An Improved Tabu Search Algorithm for a Multi-Period Bid Generation Problem with the Consideration of Delivery Lead Time

Mamaghani, Elham Jelodari; Chen, Haoxun; Prins, Christian; Demir, Emrah

doi:10.1016/j.ifacol.2019.11.599

Cited by 6 publications

(13 citation statements)

References 6 publications

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“…Other studies in this area have considered pickup and delivery problem and service time window of selective request. For example, a mixed integer programming model with single objective function was presented in which delivery lead time is considered (Mamaghani et al, 2019). In addition, for the bi-objective full truckload TSP, a two-phase evolutionary algorithm was developed in which the total transportation costs and transit time were minimized at the same time (Zhang and Hu, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Perspective Shipper (Buer and Pankratz, 2010), (Ma et al, 2010), (Rekik and Mellouli, 2012), (Remli and Rekik, 2013), (Buer and Kopfer, 2014), (Ignatius et al, 2014), (Jothi Basu et al, 2015a), (Jothi Basu et al, 2015b), (Chi, 2015), (Hu et al, 2016), (Feki et al, 2016), (Yan et al, 2017), (Yan et al, 2018a), (Yan et al, 2018b), (Zhang and Hu, 2019), (Othmane et al, 2019), (Yang et al, 2019), (Yang and Huang, 2020), (Triki, 2021), This paper Carrier (Wang and Xia, 2005), (Kwon et al, 2005), (Lee et al, 2007), (Zhang et al, 2014), (Triki et al, 2014), (Chi, 2015), (Kuyzu et al, 2015), (Jothi Basu et al, 2015b), (Li et al, 2016), (Rekik et al, 2017), (Yan et al, 2017), (Yan et al, 2018a), (Yan et al, 2018b), (Mamaghani et al, 2019), (Hammami et al, 2019), (Olcaytu and Kuyzu, 2019), (Qian et al, 2020), (Yan et al, 2020), (Hammami et al, 2020) TSP Type Bid Generation Problem (Lee et al, 2007), (Triki et al, 2014), (Kuyzu et al, 2015),…”

Section: Featurementioning

confidence: 99%

“…Cost (Wang and Xia, 2005), (Kwon et al, 2005), (Buer and Pankratz, 2010), (Ma et al, 2010), (Rekik and Mellouli, 2012), (Remli and Rekik, 2013), (Buer and Kopfer, 2014), (Ignatius et al, 2014), (Zhang et al, 2014), (Chi, 2015), (Jothi Basu et al, 2015a), (Jothi Basu et al, 2015b), (Hu et al, 2016), (Feki et al, 2016), (Yan et al, 2017), (Yan et al, 2018a), (Yan et al, 2018b), (Zhang and Hu, 2019), (Yang et al, 2019), (Qian et al, 2020), (Yang and Huang, 2020), (Triki, 2021) Profit/ Revenue (Lee et al, 2007), (Triki et al, 2014), (Kuyzu et al, 2015), (Jothi Basu et al, 2015b), (Li et al, 2016), (Yan et al, 2017), (Rekik et al, 2017), (Yan et al, 2018a), (Yan et al, 2018b), (Hammami et al, 2019), (Mamaghani et al, 2019), (Olcaytu and Kuyzu, 2019), (Yan et al, 2020), (Hammami et al, 2020), This paper Others (Kwon et al, 2005), (Buer and Pankratz, 2010), (Rekik and Mellouli, 2012), (Ignatius et al, 2014), (Chi, 2015), (J...…”

Section: Economicalmentioning

confidence: 99%

“…(Ma et al, 2010), (Rekik and Mellouli, 2012), (Buer and Kopfer, 2014), (Jothi Basu et al, 2015a), (Jothi Basu et al, 2015b), (Hu et al, 2016), (Zhang and Hu, 2019), (Othmane et al, 2019), (Triki, 2021) mixed-integer prog. (Wang and Xia, 2005), (Kwon et al, 2005), (Lee et al, 2007), (Buer and Pankratz, 2010), (Remli and Rekik, 2013), (Triki et al, 2014), (Zhang et al, 2014), (Ignatius et al, 2014), (Kuyzu et al, 2015), (Chi, 2015), (Jothi Basu et al, 2015b), (Li et al, 2016), (Feki et al, 2016), (Li et al, 2016), (Rekik et al, 2017), (Yan et al, 2017), (Yan et al, 2018a), (Yan et al, 2018b), (Mamaghani et al, 2019), (Olcaytu and Kuyzu, 2019), (Yang et al, 2019), (Hammami et al, 2019), (Yan et al, 2020), (Qian et al, 2020), (Hammami et al, 2020) mixed-integer nonlinear prog. (Yang and Huang, 2020), This paper Conditions Uncertain Stochastic (Ma et al, 2010), (Remli and Rekik, 2013), (Zhang et al, 2014), (Triki et al, 2014), (Chi, 2015), (Kuyzu et al, 2015),…”

Section: Economicalmentioning

confidence: 99%

“…(Wang and Xia, 2005), (Kwon et al, 2005), (Lee et al, 2007), (Buer and Pankratz, 2010), (Remli and Rekik, 2013), (Triki et al, 2014), (Zhang et al, 2014), (Ignatius et al, 2014), (Kuyzu et al, 2015), (Chi, 2015), (Jothi Basu et al, 2015b), (Li et al, 2016), (Feki et al, 2016), (Li et al, 2016), (Rekik et al, 2017), (Yan et al, 2017), (Yan et al, 2018a), (Yan et al, 2018b), (Mamaghani et al, 2019), (Olcaytu and Kuyzu, 2019), (Yang et al, 2019), (Hammami et al, 2019), (Yan et al, 2020), (Qian et al, 2020), (Hammami et al, 2020) mixed-integer nonlinear prog. (Yang and Huang, 2020), This paper Conditions Uncertain Stochastic (Ma et al, 2010), (Remli and Rekik, 2013), (Zhang et al, 2014), (Triki et al, 2014), (Chi, 2015), (Kuyzu et al, 2015), (Jothi Basu et al, 2015b), (Feki et al, 2016), (Olcaytu and Kuyzu, 2019), (Qian et al, 2020), (Hammami et al, 2020) Fuzzy (Jothi Basu et al, 2015b), (Yan et al, 2017), (Yan et al, 2018a), (Yan et al, 2018b), This paper Deterministic …”

Section: Economicalmentioning

confidence: 99%

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Leader-based diffusion optimization model in transportation service procurement under heterogeneous drivers’ collaboration networks

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One of the key issues in transportation systems is allocating shipping orders to the most appropriate drivers, in the shortest time and with the maximum profit. Many studies were carried out in the transportation service procurement process for allocating orders, but none of them considered driver-to-driver interactions and applied information diffusion concepts as a framework to maximize the profit, due to the lack of a framework to model the interactions. In this paper, we present a weighted drivers' collaboration network to form the interactions. To predict the behavior of drivers, a new community detection algorithm is developed to extract communities and their leaders, in terms of the speed and the power of receiving and diffusing shipping orders. Also, we present a profit maximization model using information diffusion power of community leaders. The results show the model is able to allocate shipping orders to the most suitable drivers, in the best possible time and with the highest profit. To demonstrate the performance of the developed algorithm, we present a numerical example. Finally, a case study is applied to solve the optimization problem. The results show that the optimized behavior of companies in allocating orders to drivers is based on their risk level, reputation, and the average number of their customers.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Featurementioning

confidence: 99%

Section: Economicalmentioning

confidence: 99%

Section: Economicalmentioning

confidence: 99%

Section: Economicalmentioning

confidence: 99%

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Leader-based diffusion optimization model in transportation service procurement under heterogeneous drivers’ collaboration networks

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Pre-auction optimization for the selection of shared customers in the last-mile delivery

Antit,

Jaoua,

Layeb

et al. 2023

Ann Oper Res

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Companies are constantly looking for new strategies to improve their logistics performance and ensure their competitiveness in the global market. This article provides a new scheme for managing the selection of shared customers for a logistics company. The new mechanism proposes the use of the auction as a tool to manage the selection of shared clients through the coalition pool. Thus, all unprofitable shared customers will be pushed to the pool for outsourcing by the other collaborating carriers. Then, some profitable auctioned ones will be selected. The selection system is designed based on solving a vehicle routing problem that aims to maximize the carrier's profit in a decentralized context. At first, a mixed integer linear programing model is derived to solve the deterministic version of the problem. Then in order to efficiently address the stochastic version of the problem, a simulation-based optimization model is developed. This model is employed to solve a real case study of a parcel delivery company, considering the travel times as a bimodal distribution. A comparative study is conducted to demonstrate the effectiveness of the auction approach in managing shared customers. The results of our study reveal that the proposed auction approach efficiently manages the shared customers which leads to the substantial increase of 22.65% in profits for the delivery company. These findings have significant implications for logistics companies seeking to improve their performance and competitiveness in the global market.

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Multi-objective optimisation model under multiplex weighted drivers’ collaboration network: Risk, time and profit management perspectives

Kalantari¹,

Badiee²,

Tabaie³

et al. 2023

Computers & Industrial Engineering

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An Improved Tabu Search Algorithm for a Multi-Period Bid Generation Problem with the Consideration of Delivery Lead Time

Cited by 6 publications

References 6 publications

Leader-based diffusion optimization model in transportation service procurement under heterogeneous drivers’ collaboration networks

Leader-based diffusion optimization model in transportation service procurement under heterogeneous drivers’ collaboration networks

Pre-auction optimization for the selection of shared customers in the last-mile delivery

Multi-objective optimisation model under multiplex weighted drivers’ collaboration network: Risk, time and profit management perspectives

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