Preemptive scheduling with position costs

This paper is devoted to basic scheduling problems in which the scheduling cost of a job is not a function of its completion time. Instead, the cost is derived from the integration of a cost function over the time intervals on which the job is processed. This criterion is specially meaningful when job preemption is allowed. Polynomial algorithms are presented to solve some special cases including a one-machine problem with a common due date and a two-machine problem with linear nondecreasing cost functions

A survey of variants and extensions of the resource-constrained project scheduling problem

The resource-constrained project scheduling problem (RCPSP) consists of activities that must be scheduled subject to precedence and resource constraints such that the makespan is minimized. It has become a well-known standard problem in the context of project scheduling which has attracted numerous researchers who developed both exact and heuristic scheduling procedures. However, it is a rather basic model with assumptions that are too restrictive for many practical applications. Consequently, various extensions of the basic RCPSP have been developed. This paper gives an overview over these extensions. The extensions are classified according to the structure of the RCPSP. We summarize generalizations of the activity concept, of the precedence relations and of the resource constraints. Alternative objectives and approaches for scheduling multiple projects are discussed as well. In addition to popular variants and extensions such as multiple modes, minimal and maximal time lags, and net present value-based objectives, the paper also provides a survey of many less known concepts. --project scheduling,modeling,resource constraints,temporal constraints,networks

Optimising airline maintenance scheduling decisions

Airline maintenance scheduling (AMS) studies how plans or schedules are constructed to ensure that a fleet is efficiently maintained and that airline operational demands are met. Additionally, such schedules must take into consideration the different regulations airlines are subject to, while minimising maintenance costs. In this thesis, we study different formulations, solution methods, and modelling considerations, for the AMS and related problems to propose two main contributions. First, we present a new type of multi-objective mixed integer linear programming formulation which challenges traditional time discretisation. Employing the concept of time intervals, we efficiently model the airline maintenance scheduling problem with tail assignment considerations. With a focus on workshop resource allocation and individual aircraft flight operations, and the use of a custom iterative algorithm, we solve large and long-term real-world instances (16000 flights, 529 aircraft, 8 maintenance workshops) in reasonable computational time. Moreover, we provide evidence to suggest, that our framework provides near-optimal solutions, and that inter-airline cooperation is beneficial for workshops. Second, we propose a new hybrid solution procedure to solve the aircraft recovery problem. Here, we study how to re-schedule flights and re-assign aircraft to these, to resume airline operations after an unforeseen disruption. We do so while taking operational restrictions into account. Specifically, restrictions on aircraft, maintenance, crew duty, and passenger delay are accounted for. The flexibility of the approach allows for further operational restrictions to be easily introduced. The hybrid solution procedure involves the combination of column generation with learning-based hyperheuristics. The latter, adaptively selects exact or metaheuristic algorithms to generate columns. The five different algorithms implemented, two of which we developed, were collected and released as a Python package (Torres Sanchez, 2020). Findings suggest that the framework produces fast and insightful recovery solutions

Cut generation based algorithms for unrelated parallel machine scheduling problems

Research on scheduling in the unrelated parallel machine environment is at best scarce. Moreover, almost all existing work in this area is focused on the minimization of completion time related performance measures and the solution approaches available in the literature suffer from scalability issues. In this dissertation, we leverage on the success of the mathematical programming based decomposition approaches and devise scalable, efficient, and effective cut generation based algorithms for four NP-hard unrelated parallel machine scheduling problems. In the first part,we develop a newpreemptive relaxation for the totalweighted tardiness and total weighted earliness/tardiness problems and devise a Benders decomposition algorithm for solving this preemptive relaxation formulated as a mixed integer linear program. We demonstrate the effectiveness of our approach with instances up to 5 machines and 200 jobs The second part deals with the problem of minimizing the total weighted completion time and proves that the preemptive relaxation developed in part one is an exact formulation for this problem. By exploiting the structural properties of the performance measure, we attain an exact Benders decomposition algorithm which solves instances with up to 1000 jobs and 8 machines to optimality within a few seconds. In the last part, we tackle the unrestricted common due date just-in-time scheduling problemand develop a logic-based Benders decomposition algorithm. Aside from offering the best solution approach for this problem, we demonstrate that it is possible to devise scalable logic-based algorithms for scheduling problems with irregular minsum objectives

A Survey of Fault-Tolerance Techniques for Embedded Systems from the Perspective of Power, Energy, and Thermal Issues

The relentless technology scaling has provided a significant increase in processor performance, but on the other hand, it has led to adverse impacts on system reliability. In particular, technology scaling increases the processor susceptibility to radiation-induced transient faults. Moreover, technology scaling with the discontinuation of Dennard scaling increases the power densities, thereby temperatures, on the chip. High temperature, in turn, accelerates transistor aging mechanisms, which may ultimately lead to permanent faults on the chip. To assure a reliable system operation, despite these potential reliability concerns, fault-tolerance techniques have emerged. Specifically, fault-tolerance techniques employ some kind of redundancies to satisfy specific reliability requirements. However, the integration of fault-tolerance techniques into real-time embedded systems complicates preserving timing constraints. As a remedy, many task mapping/scheduling policies have been proposed to consider the integration of fault-tolerance techniques and enforce both timing and reliability guarantees for real-time embedded systems. More advanced techniques aim additionally at minimizing power and energy while at the same time satisfying timing and reliability constraints. Recently, some scheduling techniques have started to tackle a new challenge, which is the temperature increase induced by employing fault-tolerance techniques. These emerging techniques aim at satisfying temperature constraints besides timing and reliability constraints. This paper provides an in-depth survey of the emerging research efforts that exploit fault-tolerance techniques while considering timing, power/energy, and temperature from the real-time embedded systems’ design perspective. In particular, the task mapping/scheduling policies for fault-tolerance real-time embedded systems are reviewed and classified according to their considered goals and constraints. Moreover, the employed fault-tolerance techniques, application models, and hardware models are considered as additional dimensions of the presented classification. Lastly, this survey gives deep insights into the main achievements and shortcomings of the existing approaches and highlights the most promising ones

Platelet inventory management in blood supply chain under demand and supply uncertainty

Supply chain management of blood and its products are of paramount importance in medical treatment due to its perishable nature, uncertain demand, and lack of auxiliary substitutes. For example, the Red Blood Cells (RBC's) have a life span of approximately 40 days, whereas platelets have a shelf life of up to five days after extraction from the human body. According to the World Health Organization, approximately 112 million blood units are collected worldwide annually. However, nearly 20 percent of units are discarded in developed nations due to being expired before the final use. A similar trend is noticed in developing countries as well. On the other hand, blood shortage could lead to elective surgeries cancellations. Therefore, managing blood distribution and developing an efficient blood inventory management is considered a critical issue in the supply chain domain. A standard blood supply chain (BSC) achieves the movement of blood products (red blood cells, white blood cells, and platelets) from initial collection to final patients in several echelons. The first step comprises of donation of blood by donors at the donation or mobile centers. The donation sites transport the blood units to blood centers where several tests for infections are carried out. The blood centers then store either the whole blood units or segregate them into their individual products. Finally, they are distributed to the healthcare facilities when required. In this dissertation, an efficient forecasting model is developed to forecast the supply of blood. We leverage five years' worth of historical blood supply data from the Taiwan Blood Services Foundation (TBSF) to conduct our forecasting study. With the generated supply and demand distributioins from historial supply and demand data as inputs, a single objective stochastic model is developed to determine the number of platelet units to order and the time between orders at the hospitals. To reduce platelet shortage and outdating, a collaborative network between the blood centers and hospitals is proposed; the model is extended to determine the optimal ordering policy for a divergent network consisting of multiple blood centers and hospitals. It has been shown that a collaborative system of blood centers and hospitals is better than a decentralized system in which each hospital is supplied with blood only by its corresponding blood center. Furthermore, a mathematical model is proposed based on multi-criteria decision-making (MCDM) techniques, in which different conflicting objective functions are satisfied to generate an efficient and satisfactory solution for a blood supply chain comprising of two hospitals and one blood center. This study also conducted a sensitivity analysis to examine the impacts of the coefficient of demand and supply variation and the settings of cost parameters on the average total cost and the performance measures (units of shortage, outdated units, inventory holding units, and purchased units) for both the blood center and hospitals. The proposed models can also be applied to determine ordering policies for other supply chain of perishable products, such as perishable food or drug supply chains.Includes bibliographical references