Minimizing makespan for single machine batch processing with non-identical job sizes using simulated annealing

Melouk, Sharif H.; Damodaran, Purushothaman; Chang, Ping-Yu

doi:10.1016/s0925-5273(03)00092-6

Cited by 174 publications

(71 citation statements)

References 21 publications

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“…For example, 1 2 3 represented the category of instances with 10 jobs, jobs' processing time were randomly generated from a discrete uniform [1,20] distribution, and jobs' sizes were randomly generated from a discrete uniform [4,8] distribution. [4,8] means data are generated from discrete uniform distribution.…”

Section: Experimental Designmentioning

confidence: 99%

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Scheduling Batch Processing Machine Using Max–Min Ant System Algorithm Improved by a Local Search Method

Wang

2018

Mathematical Problems in Engineering

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The problem of minimizing the makespan on single batch processing machine is studied in this paper. Both job sizes and processing time are nonidentical and the processing time of each batch is determined by the job with the longest processing time in the batch. Max-Min Ant System (MMAS) algorithm is developed to solve the problem. A local search method MJE (Multiple Jobs Exchange) is proposed to improve the performance of the algorithm by adjusting jobs between batches. Preliminary experiment is conducted to determine the parameters of MMAS. The performance of the proposed MMAS algorithm is compared with CPLEX as well as several other algorithms including ant cycle (AC) algorithm, genetic algorithm (GA), and two heuristics, First Fit Longest Processing Time (FFLPT) and Best Fit Longest Processing Time (BFLPT), through numerical experiment. The experiment results show that MMAS outperformed others especially for large population size.

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Section: Experimental Designmentioning

confidence: 99%

“…Melouk et al [8] studied the problem using Simulated Annealing (SA), and random instances were generated to evaluate the effectiveness of the algorithm. The same problem was considered by Damodaran et al [9] with genetic algorithm (GA).…”

Section: Introductionmentioning

confidence: 99%

Scheduling Batch Processing Machine Using Max–Min Ant System Algorithm Improved by a Local Search Method

Wang

2018

Mathematical Problems in Engineering

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“…For example, in Garibaldi and Ifeachor (1999) SA was used to tune a fuzzy Umbilical Acid-Base assessment model for determining information about a newborn infant's health, and Tang (2004) conducted a series of experiments that show SA to be a good choice for optimising a production/inventory system. Melouk et al (2004) and Bouleimen and Lecocq (2003) use SA to solve production scheduling problems. The SA algorithm used in this research is detailed in Section 4.3…”

Section: Simulated Annealingmentioning

confidence: 99%

Interval type-2 fuzzy modelling and stochastic search for real-world inventory management

et al. 2012

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Real-world systems present a variety of challenges to the modeller, not least of which is the problem of uncertainty inherent in their operation. In this research, an Interval Type-2 Fuzzy model is applied to a real-world problem, the goal being to discover a suitable optimisation configuration to enable a search for an inventory plan using the model. To this end, a series of Simulated Annealing configurations and the Interval Type-2 Fuzzy model were used to search for appropriate inventory plans for a large-scale real-world problem. A further set of tests were conducted in which the performance of the Interval Type-2 Fuzzy model was compared with a corresponding Type-1 Fuzzy model. In these tests the results were inconclusive, though, as will be discussed there are many ways in which Type-2 Fuzzy Logic can be exploited to demonstrate its advantages over a Type-1 approach. To conclude, in this research we have shown that a combination of Interval Type-2 Fuzzy Logic and Simulated Annealing is a logical choice for inventory management modelling and inventory plan search, and propose that the bene-

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“…Chang and Wang (2004) develop a heuristic algorithm for the 1|rj, batch| j C  problem for categories (2) and (b). Melouk et al (2004) and Damodaran et al (2006) develop several meta-heuristic algorithms based on simulated annealing (SA) and genetic algorithm (GA) for the 1|batch|Cmax problem for categories (2) and (b). Damodaran et al (2007) cope with the 1|batch|Cmax problem for categories (2) and (c) and develop a meta-heuristic algorithm based on SA to solve the problem.…”

Section: Introductionmentioning

confidence: 99%

Single machine batch processing problem with release dates to minimize total completion time

Beldar¹,

Costa²

2018

10.5267/j.ijiec

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A single machine batch processing problem with release dates to minimize the total completion time (1|rj,batch| j C  ) is investigated in this research. An original mixed integer linear programming (MILP) model is proposed to optimally solve the problem. Since the research problem at hand is shown to be NP-hard, several different meta-heuristic algorithms based on tabu search (TS) and particle swarm optimization (PSO) are used to solve the problem. To find the most performing heuristic optimization technique, a set of test cases ranging in size (small, medium, and large) are randomly generated and solved by the proposed meta-heuristic algorithms. An extended comparison analysis is carried out and the outperformance of a hybrid meta-heuristic technique properly combining PSO and genetic algorithm (PSO-GA) is statistically demonstrated.

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Minimizing makespan for single machine batch processing with non-identical job sizes using simulated annealing

Cited by 174 publications

References 21 publications

Scheduling Batch Processing Machine Using Max–Min Ant System Algorithm Improved by a Local Search Method

Scheduling Batch Processing Machine Using Max–Min Ant System Algorithm Improved by a Local Search Method

Interval type-2 fuzzy modelling and stochastic search for real-world inventory management

Single machine batch processing problem with release dates to minimize total completion time

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