A green pump scheduling algorithm for minimizing power consumption and land depletion

Wang, Jen-Ya; Chen, Fuh-Gwo; Chen, Jr-Shian

doi:10.1177/1063293x13481613

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…Environmental protection and cost efficiency have always been a dilemma and we need to weigh their severities. In [27], there were several pumps of different capacities and a reservoir storing groundwater from different sources. Because the nighttime electricity was charged at a low price and frequent pump switching will caused wear and tear, we had better continue to pump water for a long time at night.…”

Section: B Environmental Issuesmentioning

confidence: 99%

Algorithms for Minimizing Resource Consumption Over Multiple Machines With a Common Due Window

Wang

2019

IEEE Access

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Scheduling on multiple machines has attracted considerable attention recently. However, most of traditional studies focused only on commercial cost and customer satisfaction. In fact, we are able to alleviate environmental damages via proper scheduling. This study explores a multi-machine scheduling problem with a due window. The objective is to minimize the total cost (including environmental cost). We develop a branch-and-bound algorithm (B&B) and two complementary lower bounds for optimally solving the problem when n <16. Moreover, we propose an imperialist competitive algorithm (ICA) to obtain near-optimal schedules for large problem instances. Experimental results are provided to show the performance of the proposed algorithms. INDEX TERMS Chemical industry, multi-machine scheduling, resource consumption, due window, imperialist competitive algorithm, branch-and-bound algorithm.

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Section: B Environmental Issuesmentioning

confidence: 99%

Algorithms for Minimizing Resource Consumption Over Multiple Machines With a Common Due Window

Wang

2019

IEEE Access

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“…Therefore, we need a metaheuristic algorithm for obtaining near-optimal schedules. The genetic algorithm ( GA ) has been successfully applied to many combinatorial problems (Simpson and D’Souza, 2004; Wang et al, 2013). Therefore, we utilize the GA to derive the near-optimal solution for this problem.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Algorithms for single-machine scheduling to minimize the total tardiness with learning effects and two competing agents

Lee

Wang

2014

Concurrent Engineering

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Scheduling with learning effects or scheduling with two competing agents has been widely studied in recent years. However, they are seldom discussed at the same time. In this article, we consider a single-machine scheduling problem with learning effects where the objective is to minimize the total tardiness of jobs from one agent, given that the maximum tardiness of jobs from the other agent cannot exceed an upper bound. We provide a branch-and-bound algorithm to search for the optimal solution and a genetic algorithm equipped with a local search to obtain near-optimal solutions. In addition, we conduct some computational experiments to investigate the solution quality and execution speed of both algorithms.

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“…Most of the previous studies focused on a small-scale elevated tanks [5,10,28]. A small portion of real systems are similar to small test networks of these researches, but most of the time we face large networks with a couple of hundred pipes, junctions, and a considerable number of pumps, valves, tanks, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

“…There are different examples in previous research of energy consumption charge and peak power demand charge being used. Wang et al used a time-of-use electricity tariff in their optimization study [10]. Baran et al also used a time-dependent electricity tariff that was defined based on on-peak and off-peak hours [11].…”

Section: Literature Reviewmentioning

confidence: 99%

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PEPSO: Reducing Electricity Usage and Associated Pollution Emissions of Water Pumps

Miller

2017

Water

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Using metaheuristic optimization methods has enabled researchers to reduce the electricity consumption cost of small water distribution systems (WDSs). However, dealing with complicated WDSs and reducing their environmental footprint remains a challenge. In this study a multi-objective version of Pollution Emission Pump Station Optimization tool (PEPSO) is introduced that can reduce the electricity cost and pollution emissions (associated with the energy consumption) of pumps of WDSs. PEPSO includes a user-friendly graphical interface and a customized version of the non-dominated sorting genetic algorithm. A measure that is called "Undesirability Index" (UI) is defined to assist the search for a promising optimization path. The UI also ensures that the final results are desirable and practical. The various features of PEPSO are tested under six scenarios for optimizing the WDS of Monroe City, MI, and Richmond, UK. The test results indicate that in a reasonable amount of time, PEPSO can optimize and provide practical results for both WDSs.

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A green pump scheduling algorithm for minimizing power consumption and land depletion

Cited by 5 publications

References 16 publications

Algorithms for Minimizing Resource Consumption Over Multiple Machines With a Common Due Window

Algorithms for Minimizing Resource Consumption Over Multiple Machines With a Common Due Window

Algorithms for single-machine scheduling to minimize the total tardiness with learning effects and two competing agents

PEPSO: Reducing Electricity Usage and Associated Pollution Emissions of Water Pumps

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