Simulation-Based Fitness Landscape Analysis and Optimisation for Vehicle Scheduling Problem

Merkuryeva, Galina; Большаков, В И

doi:10.1007/978-3-642-27549-4_36

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…To perform a random walk on the fitness landscape, a mutation operator changes one randomly selected trip in the solution candidate by assigning a new randomly chosen vehicle, and start time is shifted to the later one by a certain constant value (Merkuryeva, Bolshakov 2012a). A sample random walk in one experiment is shown in Figure 3.…”

Section: Fitness Evaluation and Analysis Through Simulationmentioning

confidence: 99%

Simulation-Based Fitness Landscape Analysis and Optimisation of Complex Problems

Merkuryeva

Большаков

2015

Technological and Economic Development of Economy

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Widespread hard optimisation problems in economics and logistics are characterised by large dimensions, uncertainty and nonlinearity and require more powerful methods of stochastic optimisation that traditional ones. Simulation optimisation is a powerful tool for solving these problems. Moreover, fitness landscape analysis techniques provide an efficient approach to better selection of a suitable optimisation algorithm. The concept and techniques of fitness landscape analysis are described. A formalised scheme for simulation optimisation enhanced with fitness landscape analysis is given. Benchmark fitness landscape analysis is performed to find relations between efficiency of an optimisation algorithm and structural features of a fitness landscape. Case study in simulation optimisation of vehicle routing and scheduling is described. Various optimisation scenarios with application of the fitness landscape analysis are discussed and investigated.

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Section: Fitness Evaluation and Analysis Through Simulationmentioning

confidence: 99%

Simulation-Based Fitness Landscape Analysis and Optimisation of Complex Problems

Merkuryeva

Большаков

2015

Technological and Economic Development of Economy

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“…The vehicle schedule model (Fig. 6) is built as a discrete-event simulation model (Merkuryeva and Bolshakov, 2010). Each vehicle is modelled as an active object.…”

Section: Simulation Optimisation Of Vehicle Schedulesmentioning

confidence: 99%

Integrated Delivery Planning And Scheduling Built On Cluster Analysis And Simulation Optimisation

Merkuryeva¹

2012

ECMS 2012 Proceedings Edited By: K. G. Troitzsch, M. Moehring, U. Lotzmann

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Integrated solutions for product delivery planning and scheduling in distribution centres are proposed and built on a cluster analysis and simulation optimisation methodology. A cluster analysis of product demand data of stores is used to identify typical dynamic demand patterns and product delivery tactical plans. Further, simulation optimisation techniques are applied to find optimal parameters of product transportation and vehicle delivery schedules. In the paper, a cluster analysis of the demand data by using the K-means clustering algorithm and silhouette plots mean values is performed, and an NBTree-based classification model is built. In order to define optimal parameters of vehicle schedules, a genetic algorithm is applied and interacts with a discrete-event simulation transportation model built in AnyLogic simulation environment. Integrated solutions are illustrated and adjusted to a specific business case.

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“… Tactical delivery planning -currently is made with an adjustment of a strategic plan to a new or forecasted demand.  Vehicle routing and scheduling by using scheduling optimisation meta-heuristics described in [1].…”

Section: A Detailed Descriptionmentioning

confidence: 99%

An Integrated Approach to Product Delivery Planning and Scheduling

Merkuryeva¹,

Большаков²,

Kornevs³

2011

Scientific Journal of Riga Technical University. Computer Sciences

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-Product delivery planning and scheduling is a task of high priority in transport logistics. In distribution centres this task is related to deliveries of various types of goods in predefined time windows. In real-life applications the problem has different stochastic performance criteria and conditions. Optimisation of schedules itself is time consuming and requires an expert knowledge. In this paper an integrated approach to product delivery planning and scheduling is proposed. It is based on a cluster analysis of demand data of stores to identify typical dynamic demand patterns and product delivery tactical plans, and simulation optimisation to find optimal parameters of transportation or vehicle schedules. Here, a cluster analysis of the demand data by using the K-means clustering algorithm and silhouette plots mean values is performed, and an NBTree-based classification model is built. In order to find an optimal grouping of stores into regions based on their geographical locations and the total demand uniformly distributed over regions, a multiobjective optimisation problem is formulated and solved with the NSGA II algorithm.

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Simulation-Based Fitness Landscape Analysis and Optimisation for Vehicle Scheduling Problem

Cited by 7 publications

References 5 publications

Simulation-Based Fitness Landscape Analysis and Optimisation of Complex Problems

Simulation-Based Fitness Landscape Analysis and Optimisation of Complex Problems

Integrated Delivery Planning And Scheduling Built On Cluster Analysis And Simulation Optimisation

An Integrated Approach to Product Delivery Planning and Scheduling

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