A simulated annealing approach for the multi-periodic rail-car fleet sizing problem

Tzeng

Int J Adv Manuf Technol

et al. 2011

This study analyzes variations in metal removal rate (MRR) and quality performance of roughness average (R a ) and corner deviation (CD) depending on parameters of wire electrical discharge machining (WEDM) process in relation to the cutting of pure tungsten profiles. A hybrid method including response surface methodology (RSM) and back-propagation neural network (BPNN) integrated simulated annealing algorithm (SAA) were proposed to determine an optimal parameter setting. The results of 18 experimental runs via a Taguchi orthogonal table were utilized to train the BPNN to predict the MRR, R a , and CD properties. Simultaneously, RSM and SAA approaches were individually applied to search for an optimal setting. In addition, analysis of variance was implemented to identify significant factors for the processing parameters. Furthermore, the field-emission scanning electron microscope images show that a lot of built-edge layers were presented on the finishing surface after the WEDM process. Finally, the optimized result of BPNN with integrated SAA was compared with that obtained by an RSM approach. Comparisons of the results of the algorithms and confirmation experiments show that both RSM and BPNN/SAA methods are effective tools for the optimization of parameters in WEDM process.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of wire electrical discharge machining process parameters for cutting tungsten

Tzeng

Int J Adv Manuf Technol

et al. 2011

“…Furthermore, the advantages of a multi-periodic dynamic formulation that considers package (and fleet) sizing as well allocation optimization over a series of time steps are substantial and should be leveraged, as suggested by Sayarshad and Ghoseiri (2009). Currently, there are no optimal models that combine the following capabilities: (1) incorporation of MOO and use of the Pareto optimal set for problem analysis, (2) consideration of both fleet sizing and allocation, and 3) application to rail-cars with the associated rail-yard restrictions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Anderson and Christiansen (2009) develop a non-linear mixed integer optimization formulation to maximize profit while considering service quality for vehicle fleet sizing problems. Most recently, Sayarshad and Ghoseiri (2009) use simulated annealing to maximize profit in a multi-periodic problem.…”

Section: Literature Reviewmentioning

confidence: 99%

A new multi-objective optimization formulation for rail-car fleet sizing problems

Sayarshad

Marler

2009

Oper Res Int J

Self Cite

With potential application to a variety of industries, fleet sizing problems present a prevalent and significant challenge for engineers and managers. This is especially true of applications involving rail cars, where origins and destinations have capacity restrictions. This paper presents a new multi-objective optimization formulation, solution method, and analysis for multi-periodic fleet sizing problems of various sizes. Profit and quality (minimal unmet demands) represent conflicting objectives and are maximized simultaneously. The Pareto optimal set is depicted and is used for trade-off analysis. The solution involves the optimal fleet size as well as the optimal rail-car allocation strategy. The proposed approach is applied to an example problem and is shown to be successful, ultimately providing a new managerial tool for planning and analyzing rail-car fleets more effectively.

Int. J. Model. Simul. Sci. Comput.

“…Vaidyanathan and Anthony [19] studied the applicability of simulated annealing as a very effective and useful solution approach to complex problems involved in supply chain management to reduce costs and improve efficiency. Hamid and Keivan [20] uses a simulated annealing approach for optimizing the fleet size and freight car allocation problem wherein car demands and travel times were assumed to be deterministic and unmet demands were backordered. Allaoui and Artiba [21] considered a multi-objective optimization problem taking into consideration the system constrains.…”

Section: Review Methodologymentioning

confidence: 99%

Simulation Model Using Meta Heuristic Algorithms for Achieving Optimal Arrangement of Storage Bins in a Sawmill Yard

Rahman¹,

Yella²,

Dougherty³

2015

Bin planning (arrangements) is a key factor in the timber industry. Improper planning of the storage bins may lead to inefficient transportation of resources, which threaten the overall efficiency and thereby limit the profit margins of sawmills. To address this challenge, a simulation model has been developed. However, as numerous alternatives are available for arranging bins, simulating all possibilities will take an enormous amount of time and it is computationally infeasible. A discrete-event simulation model incorporating meta-heuristic algorithms has therefore been investigated in this study. Preliminary investigations indicate that the results achieved by GA based simulation model are promising and better than the other meta-heuristic algorithm. Further, a sensitivity analysis has been done on the GA based optimal arrangement which contributes to gaining insights and knowledge about the real system that ultimately leads to improved and enhanced efficiency in sawmill yards. It is expected that the results achieved in the work will support timber industries in making optimal decisions with respect to arrangement of storage bins in a sawmill yard.