A general variable neighborhood search for the swap-body vehicle routing problem

Todosijević, Raca; Hanafi, Saïd; Urošević, Dragan; Jarboui, Bassem; Gendron, Bernard

doi:10.1016/j.cor.2016.01.016

Cited by 33 publications

(18 citation statements)

References 23 publications

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“…The shaking step increases the chances of finding a global optimum solution by a random selection strategy for several neighborhood structures associated with the current best solution. Local search is an improvement procedure that makes use of a number of neighborhood structures (Todosijević et al., ). The phases of our proposed MVNSH are explained in Fig.…”

Section: The Modified Variable Neighborhood Search Heuristicmentioning

confidence: 99%

“…In the case of VNS, the moves that are used to constitute neighborhoods are divided into two groups: intraroute moves and interroute moves (Todosijević et al., ). In this content, swap, 2‐opt, 3‐opt, and insertion are mostly used as intraroute neighborhood search operators and exchange ( m , n ), Cross, Shift (0,1), and Replace (1,1) structures are used as interroute search operators (Polat, ).…”

Section: The Modified Variable Neighborhood Search Heuristicmentioning

confidence: 99%

“…In general, VNS methods need local search strategy, which improves the use of a number of neighborhood structures (Todosijević et al., ). After generating all possible precedence feasible schedules (i.e.,

F e a s_{p r e c}

) by enhanced activity swapping, we employed one local search strategy (i.e., Algorithm (c)) for generating updated schedules with better makespan performance (if any).…”

Section: The Modified Variable Neighborhood Search Heuristicmentioning

confidence: 99%

“…The basic variant of VNS was first proposed in Mladenović and Hansen (), which includes an improvement phase in which a local search is applied and one of the so‐called shaking‐phase or swap‐move approaches is used to resolve local minima traps. The local search and the swap moves, together with the neighborhood change step, are executed alternately until a predefined stopping criterion is fulfilled (Todosijević et al., ). However, the problem of searching a given neighborhood of an NP‐hard problem can itself be defined as a combinatorial optimization problem.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, any VNS approach can provide a systematic exploration of a number of neighborhood structures used in shaking and local search steps, with the aim of reaching an improved solution (Polat, ). With the successful implementation of shaking phases and local search procedures, a VNS‐based heuristic can increase the chances of finding a global optimum solution by a random selection strategy for a number of neighborhood structures associated with the current best solution (Todosijević et al., ). Considering its utmost importance, this study proposes a VNS‐based heuristic for competitively solving MRCPSPs.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐mode resource‐constrained project scheduling using modified variable neighborhood search heuristic

Chakrabortty

Abbasi

Ryan

2019

Int Trans Operational Res

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This paper aims at providing a fast near‐optimum solution to the multi‐mode resource‐constrained project scheduling problems (MRCPSPs), for projects with activities that have known deterministic renewable and nonrenewable resource requirements. The MRCPSP is known to be nondeterministic polynomial‐time hard and has been solved using various exact, heuristic, and meta‐heuristic procedures. In this paper, a modified variable neighborhood search heuristic algorithm is used as an advanced optimization technique that suits scheduling problems. For the experimental study, we have considered a standard set of 3929 multi‐mode benchmark instances from the project scheduling library with a range of projects comprising 10–30 activities. Moreover, for a better comparison, this research also considers a standard set of 4320 newly developed multi‐mode instances from MMLIB50, MMLIB100, and MMLIB+ datasets. With the limit of 50,000 schedules on these datasets, our proposed algorithm provides better makespan for 106, 34, and 1601 instances, respectively, which justifies the efficiency of the proposed algorithm, particularly for projects with a larger number of activities. The results reported in this paper can be used as a benchmark for other researchers to compare and improve.

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Section: The Modified Variable Neighborhood Search Heuristicmentioning

confidence: 99%

Section: The Modified Variable Neighborhood Search Heuristicmentioning

confidence: 99%

F e a s_{p r e c}

) by enhanced activity swapping, we employed one local search strategy (i.e., Algorithm (c)) for generating updated schedules with better makespan performance (if any).…”

Section: The Modified Variable Neighborhood Search Heuristicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multi‐mode resource‐constrained project scheduling using modified variable neighborhood search heuristic

Chakrabortty

Abbasi

Ryan

2019

Int Trans Operational Res

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A Modified Variable Neighbourhood Search Heuristic for the Multi‐Mode Resource Constrained Project Scheduling Problem with A Case Study

Chakrabortt

Abbasi

Ryan

2019

INCOSE International Symp

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This paper provides a fast near‐optimum solution to the Multi‐Mode Resource Constrained Project Scheduling Problem (MRCPSP), for projects with activities that have known deterministic renewable and non‐renewable resource requirements. Multi‐modes allows any activity to execute one of many alternative combinations of resource usages and durations, while each activity needs to pick the most feasible one. This type of setting is more prevalent for real‐life project schedules, which necessitates systematic methodologies and advanced solution approaches in harmony with real‐world implementations. This study proposes a Modified Variable Neighbourhood Search Heuristic (MVNSH) algorithm as an advanced optimization technique for scheduling projects. To validate our approach, exhaustive computational tests were carried out by using relevant benchmark instances. The MVNSH algorithm has been shown to be faster and more accurate than many existing heuristic methods. This research also contributes to developing a practical decision support system for resolving real‐life constraints in multi‐mode resource‐constrained projects.

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Detecting Patterns in Benchmark Instances of the Swap-Body Vehicle Routing Problem

Souravlias

Huber

2018

Lecture Notes in Computer Science

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A general variable neighborhood search for the swap-body vehicle routing problem

Cited by 33 publications

References 23 publications

Multi‐mode resource‐constrained project scheduling using modified variable neighborhood search heuristic

Multi‐mode resource‐constrained project scheduling using modified variable neighborhood search heuristic

A Modified Variable Neighbourhood Search Heuristic for the Multi‐Mode Resource Constrained Project Scheduling Problem with A Case Study

Detecting Patterns in Benchmark Instances of the Swap-Body Vehicle Routing Problem

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