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Allocation of products to a heterogeneous fleet of trucks in a cross-docking center based on carbon emissions and costs in food and beverage industry: Novel uncertain solution approaches

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023年 332卷 117071页

作者： Rajabzadeh, Mohsen Mousavi, Seyed Meysam Shahed Univ Dept Ind Engn Tehran Iran

The threat of climate change continues to grow, which calls for strategies to reduce emissions. Carbon emissions from transportation are among the highest in the world, so it is essential to improve its efficiency. Cross-docking is a smart way to improve the efficiency of transportation operations through the optimal use of truck capacity. This paper develops a novel bi-objective mixed integer linear programming (MILP) model to determine which products should be shipped together, select the most appropriate truck among the available ones, and schedule them. It reveals a new class of cross-dock truck scheduling problems, in which products are not interchangeable and are sent to different destinations. The first objective is to minimize overall system costs, while the second is to minimize total carbon emissions. To deal with uncertainties in factors, such as costs, time, and emission rate, these parameters are considered interval numbers. Furthermore, innovative uncertain approaches are introduced under interval uncertainty based on optimistic and pessimistic Pareto solutions for solving MILP problems via epsilon-constraint and weighting methods. The proposed model and solution procedures are used for planning an operational day at a regional distribution center (RDC) of a real food and beverage company, and results are compared. The results show that the proposed epsilon-constraint method outperforms the other implemented methods in terms of quantity and variety of optimistic and pessimistic Pareto solutions. Using the newly developed procedure, the amount of carbon produced by trucks could decrease by 18% under optimistic assumptions and 44% under pessimistic assumptions. As a result of the proposed solution approaches, managers can observe how their optimism level and the importance of objective functions influence their decisions.

关键词： Cross -docking Carbon emissions Multi -objective mixed integer programming interval programming Epsilon -constraint method Weighting method Food and beverage industry

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An inexact fractional multi-stage programming (IFMSP) method for planning renewable electric power system

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2023年 187卷

作者： Lin, Xiajing Huang, Guohe Zhou, Xiong Zhai, Yuanyuan Univ Regina Fac Engn & Appl Sci Regina SK S4S 0A2 Canada Beijing Normal Univ Sch Environm China Canada Ctr Energy Environm & Ecol Res UR BNUState Key Joint Lab Environm Simulat & Poll Beijing 100875 Peoples R China

The growing levels of greenhouse gas (GHG) emissions have made renewable energy sources a crucial aspect of power generation, especially in developing countries. However, with more investment in additional renewable energy components and the mandatory decommissioning requirement of the thermal stations, the uncertainty of renewable energy availability and the changeable policy environment have brought great complexity to the electric power systems. Hence, an innovative approach, the inexact fractional multistage programming (IFMSP), has been proposed to address the challenges of electric power system planning in transitioning to clean energy, specifically in South Africa from 2021 to 2050. The IFMSP approach balances economic benefits and GHG emission mitigation, providing a more constrained optimum interval solution than binary integer programming. The results illustrated a decline in the expansion of conventional energy sources, with natural gas, wind energy and solar energy taking the place of thermal power generation. In terms of performance, solar power ranks second to wind. By assessing various expansion scenarios, it is demonstrated that zero nuclear energy growth would yield more competitive economic advantages and fewer GHG emissions than no geothermal expansion, whereas a 20% increase in gas expansion capacity would achieve the most optimal ratio at [304.2, 317.3] (Rand/ t CO2 eq). By utilizing the proposed approach, decision-makers in developing countries can generate multiple expansion scenarios for Belt and Road Initiative (BRI) investments. This enables them to identify generation modes, capacity expansion schemes, financial planning, and carbon tax regulations, even under complex uncertainties.

关键词： Developing country interval programming Linear fractional programming Stochastic multi-stage programming Non-coal-fired technology Greenhouse gas (GHG) emissions

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Sparrow search algorithm with adaptive t distribution for multi-objective low-carbon multimodal transportation planning problem with fuzzy demand and fuzzy time

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 238卷

作者： Zhang, Huizhen Huang, Qin Ma, Liang Zhang, Ziying Univ Shanghai Sci & Technol Sch Management Mail Box 459516 Jungong Rd Shanghai 200093 Peoples R China Shanghai Univ Engn Sci Sch Mat Engn 333 Longteng Rd Shanghai 201620 Peoples R China

In this paper, we present a multi-objective low-carbon multimodal transportation planning problem with fuzzy demand and fuzzy time (MOLCMTPP-FDFT) that minimizes both cost and time while incorporating mandatory carbon emission, carbon tax, carbon trading, and carbon offset policies. Chance constrained programming and interval programming are introduced to formulate the fuzzy demand chance constrained programming model and the fuzzy time interval programming model, respectively. Then, the mathematical model of uncertain MOLCMTPP-FDFT is transformed into a deterministic model. Based on the sparrow search algorithm, t distribution and the concept of Pareto optimality for multi-objective optimization, we also propose a solution strategy for the proposed model. In this algorithm, the number of iterations is used as the degree of freedom of -to update the sparrow location, which strikes a balance between the capabilities of global search and local search. Finally, the proposed algorithm and MOLCMTPP-FDFT are applied to a real case, resulting in a minimum cost of 260730.48 and a time duration of 13.044, which outperform the minimum cost of 268874.88 and minimum time of 18.32 obtained using single-mode transportation. The carbon emissions resulting from the lowest-cost solution obtained using single-mode transportation are 3,198.48, which are significantly more than the allowed emissions. Therefore, the proposed algorithm and mathematical model of MOLCMTPP-FDFT are valuable tools for optimizing multimodal transportation route. Additionally, the experimental results not only validate the superior efficiency and energy-saving benefits of the proposed multimodal transportation routes in comparison to the actual single-modal transportation, but also demonstrate the applicability of different low-carbon policies.

关键词： Low-carbon multimodal transportation Sparrow search algorithm Adaptive t distribution Chance constrained programming interval programming

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Inexact Fuzzy Full-Infinite Mixed-Integer programming Method for an Integrated Air and Waste Management System

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JOURNAL OF URBAN PLANNING AND DEVELOPMENT-ASCE 2011年第4期137卷 370-380页

作者： Su, Jing Xi, Beidou Yao, Bo Huang, Guohe Lu, H. W. He, Li Ji, Danfeng Chinese Res Inst Environm Sci Beijing 100012 Peoples R China Beijing Normal Univ Sch Environm Beijing 100875 Peoples R China Univ Regina Ctr Studies Energy & Environm Regina SK S4S 0A2 Canada

Inexact fuzzy full-infinite mixed-integer programming (IFFMIP) was proposed as an extension of inexact full-infinite programming. It can deal with the uncertainties that exist in most of the parameters as intervals, economic data as functional intervals, fuzzy set in capacity constraint, and discrete variables in a facility-expansion plan. Although many models for dealing with waste planning and the air quality management problem exist, this is the first attempt to integrate waste flow allocation and an air quality control system. The proposed model was applied in an integrated air and waste management system. It can solve the waste allocation plan and air quality control, and reflect the interaction between these two systems. The solutions from the proposed IFFMIP model indicate that a landfill would be the first choice in period 1, whereas an incinerator would become the main approach for waste disposal at the end of the planning horizon. This is because of the increasingly strict environmental standards and loading capacity of SO(2) for industries, upon which waste planning prefers less operation costs for waste disposal. The solutions reflected the tradeoff between the environment and system cost. To control air quality at a safe level, the system must be willing to pay more operating costs;however, a desire to reduce the costs will run into the risk of potentially violating the emissions and/or ambient air quality standards. DOI: 10.1061/(ASCE)UP.1943-5444.0000088. (C) 2011 American Society of Civil Engineers.

关键词： Full-infinite programming Regional waste management Air quality control interval programming Fuzzy flexible

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Reliable design of a closed loop supply chain network under uncertainty: An interval fuzzy possibilistic chance-constrained model

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ENGINEERING OPTIMIZATION 2013年第6期45卷 745-765页

作者： Vahdani, Behnam Tavakkoli-Moghaddam, Reza Jolai, Fariborz Baboli, Arman Univ Tehran Coll Engn Dept Ind Engn Tehran Iran Inst Natl Sci Appl DISP Lab F-69621 Villeurbanne France

This article seeks to offer a systematic approach to establishing a reliable network of facilities in closed loop supply chains (CLSCs) under uncertainties. Facilities that are located in this article concurrently satisfy both traditional objective functions and reliability considerations in CLSC network designs. To attack this problem, a novel mathematical model is developed that integrates the network design decisions in both forward and reverse supply chain networks. The model also utilizes an effective reliability approach to find a robust network design. In order to make the results of this article more realistic, a CLSC for a case study in the iron and steel industry has been explored. The considered CLSC is multi-echelon, multi-facility, multi-product and multi-supplier. Furthermore, multiple facilities exist in the reverse logistics network leading to high complexities. Since the collection centres play an important role in this network, the reliability concept of these facilities is taken into consideration. To solve the proposed model, a novel interactive hybrid solution methodology is developed by combining a number of efficient solution approaches from the recent literature. The proposed solution methodology is a bi-objective interval fuzzy possibilistic chance-constraint mixed integer linear programming (BOIFPCCMILP). Finally, computational experiments are provided to demonstrate the applicability and suitability of the proposed model in a supply chain environment and to help decision makers facilitate their analyses.

关键词： closed loop supply chain network design reliability interval programming fuzzy possibilistic chance-constrained programming

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An interval Two-Stage Classified-Allocation Model for Regional Water Management under Nonstationary Condition

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JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 2021年第4期57卷 610-627页

作者： Chen, Shu Li, Qingqing Xu, Jijun Wang, Dong Wang, Yongqiang Yuan, Zhe Changjiang River Sci Res Inst Wuhan Water Resources Dept Wuhan Peoples R China Hubei Key Lab River Basin Water Resources & Ecol Wuhan Peoples R China

Due to climate change and human activities, the assumption of the stationarity of hydrologic features will no longer hold. Moreover, uncertainties, such as the apparent randomness of hydrologic elements, and complexities, such as the existence of various water users with different characteristics, also introduce huge challenges for water managers. To address nonstationarity, uncertainty, and complexity, a new approach is proposed for the optimal allocation of regional water resources. This objective was achieved via two steps: First, the generalized additive model was chosen to analyze the nonstationary probability distribution of the hydrologic dataset;then, an interval two-stage classified-allocation model is formulated by incorporating two-stage stochastic programming, interval parameter programming and classification thought. The model can not only address uncertainties, which were expressed as interval parameters and probability distributions, but can also handle complexities by classifying the water users into agricultural and nonagricultural users. The approach was applied to the Zhanghe Irrigation District to optimize available water allocation for municipality, industry, hydropower, and agriculture in two planning years (namely 2010 and 2015). The annual inflow of the Zhanghe Reservoir is found to be nonstationary and can be well fitted by Gamma distribution with one location parameter based on a nonlinear function of time. Moreover, the difference in output between the two years with different inflow probability distributions indicates the need for nonstationary analysis. Comparison to the inexact two-stage water management model that did not consider the variation of agricultural water requirement shows the meaning of classification. From the results, municipality and industry are more competitive than agriculture and then hydropower. For agriculture, winter rape and cotton have higher priority than rice. These solutions of the optimal targets and optimal

关键词： classification thought interval programming nonstationary analysis two‐ stage stochastic programming uncertainty water management

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A multiobjective optimization framework for optimal selection of supplier portfolio

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OPTIMIZATION 2014年第10期63卷 1491-1512页

作者： Guu, Sy-Ming Mehlawat, Mukesh Kumar, Santosh Chang Gung Univ Coll Management Grad Inst Business & Management Taoyuan Hsein Taiwan Univ Delhi Dept Operat Res Delhi 110007 India

Supplier selection problem has been a typical concern of optimization where good supplier selection is essential for firms in the context of supply chain management. If a firm networks with different suppliers, each one having its own advantages over the others, then there is great opportunity to reduce a firm's risk arising from supply concentration and also to provide it with the opportunity to leverage its comparative strength. In this paper, we propose a new approach referred to as Supplier Portfolio Optimization (SPO) that improves the state of the art of supplier selection by incorporating the benefits of supplier diversification using the trade-offs between the criteria of expected unit price, expected score of quality and expected score of delivery. We present three different optimization models with interval coefficients to model the uncertain SPO problem corresponding to optimistic, pessimistic and combination strategies. Further, using lower and upper bonds for fractions of order quantity, we ensure supplier portfolio diversification and also avoidance of the situation of impractical fraction of quantity ordered. Numerical experiments conducted on a dataset of a multinational firm are provided to demonstrate the applicability and efficiency of the SPO models to real-world applications of supplier selection.

关键词： multiobjective optimization interval programming supplier selection portfolio optimization supply chain management diversification

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IFTSQP: An inexact optimization model for water resources management under uncertainty

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WATER INTERNATIONAL 2007年第3期32卷 439-456页

作者： Li, Y. P. Huang, G. H. Nie, S. L. Univ Regina Environm Syst Engn Program Fac Engn Regina SK S4S 0A2 Canada N China Elect Power Univ Chinese Res Acad Environm Sci Beijing Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China

An interval-fuzzy two-stage quadratic programming (IFTSQP) method is developed for water resources management under uncertainty. The method incorporates techniques of interval-parameter programming, two-stage stochastic programming, and fuzzy quadratic programming within a general optimization framework to tackle multiple uncertainties presented as intervals, fuzzy sets and probability distributions. In the model formulation, multiple control variables are adopted to handle independent uncertainties in the model's right-hand sides;fuzzy quadratic terms are used in the objective function to minimize the variation in satisfaction degrees among the constraints. Moreover, the method can support the analysis of policy scenarios that are associated with economic penalties when the promised targets are violated. The developed method is then applied to a case study of water resources management planning. The results indicate that reasonable solutions have been obtained. They can help provide bases for identifying desired water-allocation plans with a maximized system benefit and a minimized constraint-violation risk.

关键词： fuzzy sets interval programming management optimization quadratic two-stage uncertainty water resources

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An interactive approach for the multi-objective transportation problem with interval parameters

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2015年第4期53卷 1051-1064页

作者： Yu, Vincent F. Hu, Kuo-Jen Chang, An-Yuan Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei Taiwan Natl Formosa Univ Dept Ind Management Huwei Taiwan Natl Formosa Univ Inst Ind Engn & Management Huwei Taiwan

The basic transportation problem (TP) deals with the transportation of goods from a set of supply points to a set of demand points so as to minimise linear transportation costs. The multi-objective transportation problem (MOTP) extends TP to take into consideration multiple conflicting objectives such as transportation cost, average delivery time of the commodities, unfulfilled demand and so forth. We develop an interactive approach to determine the preferred compromise solution for the MOTP where the coefficients of the objective functions and the source and destination parameters have been expressed as interval values by the decision-maker to deal with the impreciseness in the parameters. The proposed method is applied to a real-world MOTP faced by a leading provider of complete broadband access solutions. Results of the case study indicate that the developed method is a practical technique for solving the MOTP with interval parameters.

关键词： interval programming transportation problem multi-objective decision-making

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Analysis of Electricity Generation and Pollution Control in Power Industry System

Analysis of Electricity Generation and Pollution Control in ...

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2012 IEEE 3rd International Conference on Emergency Management and ManagementSciences(ICEMMS 2012)

作者： Zhanquan Ren State Nuclear Electric Power Planning Design Research Institute Beijing, China Lin Deng State Nuclear Electric Power Planning Design Research Institute Beijing, China Yexin Li School of Human Settlement and Civing Engineering Xi'an Jiaotong University Xi'an, China Ying Zhu Sino-Canada Rsources and Environment Research academy North China Electric Power University Beijing, China

An interval linear programming model is developed in this study for supporting electricity generation and pollution control under uncertainty. interval linear programming model has advantages in uncertainty reflection and policy analysis, especially when the input parameters are provided as intervals. A management problem in terms of power industry system in is studied to illustrate applicability of the proposed *** model provides an effective linkage between the power industry management policies and the associated economic implications. The results can help to generate a range of alternatives under various system conditions, and thus help decision makers to identify desired policies under various economic and system constraints to coordinate the conflict interactions among economic cost, system efficiency, pollutant mitigation and energy-supply security.

关键词： power industry electricity suppy uncertainty interval programming decision making

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