Fuzzy multi-objective programming algorithm for vehicle routing problems with backhauls

Yalçın, Gülçin Dinç; Erginel, Nihal

doi:10.1016/j.eswa.2015.02.060

Cited by 25 publications

(6 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Adapted FMOP algorithm for VRP is based on the paper by Yalcin and Erginel (2015). The proposed Adapted max λ subject to…”

Section: Problem Formulation and Resolution Methodologymentioning

confidence: 99%

“…Numerous variants of VRP have been introduced for many years in the literature. The variants of classical VRP problems can be classified as (see also Figure 1) capacitated VRP (Letchforda & Salazar-González, 2015;Wei et al, 2015;Cardoso et al, 2015), VRP with time windows (Hong & Park, 1999;Qi et al, 2015), VRP with pickup and deliveries (Dimitrakos & Kyriakidis, 2015;Avcı & Topaloglu, 2015), VRP with backhauls (Goetschalckx & Jacobs-Blecha, 1989;Ropke & Pisinger, 2006;Wang & Wang, 2009;Yalcin & Erginel, 2015;Koc & Laporte, 2018), open VRP (Zachariadis & Kiranoudis, 2010;Aksen et al, 2007;Brito et al, 2015;Erbao et al, 2014), VRP with multiple depots (Montoya-Torres, 2015; Chan & Baker, 2005;Ghafurian & Javadian, 2011), and VRP with the heterogeneous fleet (Koc et al, 2015;Salhi et al, 2014). Furthermore, VRP with time-dependent travel times has been studied recently because travel times are also dependent on the traffic congestion of the roads, which is based on queuing theory (van Woensel et al, 2007;van Woensel & Cruz, 2009;Oyola et al, 2018 for review).…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Adapted Fuzzy Multi-Objective Programming Algorithm for Vehicle Routing

Yalçın

Erginel²

2022

Universal Journal of Operations and Management

View full text Add to dashboard Cite

The vehicle routing problem (VRP) is a well-known problem in the logistics sector. In this study, two objectives, minimizing the total distance and maximizing the saving value, were considered in VRP with a fuzzy environment. The game theory approach is proposed for determining the weights of objectives when decision-makers have insufficient knowledge of assigning the weights. Thus, a fuzzy pay-off matrix is proposed for determining the weights of objectives by combining the fuzzy two-person zero-sum game with mixed strategies (FTZG with MS) and membership functions. Therefore, the fuzzy multi-objective programming (FMOP) model is adapted to the VRP model, which is named Adapted FMOP algorithm for VRP. Proposed algorithm clusters customers according to two objectives and by using four fuzzy operators, and routes customers with the traveling salesman problem (TSP) model in order to avoid the non-deterministic polynomial-time hardness (NP-hard) structure of VRP. In the end, the results are improved using local search methods. The main contribution of the Adapted FMOP algorithm for VRP is that it provides a solution that considers more than one objective without the need for decision makers’ view on the weights of objectives in all decision models in the fuzzy environment. Also, the proposed algorithm can find the solution with the help of a mathematical model without requiring any heuristics or metaheuristics, since it primarily performs clustering. Firstly, the efficiency of this algorithm was tested on problems in the literature. The Adapted FMOP algorithm for VRP achieved the best-known solutions by some small margins and exceeded the best-known solution for one problem in the literature. After seeing that the performance of the algorithm was sufficient, a data set of a firm in the construction sector was implemented to see how the algorithm works in real life and the obtained results were discussed. The solutions demonstrate that the Adapted FMOP algorithm for VRP also works well for real-world problems.

show abstract

“…The Adapted FMOP algorithm for VRP is based on the paper by Yalcin and Erginel (2015). The proposed Adapted max λ subject to…”

Section: Problem Formulation and Resolution Methodologymentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Adapted Fuzzy Multi-Objective Programming Algorithm for Vehicle Routing

Yalçın

Erginel²

2022

Universal Journal of Operations and Management

View full text Add to dashboard Cite

show abstract

“…Transforming the fuzzy multi-objective model is to calculate the linear membership functions (Yalcın and Erginel, 2015) via Eq. ( 21) for a maximization objective and Eq.…”

Section: Proposed Hybrid Methodsmentioning

confidence: 99%

Resource efficiency improvement: zero waste discharge planning in a pulp-and-paper firm under uncertainties

Jeng

Lin

Tseng

et al. 2021

MEQ

View full text Add to dashboard Cite

PurposeThis study develops an integrated zero waste discharge planning approach for improving resource efficiency in a pulp-and-paper manufacturing firm.Design/methodology/approachThe objectives of this study are to (1) identify the environmental, technical and social metrics in resource efficiency; (2) utilize fuzzy multi-objective programming and the hybrid particle swarm optimization algorithm to solve the fuzzy problem; and (3) develop an assessment for resource efficiency improvement in an industrial case study.FindingsThe findings demonstrate the superiority of hybrid particle swarm optimization algorithm in generating optimal results for a pulp-and-paper manufacturing firm.Practical implicationsThe findings demonstrate the superiority of hybrid particle swarm optimization algorithm in generating optimal results for a pulp-and-paper manufacturing firm.Originality/valueResource efficiency is a multi-objective problem in an uncertain environment. In particular, zero waste discharge planning involves minimizing the total cost and maximizing the waste material recovery rate, wastewater reuse, and waste heat recovery.

show abstract

“…Approaches for multi-objective versions of the VRPB have been proposed by Anbuudayasankar et al (2012), García-Nájera et al (2015) and Yalcın & Erginel (2015). Three heuristics approaches for solving a bi-objective vehicle routing problem with forced backhauls were introduced by Anbuudayasankar et al (2012).…”

Section: Introductionmentioning

confidence: 99%

“…In particular, two heuristics are based on the well-known savings algorithm and the third heuristic is based on a Genetic Algorithm (GA). Finally, an evolutionary approach and a fuzzy programming for the multi-objective vehicle routing problems with backhauls were presented by García-Nájera et al (2015) and Yalcın and Erginel (2015), respectively. Other multiobjective algorithms proposed for solving related logistic combinatorial problems could be consulted in Nezhad et al (2013), Mortezaei and JabalAmeli (2011), Mohammadi et al (2011), Rao and Patel (2014, Yazdian andShahanaghi (2011), Escobar et al (2013), Escobar et al (2014b), Escobar et al (2015) and Bolaños et al (2015).…”

Section: Introductionmentioning

confidence: 99%

A multi-objective Pareto ant colony algorithm for the Multi-Depot Vehicle Routing problem with Backhauls

Chávez¹,

Escobar²,

Echeverri³

2016

10.5267/j.ijiec

View full text Add to dashboard Cite

This paper presents a multiobjective ant colony algorithm for the Multi-Depot Vehicle Routing Problem with Backhauls (MDVRPB) where three objectives of traveled distance, traveling times and total consumption of energy are minimized. An ant colony algorithm is proposed to solve the MDVRPB. The solution scheme allows one to find a set of ordered solutions in Pareto fronts by considering the concept of dominance. The effectiveness of the proposed approach is examined by considering a set of instances adapted from the literature. The computational results show high quality results within short computing times.

show abstract

Fuzzy multi-objective programming algorithm for vehicle routing problems with backhauls

Cited by 25 publications

References 35 publications

Adapted Fuzzy Multi-Objective Programming Algorithm for Vehicle Routing

Adapted Fuzzy Multi-Objective Programming Algorithm for Vehicle Routing

Resource efficiency improvement: zero waste discharge planning in a pulp-and-paper firm under uncertainties

A multi-objective Pareto ant colony algorithm for the Multi-Depot Vehicle Routing problem with Backhauls

Contact Info

Product

Resources

About