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Spatio-Temporal Pricing for Ridesharing Platforms
Ridesharing platforms have radically changed the way people get around in urban areas, but there remain challenges undercutting the mission of “making transportation as reliable as running water.” A particular concern is that drivers strategize: calling riders to find out their destinations and canceling trips that are not worthwhile, declining trips and chasing surge prices in neighboring areas, and going off-line before large events will end in anticipation of a price increase. In this work, we show that such strategic behaviors are symptoms of inefficiencies in the pricing and dispatching rules governing today's platforms. We propose the Spatio-Temporal Pricing mechanism, which solves for the welfare-optimal matching of drivers to trips, and sets prices that are appropriately smooth in both space and time such that the best thing for drivers to do is accept any proposed trip dispatch. This demonstrates that ridesharing platforms can succeed in optimally orchestrating trips and providing reliable transpiration for riders, while still leaving drivers with the flexibility to decide how to work.
<p>The Job Shop Scheduling (JSS) problem is considered to be a challenging one due to practical requirements such as multiple objectives and the complexity of production flows. JSS has received great attention because of its broad applicability in real-world situations. One of the prominent solutions approaches to handling JSS problems is to design effective dispatching rules. Dispatching rules are investigated broadly in both academic and industrial environments because they are easy to implement (by computers and shop floor operators) with a low computational cost. However, the manual development of dispatching rules is time-consuming and requires expert knowledge of the scheduling environment. The hyper-heuristic approach that uses genetic programming (GP) to solve JSS problems is known as GP-based hyper-heuristic (GP-HH). GP-HH is a very useful approach for discovering dispatching rules automatically. Although it is technically simple to consider only a single objective optimization for JSS, it is now widely evidenced in the literature that JSS by nature presents several potentially conflicting objectives, including the maximal flowtime, mean flowtime, and mean tardiness. A few studies in the literature attempt to solve many-objective JSS with more than three objectives, but existing studies have some major limitations. First, many-objective JSS problems have been solved by multi-objective evolutionary algorithms (MOEAs). However, recent studies have suggested that the performance of conventional MOEAs is prone to the scalability challenge and degrades dramatically with many-objective optimization problems (MaOPs). Many-objective JSS using MOEAs inherit the same challenge as MaOPs. Thus, using MOEAs for many-objective JSS problems often fails to select quality dispatching rules. Second, although the reference points method is one of the most prominent and efficient methods for diversity maintenance in many-objective problems, it uses a uniform distribution of reference points which is only appropriate for a regular Pareto-front. However, JSS problems often have irregular Pareto-front and uniformly distributed reference points do not match well with the irregular Pareto-front. It results in many useless points during evolution. These useless points can significantly affect the performance of the reference points-based algorithms. They cannot help to enhance the solution diversity of evolved Pareto-front in many-objective JSS problems. Third, Pareto Local Search (PLS) is a prominent and effective local search method for handling multi-objective JSS optimization problems but the literature does not discover any existing studies which use PLS in GP-HH. To address these limitations, this thesis's overall goal is to develop GP-HH approaches to evolving effective rules to handle many conflicting objectives simultaneously in JSS problems. To achieve the first goal, this thesis proposes the first many-objective GP-HH method for JSS problems to find the Pareto-fronts of nondominated dispatching rules. Decision-makers can utilize this GP-HH method for selecting appropriate rules based on their preference over multiple conflicting objectives. This study combines GP with the fitness evaluation scheme of a many-objective reference points-based approach. The experimental results show that the proposed algorithm significantly outperforms MOEAs such as NSGA-II and SPEA2. To achieve the second goal, this thesis proposes two adaptive reference point approaches (model-free and model-driven). In both approaches, the reference points are generated according to the distribution of the evolved dispatching rules. The model-free reference point adaptation approach is inspired by Particle Swarm Optimization (PSO). The model-driven approach constructs the density model and estimates the density of solutions from each defined sub-location in a whole objective space. Furthermore, the model-driven approach provides smoothness to the model by applying a Gaussian Process model and calculating the area under the mean function. The mean function area helps to find the required number of the reference points in each mean function. The experimental results demonstrate that both adaptive approaches are significantly better than several state-of-the-art MOEAs. To achieve the third goal, the thesis proposes the first algorithm that combines GP as a global search with PLS as a local search in many-objective JSS. The proposed algorithm introduces an effective fitness-based selection strategy for selecting initial individuals for neighborhood exploration. It defines the GP's proper neighborhood structure and a new selection mechanism for selecting the effective dispatching rules during the local search. The experimental results on the JSS benchmark problem show that the newly proposed algorithm can significantly outperform its baseline algorithm (GP-NSGA-III).</p>
<p>The Job Shop Scheduling (JSS) problem is considered to be a challenging one due to practical requirements such as multiple objectives and the complexity of production flows. JSS has received great attention because of its broad applicability in real-world situations. One of the prominent solutions approaches to handling JSS problems is to design effective dispatching rules. Dispatching rules are investigated broadly in both academic and industrial environments because they are easy to implement (by computers and shop floor operators) with a low computational cost. However, the manual development of dispatching rules is time-consuming and requires expert knowledge of the scheduling environment. The hyper-heuristic approach that uses genetic programming (GP) to solve JSS problems is known as GP-based hyper-heuristic (GP-HH). GP-HH is a very useful approach for discovering dispatching rules automatically. Although it is technically simple to consider only a single objective optimization for JSS, it is now widely evidenced in the literature that JSS by nature presents several potentially conflicting objectives, including the maximal flowtime, mean flowtime, and mean tardiness. A few studies in the literature attempt to solve many-objective JSS with more than three objectives, but existing studies have some major limitations. First, many-objective JSS problems have been solved by multi-objective evolutionary algorithms (MOEAs). However, recent studies have suggested that the performance of conventional MOEAs is prone to the scalability challenge and degrades dramatically with many-objective optimization problems (MaOPs). Many-objective JSS using MOEAs inherit the same challenge as MaOPs. Thus, using MOEAs for many-objective JSS problems often fails to select quality dispatching rules. Second, although the reference points method is one of the most prominent and efficient methods for diversity maintenance in many-objective problems, it uses a uniform distribution of reference points which is only appropriate for a regular Pareto-front. However, JSS problems often have irregular Pareto-front and uniformly distributed reference points do not match well with the irregular Pareto-front. It results in many useless points during evolution. These useless points can significantly affect the performance of the reference points-based algorithms. They cannot help to enhance the solution diversity of evolved Pareto-front in many-objective JSS problems. Third, Pareto Local Search (PLS) is a prominent and effective local search method for handling multi-objective JSS optimization problems but the literature does not discover any existing studies which use PLS in GP-HH. To address these limitations, this thesis's overall goal is to develop GP-HH approaches to evolving effective rules to handle many conflicting objectives simultaneously in JSS problems. To achieve the first goal, this thesis proposes the first many-objective GP-HH method for JSS problems to find the Pareto-fronts of nondominated dispatching rules. Decision-makers can utilize this GP-HH method for selecting appropriate rules based on their preference over multiple conflicting objectives. This study combines GP with the fitness evaluation scheme of a many-objective reference points-based approach. The experimental results show that the proposed algorithm significantly outperforms MOEAs such as NSGA-II and SPEA2. To achieve the second goal, this thesis proposes two adaptive reference point approaches (model-free and model-driven). In both approaches, the reference points are generated according to the distribution of the evolved dispatching rules. The model-free reference point adaptation approach is inspired by Particle Swarm Optimization (PSO). The model-driven approach constructs the density model and estimates the density of solutions from each defined sub-location in a whole objective space. Furthermore, the model-driven approach provides smoothness to the model by applying a Gaussian Process model and calculating the area under the mean function. The mean function area helps to find the required number of the reference points in each mean function. The experimental results demonstrate that both adaptive approaches are significantly better than several state-of-the-art MOEAs. To achieve the third goal, the thesis proposes the first algorithm that combines GP as a global search with PLS as a local search in many-objective JSS. The proposed algorithm introduces an effective fitness-based selection strategy for selecting initial individuals for neighborhood exploration. It defines the GP's proper neighborhood structure and a new selection mechanism for selecting the effective dispatching rules during the local search. The experimental results on the JSS benchmark problem show that the newly proposed algorithm can significantly outperform its baseline algorithm (GP-NSGA-III).</p>
<p>Scheduling is an important problem in artificial intelligence and operations research. In production processes, it deals with the problem of allocation of resources to different tasks with the goal of optimizing one or more objectives. Job shop scheduling is a classic and very common scheduling problem. In the real world, shop environments dynamically change due to events such as the arrival of new jobs and machine breakdown. In such manufacturing environments, uncertainty in shop parameters is typical. It is of vital importance to develop methods for effective scheduling in such practical settings. Scheduling using heuristics like dispatching rules is very popular and suitable for such environments due to their low computational cost and ease of implementation. For a dynamic manufacturing environment with varying shop scenarios, using a universal dispatching rule is not very effective. But manual development of effective dispatching rules is difficult, time consuming and requires expertise. Genetic programming is an evolutionary approach which is suitable for automatically designing effective dispatching rules. Since the genetic programming approach searches in the space of heuristics (dispatching rules) instead of building up a schedule, it is considered a hyper-heuristic approach. Genetic programming like many other evolutionary approaches is computationally expensive. Therefore, it is of vital importance to present the genetic programming based hyper-heuristic (GPHH) system with scheduling problem instances which capture the complex shop scenarios capturing the difficulty in scheduling. Active learning is a related concept from machine learning which concerns with effective sampling of those training instances to promote the accuracy of the learned model. The overall goal of this thesis is to develop effective and efficient genetic programming based hyper-heuristic approaches using active learning techniques for dynamic job shop scheduling problems with one or more objectives. This thesis develops new representations for genetic programming enabling it to incorporate the uncertainty information about processing times of the jobs. Furthermore, a cooperative co-evolutionary approach is developed for GPHH which evolves a pair of dispatching rules for bottleneck and non-bottleneck machines in the dynamic environment with uncertainty in processing times arising due to varying machine characteristics. The results show that the new representations and training approaches are able to significantly improve the performance of evolved dispatching rules. This thesis develops a new GPHH framework in order to incorporate active learning methods toward sampling DJSS instances which promote the evolution of more effective rules. Using this framework, two new active sampling methods were developed to identify those scheduling problem instances which promoted evolution of effective dispatching rules. The results show the advantages of using active learning methods for scheduling under the purview of GPHH. This thesis investigates a coarse-grained model of parallel evolutionary approach for multi-objective dynamic job shop scheduling problems using GPHH. The outcome of the investigation was utilized to extend the coarse-grained model and incorporate an active sampling heuristic toward identifying those scheduling problem instances which capture the conflict between the objectives. The results show significant improvement in the quality of the evolved Pareto set of dispatching rules. Through this thesis, the following contributions have been made. (1) New representations and training approaches for GPHH to incorporate uncertainty information about processing times of jobs into dispatching rules to make them more effective in a practical shop environment. (2) A new GPHH framework which enables active sampling of scheduling problem instances toward evolving dispatching rules effective across complex shop scenarios. (3) A new active sampling heuristic based on a coarse-grained model of parallel evolutionary approach for GPHH for multi-objective scheduling problems.</p>
<p>Scheduling is an important problem in artificial intelligence and operations research. In production processes, it deals with the problem of allocation of resources to different tasks with the goal of optimizing one or more objectives. Job shop scheduling is a classic and very common scheduling problem. In the real world, shop environments dynamically change due to events such as the arrival of new jobs and machine breakdown. In such manufacturing environments, uncertainty in shop parameters is typical. It is of vital importance to develop methods for effective scheduling in such practical settings. Scheduling using heuristics like dispatching rules is very popular and suitable for such environments due to their low computational cost and ease of implementation. For a dynamic manufacturing environment with varying shop scenarios, using a universal dispatching rule is not very effective. But manual development of effective dispatching rules is difficult, time consuming and requires expertise. Genetic programming is an evolutionary approach which is suitable for automatically designing effective dispatching rules. Since the genetic programming approach searches in the space of heuristics (dispatching rules) instead of building up a schedule, it is considered a hyper-heuristic approach. Genetic programming like many other evolutionary approaches is computationally expensive. Therefore, it is of vital importance to present the genetic programming based hyper-heuristic (GPHH) system with scheduling problem instances which capture the complex shop scenarios capturing the difficulty in scheduling. Active learning is a related concept from machine learning which concerns with effective sampling of those training instances to promote the accuracy of the learned model. The overall goal of this thesis is to develop effective and efficient genetic programming based hyper-heuristic approaches using active learning techniques for dynamic job shop scheduling problems with one or more objectives. This thesis develops new representations for genetic programming enabling it to incorporate the uncertainty information about processing times of the jobs. Furthermore, a cooperative co-evolutionary approach is developed for GPHH which evolves a pair of dispatching rules for bottleneck and non-bottleneck machines in the dynamic environment with uncertainty in processing times arising due to varying machine characteristics. The results show that the new representations and training approaches are able to significantly improve the performance of evolved dispatching rules. This thesis develops a new GPHH framework in order to incorporate active learning methods toward sampling DJSS instances which promote the evolution of more effective rules. Using this framework, two new active sampling methods were developed to identify those scheduling problem instances which promoted evolution of effective dispatching rules. The results show the advantages of using active learning methods for scheduling under the purview of GPHH. This thesis investigates a coarse-grained model of parallel evolutionary approach for multi-objective dynamic job shop scheduling problems using GPHH. The outcome of the investigation was utilized to extend the coarse-grained model and incorporate an active sampling heuristic toward identifying those scheduling problem instances which capture the conflict between the objectives. The results show significant improvement in the quality of the evolved Pareto set of dispatching rules. Through this thesis, the following contributions have been made. (1) New representations and training approaches for GPHH to incorporate uncertainty information about processing times of jobs into dispatching rules to make them more effective in a practical shop environment. (2) A new GPHH framework which enables active sampling of scheduling problem instances toward evolving dispatching rules effective across complex shop scenarios. (3) A new active sampling heuristic based on a coarse-grained model of parallel evolutionary approach for GPHH for multi-objective scheduling problems.</p>
<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>
<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>
This study addresses a new problem on scheduling of nonidentical multiple diffusion furnaces (NMDF) with machine eligibility restriction (MER) along with important real-life problem characteristics such as multiple and incompatible job-families, different release time, different due-date, non-agreeable release time & due-date, and occurrences of randomly and dynamically generated real time events (jobs and/or resources related) to minimize total weighted tardiness. Due to the computational difficulty in obtaining an optimal solution for large real-life sized scheduling of NMDF, an alternate solution procedure: heuristic algorithm based on due-date based dispatching rule, called due-date based heuristic algorithm (DDHA) is constructed in this study. Though there has been extensive scheduling research relating to the use of existing dispatching rules in semiconductor manufacturing along with/without new dispatching rules, there is no comparative analysis of all due-date based dispatching rules for real time scheduling (RTS) of NMDF-MER. Hence, this study proposes 20 variants of DDHA, considering various due-date based dispatching rules, for RTS-NMDF-MER and compares their performance. A suitable experimental design is developed and randomly generated 270 instances to represent the research problem considered in this study. From the empirical and statistical analysis carried out in this study, the better performing DDHA(s), for RTS-NMDF-MER, are identified.
