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5th International Symposium on Uncertainty Quantification and Stochastic Modelling, Uncertainties 2020

作者： Zidani, Hafid Ellaia, Rachid De Cursi, Edouardo Souza Mohammadia School of Engineers Mohammed V University of Rabat Ibn Sina Avenue Agdal BP 765 Rabat Morocco LOFIMS Laboratory INSA-Rouen Université Avenue BP. 08 St Etienne du Rouvray Cedex76801 France

ISBN: (纸本)9783030536688

In this paper, a novel approach is proposed to generate set of Pareto points to represent the optimal solutions along the Pareto frontier. This approach, which introduces a new definition of dominance, can be interpreted as a representation of the solution of the multi-objective optimization under the form of the solution of a problem in variational calculus. The method deals with both convex and non-convex problems. In order to validate the method, multi-objective numerical optimization problems are considered. © Springer Nature Switzerland AG 2021.

关键词： Multi-objective optimization multiple objective programming Variational calculation

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Knapsack Balancing via Multiobjectivization

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APPLIED SCIENCES-BASEL 2024年第20期14卷 9236页

作者： Kaliszewski, Ignacy Miroforidis, Janusz Polish Acad Sci Syst Res Inst 6 Newelska St PL-01447 Warsaw Poland

In this paper, we address the aspect of knapsack balancing in the classic knapsack problem. Recognizing that excessive dispersion in the objective function or constraint coefficients of the optimal solution can be undesirable, we propose, when appropriate, to control this effect through problem multiobjectivization. By multiobjectivization, we mean the addition of one or more objective functions that aim to shift the original problem's optimal solutions towards Pareto optimal solutions of the multiobjectivized problem, reducing the dispersion of the respective coefficients. We detail how the knapsack balance aspect can be incorporated into the standard knapsack problem model and demonstrate the functionality of this enriched model through illustrative examples.

关键词： combinatorial optimization knapsack problem product objective function knapsack balancing multiple objective programming

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INTEGRATED PRODUCTION-DISTRIBUTION PLANNING OPTIMIZATION USING NEUTROSOPHIC programming

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2022年第2期29卷 174-191页

作者： Badhotiya, Gaurav Kumar Soni, Gunjan Nepal, Bimal Mittal, Murari Lal Graph Era Deemed Univ Dept Mech Engn Dehra Dun Uttarakhand India Malaviya Natl Inst Technol Jaipur Dept Mech Engn Jaipur Rajasthan India Texas A&M Univ Engn Technol & Ind Distribut College Stn TX 77843 USA

Consideration and management of uncertainties are critical in effective decision-making in production-distribution planning problems. Further, these decisions often involve multiple objectives which are conflicting in nature. More importantly, the decisions represented by these objective functions are often based on imprecise or uncertain data either due to unavailability or lack of objectivity of information, therefore cannot be solved by classical deterministic modeling techniques. To that end, this paper presents a neutrosophic programming-based approach to solve an integrated production-distribution planning problem in a two-echelon supply chain by considering uncertainties and indeterminateness in the data. The problem is formulated as a tri-objective mixed-integer linear programming model considering important features of production distribution planning decisions. The three objectives considered are to minimize: total cost, delivery time, and backorder level. These objectives are represented by membership functions of the neutrosophic set, i.e., truth, indeterminacy, and falsity. The efficacy of the proposed methodology is illustrated by considering problem instances inspired by a real-world case in the automotive industry. A Pareto optimality test performed on the proposed neutrosophic model shows the existence of a strong optimal solution of the proposed neutrosophic model.

关键词： Information indeterminacy Production and distribution planning Neutrosophic sets multiple objective programming production planning Uncertainty

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"Combining equity and utilitarianism"-additional insights into a novel approach

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2018年第3期25卷 983-1000页

作者： Gerdessen, Johanna C. Kanellopoulos, Argyris Claassen, G. D. H. (Frits) Wageningen Univ Operat Res & Logist Wageningen Netherlands

Recently, a novel approach (to be referred to as CEU) was introduced for the frequently arising problem of combining the conflicting criteria of equity and utilitarianism. This paper provides additional insights into CEU and assesses its added value for practice by comparing it with a commonly used extended goal programming (EGP) approach. The comparison comprises the way of balancing equity and utilitarianism, the number and spacing of solutions, discrete versus continuous nature, method-specific parameters, distance to the Pareto front, and computational effort. CEU balances between equity and utilitarianism in a way that is basically different from using a convex combination of these two criteria. Moreover, CEU's parameter has an intuitive interpretation. The set of solutions generated by CEU is smaller and more widely spaced than EGP's set of solutions, which can be an advantage for the decision maker. CEU generates solutions on the Pareto front of the decision maker's n-criteria problem. However, CEU's way of balancing equity and utilitarianism causes a (small) distance to the Pareto front of the associated bicriteria problem on the aggregate criteria. Reporting this distance will support the decision maker to assess whether the achieved balance is worth its price. Using CEU may require a larger computational effort than using EGP.

关键词： multiple criteria decision analysis multiple objective programming goal programming comparison

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MRP-WSCI: multiple reference point based weak and strong composite indicators

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2020年 95卷 102060-102060页

作者： Ruiz, Francisco El Gibari, Samira Cabello, Jose M. Gomez, Trinidad Univ Malaga Dept Appl Econ Math Calle Ejido 6 Malaga 29071 Spain Univ Malaga Programa Doctorado Econ & Empresa Malaga Spain

The construction of composite indicators is becoming an increasingly important way of managing great amounts of information obtained from multiple single indicators. Obviously, the aggregation of several indicators in a single synthetic measure always implies some loss of information on the way. For this reason, this aggregation must be done in the most informative possible way, so that the results obtained can be easily interpreted, and in an efficient way, so that the information contained in the composite indicator is maximal. In this paper, we propose a novel methodology based on the multicriteria reference point scheme. The decision maker can establish any number of reference levels for each indicator, and the final outcome can be interpreted in terms of the position with respect to these levels. Besides, two different aggregations are proposed: the weak indicator, allowing for full compensation among the single indicators, and the strong indicator, not allowing for any compensation. The joint visualization of both composite indicators provides valuable information that may be unnoticed using other existing approaches. The approach is illustrated using an example based on the EU-Regional Social Progress Index (EU-SPI). (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Composite indicators multiple objective programming multiple reference point scheme Weak and strong indicators Compensation

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Industrial information integration method to vehicle routing optimization using grey target decision

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JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 2022年 27卷

作者： Guo, Kai Hu, Shanshan Zhu, Hai Tan, Wenan Henan Univ Sci & Technol Sch Management Luoyang 471023 Henan Peoples R China Henan Collaborat Innovat Ctr Nonferrous Met Luoyang 471000 Henan Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol 29 Jiangjun Ave Nanjing 211106 Peoples R China Shanghai Polytech Univ Sch Comp & Informat Engn Shanghai 201209 Peoples R China

Industrial information integration can help companies develop supply chain systems and is a good framework for vehicle routing problem. To address the problems of excessive energy consumption, environmental pollution caused by carbon dioxide emission, and timeliness in the transportation process, a multi-objective vehicle routing optimization model was proposed to minimise transportation, carbon emission, and time window penalty costs. In the proposed model, an elitist nondominated sorting genetic algorithm was used to obtain the Pareto optimal solution. Furthermore, the optimal distribution path was selected by using multi-objective grey target decision making according to entropy value. Finally, a real distribution case was analysed, and the calculated optimal path was compared with the actual path of the case to verify the feasibility of the proposed model and algorithm. The results showed that the proposed model and algorithm can be effectively used to reduce the target cost.

关键词： Industrial information integration Grey target decision multiple objective programming Routing optimization Elitist nondominated sorting genetic algorithm

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Combining Traveling Salesman and Traveling Repairman Problems: A multi-objective approach based on multiple scenarios

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COMPUTERS & OPERATIONS RESEARCH 2019年 112卷 104766-000页

作者： Bock, Stefan Klamroth, Kathrin Univ Wuppertal Schumpeter Sch Business & Econ Inst Business Comp & Operat Res Wuppertal Germany Univ Wuppertal Dept Math & Comp Sci Wuppertal Germany

This paper analyzes a multi-objective variant of the well-known Traveling Salesman Problem (TSP) and the Traveling Repairman Problem (TRP) in order to address the classical conflict between cost minimization (represented by the TSP) and customer waiting time minimization (represented by the TRP). By simultaneously considering different scenarios with individual travel times, uncertainty in travel data is handled. We interpret each travel time scenario as an individual objective function and introduce deterministic multi-objective counterpart models denoted as the Multi-objective TSP (MOTSP), Multi-objective TRP (MOTRP), and the combined Multi-objective TSP and TRP models (MOTSRP), respectively. Problems with and without additional deadline restrictions are considered, and the complexity status of computing the Pareto fronts of various problem variants for different underlying networks is resolved. As a particularly interesting case, we consider the MOTSRP with deadlines on a line and show that the problem is intractable even in this simple setting. Nevertheless, we propose a Dynamic programming approach that solves random instances to optimality in reasonable time. Moreover, the computational study additionally evaluates the average complexity of the Line-MOTSRP with deadlines for different numbers of scenarios. The computational study also analyzes the Pareto fronts that are generated for specifically designed extremal instances. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： multiple objective programming Multi-objective TSP and TRP Complexity analysis of Pareto fronts Multi-objective dynamic programming Line-TSP and Line-TRP

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UAV routing for reconnaissance mission: A multi-objective orienteering problem with time-dependent prizes and multiple connections

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COMPUTERS & OPERATIONS RESEARCH 2022年 145卷 1页

作者： Dasdemir, Erdi Batta, Rajan Koksalan, Murat Ozturk, Diclehan Tezcaner Hacettepe Univ Dept Ind Engn TR-06800 Ankara Turkey Univ Buffalo SUNY Dept Ind & Syst Engn Buffalo NY 14260 USA Middle East Tech Univ Dept Ind Engn TR-06800 Ankara Turkey Univ Michigan Ross Sch Business Ann Arbor MI 48109 USA

We address the route planning problem of an unmanned air vehicle (UAV) tasked with collecting information from a radar-monitored environment for a reconnaissance mission. The UAV takes off from a home base, visits a set of targets, and finishes its movement at a final base. Collectable information at a target depends on the time the target is visited by the UAV. There are multiple trajectory alternatives between the target pairs with different travel time and threat attributes. A route plan involves the selection of the targets to visit, the order of visit to the targets, and the trajectories to follow between the targets. multiple routing objectives, information collection, mission duration and mission safety, are considered to present the trade-offs among the objectives to the route planner. The problem is classified as a multi-objective orienteering problem with time-dependent prizes and multiple connection options. A mixed integer programming model that can be used for small-sized problems is formulated. Larger problems are addressed with a hybrid algorithm involving heuristics and exact approaches. A case study based on a terrain in the State of Colorado is presented. Finally, some practical issues for the UAV route planning problem is discussed.

关键词： multiple objective programming UAV routing Orienteering Time-dependent prizes Mixed integer programming

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Control of perishable inventory system with uncertain perishability process using neural networks and robust multicriteria optimization

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BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES 2022年第3期70卷

作者： Cholodowicz, Ewelina Orlowski, Przemyslaw West Pomeranian Univ Technol Szczecin Fac Elect Engn Dept Automat & Robot Al Piastow 17 PL-70310 Szczecin Poland

The inventory systems are highly variable and uncertain due to market demand instability, increased environmental impact, and perishability processes. The reduction of waste and minimization of holding and shortage costs are the main topics studied within the inventory management area. The main difficulty is the variability of perishability and other processes that occurred in inventory systems and the solution for a trade-off between sufficient inventory level and waste of products. In this paper, the approach for resolving this trade-off is proposed. The presented approach assumes the application of a state-feedback neural network controller to generate the optimal quantity of orders considering an uncertain deterioration process and the FIFO issuing policy. The development of the control system is based on state-space close loop control along with neural networks. For modelling the perishability process Weibull distribution and FIFO policy are applied. For the optimization of the designed control system, the evolutionary NSGA-II algorithm is used. The robustness of the proposed approach is provided using the minimax decision rule. The worst-case scenario of an uncertain perishability process is considered. For assessing the proposed approach, simulation research is conducted for different variants of controller structure and model parameters. We perform extensive numerical simulations in which the assessment process of obtained solutions is conducted using hypervolume indicator and average absolute deviation between results obtained for the learning and testing set. The results indicate that the proposed approach can significantly improve the performance of the perishable inventory system and provides robustness for the uncertain changes in the perishability process.

关键词： multiple objective programming optimal control genetic algorithm perishable inventories inventory control

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A web-based interactive decision support system for a multi-objective lot-sizing and production scheduling model

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2024年 271卷

作者： Zeitrag, Yannik Figueira, Jose Rui Pereira, Miguel Alves Univ Lisbon CEGIST Inst Super Tecn Av Rovisco Pais 1 P-1049001 Lisbon Portugal

In the course of globalization and the corresponding steadily growing market dynamics, companies are faced with an increasing challenge to remain competitive. Therefore, the efficient use of the available resources, while maintaining a high level of service and customer satisfaction, is a key instrument for ensuring success. Within this framework, production scheduling can be seen as one of the most decisive operational activities of a manufacturing company, with the production orders being sequenced in a way that allows the achievement of an efficient allocation of the available resources. Furthermore, the correlated lot -sizing represents a crucial influencing factor in production scheduling. In this article, a novel methodology to solve multi -objective lotsizing and production scheduling problems is proposed. Essentially, it provides an interactive three-phase framework that supports a procedure for simultaneous decision -making regarding lot -sizing and production scheduling. The main goal is not only to find an appropriate production schedule that best meets the decision-maker's preferences but also to gain a deeper insight into the relationship among criteria and a better understanding of possible trade-offs between competing objectives. To guide the user through the whole decision -making process, a web -based interactive application was developed. The proposed methodology was applied to a case study regarding an automotive supplier company, revealing itself as a viable and practical technique able to solve real -world multi -objective lot -sizing and production scheduling problems in the manufacturing industry.

关键词： multiple objective programming Decision support systems Lot-sizing Production scheduling Web-based platform

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