An efficient meta-heuristic algorithm for solving capacitated vehicle routing problem

Faiz, Alfian; Subiyanto, Subiyanto; Arief, Ulfah Mediaty

doi:10.26555/ijain.v4i3.244

Cited by 10 publications

(6 citation statements)

References 57 publications

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“…Since the k-means algorithm in the initial centroid selection is random and the results are unstable, this work conducted 50 tests and took the optimal results. A method PVNS-ASM (denoted as method 4) proposed by Faiz et al [ 37 ]. Method 4 is an enhanced perturbation-based variable neighborhood search with an adaptive selection mechanism.…”

Section: Simulation Experiments and Discussionmentioning

confidence: 99%

“…Method 4 is an enhanced perturbation-based variable neighborhood search with an adaptive selection mechanism. On the parameter setting, this work adopts the optimal configuration of method 3 in [ 37 ], the best configuration of the shaking step: N1: exchange (m, n), N2: cross, and N3: shift (m, 0) and the best configuration of the local search step: N1: insertion, N2: exchange, N3: 3-opt, N4: 2-opt, N5: replace, N6: shift, and N7: cross; that is, k max is set to 3 and m max is 7, the perturbation counter p max is 50, the VNS termination counter s max is 100, and the parameter of scoring system θ 1 is set to 5 and θ is 10. This work conducted 50 tests of this method and finally took the optimal value of the results.…”

Section: Simulation Experiments and Discussionmentioning

confidence: 99%

“…A method PVNS-ASM (denoted as method 4) proposed by Faiz et al [ 37 ]. Method 4 is an enhanced perturbation-based variable neighborhood search with an adaptive selection mechanism.…”

Section: Simulation Experiments and Discussionmentioning

confidence: 99%

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A Cross-Regional Scheduling Strategy of Waste Collection and Transportation Based on an Improved Hierarchical Agglomerative Clustering Algorithm

Wei

Liang

Tang

2022

Computational Intelligence and Neuroscience

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The disposal of municipal solid waste (MSW) is based on the divide-regional operation model at present, which brings management convenience. However, due to the constraints of the division between the operating regions, there are problems such as inflexible vehicle scheduling and low efficiency. Changing this mode will put pressure on management. It is necessary to break the constraint of the region and form a fully automatic operating scheme without too much manual management. First, the initial clusters are formed by considering the distance in the initial allocation module. Secondly, through the type labeling and reallocation module, the single big data set is transformed into multiple small data sets by considering the allocated amount of garbage and the carrying capacity of garbage vehicles. Then, this work proposes the improved hierarchical agglomerative clustering (IHAC) algorithm and the garbage collecting path planning (GCPP) algorithm to realize the intelligent allocation of waste and scheduling route planning of garbage vehicles. Finally, through simulation by real example and comparative analysis, the advantages of the proposed scheme are discussed. The results show that the proposed scheme is more effective than the original scheme and other advanced methods, which can provide decision support for the scientific and intelligent collection and transportation of MSW.

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Section: Simulation Experiments and Discussionmentioning

confidence: 99%

Section: Simulation Experiments and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A Cross-Regional Scheduling Strategy of Waste Collection and Transportation Based on an Improved Hierarchical Agglomerative Clustering Algorithm

Wei

Liang

Tang

2022

Computational Intelligence and Neuroscience

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“…An enhanced perturbation-based VNS with adaptive selection mechanism method (PVNS-ASM) is developed in [18] by combining perturbation-based VNS (PVNS) with an adaptive selection mechanism (ASM).…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Genetic Algorithms for the Asymmetric Distance-Constrained Vehicle Routing Problem

Ahmed

Hameed

Mutar

2022

Mathematical Problems in Engineering

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We aim to suggest a simple genetic algorithm (GA) and other four hybrid GAs (HGAs) for solving the asymmetric distance-constrained vehicle routing problem (ADVRP), a variant of vehicle routing problem (VRP). The VRP is a difficult NP-hard optimization problem that has numerous real-life applications. The VRP aims to find an optimal tour that has least total distance (or cost) to provide service to n customers (or nodes or cities) utilizing m vehicles so that every vehicle starts journey from and ends journey at a depot (headquarters) and visits every customer only once. The problem has many variations, and we consider the ADVRP for this study, where distance traveled by every vehicle must not exceed a predefined maximum distance. The proposed GA uses random initial population followed by sequential constructive crossover and swap mutation. The HGAs enhance the initial solution using 2-opt search method and incorporate a local search technique along with an immigration procedure to obtain effective solution to the ADVRP. Experiments have been conducted among the suggested GAs by solving several restricted and unrestricted ADVRP instances on asymmetric TSPLIB utilizing several vehicles. Our experiments claim that the suggested HGAs using local search methods are very effective. Finally, we reported a comparative study between our best HGA and a state-of-the-art algorithm on asymmetric capacitated VRP and found that our algorithm is better than the state-of-the-art algorithm for the instances.

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“…This is due to its capability to find the optimal solution within reasonable time [11]. Some of the metaheuristic techniques used in scheduling problems are evolutionary algorithms including genetic algorithm, differential evolution, artificial bee colony, ant colony optimization, memetic algorithm and many more [8], [12]- [14]. In JSP, genetic algorithm is the most used method [15].…”

Section: Introductionmentioning

confidence: 99%

Staff scheduling for a courier distribution centre using evolutionary algorithm

Ying

Yusoh

2022

IJEECS

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Staff scheduling is a <span lang="EN-US">combinatorics optimization problem and companies face this complex task on daily basis in constructing a schedule fitting all conditions. In a courier distribution center, staffs are assigned to work in processes of a continuous workflow. Staffs have varying work ability for each process. Instead of generating staff schedule instinctively, it is an advantage to optimize staff’s schedule by measuring the performance of each staff. An optimized schedule improves the operation’s efficiency and fully utilize staffs’ work ability, hence, minimizing the cost. This paper proposed evolutionary algorithm, namely genetic algorithm as the solution to courier center staff scheduling. Based on the result, the produced schedule can reduce up to 30% of the staff in schedule while not affecting operation workflow. The cut down on number of working staffs could amount to a substantial reduction of operation cost every month. The generated schedule is significantly customized and take less time to complete an operation. Although the proposed solution is specific to the use case of a courier distribution center, it is however, potentially a generalize model for the logistics industry, introducing a more effective staff scheduling system to cope with the industry’s ever-rising demands.</span>

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An efficient meta-heuristic algorithm for solving capacitated vehicle routing problem

Cited by 10 publications

References 57 publications

A Cross-Regional Scheduling Strategy of Waste Collection and Transportation Based on an Improved Hierarchical Agglomerative Clustering Algorithm

A Cross-Regional Scheduling Strategy of Waste Collection and Transportation Based on an Improved Hierarchical Agglomerative Clustering Algorithm

Hybrid Genetic Algorithms for the Asymmetric Distance-Constrained Vehicle Routing Problem

Staff scheduling for a courier distribution centre using evolutionary algorithm

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