No-idle parallel-machine scheduling of unit-time jobs with a small number of distinct release dates and deadlines

Brauner, Nadia; Kovalyov, Mikhail Y.; Quilliot, Alain; Toussaint, Hélène

doi:10.1016/j.cor.2021.105315

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…In this context, the no-idling constraint is taken into account. The authors in [44] studied the identical parallel machine scheduling problem with unit processing times, release dates, deadlines, and no-idle machines constraints. In the latter study, several theoretical proprieties and an efficient heuristic were presented.…”

Section: Green Parallel Machine Scheduling Problem With Idle Machine ...mentioning

confidence: 99%

Green Scheduling of Identical Parallel Machines with Release Date, Delivery Time and No-Idle Machine Constraints

et al. 2021

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Global warming and climate change are threatening life on earth. These changes are due to human activities resulting in the emission of greenhouse gases. This is caused by intensive industrial activities and excessive fuel energy consumption. Recently, the scheduling of production systems has been judged to be an effective way to reduce energy consumption. This is the field of green scheduling, which aims to allocate jobs to machines in order to minimize total costs, with a focus on the sustainable use of energy. Several studies have investigated parallel-machine shops, with a special focus on reducing and minimizing the total consumed energy. Few studies explicitly include the idle energy of parallel machines, which is the energy consumed when the machines are idle. In addition, very few studies have considered the elimination of idle machine times as an efficient way to reduce the total consumed energy. This is the no-idle machine constraint, which is the green aspect of the research. In this context, this paper addresses the green parallel-machine scheduling problem, including release dates, delivery times, and no-idle machines, with the objective of minimizing the maximum completion time. This problem is of practical interest since it is encountered in several industry processes, such as the steel and automobile industries. A mixed-integer linear programming (MILP) model is proposed for use in obtaining exact solutions for small-sized instances. Due to the NP-hardness of the studied problem, and encouraged by the successful adaptation of metaheuristics for green scheduling problems, three genetic algorithms (GAs) using three different crossover operators and a simulated annealing algorithm (SA) were developed for large-sized problems. A new family of lower bounds is proposed. This was intended for the evaluation of the performance of the proposed algorithms over the average percent of relative deviation (ARPD). In addition, the green aspect was evaluated over the percentage of saved energy, while eliminating the idle-machine times. An extensive experimental study was carried out on a benchmark of test problems with up to 200 jobs and eight machines. This experimental study showed that one GA variant dominated the other proposed procedures. Furthermore, the obtained numerical results provide strong evidence that the proposed GA variant outperformed the existing procedures from the literature. The experimental study also showed that the adoption of the no-idle machine time constraints made it possible to reduce the total consumed energy by 29.57%, while the makespan (cost) increased by only 0.12%.

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Section: Green Parallel Machine Scheduling Problem With Idle Machine ...mentioning

confidence: 99%

Green Scheduling of Identical Parallel Machines with Release Date, Delivery Time and No-Idle Machine Constraints

et al. 2021

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“…In the parallel machine scheduling papers, Naderi et al [4] showed that order acceptance and the identical parallel machine scheduling problem are NP-hard; researches in this direction should focus on the description of the model. For example, Brauner et al [5] used a small square to represent the processing of one job in the corresponding unit-time interval. Expressing all time interval with this small square solves the schedule problem with the release dates and deadlines.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Stove Scheduling for Sustainable On-Demand Food Delivery

Dai

Fan

2021

Sustainability

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Ordering food through mobile apps and crowdsourcing resources has become increasingly popular in the digital age. Restaurants can improve customer satisfaction to satisfy on-demand food orders by shortening waiting time and achieving sustainability through fuel reduction. In the present study, we construct a double-layer scheduling model, which is developed using the characteristics of on-demand food preparation, including the use of multiple stoves, a variety of dishes in one order, and the integration of the same dishes from different customers. The bottom layer is a multi-stove dish package scheduling model based on parallel machine scheduling. The upper layer is an order selection model based on the knapsack problem. To identify the optimal solution, four strategies for calculating the weight coefficient of the dish package are proposed to shorten the waiting time and realize sustainability. Numerical experiments are designed to analyze the differences of the final scheduling results under the four strategies. The bottom layer is extended to another model based on the vehicle routing optimization model, given the switch time between different dishes. The extension of the model is also compared in the numerical experiments. Our paper confirms the necessity of using a double-layer model for multi-strategy comparison in order to achieve sustainable on-demand scheduling.

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Energy-efficient scheduling in an identical parallel machine environment with peak power consumption and deadline constraints

Li,

Liu

2024

Computers & Operations Research

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No-idle parallel-machine scheduling of unit-time jobs with a small number of distinct release dates and deadlines

Cited by 4 publications

References 33 publications

Green Scheduling of Identical Parallel Machines with Release Date, Delivery Time and No-Idle Machine Constraints

Green Scheduling of Identical Parallel Machines with Release Date, Delivery Time and No-Idle Machine Constraints

Multi-Stove Scheduling for Sustainable On-Demand Food Delivery

Energy-efficient scheduling in an identical parallel machine environment with peak power consumption and deadline constraints

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