A Flowshop Scheduling Problem With Transportation Times and Capacity Constraints

Although there are numerous methodologies and research studies on machine scheduling, most of the literature assumes that there is an unlimited number of transporters to deliver jobs from one machine to another for further processing and that transportation times can be neglected. These two assumptions are not applicable if one intends to generate an accurate schedule for the shop floor. In this research, a flowshop scheduling problem with two machines, denoted as M1 and M2, and a single transporter with capacity c is considered. The main focus is on the development of a dynamic programming algorithm to generate a schedule that minimizes the makespan. The transporter takes t1 time units to travel with at least one job from machine M1 to machine M2, and t2 time units to return empty to machine M1. When the processing times for all n jobs on machine M1 are constant, denoted as pj1≡p1, and the capacity of the transporter c is at least ()12121−⎥⎥⎤⎢⎢⎡+ptt, the computational complexity of the proposed algorithm is shown to be

Single machine scheduling with consideration of preventive maintenance and machine health

Because of Industry 4.0 and Internet of Things, it is easier to collect data from machines through sensors that are embedded inside machines. Once the status change of a machine is detected, production on that machine may need to be adjusted accordingly. In this research, we focus on single machine scheduling with considering the Preventive Maintenance (PM) and machine health index. Machine health index is categorized into three states: good, fair, and breakdown. When the machine moves from one state to another, the processing time of jobs will change as well as the machine failure rate. We develop a model to determine an optimal interval of performing PM and production sequence of jobs. A two-phase heuristic method is proposed to solve a large-size problem. Through different parameter settings, such as the machine failure rate, number of jobs, repair and maintenance cost, we show that the two-phase heuristic can obtain a solution with high quality

Single machine scheduling with consideration of preventive maintenance and machine health

Because of Industry 4.0 and Internet of Things, it is easier to collect data from machines through sensors that are embedded inside machines. Once the status change of a machine is detected, production on that machine may need to be adjusted accordingly. In this research, we focus on single machine scheduling with considering the Preventive Maintenance (PM) and machine health index. Machine health index is categorized into three states: good, fair, and breakdown. When the machine moves from one state to another, the processing time of jobs will change as well as the machine failure rate. We develop a model to determine an optimal interval of performing PM and production sequence of jobs. A two-phase heuristic method is proposed to solve a large-size problem. Through different parameter settings, such as the machine failure rate, number of jobs, repair and maintenance cost, we show that the two-phase heuristic can obtain a solution with high quality

Optimal contract design for cloud computing service with resource service guarantee

<p>The optimal contract design for cloud computing service with resource guarantee under the consideration of resource redundancy and network externality is studied in this research. A model in which a service provider determines joint pricing and resource allocation decisions is constructed by proposing two types of contracts with different service-level agreements (SLAs). The SLA of each contract describes the price and associated penalty if the provider cannot provide the resource requested by the customers. Optimal pricing and resource allocation decisions as well as the equilibrium contracts of the service provider are analyzed based on the dynamics of the model characteristics. We found that optimal contract design is sensitive to both service levels and customers’ beliefs of compensation ratio when the requested resource is unfulfilled. Furthermore, service providers should evaluate the trade-off between benefit of price discrimination and effect of network externality when determining the optimal contract design.</p

Additional file 2: of Integrating RNA-seq and ChIP-seq data to characterize long non-coding RNAs in Drosophila melanogaster

Summary of the curated lncRNAs. (XLS 1510 kb