Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

Abstract

Abstract:A promising approach for an effective shop scheduling that synergizes the benefits of the combinatorial optimization, supervised learning and discrete-event simulation is presented. Though dispatching rules are in widely used by shop scheduling practitioners, only ordinary performance rules are known; hence, dynamic generation of dispatching rules is desired to make them more effective in changing shop conditions. Meta-heuristics are able to perform quite well and carry more knowledge of the problem domain, however at the cost of prohibitive computational effort in real-time. The primary purpose of this research lies in an offline extraction of this domain knowledge using decision trees to generate simple if-then rules that subsequently act as dispatching rules for scheduling in an online manner. We used similarity index to identify parametric and structural similarity in problem instances in order to implicitly support the learning algorithm for effective rule generation and quality index for relative ranking of the dispatching decisions. Maximum lateness is used as the scheduling objective in a job shop scheduling environment.

Genetic programming for production scheduling: a survey with a unified framework

“…Supervised learning techniques such as decision tree (Olafsson and Li, 2010;Shahzad and Mebarki, 2016), logistic regression (Ingimundardottir and Runarsson, 2011), support vector machines (Shiue, 2009), and artificial neural networks (Weckman et al, 2008;Eguchi et al, 2008) have also been investigated in literature for automated design of production scheduling heuristics. For supervised learning, optimal decisions from solving small instances with exact optimisation methods or from the historical data are needed to build the training set.…”

“…As compared to other hyper-heuristics based on supervised learning such as decision tree (Olafsson and Li, 2010;Shahzad and Mebarki, 2016), logistic regression (Ingimundardottir and Runarsson, 2011), support vector machine (Shiue, 2009), and artificial neural networks (Weckman et al, 2008;Eguchi et al, 2008), genetic programming (GP) has shown a number of key advantages. First, GP has flexible representations which allow various heuristics to be represented as different computer programs.…”

Genetic programming for production scheduling: a survey with a unified framework

“…Supervised learning techniques such as decision tree [107,127], logistic regression [57], support vector machines [129], and artificial neural networks [32,138] have also been investigated in the literature for automated design of production scheduling heuristics. For supervised learning, optimal decisions from solving small instances with exact optimisation methods or from the historical data are needed to build the training set.…”

“…As compared to other hyper-heuristics based on supervised learning such as decision tree [107,127], logistic regression [57], support vector machine [129], and artificial neural networks [32,138], genetic programming (GP) has shown a number of key advantages. First, GP has flexible representations which allow various heuristics to be represented as different computer programs.…”

Critical Machine Based Scheduling -A Review

“…The parallel flow shops scheduling (flowshops with parallel machines) and parallel Hybrid flowshops are example for them [19,20]. The open shop and closed shop nature, dynamic job/ machine [21][22][23] (stochastic) availability and are static (deterministic) also the notable scheduling environments [24]. The most of the problems in the literature solved with the ultimate criteria of throughput and cost measures and assumed that machines/ facilities are available continuously, set up times neglected, etc.…”