairness multi-objective programming Mixed-Integer Linear programming

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Genetic algorithm based fuzzy stochastic transportation programming problem with continuous random variables

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OPSEARCH 2016年第4期53卷 835-872页

作者： Dutta, S. Acharya, S. Mishra, Rajashree KIIT Univ Sch Appl Sci Dept Math Bhubaneswar Orissa India

This paper is concerned with the solution procedure of a multi-objective transportation problem with fuzzy stochastic simulation based genetic algorithm. Supplies and demands are considered as a fuzzy random variables with fuzzy means and fuzzy variances in proposed multi-objective fuzzy stochastic transportation problem. The first step in fuzzy simulation based genetic algorithm is to deal with aspiration level of the constraints with the help of alpha-cut technique to obtain multi-objective stochastic transportation problem. In next step, fuzzy probabilistic constraints (fuzzy chance constraints) are handled within fuzzy stochastic simulation based genetic algorithm to obtain a feasible region. The feasibilities of the chance constraints are checked by the stochastic programming with the genetic process without deriving the deterministic equivalents. The feasibility condition for the transportation problem is maintained through out the problem. Finally, multiple objective functions are considered in order to generate a Pareto optimal solutions for the fuzzy stochastic transportation problem using the proposed algorithm. The proposed procedure is illustrated by two numerical examples.

关键词： Stochastic programming multi-objective programming Fuzzy programming Chance constrained programming Genetic algorithm

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Research on optimal train operation control strategy based on dynamic programming algorithm 3

Research on optimal train operation control strategy based o...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Zhou, Luyao Shen, Zhijie Liu, Haoran Zhang, Qingyang Gao, Zehua Liu, Jiawang Wenzhou University of Technology Wenzhou China Dongguan University of Technology Dongguan China Jiangsu University Zhenjiang China Nanjing Institute of Technology Nanjing China Hebei University Baoding China Jishou University Jishou China

ISBN: (纸本)9798350341546

Energy-saving train operation is an important means to reduce the energy consumption of rail transit system. This paper mainly aims at the problem that different driving strategies will produce different energy and time consumption during train operation, and establishes a mathematical model to study the optimal train driving strategies in different running processes. Firstly, the train dynamics model is established, and the running parameters of the train under different road conditions are obtained by Euler method. Then, optimization models of single objective programming and multi-objective programming are constructed according to different road conditions, and then dynamic programming algorithm is used to solve the models, and the optimal train control strategy under different objectives and different road conditions is obtained, and the relevant parameter sequence curves are obtained. © 2023 IEEE.

关键词： Dynamic programming algorithm Eulerian method multi-objective programming Nonlinear programming Single objective programming

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multi-objective Evolutionary Algorithms for MILP and MINLP in Process Synthesis

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Chinese Journal of Chemical Engineering 2001年第2期9卷 173-178页

作者：石磊姚平经 Dalian Univ Technol Lab Proc Syst Engn Dalian 116012 Peoples R China

Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is improved and a new self-adjustment scheme of is proposed. This algorithm is proved to be very efficient both computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis.

关键词： multi-objective programming multi-objective evolutionary algorithm steady-state non-dominated sorting genetic algorithm process synthesis

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A data-driven sustainable scheduling model for dispatch-steerable last-mile delivery systems with negotiable time windows

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Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Keivan Tafakkori Reza Tavakkoli-Moghaddam Ali Siadat School of Industrial Engineering College of Engineering University of Tehran Tehran Iran Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey Arts et Métiers Institute of Technology Université de Lorraine LCFC F-57070 Metz France

With the surge in e-commerce, last-mile delivery (LMD) faces challenges in meeting the dynamic arrival of large-scale customer demand. Existing research considers traditional, static, single or two-echelon, and inflexible vehicle routing-based dispatch strategies, yielding inefficiencies while the sustainability impacts remain unclear. This paper proposes a novel tri-objective mathematical model for dispatch-steerability in sustainable LMD systems by integrating insourced and crowdsourced transport modes with heterogeneous capacities. Deliveries originate from main dispatch points (MDPs) or dynamically generated alternative dispatch points (ADPs), adapting to stakeholder, driver, and customer preferences via negotiable timing through temporary rejections or incentives. The model optimizes economic efficiency (minimizing routing, rejection, and negotiation costs), environmental impact (reducing underutilization of transport modes), and social responsibility (balancing driver workload and just-in-time deliveries). A data-driven density-based spatial clustering of applications with noise (DBSCAN) algorithm identifies ADP locations, enhancing the adaptability of the decisions. Scalability is achieved via a rolling horizon strategy, an improved augmented epsilon-constraint method, and multi-objective meta-heuristics, including a genetic algorithm (GA) and simulated annealing (SA) featuring an archive and grid mechanism with a tailored representation scheme. Results show a 200 % increase in Pareto-optimal solutions as demand rises, and that a dispatch steerable LMD system with ADPs offers 150 % more diverse delivery options than MDP-only systems. Pareto surface analysis underscores LMD adaptation trends and the need for multi-criteria decision-making in sustainable LMD systems.

关键词： Last-mile delivery Sustainability multi-objective programming Order acceptance scheduling Meta-heuristics Machine learning

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Construction of a Clinical Path Discrimination Model for Stroke Patients Based on the XGBoost Integrated Learning Algorithm and Its Application Analysis in the MOP under the DIP Payment Model

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Journal of Clinical and Nursing Research 2025年第4期9卷 291-298页

作者： Huisi Hong Yiming Yuan Jianlong Huang Liqun Zheng Wentao Guo Bingteng Sun Xiaoxing Huang Zhongshan Hospital of Traditional Chinese Medicine Affiliated to Guangzhou University of Chinese Medicine Zhongshan 528401GuangdongChina

objective:Aiming at the problems of clinical pathway optimization and medical cost control for stroke patients,this study proposes a clinical pathway discrimination model based on the XGBoost integrated learning *** with the multi-objective programming(MOP)method,this study explores its application value under the Diagnosis-Intervention Packet(DIP)payment ***:The data of stroke patients(ICD codes I60-I63)from 2018 to 2024 were obtained through the medical record statistical management system of medical institutions in Guangdong *** indicators(average length of hospital stay),health economics indicators(total cost),effectiveness indicators(mortality rate),and specific indicators(NIHSS score)were *** XGBoost algorithm was used to construct the clinical pathway discrimination model,and the hyperparameters were optimized through grid *** on the DIP payment rules,the prediction results of the model were used as inputs to establish a dynamic programming model,aiming to minimize costs and maximize curative effects to obtain the optimal clinical pathway ***:The goodness-of-fit(R 2)of the XGBoost model on the test set reached 0.768,which was significantly better than that of the random forest(0.691)and the BP neural network(0.343).The total cost of the clinical pathway optimized by the dynamic programming model decreased by 12.7%(95%CI:10.2-15.1%),the average length of hospital stay was shortened by 1.8 days,and the NIHSS improvement rate increased by 8.3%.Conclusion:The integrated model proposed in this study has high accuracy and robustness in clinical pathway *** with the MOP method,it can provide a scientific basis for the optimization of medical resources under the DIP model,helping medical institutions achieve the dual goals of precise cost control and curative effect improvement.

关键词： XGBoost multi-objective programming Clinical pathway DIP payment Stroke

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Strategic vegetable crop distribution in Himachal Pradesh for sustainable economic and environmental outcomes

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Scientific reports 2025年第1期15卷 19437页

作者： Nikhil Chandel S A Wani Mushtaq A Lone Ohud A Alqasem Aafaq A Rather S A Mir Danish Qayoom M Nagy Getachew Tekle Mekiso Oluwafemi Samson Balogun School of Agricultural Economics and Horti-Business Management SKUAST Kashmir Srinagar 190025 India. Directorate of Planning & Monitoring SKUAST Kashmir Srinagar 190025 India. Department of Mathematical Sciences College of Science Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia. Symbiosis Statistical Institute Symbiosis International (Deemed University) Pune 411004 India. Department of Statistics and Operations Research King Saud University P.O. Box 2455 11451 Riyadh Saudi Arabia. Wachemo University Hosaina Ethiopia. getachewtekle@wcu.edu.et. Department of Computing University of Eastern Finland 70211 Kuopio Finland.

India, the second-largest global producer of fruits and vegetables, faces challenges in translating productivity into profitability, particularly in vegetable farming. This study examined resource use efficiency in Himachal Pradesh, focusing on optimizing land allocation using multi-objective programming (MOP) to maximize income and employment while minimizing fertilizer and pesticide use. Primary data were gathered from Solan and Sirmaur districts through structured questionnaires employing a multi-stage sampling design. Optimization models were developed annually for marginal, small, and medium farmers over three cropping seasons. The study demonstrated significant productivity, profitability, and sustainability improvements through optimization techniques. For marginal farmers, income increased from Rs 229,359 to Rs 280,058 per hectare, employment peaked at 252 man-days from 190, and fertilizer costs reduced from Rs 6180 to Rs 4420 per hectare, with the MOP approach allocating Chinese cabbage (March-May), cauliflower (August-November), and peas (November-March). Small farmers saw income rise from Rs 238,095 to Rs 296,214 per hectare, employment reaches 263 man-days from 217, and fertilizer costs decrease from Rs 6686 to Rs 3977 per hectare, with the MOP approacgh allocating Chinese cabbage (March-May), beans (August-November), and peas (November-March). For medium farmers, income maximized from Rs 256,071 to Rs 374,673 per hectare, employment at 253 man-days from 219, and fertilizer costs minimized from Rs 7092 to Rs 4428 per hectare, with the MOP approach allocating Chinese cabbage (March-May), beans (August-November), and peas (November-March). The study highlighted the need for efficient resource use and strategic crop selection for sustainable agriculture. Key policy recommendations include targeted support programs, resource efficiency initiatives, customized training, improved access to credit, and infrastructure development. These measures can boost produc

关键词： Agricultural productivity multi-objective programming Optimization Resource use efficiency Vegetable farming

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Designing an integrated pharmaceutical relief chain network under demand uncertainty

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2020年 136卷 101867-000页

作者： Akbarpour, Mina Torabi, S. Ali Ghavamifar, Ali Univ Tehran Sch Ind Engn Coll Engn Tehran Iran

This paper addresses an integrated relief network design problem for pharmaceutical items. The proposed bi-objective model accounts for perishability of pharmaceutical items, mobility of relief facilities, and benefits from a cooperative coverage mechanism in designing the network. A min-max robust model is developed to tackle the demand uncertainty. Several numerical experiments are conducted to explore the performance of the robust model. Also, by conducting a real case study, useful managerial insights are derived through performing several sensitivity analyses. The numerical results reveal that using the min-max robust model enhances the pharmaceutical relief network's effectiveness and efficiency considerably.

关键词： Humanitarian logistics Pharmaceutical relief items Perishability multi-objective programming Robust optimization

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