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multi-objective simulation-optimization for a distribution center resource planning

IISE TRANSACTIONS 2025年

作者： Li, Yan Swee, Steve Yeo Keng Li, Haobin Chew, Ek Peng Natl Univ Singapore Dept Ind Syst Engn & Management Singapore Singapore Natl Univ Singapore Ctr Next Generat Logist Singapore Singapore

This study tackles resource planning in distribution centers, aiming to enhance efficiency while minimizing the number of deployed workers and forklifts. We achieve this by optimizing resource schedules across different areas and time periods. Initially, we developed a detailed warehouse simulation model to reflect resource limitations and their impact on operations. Specifically, we define staging areas, including the receiving dock, inbound buffer area, and outbound buffer area, which serve as the start and end points of all activities and the nexus of resource allocation. Utilizing modular modeling and an event-driven discrete-event mechanism, our proposed model can accelerate the simulation process while ensuring high fidelity. We then design multi-objective simulation-optimization models, aiming for Pareto optimality, minimizing average queuing time, workforce size, and forklift fleet size under different skill set scenarios. Using a customized multiple gradient descent-simultaneous perturbation stochastic approximation algorithm, our approach effectively handles multi-objective problems and significantly reduces CPU time compared to evolutionary algorithms. A case study highlights the inbound buffer area as a key bottleneck and reveals the impact of incomplete skills on workforce deployment. Drawing insights from the results, we discuss several strategies for enhancing efficiency-including deploying part-time workers-offering new solutions to warehouse resource planning challenges.

关键词： Distribution center multi-objective simulation-optimization resource planning

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multi-objective simulation-optimization via kriging surrogate models applied to natural gas liquefaction process design

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ENERGY 2023年第PartB期262卷

作者： Santos, Lucas F. Costa, Caliane B. B. Caballero, Jose A. Ravagnani, Mauro A. S. S. Univ Estadual Maringa Dept Chem Engn Ave Colombo 5790 BR-87020900 Maringa Brazil Univ Alicante Inst Chem Proc Engn Ap Correos 99 Alicante 03080 Spain

A surrogate-based multi-objective optimization framework is employed in the design of natural gas liquefaction processes using reliable, black-box process simulation. The conflicting objectives are minimizing both power consumption and heat exchanger area utilization. The Pareto solutions of the single-mixed refrigerant (SMR) and propane-precooled mixed refrigerant (C3MR) processes are compared to determine the suitability of each process in terms of energy consumption and heat exchanger area. Kriging models and the z-constraint methodology are used to sequentially provide simple surrogate optimization subproblems, whose minimizers are promising feasible and non-dominated solutions to the original black-box problem. The surrogate-based..-constrained optimization subproblems are solved in GAMS using CONOPT. The Pareto Fronts achieved with the surrogate-based framework dominate the results from the NSGA-II, a well-established meta-heuristics of multi-objective optimization. The objective functions of non-dominated solutions go as low as 1045 and 980.3 kJ/kg-LNG and specific UA values of 212.2 and 266.9 kJ/(degrees C kg-LNG) for SMR and C3MR, respectively. The trade-off solutions that present the minimum sum of relative objectives are analyzed as well as the dominance of C3MR over SMR at low power consumption values and conversely at low heat exchanger area utilization.

关键词： multi-objective simulation-optimization Natural gas liquefaction Surrogate-based optimization Process design Kriging Mathematical programming

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Maintenance scheduling of a fleet of fighter aircraft through multi-objective simulation-optimization

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simulation-TRANSACTIONS OF THE SOCIETY FOR MODELING AND simulation INTERNATIONAL 2014年第9期90卷 1023-1040页

作者： Mattila, Ville Virtanen, Kai Aalto Univ Sch Sci Dept Math & Syst Anal Syst Anal Lab FI-00076 Aalto Finland

The maintenance scheduling problem of a fleet of fighter aircraft is considered through multi-objective simulation-optimization (MOSO). In the problem, a maintenance schedule consisting of target starting times of the maintenance activities of the aircraft is determined. The objectives are to minimize average deviation between the target and actual starting times of the activities and to maximize average aircraft availability. The objectives depend on the maintenance schedule through complex interactions due to a policy in which the need for maintenance is based on the flight hours of the aircraft cumulated during flight missions. In addition, the durations of the flight missions, maintenance activities, and failure repairs are uncertain. Therefore, an MOSO approach is applied to the problem. The approach includes a discrete-event simulation model and a state-of-the art multi-objective simulated annealing algorithm for determining non-dominated schedules. Moreover, a multi-attribute value (MAV) function is used for supporting a maintenance decision-maker (DM) in selecting the preferred non-dominated schedule for implementation. The MAV function captures incomplete information on the values of the objectives as well as on the DM's preference statements regarding the weights of the objectives. The approach is implemented as an MOSO tool whereby the DM can consider the complex interactions and uncertainties of the problem which have not been addressed in the existing literature on maintenance scheduling. The approach and the tool are illustrated with a set of test problems as well as a real-life example problem.

关键词： Aircraft maintenance scheduling multi-attribute value function multi-objective simulation-optimization simulated annealing

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A Three-Level Decision Support Approach Based on multi-objective simulation-optimization and DEA: A Supply Chain Application 22nd

A Three-Level Decision Support Approach Based on Multi-objec...

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22nd Regular meeting of the Portuguese-Association-of-Operational-Research (APDIO)

作者： Gomes, Luis Pedro Vieira, Antonio Fragoso, Rui Almeida, Dora Coelho, Luis Neves, Jose Maia Univ Evora IFFA CEFAGE Palacio VimiosoLargo Marques de Marialva 8 P-7000809 Evora Portugal Polytech Inst Portalegre VALORIZA Res Ctr Endogenous Resource Valorizat Ed BioBipCampus Politecn 10 P-7300555 Portalegre Portugal Univ Minho Escola Engn Ctr Algoritmi Campus Azurem P-4800058 Guimaraes Portugal

ISBN: (纸本)9783031464393;9783031464386

Decision-making in multi-objective problems is a complex problem with several approaches existing in the literature. Many such approaches typically combine simulation and optimization methods to achieve a robust tool capable of providing useful information to decision-makers. While simulation helps decision-makers to test alternative scenarios and allows uncertainty to be considered, optimization enables them to find the best alternatives for specific conditions. This paper proposes an alternative approach consisting of three levels, wherein we use simulation to model complex scenarios, simulation-optimization to identify the scenarios of the Pareto-front, and Data Envelopment Analysis to identify the most efficient solutions, including those not belonging to the Pareto-front, thereby exploiting the benefits of efficiency analysis and simulation-optimization. This can be useful when decision-makers decide to consider scenarios that, while not optimum, are efficient for their decision-making profile. To test our approach, we applied it to a supply chain design problem. Our results show how our approach can be used to analyze a given system from three different perspectives and that some of the solutions, while not optimum, are efficient. In traditional approaches, such scenarios could be overlooked, despite their efficiency for specific decision-making profiles.

关键词： simulation multi-objective simulation-optimization Data envelopment analysis Supply chain design Decision-making Efficiency

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A multi-objective simulation-optimization for a joint problem of strategic facility location, workforce planning, and capacity allocation: A case study in the Royal Australian Navy

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 186卷 115751-115751页

作者： Turan, Hasan Hueseyin Kahagalage, Sanath Darshana Jalalvand, Fatemeh El Sawah, Sondoss Univ New South Wales Canberra ADFA Capabil Syst Ctr Canberra ACT Australia

The availability of assets crucially depends on the interplay between the facility locations, workforce planning, and capacity allocation. Therefore, in this paper, we model and solve a novel multi-objective optimization problem for strategic facility location, workforce planning, and capacity allocation in the context of the military. The developed model deviates from more common approaches and adapts a dynamic capacity scheme for both the workforce and facilities to capture the dynamic nature of future demand, such as maintenance requirements for assets. The capacity allocation and workforce planning problem focus on mainly strategic decisions involving from workforce and facilities. A system dynamics (SD) simulation model enables a decision-maker to analyze the effects of decision variables by modeling the system complexities, uncertainties, and interactions between facilities, workforce, and assets. However, the simulation model neither suggests nor seeks the best solution strategy or strategies. To overcome this shortcoming, we propose a simulationoptimization approach that uses a Non-dominated Sorting Genetic Algorithm-II (NSGA-II). NSGA-II generates feasible solution strategies (candidate solutions) such as time and amount of capacity expansion and downsizing for both the workforce and facilities as well as the amount of crew recruitment. These solution strategies are fed to the simulation model where it evaluates the fitness of candidate solutions. We test the applicability of the proposed method on a realistic case study from the Royal Australian Navy. Finally, we present scenario-based sensitivity analysis arising from the decision variables to support decision-makers.

关键词： Military facility location Military workforce planning Dynamic capacity multi-objective simulation-optimization NSGA-II

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Handling Large Decision Variables in multi-objective Groundwater optimization Problems: Aquifer Parameter-Based Clustering Approach

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WATER RESOURCES MANAGEMENT 2023年第11期37卷 4553-4568页

作者： Mishra, Shreyansh Bosc, Lilian Gaur, Shishir Kacem, Mariem Ohri, Anurag Banaras Hindu Univ Indian Inst Technol Dept Civil Engn Varanasi 221005 India Univ Lyon Tribol & Syst Dynam Lab Cent Lyon ENISE CNRS UMR 5513LTDS F-42000 St Etienne France Beihang Univ Sino French Engineer Sch Cent Pekin Beijing 100191 Hebei Peoples R China Ecole Mines St Etienne F-42100 St Etienne France

Number of decision variables (DVs) significantly impacts the convergence of multi-objective groundwater simulation-optimization problems (MO-GSOPs). Previous studies of reducing DV by decomposition methods based on the proximity between the pumping wells have yet to assess its implication on the Pareto fronts. This study introduces a novel approach to clustering known as aquifer parameter-based clustering. This work aims to decrease the number of wells involved in MO-GSOPs via clustering based on essential aquifer properties that govern groundwater flow, including initial head, recharge, top elevation of the aquifer layer, and hydraulic conductivity. The simulation-optimization model solves the objectives of maximizing pumping discharge and river-aquifer (R-A) exchanges. The resulting Pareto fronts are compared in terms of convergence and diversity. The analysis reveals that initial head-based clustering exhibits superior performance, leading to a significant increase in hypervolume (46%) and a decrease in the inverted generational distance (22%) compared to distance-based DV clustering. Comparison between results shows that aquifer parameter-based clustering has superior optimal results overall than traditional clustering based upon Euclidian distance. Furthermore, the discharge variation resulting from the parameter-based clustering is examined at the commune level. Notably, Chazey Sur Ain, located near the river, experiences a substantial increase in discharge (12659.13 m(3)/d), while communes situated near the study area's boundary, namely Douvres and Jujurieux, observe marginal discharge increases (500 m(3)/d and 130.33 m(3)/d, respectively).

关键词： multi-objective simulation-optimization Groundwater Management Clustering Decision Variable MOPSO K-means

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A multi-objective simulation-based decision support tool for wine supply chain design and risk management under sustainability goals

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期232卷

作者： Vieira, Antonio A. C. Figueira, Jose Rui Fragoso, Rui Univ Evora CEFAGE Res Ctr Largo ColegiaisN 2 P-7004516 Evora Portugal Univ Lisbon CEGIST Inst Super Tecn Ave Rovisco Pais P-1049001 Lisbon Portugal

Sustainable Supply Chain Network Design (SCND) poses a complex challenge, as it often requires trade-offs in decision-making. The problem becomes even more challenging when there is a need to integrate risk management into the design process. It is steered by this challenge that the purpose of the paper is twofold. First, it proposes a simulation-Based Decision Support System (SBDSS) for a real case study of a Portuguese wine SCND problem. Second, it analyzes different wine Supply Chain (SC) configurations under risk situations and sustainability objectives. The main contributions of this research are as follows. First, it concerns the method that was employed, which, contrarily to traditional approaches in literature (e.g., stochastic or genetic programming), consists of a simulation approach, in which agents and their interactions in an environment with uncertainty are modeled. Second, for the generation of non-dominated solutions, a simheuristic was employed, which combines multi-objective optimization with simulation. Finally, the type of industry that was considered, due to the lack of cases focusing on it in literature. The results suggest that the proposed approach determines a set of Pareto-front solutions that consider the sustainability dimensions of SCND, as well as risk situations. Furthermore, the implications that several parameters have on the sustainability of the designed SCs were assessed, as well as the impacts that the considered risks have on the performance of SCs. In particular, it was found that transportation risks impacted considerably more the performance of the SC than the risks on the prices of energy and raw materials.

关键词： Supply chain design Sustainability Risk management multi-objective simulation-optimization simulation-based heuristic

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