Scheduling Non-Preemptible Jobs to Minimize Peak Demand

Yaw, Sean; Mumey, Brendan

doi:10.3390/a10040122

Cited by 8 publications

(5 citation statements)

References 16 publications

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“…However, for other applications, the geometric constraint in GSP does not seem to be necessary, and hence it makes sense to drop it (i.e., to rather consider DSP): this might lead to better solutions, possibly via simpler and/or more efficient algorithms. Consider for example the minimization of the peak energy consumption in smart-grids [31,44,39].…”

Section: H(i)mentioning

confidence: 99%

“…There is a very rich line of research on generalizations and variants of DSP such as online versions [34,35], tasks with availability constraints or time windows [45,44,31], a mixture of preemptable and non-preemptable tasks [39] or generalized cost functions based on the demand at each edge [10,35]. The variant of DSP with the extra feature of interrupting the tasks is known as Strip Packing with Slicing, for which there exists an FPTAS [3]; on the other hand, the case of DSP is still hard to approximate by a factor better than 3/2 as noted by Tang et al [43].…”

Section: Related Workmentioning

confidence: 99%

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Approximation Algorithms for Demand Strip Packing

Gálvez¹,

Grandoni²,

Ameli³

et al. 2021

Preprint

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In the Demand Strip Packing problem (DSP), we are given a time interval and a collection of tasks, each characterized by a processing time and a demand for a given resource (such as electricity, computational power, etc.). A feasible solution consists of a schedule of the tasks within the mentioned time interval. Our goal is to minimize the peak resource consumption, i.e. the maximum total demand of tasks executed at any point in time.It is known that DSP is NP-hard to approximate below a factor 3/2, and standard techniques for related problems imply a (polynomial-time) 2-approximation. Our main result is a (5/3 + ε)approximation algorithm for any constant ε > 0. We also achieve best-possible approximation factors for some relevant special cases.

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Section: H(i)mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Approximation Algorithms for Demand Strip Packing

Gálvez¹,

Grandoni²,

Ameli³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Dealing with the allocation of processing resources several contributions are intended to the minimisation of the workload peaks. The minimisation of peaks has a long tradition in job scheduling and resource allocation problems, both in production systems and in power grids management (Ranjan et al , 2014; Yaw and Mumey, 2017). As for power grids which need for the balance of power load among the users, the workload in production systems needs to be balanced between several processing resources (e.g.…”

Section: Literature Reviewmentioning

confidence: 99%

A simulated annealing algorithm for the allocation of production resources in the food catering industry

Tufano

Accorsi

Manzini

2020

BFJ

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PurposeThis paper addresses the trade-off between asset investment and food safety in the design of a food catering production plant. It analyses the relationship between the quality decay of cook-warm products, the logistics of the processes and the economic investment in production machines.Design/methodology/approachA weekly cook-warm production plan has been monitored on-field using temperature sensors to estimate the quality decay profile of each product. A multi-objective optimisation model is proposed to (1) minimise the number of resources necessary to perform cooking and packing operations or (2) to maximise the food quality of the products. A metaheuristic simulated annealing algorithm is introduced to solve the model and to identify the Pareto frontier of the problem.FindingsThe packaging buffers are identified as the bottleneck of the processes. The outcome of the algorithms highlights that a small investment to design bigger buffers results in a significant increase in the quality with a smaller food loss.Practical implicationsThis study models the production tasks of a food catering facility to evaluate their criticality from a food safety perspective. It investigates the tradeoff between the investment cost of resources processing critical tasks and food safety of finished products.Social implicationsThe methodology applies to the design of cook-warm production. Catering companies use cook-warm production to serve school, hospitals and companies. For this reason, the application of this methodology leads to the improvement of the quality of daily meals for a large number of people.Originality/valueThe paper introduces a new multi-objective function (asset investment vs food quality) proposing an original metaheuristic to address this tradeoff in the food catering industry. Also, the methodology is applied and validated in the design of a new food production facility.

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“…This problem was proven to be NP-hard [25,26]. The algorithms proposed by Yaw et al are, to some extent, applicable to the bus depot problem but need to be adjusted.…”

Section: Appl Sci 2019 9 X For Peer Review 8 Of 16mentioning

confidence: 99%

“…The jobs in the bus depot problem have three possible heights and Cieliebak et al schedule jobs with the same height. Yaw et al propose an exact algorithm, an approximation and a simple heuristic for the problem of scheduling non-preemptive jobs to minimize the peak demand in a smart grid [26]. They are scheduling jobs such as household appliances (dishwasher or water heater).…”

mentioning

confidence: 99%

Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots

2019

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The city of Hamburg has decided to electrify its bus fleets. The two public transportation companies in this city expect to operate up to 1500 buses by 2030. In order to accomplish this ambitious goal, both companies need to build an appropriate charging infrastructure. They have both decided to implement the centralized depot charging concept. Buses can therefore charge only at the depot and do not have the possibility for opportunity charging at intermediate stations. The load profile of such a bus depot is highly dependent on the charging schedule of buses. Without an intelligent scheduling system, the buses charge on demand as soon as they arrive to the depot. This can lead to an unevenly distributed load profile with high load peaks, which is problematic for the local grid as well as for the equipment dimensioning at the depot. Charging scheduling on large-scale bus depots is a relatively new and poorly researched topic. This paper addresses the issue and proposes two algorithms for charging scheduling on large-scale bus depots with the goal to minimize the peak load. The schedules created with the proposed algorithms were both tested and validated in the Bus Depot Simulator, a cosimulation platform used for bus depot simulations.

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Scheduling Non-Preemptible Jobs to Minimize Peak Demand

Cited by 8 publications

References 16 publications

Approximation Algorithms for Demand Strip Packing

Approximation Algorithms for Demand Strip Packing

A simulated annealing algorithm for the allocation of production resources in the food catering industry

Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots

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