An Improved Revenue Distribution Model for Logistics Service Supply Chain Considering Fairness Preference

Lu, Fuqiang; Wang, Liying; Bi, Hualing; Du, Zichao; Wang, Suxin

doi:10.3390/su13126711

Cited by 19 publications

(15 citation statements)

References 46 publications

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“…The hybrid particle swarm optimization algorithm uses the fitness value of each individual particle's current position, and assigns these particles a suitable order according to the fitness value of each particle, and then assigns good positions and velocities to the group with poor fitness values. Individuals, where the second half of the whole ranking with low fitness value is defined as the individual with poor fitness value, maintain the best position of each individual [21]. Therefore, the group can concentrate on the space with relatively good fitness value, and at the same time, it is also affected by the individual's own memory of retaining the best previous position.…”

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confidence: 99%

Construction of Mathematical Model of Logistics Delivering Based on Intelligent Mobilization

Liang¹,

Guo²

2022

Journal of Function Spaces

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Distribution process is the core of logistics enterprise system. Efficient distribution process is the key for enterprises to improve logistics service level, gain competitive advantage, and win customers. In order to reduce the design error of the system and ensure the effective operation of the system, this paper establishes the system model and analyzes the properties of the model through the evaluation and analysis of various resources of the distribution system. The future development of intelligent logistics system is not only the key for logistics enterprises to win competition, but also a new measure to promote China’s economic development. This is not only a typical NP hard problem, but also a major challenge for the rational and scientific development of the intelligent logistics industry. Based on the previous theoretical research results, this paper intends to explore the intelligent logistics distribution route selection scheme by using mathematical modeling methods such as particle swarm optimization, so as to provide new technologies and methods for logistics distribution operation and management decision-making. The results show that compared with the traditional genetic algorithm, the accuracy of the improved particle swarm optimization algorithm is improved by 10.36%. This method effectively improves the operation cycle and link efficiency and achieves the purpose of optimization. The improved particle swarm optimization algorithm proposed in this paper is not only more suitable and effective for enterprise decision makers to deal with subjective judgments in an imprecise environment. It is also based on the evaluation sequence of each indicator of the alternative obtained from the evaluation and the comprehensive evaluation of all indicators. By weighting and considering the results, the best solution for enterprise logistics and distribution is selected.

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confidence: 99%

Construction of Mathematical Model of Logistics Delivering Based on Intelligent Mobilization

Liang¹,

Guo²

2022

Journal of Function Spaces

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“…It provides an overview of the concept of fairness and its dimensions from a general perspective. It incorporates the concepts of distributive fairness, interpreted as the fairness of price received (Adams, 1965;Bolton et al, 2003;Haitao Cui et al, 2007;Gielissen and Graafland, 2009;Zitzmann and Dobhan, 2010;Briggeman and Lusk, 2011;Lu et al, 2021), procedural fairness, the perceived fairness of the procedures used to determine price distributions (Thibaut and Walker, 1978), interactional fairness, the quality of employees' interpersonal treatment (Bies and Moag, 1986;Colquitt et al, 2001), and environmental fairness, respect for the environment (Food Ethics Council, 2020). Past research conceptualised interactional fairness as the third dimension of fairness (Colquitt et al, 2001) or as the social part of procedural fairness (Folger and Konovsky, 1989).…”

Section: Review Of Literaturementioning

confidence: 99%

Does fairness matter? Consumers' perception of fairness in the agro-food chain

Prete

Samoggia

2023

Front. Sustain. Food Syst.

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IntroductionDefining ‘fairness’ in the agro-food sector is a challenging task. There is no single definition of fairness and the literature does not provide a complete conceptualization from the consumer's point of view. The current research seeks to explore the consumers' interest in fairness and ethics in the agro-food chain by exploring (i) a comprehensive theoretical framework to conceptualize fairness from a consumer perspective, and (ii) the consumers' perceived importance of different food attributes as fairness-related aspects.MethodLiterature review and focus groups allowed for the creation of the final survey to be submitted to consumers. 529 valid responses from a predominantly Italian female sample were collected. Data were elaborated with Exploratory Factor Analysis and ANOVA test.ResultsThe research identified five dimensions of fairness: Fair price, environment, networking, short chain, and working condition. Also, it emerged that age influences consumers' perceived importance of products with fair attributes.DiscussionThis research contributes to the development of a fairer and more sustainable food system by identifying perceptions of agro-food chain fairness and establishing a link with food shopping intentions. The research provides companies with suggestions on how to expand sales by reaching a greater number of consumers.

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“…These stock products will be participated in the distribution of the next period. Additionally, each retailer is distributed by one distribution center， however, the distribution center can serve different retailers [22]. 5) Push-pull junction point…”

Section: ) Distribution Modelmentioning

confidence: 99%

A Multiobjective Multiperiod Mixed-Integer Programming Optimization Model for Integrated Scheduling of Supply Chain Under Demand Uncertainty

Cao

Wang

2022

IEEE Access

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The problem of integrated scheduling of supply chain has a huge impact on operational efficiency and cost effectiveness. The increasing number of nodes, different time window constraints for customers, and a variety of uncertain scenarios make supply chain scheduling complicated. This research develops a multi-objective multi-period mixed-integer programming optimization model. We consider comprehensively the effect of demand uncertainty, time window constraints, the constraints of node capability, multi-period and sub-period factors. As the conflicting benefit factors, the cost and service level are two optimization objectives. The first objective function aims to minimize the total cost in all periods. The second objective function considers service level through minimizing the material flow of out-of-stock items in all periods to maximize the service level. And the suppliers' capacity, the selection of suppliers, manufacturers' productivity, the transaction relationship, the sub-period time, the inventory capacity and lead time for delivery are also considered. Then total costs and service levels are normalized, whose sum is the objective function. And the problem is transformed into a multi-period non-linear optimization problem. An improved Mixed Genetic Algorithm is designed to solve the model. Finally, the practicability of the proposed model and algorithm is demonstrated through its application in an electronics supply chain case study. The results indicate that the proposed model and algorithm can provide a promising approach to fulfill a multi-objective multi-period integrated scheduling plan under uncertain demand scenarios.INDEX TERMS Integrated scheduling, mixed-integer programming optimization, supply chain, improved mixed genetic algorithm, demand uncertainty.

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An Improved Revenue Distribution Model for Logistics Service Supply Chain Considering Fairness Preference

Cited by 19 publications

References 46 publications

Construction of Mathematical Model of Logistics Delivering Based on Intelligent Mobilization

Construction of Mathematical Model of Logistics Delivering Based on Intelligent Mobilization

Does fairness matter? Consumers' perception of fairness in the agro-food chain

A Multiobjective Multiperiod Mixed-Integer Programming Optimization Model for Integrated Scheduling of Supply Chain Under Demand Uncertainty

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