Single machine scheduling problem with interval processing times to minimize mean weighted completion time

Allahverdi, Ali; Aydilek, Harun; Aydilek, Asiye

doi:10.1016/j.cor.2014.06.003

Cited by 31 publications

(15 citation statements)

References 14 publications

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“…In the conventional production scheduling, the commonly existing objectives for optimization include makespan [14], completion time [15], job flow time [16], and tardiness-earliness penalties [17]. Some novel objectives are further considered to be integrated into scheduling, e.g., decreasing energy consumption [18], reducing carbon footprint [19], and improving robustness [20].…”

Section: Background On Energy-cost-aware Schedulingmentioning

confidence: 99%

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An Energy-Cost-Aware Scheduling Methodology for Sustainable Manufacturing

et al. 2015

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With the rising energy price and the ever-increasing consciousness of environmental friendliness, it is becoming practically helpful for manufacturers to have a clear view on how the energy is consumed at their shop floors, what the corresponding energy cost is, and how to reduce the energy consumption or the energy cost. However, there is currently limited literature investigating the energy cost minimization in manufacturing through production scheduling under volatile energy prices. This paper proposes a generic mixed-integer linear programming model to enable the job scheduling on a single machine for the purpose of minimizing the necessary energy cost without exceeding the due date. The results given by a case study on a surface grinding machine demonstrate this scheduling methodology effectively contributes to the reduction of greenhouse gas emissions during peak time periods by shifting the production load to off-peak periods, and leads to energyefficient, demand-responsive, and cost-effective manufacturing processes.

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Section: Background On Energy-cost-aware Schedulingmentioning

confidence: 99%

“…Constraint (14) makes sure that only one job is executed at one time on respecting the scheduled job sequence, and pre-emption is prohibited. Constraint (15) shows the requirement that all the jobs should be fulfilled before the due time, and finally the machine is set off. …”

Section: Scheduling Model For Energy Cost Minimizationmentioning

confidence: 99%

An Energy-Cost-Aware Scheduling Methodology for Sustainable Manufacturing

et al. 2015

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“…Ćwik and Józefczyk [41] adopted interval-valued processing times to represent the uncertain parameters used the lower bound instead of solving the internal deterministic flow-shop, and employed the maximum regret to evaluate the uncertainty. For the single machine scheduling problems with uncertain processing time, Allahverdi et al [42] addressed some heuristics based on the polynomial time using the values of the upper and lower bounds of processing time. Feng et al [43] designed a min-max-regret scheduling model to reflect the uncertain processing time, in which a worstcase scenario and heuristic methods are proposed to solve the above problem.…”

Section: Literature Review a Flow Shop And Job Shop Scheduling Pmentioning

confidence: 99%

Multi-Objective Migrating Birds Optimization Algorithm for Stochastic Lot-Streaming Flow Shop Scheduling With Blocking

Han

Gong

et al. 2019

IEEE Access

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Blocking lot-streaming flow shop scheduling problem with the stochastic processing time has a wide range of applications in various industrial systems. However, this problem has not yet been well studied. In this paper, the above-mentioned problem is transformed into a determinate multi-objective optimization one using the Monte Carlo sampling method. A Multi-Objective Migrating Birds Optimization (MOMBO) algorithm is then proposed to solve the above-mentioned re-formulated multi-objective scheduling problem, in which the multiple-based PFE is proposed to yield the initial solutions with high quality, the information of the non-dominated solutions is learned and sampled to improve the global searching ability of MOMBO, and a reference-point-assisted local search method for multi-objective optimization is applied to further enhance the exploitation capability of MOMBO. To evaluate the performance of the MOMBO, several comparative experiments are executed on 180 test scheduling instances. The experimental results demonstrate that the MOMBO outperforms the compared algorithms in convergence and distributivity and has capacities to tackle the uncertainties. INDEX TERMS Scheduling, multi-objective, blocking lot-streaming flow shop, stochastic processing time, migrating birds optimization.

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“…Also, it represents a first step towards solving problems in the other uncertain frameworks. Indeed, an interval can be seen as a uniform probability distribution or the support of an unknown probability distribution [1]. Also, an interval not only is a particular case of a fuzzy interval, but also the α-cuts of fuzzy intervals are intervals, so a fuzzy scheduling problem can be decomposed in multiple interval scheduling problems.…”

Section: Introductionmentioning

confidence: 99%

A Genetic Approach to the Job Shop Scheduling Problem with Interval Uncertainty

Díaz

González-Rodríguez

Palacios

et al. 2020

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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In this paper we tackle a variant of the job shop scheduling problem where task durations are uncertain and only an interval of possible values for each task duration is known. We propose a genetic algorithm to minimise the schedule's makespan that takes into account the problem's uncertainty during the search process. The behaviour of the algorithm is experimentally evaluated and compared with other stateof-the-art algorithms. Further analysis in terms of solution robustness proves the advantage of taking into account interval uncertainty during the search process with respect to considering only the expected processing times and solving the problem's crisp counterpart. This robustness analysis also illustrates the relevance of the interval ranking method used to compare schedules during the search.

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Single machine scheduling problem with interval processing times to minimize mean weighted completion time

Cited by 31 publications

References 14 publications

An Energy-Cost-Aware Scheduling Methodology for Sustainable Manufacturing

An Energy-Cost-Aware Scheduling Methodology for Sustainable Manufacturing

Multi-Objective Migrating Birds Optimization Algorithm for Stochastic Lot-Streaming Flow Shop Scheduling With Blocking

A Genetic Approach to the Job Shop Scheduling Problem with Interval Uncertainty

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