An ordinal optimization theory-based algorithm for a class of simulation optimization problems and application

Horng, Shih-Cheng; Lin, Shieh-Shing

doi:10.1016/j.eswa.2009.01.023

Cited by 23 publications

(4 citation statements)

References 33 publications

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“…The former approach is utilized for identification of global best solutions in the MOPSO while the latter is used in all other situations when a solution needs to be evaluated. Such a design is inspired by the ordinal optimization philosophy (i.e., using crude models for solution evaluation will not lead to significant loss of optimal solutions) [ 44 ], which helps to bring down the computational burden of the whole algorithm.…”

Section: Discussionmentioning

confidence: 99%

Environment-Aware Production Scheduling for Paint Shops in Automobile Manufacturing: A Multi-Objective Optimization Approach

Zhang

2017

IJERPH

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The traditional way of scheduling production processes often focuses on profit-driven goals (such as cycle time or material cost) while tending to overlook the negative impacts of manufacturing activities on the environment in the form of carbon emissions and other undesirable by-products. To bridge the gap, this paper investigates an environment-aware production scheduling problem that arises from a typical paint shop in the automobile manufacturing industry. In the studied problem, an objective function is defined to minimize the emission of chemical pollutants caused by the cleaning of painting devices which must be performed each time before a color change occurs. Meanwhile, minimization of due date violations in the downstream assembly shop is also considered because the two shops are interrelated and connected by a limited-capacity buffer. First, we have developed a mixed-integer programming formulation to describe this bi-objective optimization problem. Then, to solve problems of practical size, we have proposed a novel multi-objective particle swarm optimization (MOPSO) algorithm characterized by problem-specific improvement strategies. A branch-and-bound algorithm is designed for accurately assessing the most promising solutions. Finally, extensive computational experiments have shown that the proposed MOPSO is able to match the solution quality of an exact solver on small instances and outperform two state-of-the-art multi-objective optimizers in literature on large instances with up to 200 cars.

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Section: Discussionmentioning

confidence: 99%

Environment-Aware Production Scheduling for Paint Shops in Automobile Manufacturing: A Multi-Objective Optimization Approach

Zhang

2017

IJERPH

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“…However for the QoS-aware service selection problems, the performance of the simulations is a critical issue. Using the simulation to evaluate the output variables for a given setting of the input variables is already computationally expensive not even mention the search of the best policy provided that the input-variable space is huge, and furthermore variability is an integral part of the problem making the simulations more complex [4]. Thus, the simulation approaches sometimes face space explosion problem for both state space and action space.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

A Simulation‐Based Approach of QoS‐Aware Service Selection in Mobile Edge Computing

Huang

Lan

2018

Wireless Communications and Mobile Computing

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Edge computing is an emerging computational model that enables efficient offloading of service requests to edge servers. By leveraging the well-developed technologies of cloud computing, the computing capabilities of mobile devices can be significantly enhanced in edge computing paradigm. However, upon the arrival of user requests, whether to dispatch them to the edge servers or cloud servers in order to guarantee the quality of service (QoS), i.e., the QoS-aware service selection problem, still remains an open problem. Due to the dynamic mobility of users and the variation of task arrivals and service processes, it is extremely costly to obtain the global optimal solution by both mathematical approaches and simulation-based schemes. To attack this challenge, this paper proposes a simulation-based approach of QoS-aware dynamic service selection for mobile edge computing systems. Stochastic system models are presented and mathematical analyses are provided. Based on the analytical results, the QoS-aware service selection problem is formulated by a dynamic optimization problem. Goal softening is applied to the original problem, and service selection algorithms are designed using ordinal optimization techniques. Simulation experiments are conducted to validate the efficacy of the approach presented in this paper.

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“…In the implementation of OO, the crude model (used in Step 2) has to be designed specifically for a concrete problem. Although generic crude models (like the artificial neural network-based model presented in [26]) may be useful for some problems, No Free Lunch Theorems [27] suggest that Discrete Dynamics in Nature and Society 5 incorporation of problem-specific information is the only way to promote the efficiency of optimization algorithms. So, here, we will devise a specialized crude model which can provide a quick and rough evaluation of the candidate solutions for the discussed parallel-machine problem.…”

Section: Basics Of Ordinal Optimizationmentioning

confidence: 99%

A Three-Stage Optimization Algorithm for the Stochastic Parallel Machine Scheduling Problem with Adjustable Production Rates

Zhang

2013

Discrete Dynamics in Nature and Society

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We consider a parallel machine scheduling problem with random processing/setup times and adjustable production rates. The objective functions to be minimized consist of two parts; the first part is related with the due date performance (i.e., the tardiness of the jobs), while the second part is related with the setting of machine speeds. Therefore, the decision variables include both the production schedule (sequences of jobs) and the production rate of each machine. The optimization process, however, is significantly complicated by the stochastic factors in the manufacturing system. To address the difficulty, a simulation-based threestage optimization framework is presented in this paper for high-quality robust solutions to the integrated scheduling problem. The first stage (crude optimization) is featured by the ordinal optimization theory, the second stage (finer optimization) is implemented with a metaheuristic called differential evolution, and the third stage (fine-tuning) is characterized by a perturbation-based local search. Finally, computational experiments are conducted to verify the effectiveness of the proposed approach. Sensitivity analysis and practical implications are also discussed. Hindawi Publishing Corporation

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An ordinal optimization theory-based algorithm for a class of simulation optimization problems and application

Cited by 23 publications

References 33 publications

Environment-Aware Production Scheduling for Paint Shops in Automobile Manufacturing: A Multi-Objective Optimization Approach

Environment-Aware Production Scheduling for Paint Shops in Automobile Manufacturing: A Multi-Objective Optimization Approach

A Simulation‐Based Approach of QoS‐Aware Service Selection in Mobile Edge Computing

A Three-Stage Optimization Algorithm for the Stochastic Parallel Machine Scheduling Problem with Adjustable Production Rates

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