The Biobjective Inventory Routing Problem – Problem Solution and Decision Support

Geiger, Martin Josef; Sevaux, Marc

doi:10.1007/978-3-642-21527-8_41

Cited by 16 publications

(28 citation statements)

References 20 publications

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“…At day 1 the values of L i,t are set to some pre-defined initial inventory level. The inventory levels are then updated according to the equation given by Geiger and Sevaux [31].…”

Section: Problem Definitionmentioning

confidence: 99%

“…The supplier has to service all customers with a fleet of homogeneous vehicles with an equal maximal capacity. The objective in [31] was to minimize the total inventory cost and the total routing cost during the planning period. Here, it is extended to a tri-objective problem that also states expected stockout cost as an objective function.…”

Section: Problem Definitionmentioning

confidence: 99%

“…Formally, the problem is set up as described in [31]: There are n customers and at most one vehicle with capacity C per customer. Deliveries cannot be split in the model, namely a customer can be visited at most once by a vehicle per day.…”

Section: Problem Definitionmentioning

confidence: 99%

“…This can be avoided by adding a back-logging strategy in case customers are willing to wait till the next delivery day [15,17]. Stockout is not permitted in most IRP with deterministic demand [2,13,31]. However, when the demand of customers is stochastic, it is impossible to avoid the stockout [33].…”

Section: Introductionmentioning

confidence: 99%

“…But they integrate three objectives problem into a single objective problem by giving a tradeoff weights to each objective. Geiger et al developed a local search strategy by modifying deliver frequencies which generated a Pareto front approximation for the bi-objective inventory routing problem [31]. They also proposed 14 benchmark instances with certain demand.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multi-objective inventory routing with uncertain demand using population-based metaheuristics

Yang¹,

Emmerich²,

Bäck³

et al. 2016

ICA

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Abstract. This article studies a tri-objective formulation of the inventory routing problem, extending the recently studied biobjective formulation. As compared to distance cost and inventory cost, which were discussed in previous work, it also considers stockout cost as a third objective. Demand is modeled as a Poisson random variable. State-of-the-art evolutionary multi-objective optimization algorithms and a new method based on swarm intelligence are used to compute approximation of the 3-D Pareto front. A bench-mark previously used in bi-objective inventory routing is extended by incorporating a uncertain demand model with an expected value that equals the average demand of the original benchmark. The results provide insights into the shape of the optimal trade-off surface. Based on this the dependences between different objectives are clarified and discussed. Moreover, the performances of the four different algorithmic methods are compared and due to the consistency in the results, it can be concluded that a near optimal approximation to the Pareto front can be found for problems of practically relevant size.

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“…At day 1 the values of L i,t are set to some pre-defined initial inventory level. The inventory levels are then updated according to the equation given by Geiger and Sevaux [31].…”

Section: Problem Definitionmentioning

confidence: 99%

Section: Problem Definitionmentioning

confidence: 99%

Section: Problem Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multi-objective inventory routing with uncertain demand using population-based metaheuristics

Yang¹,

Emmerich²,

Bäck³

et al. 2016

ICA

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Simulation of Preference Information in an Interactive Reference Point‐Based Method for the Bi‐Objective Inventory Routing Problem

Huber

Geiger

Sevaux

2014

Multi Criteria Decision Anal

Self Cite

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International audienceIn the research area of multiple criteria decision making, very few publications exist that explicitly design the simulation of a decision maker (DM) in an interactive approach. For this reason, we outline some methods widely used in the literature to identify common assumptions of simulating the DM's responses and the required input preference information. Our paper aims at covering the identified gap by introducing experimental concepts. Such concepts are used for theoretical analyses of a combined search-and-decision-making procedure. Simulating the DM is a fruitful idea because the algorithm can be tested without integrating a human decision maker. Finally, we conduct experiments based on the proposed settings for a multiobjective inventory routing problem, which is a relevant and challenging logistic problem

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Editorial: Special issue on understanding complexity in multiobjective optimization

Greco

Klamroth

Knowles

et al. 2017

J. Multi-Crit. Decis. Anal.

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This special issue of JMCDA was inspired by the work of three Dagstuhl seminars aimed at strengthening the links between the scientific communities of multiple criteria decision making (MCDM) and evolutionary multiobjective optimization. These three Dagstuhl seminars were devoted to the following topics:• Hybrid and robust approaches to multiobjective optimization Mutzel investigate complexity for multiobjective combinatorial optimization problems, taking into consideration output-sensitive complexity of an algorithm for a general enumeration problem, that is, the property that its running time is bounded by a polynomial in the input and the output size. The paper shows that output-sensitive complexity is able to separate efficiently solvable from presumably not efficiently solvable problems, proving also that multiobjective s-t-path problems do not admit an output-sensitive algorithm under weak complexity theoretic assumptions as P ≠ NP. Rodrigo Lankaites Pinheiro, Dario Landa-Silva, and Jason Atkin present a technique that supports understanding the relationships between objectives in a multiobjective optimization problem through a visualization and analysis of the local and global relationships between objectives. The advantages of the proposed technique are shown in experiments on three different combinatorial optimization problems (multiobjective multidimensional knapsack problem, multiobjective nurse scheduling problem, and multiobjective vehicle routing problem with time windows).view of navigation, that is, the interactive procedure of traversing through a set of points (the navigation set) in the objective space guided by a decision maker, with the ultimate goal of identifying the single most preferred Pareto optimal solution. The authors describe a general framework to capture a wide range of navigation methods taking also into account real-world problems to which these methods have been applied and highlighting directions of future research. consider complex systems composed of strongly interrelated subsystems or subproblems with single or multiple objectives that are usually not sequentially ordered or obviously decomposable. In the literature, these systems are also referred to as "interwoven systems" or "systems of systems." Due to the correlation between the components, the overall system performance does not equal the simple sum of their performances, and inclusion of complex synergy may imply possible inaccuracies in the model and prohibitively expensive computations. The authors review recent developments in this field and present a preliminary mathematical model of an interwoven system introducing some approaches to its multiobjective optimization. start from the consideration that despite the fact that in general multiobjective combinatorial optimization problems are known to be hard problems because very often they are NP-complete and intractable, there are also variants or cases of multiobjective combinatorial optimization problems that are easy. The article focuses on particular cases of...

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The Biobjective Inventory Routing Problem – Problem Solution and Decision Support

Cited by 16 publications

References 20 publications

Multi-objective inventory routing with uncertain demand using population-based metaheuristics

Multi-objective inventory routing with uncertain demand using population-based metaheuristics

Simulation of Preference Information in an Interactive Reference Point‐Based Method for the Bi‐Objective Inventory Routing Problem

Editorial: Special issue on understanding complexity in multiobjective optimization

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