Improved Squeaky Wheel Optimisation for Driver Scheduling

This paper presents a technique called Improved Squeaky Wheel Optimisation for driver scheduling problems. It improves the original Squeaky Wheel Optimisations effectiveness and execution speed by incorporating two additional steps of Selection and Mutation which implement evolution within a single solution. In the ISWO, a cycle of Analysis-Selection-Mutation-Prioritization-Construction continues until stopping conditions are reached. The Analysis step first computes the fitness of a current solution to identify troublesome components. The Selection step then discards these troublesome components probabilistically by using the fitness measure, and the Mutation step follows to further discard a small number of components at random. After the above steps, an input solution becomes partial and thus the resulting partial solution needs to be repaired. The repair is carried out by using the Prioritization step to first produce priorities that determine an order by which the following Construction step then schedules the remaining components. Therefore, the optimisation in the ISWO is achieved by solution disruption, iterative improvement and an iterative constructive repair process performed. Encouraging experimental results are reported

Solving Challenging Real-World Scheduling Problems

This work contains a series of studies on the optimization of three real-world scheduling problems, school timetabling, sports scheduling and staff scheduling. These challenging problems are solved to customer satisfaction using the proposed PEAST algorithm. The customer satisfaction refers to the fact that implementations of the algorithm are in industry use. The PEAST algorithm is a product of long-term research and development. The first version of it was introduced in 1998. This thesis is a result of a five-year development of the algorithm. One of the most valuable characteristics of the algorithm has proven to be the ability to solve a wide range of scheduling problems. It is likely that it can be tuned to tackle also a range of other combinatorial problems. The algorithm uses features from numerous different metaheuristics which is the main reason for its success. In addition, the implementation of the algorithm is fast enough for real-world use.Siirretty Doriast

A multi-agent system for a bus crew rescheduling system

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Unpredictable events (UE) are major factors that cause disruption to everyday bus operation. In the occurrence of UE, the main resources - crews and vehicles - are affected, and this leads to crew schedule disruption. One way to deal with the problem is crew rescheduling. Most of the current approaches are based on static schedules do not support rescheduling in a real-time scenario. They have the ability to reschedule but a new complete schedule is produced without concerning the real time situation. The mathematical approaches which are used by most scheduling packages have the ability to search for optimum or near optimum schedules but they are usually slow to produce results in real-time because they are computationally intensive when faced with complex situations. In practice, crew or bus rescheduling is managed manually, based on the supervisor's capabilities and experience in managing UE. However, manual rescheduling is complex, prone to error and not optimum, especially when dealing with many UE at the same time. This research proposes the CRSMAS (Crew Rescheduling System with Multi Agent System) approach as an alternative that may help supervisors to make quick rescheduling decisions by automating the crew rescheduling process. A Multi Agent System (MAS) is considered suitable to support this rescheduling because agents can dynamically adapt their behaviour to changing environments and they can find solutions quickly via negotiations and cooperation between them. To evaluate the CRSMAS, two types of experiment are carried out: Single Event and Multiple Events. The Single Event experiment is used to find characteristics of crew schedules that influence the crew rescheduling process while the Multiple Events experiment is used to test the capability of CRSMAS in dealing with numerous events that occur randomly. A wide range of simulation results, based on real-world data, are reported and analysed. Based on the experiment it is concluded that CRSMAS is suitable for automating the crew rescheduling process and capable of quick rescheduling whether facing single events or multiple events at the same time, the success of rescheduling is not only dependant on the tool but also to other factors such as the characteristics of crew schedules and the period of the UE, and one limitation of CRSMAS that was discovered is it cannot simulate different type of events at the same time. This limitation is because in different events there are different rules but, in Virtual World, agents can only negotiate with one set of rules at a time.Financial support was obtained from the Universiti Teknikal Malaysia Melaka (UTeM)

A multi-agent system for a bus crew rescheduling system

Unpredictable events (UE) are major factors that cause disruption to everyday bus operation. In the occurrence of UE, the main resources - crews and vehicles - are affected, and this leads to crew schedule disruption. One way to deal with the problem is crew rescheduling. Most of the current approaches are based on static schedules do not support rescheduling in a real-time scenario. They have the ability to reschedule but a new complete schedule is produced without concerning the real time situation. The mathematical approaches which are used by most scheduling packages have the ability to search for optimum or near optimum schedules but they are usually slow to produce results in real-time because they are computationally intensive when faced with complex situations. In practice, crew or bus rescheduling is managed manually, based on the supervisor's capabilities and experience in managing UE. However, manual rescheduling is complex, prone to error and not optimum, especially when dealing with many UE at the same time. This research proposes the CRSMAS (Crew Rescheduling System with Multi Agent System) approach as an alternative that may help supervisors to make quick rescheduling decisions by automating the crew rescheduling process. A Multi Agent System (MAS) is considered suitable to support this rescheduling because agents can dynamically adapt their behaviour to changing environments and they can find solutions quickly via negotiations and cooperation between them. To evaluate the CRSMAS, two types of experiment are carried out: Single Event and Multiple Events. The Single Event experiment is used to find characteristics of crew schedules that influence the crew rescheduling process while the Multiple Events experiment is used to test the capability of CRSMAS in dealing with numerous events that occur randomly. A wide range of simulation results, based on real-world data, are reported and analysed. Based on the experiment it is concluded that CRSMAS is suitable for automating the crew rescheduling process and capable of quick rescheduling whether facing single events or multiple events at the same time, the success of rescheduling is not only dependant on the tool but also to other factors such as the characteristics of crew schedules and the period of the UE, and one limitation of CRSMAS that was discovered is it cannot simulate different type of events at the same time. This limitation is because in different events there are different rules but, in Virtual World, agents can only negotiate with one set of rules at a time.EThOS - Electronic Theses Online ServiceUniversiti Teknikal Malaysia Melaka (UTeM)GBUnited Kingdo

Evolutionary algorithms for scheduling operations

While business process automation is proliferating through industries and processes, operations such as job and crew scheduling are still performed manually in the majority of workplaces. The linear programming techniques are not capable of automated production of a job or crew schedule within a reasonable computation time due to the massive sizes of real-life scheduling problems. For this reason, AI solutions are becoming increasingly popular, specifically Evolutionary Algorithms (EAs). However, there are three key limitations of previous studies researching application of EAs for the solution of the scheduling problems. First of all, there is no justification for the selection of a particular genetic operator and conclusion about their effectiveness. Secondly, the practical efficiency of such algorithms is unknown due to the lack of comparison with manually produced schedules. Finally, the implications of real-life implementation of the algorithm are rarely considered. This research aims at addressing all three limitations. Collaborations with DBSchenker,the rail freight carrier, and Garnett-Dickinson, the printing company,have been established. Multi-disciplinary research methods including document analysis, focus group evaluations, and interviews with managers from different levels have been carried out. A standard EA has been enhanced with developed within research intelligent operators to efficiently solve the problems. Assessment of the developed algorithm in the context of real life crew scheduling problem showed that the automated schedule outperformed the manual one by 3.7% in terms of its operating efficiency. In addition, the automatically produced schedule required less staff to complete all the jobs and might provide an additional revenue opportunity of £500 000. The research has also revealed a positive attitude expressed by the operational and IT managers towards the developed system. Investment analysis demonstrated a 41% return rate on investment in the automated scheduling system, while the strategic analysis suggests that this system can enable attainment of strategic priorities. The end users of the system, on the other hand, expressed some degree of scepticism and would prefer manual methods

A Self-Adjusting Algorithm for Driver Scheduling

Public transport driver scheduling is a world wide problem, which is NP-hard. Although some mathematically based methods are being used in the transport industry, there is still much scope for improvements. This paper presents a novel evolutionary approach that simulates the self-adjusting process on a single schedule. Five factors characterized by fuzzy membership functions are first aggregated to evaluate the shift structure. This evaluating function is incorporated into a constructing heuristic to make shift selection. A self-adjusting algorithm is then designed to guide the constructing heuristic to improve a given initial schedule iteratively. In each generation an unfit portion of the working schedule is removed. Broken schedules are repaired by the constructing heuristic until stopping condition is met. Experimental results on real-world driver scheduling problems has demonstrated the success of the proposed approach