Evolving Dispatching Rules for Dynamic Job Shop Scheduling Problems using Genetic Programming

<p>Job shop scheduling (JSS) problems are difficult combinatorial optimisation problems that have been studied over the past 60 years. The goal of a JSS problem is to schedule the arriving jobs as effectively as possible on the limited machine resources on the shop floor. Each job has a sequence of operations that need to be processed on specific machines, but the machines can only process one job at a time. JSS and other types of scheduling are important problems in manufacturing systems, such as semiconductor manufacturing. In particular, this thesis focuses on dynamic JSS (DJSS) problems, where unforeseen events occur during processing that needs to be handled by the manufacturer. Examples of dynamic events that occur in DJSS problems are dynamic or unforeseen job arrivals, machine breakdowns, uncertain job processing times, and so on. A prominent method of handling DJSS problems is to design effective dispatching rules for the DJSS problem handled by the manufacturer. Dispatching rules are local decision makers that determine what job is processed by a machine when the machine finishes processing the previous job and becomes available. Dispatching rules have been investigated extensively by both academics and industry experts due to their simplicity, interpretability, low computational cost and their ability to cope effectively in dynamic environments. However, dispatching rules are designed for a specific DJSS problem and have no guarantee that they retain their effectiveness on other DJSS problems. In a real-world scenario, the properties of a manufacturing system can change over time, meaning that previously effective dispatching rule may longer be effective. Therefore, a manufacturer may need to redesign a dispatching rule to maintain a competitive edge on the market. However, designing an effective dispatching rule for a specific DJSS problem is expensive, and typically requires a human expert and extensive trial-and-error process to verify their effectiveness. To circumvent the manual design of dispatching rules, researchers have proposed hyper-heuristic approaches to automate the design of dispatching rules. In particular, various genetic programming based hyper-heuristic (GP-HH) approaches have been proposed in the literature to evolve effective dispatching rules for scheduling problems, including DJSS problems. However, there are many potential directions that have not been fully investigated. The overall goal of this thesis is to develop new and effective GP-HH approaches to designing high-quality dispatching rules for DJSS problems that aims to improve beyond the standard GP approach while maintaining computational efficiency. The focus will be on developing approaches which can decompose complex JSS problems down to simpler subcomponents, evolving multiple heuristics to handle the subcomponents, and developing GP-HH approaches that can handle complex DJSS problems by exploiting the problem properties. This thesis is the first to develop ensemble GP approaches that evolve ensembles of dispatching rules using cooperative coevolution. In addition, the thesis also investigates different combination schemes for one of the ensemble GP approaches to combine the ensemble member outputs effectively. The results show that ensemble GP approach evolves rules that perform significantly better than the rules evolved by the benchmark GP approach. This thesis provides the first investigation into applying GP-HH to a DJSS problem with dynamic job arrivals and machine breakdowns. In addition, the thesis also develops machine breakdown GP approach to the DJSS problem by incorporating machine breakdown GP terminals. The results show that the standard GP do not generalise well over the DJSS problem. The best rules from the machine breakdown GP approach do perform better than the best rule from the standard GP approach, and the analysis shows that the rules behaviour is similar to the shortest processing time rule in certain decision situations. This thesis is the first to develop a multitask GP approach to evolve a portfolio of dispatching rules for a DJSS problem with dynamic job arrivals and machine breakdowns. The multitask GP approach improve on the standard GP approach either in terms of the effectiveness of the output rules or the computation time required to evolve the rules. The analysis shows that the difference between DJSS problem having no machine breakdowns and having machine breakdowns is a more significant factor than the difference between two DJSS problems with different frequencies of machine breakdown investigated.</p>

Evolving Dispatching Rules for Dynamic Job Shop Scheduling Problems using Genetic Programming

<p>Job shop scheduling (JSS) problems are difficult combinatorial optimisation problems that have been studied over the past 60 years. The goal of a JSS problem is to schedule the arriving jobs as effectively as possible on the limited machine resources on the shop floor. Each job has a sequence of operations that need to be processed on specific machines, but the machines can only process one job at a time. JSS and other types of scheduling are important problems in manufacturing systems, such as semiconductor manufacturing. In particular, this thesis focuses on dynamic JSS (DJSS) problems, where unforeseen events occur during processing that needs to be handled by the manufacturer. Examples of dynamic events that occur in DJSS problems are dynamic or unforeseen job arrivals, machine breakdowns, uncertain job processing times, and so on. A prominent method of handling DJSS problems is to design effective dispatching rules for the DJSS problem handled by the manufacturer. Dispatching rules are local decision makers that determine what job is processed by a machine when the machine finishes processing the previous job and becomes available. Dispatching rules have been investigated extensively by both academics and industry experts due to their simplicity, interpretability, low computational cost and their ability to cope effectively in dynamic environments. However, dispatching rules are designed for a specific DJSS problem and have no guarantee that they retain their effectiveness on other DJSS problems. In a real-world scenario, the properties of a manufacturing system can change over time, meaning that previously effective dispatching rule may longer be effective. Therefore, a manufacturer may need to redesign a dispatching rule to maintain a competitive edge on the market. However, designing an effective dispatching rule for a specific DJSS problem is expensive, and typically requires a human expert and extensive trial-and-error process to verify their effectiveness. To circumvent the manual design of dispatching rules, researchers have proposed hyper-heuristic approaches to automate the design of dispatching rules. In particular, various genetic programming based hyper-heuristic (GP-HH) approaches have been proposed in the literature to evolve effective dispatching rules for scheduling problems, including DJSS problems. However, there are many potential directions that have not been fully investigated. The overall goal of this thesis is to develop new and effective GP-HH approaches to designing high-quality dispatching rules for DJSS problems that aims to improve beyond the standard GP approach while maintaining computational efficiency. The focus will be on developing approaches which can decompose complex JSS problems down to simpler subcomponents, evolving multiple heuristics to handle the subcomponents, and developing GP-HH approaches that can handle complex DJSS problems by exploiting the problem properties. This thesis is the first to develop ensemble GP approaches that evolve ensembles of dispatching rules using cooperative coevolution. In addition, the thesis also investigates different combination schemes for one of the ensemble GP approaches to combine the ensemble member outputs effectively. The results show that ensemble GP approach evolves rules that perform significantly better than the rules evolved by the benchmark GP approach. This thesis provides the first investigation into applying GP-HH to a DJSS problem with dynamic job arrivals and machine breakdowns. In addition, the thesis also develops machine breakdown GP approach to the DJSS problem by incorporating machine breakdown GP terminals. The results show that the standard GP do not generalise well over the DJSS problem. The best rules from the machine breakdown GP approach do perform better than the best rule from the standard GP approach, and the analysis shows that the rules behaviour is similar to the shortest processing time rule in certain decision situations. This thesis is the first to develop a multitask GP approach to evolve a portfolio of dispatching rules for a DJSS problem with dynamic job arrivals and machine breakdowns. The multitask GP approach improve on the standard GP approach either in terms of the effectiveness of the output rules or the computation time required to evolve the rules. The analysis shows that the difference between DJSS problem having no machine breakdowns and having machine breakdowns is a more significant factor than the difference between two DJSS problems with different frequencies of machine breakdown investigated.</p>

A Performance-Based Dispatching Rule for Decentralised Manufacturing Planning and Production Control System

Considering a Flow Shop production line in an Industry 4.0 setting where the Cyber-Physical System (CPS) and Internet of Things (IoTs) can be deployed, a newly Performance-based Decentralised Dispatching Rule (PDDR) is proposed. It combines known dispatching rules with the knowledge of the monitored production system state. The goal is to provide a novel dispatching rule based on production line performance oversight. The governance system considers the machine condition in terms of machine utilisation. Regarding the assessment scenario, the proposed rule has been tested and compared with the well-known Short Processing Time (SPT) and the First-In-First-Out (FIFO) rule in a higher generality way by taking into account unforeseen events that may occur in production (such as breakdowns, potential rework, micro-stops, and unplanned machine setups). The simulation results showed interesting results where the flexibility of this rule, as well as its practical use with real hypotheses are its main advantages.

A Novel Heuristic to Minimize the Bottlenecks Presence in Batch Generation. Case of Study.

This paper presents a new heuristic method capable of minimizing the presence of bottlenecks generated when production batches have a distinct makespan. The proposed heuristic groups the jobs into items, where the one with the longest processing time in the batch determines the makespan. To test the heuristic, information was collected from a real paint process with two stations: one with a single cabin and the other with two parallel cabins. The capacity of processing jobs is limited by the cabin dimensions where jobs have different sizes and processing times. A makespan comparison between the heuristic proposed versus the First in First out (FIFO) dispatching rule that the case of study uses. Additionally, ten random instances based on data taken from the real process were created with the purpose to compare the new heuristic method versus Genetic Algorithm (GA) and Simulated Annealing (SA). The result of the comparison to FIFO, GA and SA showed that the proposed heuristic minimizes the bottleneck in a and creating batches almost with the same makespan. Results indicated a bottleneck time reduction of 96% when new heuristic method were compared to FIFO rule, while compared to Generic Algorithm and Simulated Annealing the bottleneck reduction were around 89% in both cases.

Weighted grey relational analysis to evaluate multilevel dispatching rules in wafer fabrication

PurposeThis research proposes weighted grey relational analysis (WGRA) method to evaluate the performance of 325 multilevel dispatching rules in the wafer fabrication process.Design/methodology/approachThe research methodology involves multilevel dispatching rule generation, simulations, WGRA and result analysis. A complete permutation of multilevel dispatching rules, including the partial orders, is generated from five basic rules. Performance measures include cycle time, move, tool idling and queue time. The simulation model and data are obtained from a wafer fab in Malaysia. Two seasons varying in customer orders and objective weights are defined. Finally, to benchmark performance and investigate the effect of varying values of coefficient, the models are compared against TOPSIS and VIKOR.FindingsResults show that the seasons prefer different multilevel dispatching rules. In Normal season, the ideal first basic dispatching rule is critical ratio (CR) and CR followed by shortest processing time (SPT) is the best precedence pairing. In Peak season, the superiority of the rule no longer heavily relies on the first basic rule but rather depends on the combination of tiebreaker rules and on-time delivery (OTD) followed by CR is considered the best precedence pairing. Compared to VIKOR and TOPSIS, WGRA generates more stable rankings in this study. The performance of multicriteria decision-making (MCDM) methods is influenced by the data variability, as a higher variability produces a much consistent ranking.Research limitations/implicationsAs research implications, the application illustrates the effectiveness and practicality of the WGRA model in analyzing multilevel dispatching rules, considering the complexity of the semiconductor wafer fabrication system. The methodology is useful for researchers wishing to integrate MCDM model into multilevel dispatching rules. The limitation of the research is that the results were obtained from a simulation model. Also, the rules, criteria and weights assigned in WGRA were decided by the management. Lastly, the distinguishing coefficient is fixed at 0.5 and the effect to the ranking requires further study.Originality/valueThe research is the first deployment WGRA in ranking multilevel dispatching rules. Multilevel dispatching rules are rarely studied in scheduling research although studies show that the tiebreakers affect the performances of the dispatching rules. The scheduling reflects the characteristics of wafer fabrication and general job shop, such as threshold and look-ahead policies.