การจัดการเส้นทางขนส่งเพื่อลดความสูญเปล่าที่เกิดจากความล่าช้า ในกระบวนการจัดส่งสินค้าโดยใช้อัลกอริทึมแบบประหยัด กรณีศึกษา กิจเจริญทรัพย์ชุมแพขนส่ง จังหวัดขอนแก่น

การจัดการเส้นทางขนส่งโดยใช้อัลกอริทึมแบบประหยัดด้วยการลดความสูญเปล่ามีความสำคัญต่อการลดระยะทางและเวลาขนส่ง ทำให้สามารถลดต้นทุนการจัดส่งสินค้า งานวิจัยนี้จึงมีวัตถุประสงค์เพื่อศึกษาความ สูญเปล่าที่เกิดจากความล่าช้า เพื่อศึกษาปัจจัยเชิงสาเหตุที่มีอิทธิพลต่อความสูญเปล่าที่เกิดจากความล่าช้าและเสนอการจัดการเส้นทางขนส่งเพื่อลดความสูญเปล่าที่เกิดจากความล่าช้าในกระบวนการจัดส่งสินค้าโดยใช้อัลกอริทึมแบบประหยัด ซึ่งใช้ระเบียบวิธีวิจัยแบบผสมผสาน หน่วยวิเคราะห์ข้อมูลในระดับปัจเจกบุคคล เก็บรวบรวมข้อมูลเชิงคุณภาพด้วยการสังเกตแบบมีส่วนร่วมและแนวทางการสัมภาษณ์ผู้ให้ข้อมูลหลัก 17 คน และเชิงปริมาณเก็บด้วยแบบสอบถามจากกลุ่มตัวอย่าง 200 คน โดยวิเคราะห์ข้อมูลด้วยแผนภูมิการไหล คุณค่ากิจกรรม และโมเดลสมการโครงสร้าง ร่วมกับการจัดเส้นทางการขนส่งด้วยอัลกอริทึมแบบประหยัด ผลการวิจัยพบว่า กิจกรรมเคลื่อนย้ายสินค้าไปส่งให้กับลูกค้าเป็นความสูญเปล่า ซึ่งการจัดเส้นทางเป็นปัจจัยเชิงสาเหตุที่มีอิทธิพลต่อความสูญเปล่าที่เกิดจากความล่าช้าเป็นอันดับแรก จึงเสนอการจัดการเส้นทางขนส่งเพื่อลดความสูญเปล่าที่เกิดจากความล่าช้าในกระบวนการจัดส่งสินค้าโดยใช้อัลกอริทึมแบบประหยัด พบว่า สามารถลดระยะทางได้ 0.13 กิโลเมตรต่อรายการต่อวัน และลดระยะเวลาขนส่ง 1.43 นาทีต่อรายการต่อวัน ทำให้ลดต้นทุนการจัดส่งสินค้า 8,164.00 บาทต่อเดือน ซึ่งเป็นประโยชน์ต่อผู้ประกอบการธุรกิจขนส่งในการจัดการเส้นทางขนส่งอย่างมีประสิทธิภาพต่อไ

A data-driven method to assess the causes and impact of delay propagation in air transportation systems

Air transportation systems are exposed to disruptions, which have significant impact on operations. Airlines operate tight schedules to maximise resource utilisation, however, the lack of sufficient buffers often result in propagating delays. Thus, understanding how likely it is to experience delays, why they keep happening and what is their impact on airline operations are important steps for the management of the disruptions they cause. In this paper, we propose a data-driven method to empirically analyse how delays propagate and their impact on an airline schedule. Our multi-layer network method captures different variables that are influenced by schedule disruption, namely aircraft (tail), crew, passengers and their interfaces. The method is tested on the schedule disruptions of a hub-and-spoke airline where we empirically demonstrate that incorporating information in this multi-layered manner results in a more robust assessment of delay propagation. The method along with the empirical results of this study can support aviation system planners gain additional insights into flight delay propagation patterns and consequently support their resource allocation decisions while improving overall system performance

Reliable Reserve-Crew Scheduling for Airlines

We study the practical setting in which regular- and reserve-crew schedules are dynamically maintained up to the day of executing the schedule. At each day preceding the execution of the schedule, disruptions occur due to sudden unavailability of personnel, making the planned regular and reserve-crew schedules infeasible for its execution day. This paper studies the fundamental question how to repair the schedules' infeasibility in the days preceding the execution, taking into account labor regulations. We propose a robust repair strategy that maintains flexibility in order to cope with additional future disruptions. The flexibility in reserve-crew usage is explicitly considered through evaluating the expected shortfall of the reserve-crew schedule based on a Markov chain formulation. The core of our approach relies on iteratively solving a set-covering formulation, which we call the Robust Crew Recovery Problem, which encapsulates this flexibility notion for reserve crew usage. A tailored branch-and-price algorithm is developed for solving the Robust Crew Recovery Problem to optimality. The corresponding pricing problem is efficiently solved by a newly developed pulse algorithm. Based on actual data from a medium-sized hub-and-spoke airline, we show that embracing our approach leads to fewer flight cancellations and fewer last-minute alterations, compared to repairing disrupted schedules without considering our robust measure

Reliable reserve-crew scheduling for airlines

We study the practical setting in which regular- and reserve-crew schedules are dynamically maintained up to the day of executing the schedule. At each day preceding the execution of the schedule, disruptions occur due to sudden unavailability of personnel, making the planned regular and reserve-crew schedules infeasible for its execution day. This paper studies the fundamental question how to repair the schedules’ infeasibility in the days preceding the execution, taking into account labor regulations. We propose a robust repair strategy that maintains flexibility in order to cope with additional future disruptions. The flexibility in reserve-crew usage is explicitly considered through evaluating the expected shortfall of the reserve-crew schedule based on a Markov chain formulation. The core of our approach relies on iteratively solving a set-covering formulation, which we call the Robust Crew Recovery Problem, which encapsulates this flexibility notion for reserve crew usage. A tailored branch-and-price algorithm is developed for solving the Robust Crew Recovery Problem to optimality. The corresponding pricing problem is efficiently solved by a newly developed pulse algorithm. Based on actual data from a medium-sized hub-and-spoke airline, we show that embracing our approach leads to fewer flight cancellations and fewer last-minute alterations, compared to repairing disrupted schedules without considering our robust measure.</p

Optimization Approaches for Solving Large-Scale Personnel Scheduling Problems

Personnel scheduling is one of the most critical components in logistical planning for many practical areas, particularly in transportation, public services, and clinical operations. Because manpower is both an expensive and scarce resource, even a tiny improvement in utilization can provide huge expense savings for businesses. Additionally, a slightly better assignment schedule of the involved professionals can significantly increase their work satisfaction, which can in turn greatly improve the quality of the services customers or patients receive. However, practical personnel scheduling problems (PSPs) are hard to solve because modeling all of the complicated and nuanced requirements and rules is challenging. Moreover, since an iterative construction process may be necessary for handling the multiple-criteria or ill-defined objective nature of many PSPs, the model is expected to be solved in a short time while providing high-quality solutions, despite its large size and complexity. In this dissertation, we propose new models and solution approaches to address these challenges. We study in total three real-world PSPs. We first consider the crew pairing construction for a cargo airline. Each crew pairing is a sequence of flights assigned to a specific line/bid crew to operate in practice. Unlike traditional passenger aviation, due to the cargo airline's underlying network, each crew pairing will specify a complete flying schedule for the assigned crew over the entire planning horizon. Consequently, an extra and unique set of requirements must be incorporated into the construction process. We solve the problem using a delayed column generation framework. We develop a restricted shortest path model to incorporate the entire set of complicated requirements simultaneously and solve it using a labeling algorithm accelerated by a handful of proposed strategies. Computational experiments show that our approach can solve the crew pairing problem in a short time, while almost always delivering an optimal solution. Second, we consider an extension of the previous cargo crew scheduling problem, where a "break" is allowed to take place in the "middle" of each crew pairing. This break feature, working as a special type of conventional deadheading, is expected to significantly increase the flight coverage for practical deployment. However, incorporating this feature will result in an extremely dense underlying network, which introduces new computational challenges. To address this issue, we propose a bidirectional labeling based arc selection approach, which only needs to work on a tiny sub-network each time but can still guarantee the exactness of the delayed column generation process. We demonstrate through real-world instances that our proposed approach can solve this relaxed problem extension in a very short time and the resulting flight coverage will increase by over 30%. Finally, we study a medical resident annual block scheduling problem. We need to assign residents to perform services at different clinical units during each time period across the academic year so that the residents receive appropriate training while the hospital gets staffed sufficiently. We propose a two-stage partial fixing solution framework to address the long runtime issue caused by traditional approaches. A network-based model is also developed to provide a high-quality service selection to initiate this two-stage framework. Experiments using inputs from our clinical collaborator show that our approach can speed up the schedule construction at least 5 times for all instances and even over 100 times for some huge-size ones compared to a widely-used traditional approach.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169758/1/jhguo_1.pd

Operational research:methods and applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order