A Framework to Incorporate Decision-Maker Preferences Into Simulation Optimization to Support Collaborative Design

Goren, Selcuk; Baccouche, Ahlem; Pierreval, Henri

doi:10.1109/tsmc.2016.2531643

Cited by 10 publications

(5 citation statements)

References 39 publications

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“…Constraints ( 22)-( 30) are the temporal constraints. Constraint (22) guarantees that the arrival time of vehicle is earlier than the service start time. Constraint (23) maintains that the departure happens only after the service is completed.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Wang et al ( 18 ) provide a summary of methods on how to incorporate preferences into multi-objective optimization (MOO), such as weight sum method, reference point, reference direction, utility function, and so forth. According to Coello et al ( 19 ), preference-based MOO approaches are divided into three categories: priori ( 20 , 21 ), progressive ( 22 , 23 ), and posteriori ( 24 ) preference articulations, which mean making decisions before, during, and after search, respectively. In ST, it may be impractical for a decision-maker (DM) to specify their preferences completely before any alternatives are known.…”

Section: Literature Reviewmentioning

confidence: 99%

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Preference-Based Multi-Objective Optimization for Synchromodal Transport Using Adaptive Large Neighborhood Search

Zhang

Atasoy

Negenborn

2021

Transportation Research Record

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Decision-makers in synchromodal transport (ST) have different preferences toward different objectives, such as cost, time, and emissions. To solve the conflicts among objectives and obtain preferred solutions, a preference-based multi-objective optimization model is developed. In ST, containers need to be transferred across modes, therefore the optimization problem is formulated as a pickup and delivery problem with transshipment. The preferences of decision-makers are usually expressed in linguistic terms, so weight intervals, that is, minimum and maximum weights, are assigned to objectives to represent such vague preferences. An adaptive large neighborhood search is developed and used to obtain non-dominated solutions to construct the Pareto frontier. Moreover, synchronization is an important feature of ST and it makes available resources fully utilized. Therefore, four synchronization cases are identified and studied to make outgoing vehicles cooperate with changes of incoming vehicles’ schedules at transshipment terminals. Case studies in the Rhine-Alpine corridor are designed and the results show that the proposed approach provides non-dominated solutions which are in line with preferences. Moreover, the mode share under different preferences is analyzed, which signals that different sustainability policies in transportation will influence the mode share.

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Section: Mathematical Modelmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Preference-Based Multi-Objective Optimization for Synchromodal Transport Using Adaptive Large Neighborhood Search

Zhang

Atasoy

Negenborn

2021

Transportation Research Record

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“…As to supply chain collaborative design, authors in [80] developed a three-stage framework that incorporates decision makers taking into account their considerations. The first stage consists of getting a set of efficient designs, it's a multimodal optimization problem solved by the Crowding Clustering Genetic Algorithm (CCGA) combined with simulation.…”

Section: A Strategic Decision Planningmentioning

confidence: 99%

“…-Meta-heuristics (MH): metaheuristics is used in a great range of supply chain applications. Specifically genetic algorithms are mainstream, indeed GA was applied in supplier selection [38], supply chain design [78]- [80], and mostly adopted in manufacturing problems to decide in the production strategy [112], schedule operation [114], as well as manufacturing system design [104].…”

Section: A Simulation Purpose 1) Solution Evaluation Approaches (Se)mentioning

confidence: 99%

Coupling Soft Computing, Simulation and Optimization in Supply Chain Applications: Review and Taxonomy

2020

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Supply chain networks are typical examples of complex systems. Thereby, making decisions in such systems remains a very hard issue. To assist decision makers in formulating the appropriate strategies, robust tools are needed. Pure optimization models are not appropriate for several reasons. First, an optimization model cannot capture the dynamic behavior of a complex system. Furthermore, most common practical problems are very constrained to be modeled as simple tractable models. To fill in the gap, hybrid optimization/simulation techniques have been applied to improve the decision-making process. In this paper we explore the near-full spectrum of optimization methods and simulation techniques. A review and taxonomy were performed to give an overview of the broad field of optimization/simulation approaches applied to solve supply chain problems. Since the possibilities of coupling them are numerous, we launch a discussion and analysis that aims at determining the appropriate framework for the studied problem depending on its characteristics. Our study may serve as a guide for researchers and practitioners to select the suitable technique to solve a problem and/or to identify the promising issues to be further explored.

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“…We point the interested reader to the overviews by Tekin andSabuncuoglu (2004, p. 1075) and Rosen et al (2008, pp. 329-332); more recent methods include Gören et al (2017) and Steponavičė et al (2014). Despite the popularity of metaheuristics for MOSO (and for MOO and SOSO, as noted by Brockhoff (2011), Coello Coello et al (2007), and Deb (2009) and by Fu (2002), Hong and Nelson (2009), Hong et al (2015), Nelson (2010), and Ólafsson (2006), respectively), our goal is to facilitate the advancement of literature on provably convergent MOSO algorithms.…”

Section: Articulation Of the Decisionmentioning

confidence: 99%

An Introduction to Multiobjective Simulation Optimization

Hunter

Applegate

Arora

et al. 2019

ACM Trans. Model. Comput. Simul.

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The multiobjective simulation optimization (MOSO) problem is a nonlinear multiobjective optimization problem in which multiple simultaneous and conflicting objective functions can only be observed with stochastic error. We provide an introduction to MOSO at the advanced tutorial level, aimed at researchers and practitioners who wish to begin working in this emerging area. Our focus is exclusively on MOSO methods that characterize the entire efficient or Pareto-optimal set as the solution to the MOSO problem; later, this set may be used as input to the broader multicriteria decision-making process. Our introduction to MOSO includes an overview of existing theory, methods, and provably convergent algorithms that explicitly control sampling error for (1) MOSO on finite sets, called multiobjective ranking and selection; (2) MOSO with integer-ordered decision variables; and (3) MOSO with continuous decision variables. In the context of integer-ordered and continuous decision variables, we focus on methods that provably converge to a local efficient set under the natural ordering. We also discuss key open questions that remain in this emerging field.

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A Framework to Incorporate Decision-Maker Preferences Into Simulation Optimization to Support Collaborative Design

Cited by 10 publications

References 39 publications

Preference-Based Multi-Objective Optimization for Synchromodal Transport Using Adaptive Large Neighborhood Search

Preference-Based Multi-Objective Optimization for Synchromodal Transport Using Adaptive Large Neighborhood Search

Coupling Soft Computing, Simulation and Optimization in Supply Chain Applications: Review and Taxonomy

An Introduction to Multiobjective Simulation Optimization

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