Using decomposition-based multi-objective algorithm to solve Selective Pickup and Delivery Problems with Time Windows

Ben-Said, Asma; Moukrim, Aziz; Guibadj, Rym Nesrine; Verny, Jérôme

doi:10.1016/j.cor.2022.105867

Cited by 9 publications

(5 citation statements)

References 44 publications

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“…A metaheuristic method that combines a Pareto ant colony optimization algorithm and a variable neighborhood search method has been proposed for the bi-objective TOP (BTOP) [14]. Finally, a two-phase decomposition method based on Local Search has been proposed to solve Selective Pickup and Delivery Problems with Time Windows (SPDPTW) [1].…”

Section: Bi-objective Optimizationmentioning

confidence: 99%

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An Elitist Non-dominated Heuristic Resolution for the Dynamic Asset Protection Problem

Peña,

Moukrim,

Serairi

2023

Lecture Notes in Computer Science

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During escaped wildfires, community assets are at risk of damage or destruction. Preventive operations requiring dispatching resources and cooperation can be taken to protect these assets. The planning of such operations is sensitive to unforeseen disruptions that may occur. To account for the effects of the disruption, it may be necessary to alter the initial routes of the vehicles. The problem rising from the rescheduling of the vehicles is a bi-objective optimization problem known as the Dynamic Asset Protection Problem (D-APP). We propose a genetic algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve the D-APP. We define new mutation and crossover operators adapted to our problem, and we propose procedures to repair and evaluate a solution based on Mixed Integer Programming (MIP).

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Section: Bi-objective Optimizationmentioning

confidence: 99%

“…The route of a vehicle always starts at a depot and ends at the sink node. For instance, there are three vehicles in solution [1,2,6,4,11,1,5,7,3,11,1,7,3,11], the route of the first vehicle is…”

Section: Encodingmentioning

confidence: 99%

An Elitist Non-dominated Heuristic Resolution for the Dynamic Asset Protection Problem

Peña,

Moukrim,

Serairi

2023

Lecture Notes in Computer Science

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“…The most frequently employed decomposition-based MOEA is MOEA/D, introduced by Zhang and Li 30 . The MOEA/D structure can incorporate various traditional single-objective optimization and localized search methods 33 – 35 . Indicator-based MOEAs.…”

Section: Introductionmentioning

confidence: 99%

“…To gauge the potency of this proposed method, we employ distinct benchmark test functions: ZDT 65 , DTLZ 66 , Constraint 67 , 68 (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design Brushless DC wheel motor 69 (RWMOP1), Helical spring 68 (RWMOP2), Two-bar truss 68 (RWMOP3), Welded beam 70 (RWMOP4), Disk brake 71 (RWMOP5). The objective of this assessment is to compare the efficacy of our proposed method against MOMPA, NSGA-II, MOAOA, MOEA/D and MOGNDO, using metrics like generational distance (GD) 34 , inverse generational distance (IGD) 35 , hypervolume 36 , Spacing 37 , Spread 36 and run time (RT). The approximations of the Pareto-front produced by our method are evaluated using these metrics.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective exponential distribution optimizer (MOEDO): a novel math-inspired multi-objective algorithm for global optimization and real-world engineering design problems

Kalita,

Ramesh,

Cepova

et al. 2024

Sci Rep

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The exponential distribution optimizer (EDO) represents a heuristic approach, capitalizing on exponential distribution theory to identify global solutions for complex optimization challenges. This study extends the EDO's applicability by introducing its multi-objective version, the multi-objective EDO (MOEDO), enhanced with elite non-dominated sorting and crowding distance mechanisms. An information feedback mechanism (IFM) is integrated into MOEDO, aiming to balance exploration and exploitation, thus improving convergence and mitigating the stagnation in local optima, a notable limitation in traditional approaches. Our research demonstrates MOEDO's superiority over renowned algorithms such as MOMPA, NSGA-II, MOAOA, MOEA/D and MOGNDO. This is evident in 72.58% of test scenarios, utilizing performance metrics like GD, IGD, HV, SP, SD and RT across benchmark test collections (DTLZ, ZDT and various constraint problems) and five real-world engineering design challenges. The Wilcoxon Rank Sum Test (WRST) further confirms MOEDO as a competitive multi-objective optimization algorithm, particularly in scenarios where existing methods struggle with balancing diversity and convergence efficiency. MOEDO's robust performance, even in complex real-world applications, underscores its potential as an innovative solution in the optimization domain. The MOEDO source code is available at: https://github.com/kanak02/MOEDO.

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“…We aimed to gauge their capabilities in swiftly converging to the true Pareto optimal front and the distribution of the obtained non-dominated solutions. Upon assessing their convergence and coverage using MO metrics and the produced Pareto optimal fronts on benchmark suites (ZDT [ 54 ], DTLZ [ 54 ], Constraint [ 68 , 69 ] and engineering design problems [ 55 , 56 ]), we discerned that these algorithms still exhibited shortcomings in convergence and coverage using metrics like generational distance (GD) [ 70 ], inverse generational distance (IGD) [ 71 ], hypervolume [ 72 ], Spacing [ 73 ], Spread [ 72 ] and run time (RT). The approximations of the Pareto-front produced by our method are evaluated using these metrics.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

Kalita,

Naga Ramesh,

Čep

et al. 2024

Heliyon

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Using decomposition-based multi-objective algorithm to solve Selective Pickup and Delivery Problems with Time Windows

Cited by 9 publications

References 44 publications

An Elitist Non-dominated Heuristic Resolution for the Dynamic Asset Protection Problem

An Elitist Non-dominated Heuristic Resolution for the Dynamic Asset Protection Problem

Multi-objective exponential distribution optimizer (MOEDO): a novel math-inspired multi-objective algorithm for global optimization and real-world engineering design problems

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

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