Simulation-based evolution of resupply and routing policies in rich vendor-managed inventory scenarios

Vonolfen, Stefan; Affenzeller, Michael; Beham, Andreas; Lengauer, Efrem; Wagner, Stefan

doi:10.1007/s10100-011-0232-5

Cited by 14 publications

(7 citation statements)

References 14 publications

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“…The costs for using a vehicle are set to c v = 3, 000, the costs for the traveled distance is set to c d = 1. These values were set according to previous practical studies such as Vonolfen et al (2013a). We assume that enough vehicles are available to serve all requests as also observed in case studies such as Mitrovic-Minic et al (2001).…”

Section: Problem Descriptionmentioning

confidence: 98%

“…Especially the combination of simulation with metaheuristics has proven to be fruitful for various application domains (Tekin and Sabuncuoglu (2004)). In this context, HeuristicLab has been applied in several application domains such as production planning (Can et al (2008), Pitzer et al (2011)), ), inventory routing (Vonolfen et al (2013a)) and dynamic dial-a-ride problems ). Especially the evolution of policies in dynamic and volatile environments has been shown to be a powerful application of simulationbased optimization.…”

Section: Simulation-based Evolution Of Waiting Strategiesmentioning

confidence: 99%

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Distribution of waiting time for dynamic pickup and delivery problems

Vonolfen

Affenzeller

2014

Ann Oper Res

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Pickup and delivery problems have numerous applications in practice such as parcel delivery and passenger transportation. In the dynamic variant of the problem, not all information is available in advance but is revealed during the planning process. Thus, it is crucial to anticipate future events in order to generate high-quality solutions. Previous work has shown that the use of waiting strategies has the potential to save costs and maximize service quality. We adapt various waiting heuristics to the pickup and delivery problem with time windows. Previous research has shown, that specialized waiting heuristics utilizing anticipatory knowledge potentially outperform general heuristics. Direct policy search based on evolutionary computation and a simulation model is proposed as a methodology to automatically specialize waiting strategies to different problem characteristics. Based on the strengths of the previously introduced waiting strategies, we propose a novel waiting heuristic that can utilize historical request information based on an intensity measure which does not require an additional data preprocessing step. The performance of the waiting heuristics is evaluated on a single set of benchmark instances containing various instance classes that differ in terms of spatial and temporal properties. The diverse set of benchmark instances is used to analyze the influence of spatial and temporal instance properties as well as the degree of dynamism to the potential savings that can be achieved by anticipatory waiting and the incorporation of knowledge about future requests.

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Section: Problem Descriptionmentioning

confidence: 98%

Section: Simulation-based Evolution Of Waiting Strategiesmentioning

confidence: 99%

Distribution of waiting time for dynamic pickup and delivery problems

Vonolfen

Affenzeller

2014

Ann Oper Res

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“…In other words the modeling phase allows detecting the basic patterns governing the supply chain behavior and provides developers with the relevant conceptual model for recreating the real system essence in a synthetic environment. Therefore the ability to capture as faithfully as possible the working logics of a real fish supply chain in a simulation tool provided with predictive capabilities relies on the accuracy of the underlying model and as a consequence on capability of abstraction from irrelevant details [11]. As for the simulation paradigm a DES approach has been adopted.…”

Section: Model Descriptionmentioning

confidence: 99%

“…To this end M&S provides decision makers at all levels (operational, tactical, and strategic) with specific and direct responses thanks to the possibility of investigating and testing real processes and their outcomes in a synthetic environment [4][5][6]. As a matter of facts, M&S has been already applied to handle different issues pertaining to supply chain management such as optimization [7], policy analysis [8][9][10][11], and decision support [12]. Here, M&S has shown its effectiveness and its capability to take into account several factors and their mutual interdependencies along the chain from procurement to market.…”

Section: State Of the Artmentioning

confidence: 99%

Safety and Security in Fresh Good Supply Chain

Bruzzone¹,

Longo²,

Massei³

et al. 2014

International Journal of Food Engineering

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This paper proposes a model aimed at investigating safety and security issues in fresh good supply chain. Particular attention is paid on fresh goods contamination since the increasing complexity of supply chains makes these events more likely to happen. To this end, a discrete event simulator (DES) underpinned by an analytical model has been developed as a support tool for prevention and mitigation. Indeed, the simulation model is able to recreate the disruption wave propagation along the supply chain due to a contamination event as well as the effects on the dynamic evolution of contaminated products demand taking into account complex phenomena like media campaigns. In addition, even the economic impacts in terms of costs (i.e. transportation costs, inventory costs, and lost sales) can be assessed. The potentials of the proposed simulation framework have been investigated in a real case study of a fresh food supply chain located in the north of Italy.

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“…In order to get a single policy out of the later formulated amount of simple rules, rule synthesis is applied similar to [14] and [2]. First, all simple rules are normalized according to their maximum value for ensuring that their output value is in the interval [0,1].…”

Section: Principles Of Policy Optimizationmentioning

confidence: 99%

Evolutionary optimization of multi-agent controlstrategies for electric vehicle charging

Hutterer

Affenzeller

Auinger

2012

Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation

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While an increasing share of intermittent and non-dispatchable renewable energy plants cause probabilistic behavior at the power grids' supply side, the expected penetration of electric mobility at the demand side offers the opportunity of controllable load. Their optimal coordination is one major concern for future smart grids. Therefore, a multi-agent system will be proposed where each electric vehicle (agent) acts in response to dynamic conditions in its environment according to a given strategy. Optimizing these strategies will be the core of this paper, while evolutionary computation will be used for optimization. Here, simulation models will be applied for problem representation and solution evaluation. Thus, simulation allows modeling of complex as well as probabilistic systems, necessary for the herein tackled probem. In the end, the optimized strategies determine electric vehicles' charging behavior such that end-users' energy demand is satisfied and secure power grid operation is guaranteed throughout the considered grid using power from renewable plants. For solution representation, two different approaches will be compared concerning reachable solution quality as well as problem-specific metrics.

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Simulation-based evolution of resupply and routing policies in rich vendor-managed inventory scenarios

Cited by 14 publications

References 14 publications

Distribution of waiting time for dynamic pickup and delivery problems

Distribution of waiting time for dynamic pickup and delivery problems

Safety and Security in Fresh Good Supply Chain

Evolutionary optimization of multi-agent controlstrategies for electric vehicle charging

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