Local Search Methods for Solving Total Completion Times, Range of Lateness and Maximum Tardiness Problem

Ali, Faez Hassan; Ahmed, Manal Ghassan

doi:10.1109/iec49899.2020.9122821

Cited by 3 publications

(5 citation statements)

References 4 publications

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“…The indicates where job falls in the ordering and represents the collection of all schedules. Finding all efficient solutions to solve the problem ( ( )) is challenging, since it is an NP-hard problem because the problems 1// , 1// are NP-hard [7] [12].…”

Section: Description Of Multi-criteria Scheduling Problemmentioning

confidence: 99%

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Solving the Multi-Criteria Problem: Total Completion Time, Total Late Work, Total Earliness Time, Maximum Earliness, and Maximum Tardine

Neamah,

Kalaf

2024

Iraqi Journal of Science

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In this paper, we study the scheduling of jobs on a single machine. Each of the n jobs will be processed without interruption and becomes available for processing at time zero. The goal is to find a processing order for the jobs, minimizing the total completion time, total late work, total earliness time, and maximum earliness maximum tardiness. The posed problems in this paper are as follows: The first problem is to minimize the multi-criteria, which includes minimizing the total completion time, total late work, total earliness time, maximum earliness, and maximum tardiness that are denoted by , respectively. The second problem is to minimize the multi-objective functions ( ). The theoretical section will present the mathematical formula for the discussed problem. Because these problems are NP-hard problems. It is difficult to determine the efficient (optimal) solution set for these problems. Some special cases are shown and proven to find efficient (optimal) solutions to the discussed problem. The significance of the dominance rule can be applied to problems to improve and to get good solutions that will be highlighted.

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Section: Description Of Multi-criteria Scheduling Problemmentioning

confidence: 99%

“…They suggested an Upper Bound (limits) UB and a Lower Bound (limits) LB to be used in the application of the Branch and Bound method. F. H. Ali and M. G. Ahmed 2022 [12] studied the multi-criteria( ), multi-objective function (…”

Section: Introductionmentioning

confidence: 99%

Solving the Multi-Criteria Problem: Total Completion Time, Total Late Work, Total Earliness Time, Maximum Earliness, and Maximum Tardine

Neamah,

Kalaf

2024

Iraqi Journal of Science

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“…When more than one objective (criteria) is needed, scheduling problems become more difficult to model and solve. It is frequently implausible that different objectives will be best served by the same set of decision variables [5][6][7][8]. As a result, there is a trade-off between the multiple objectives.…”

Section: Introductionmentioning

confidence: 99%

“…[16] examined the multiobjective problem, which is the sum of completion time, tardiness, earliness, and late work. Shortest Processing Tim (SPT): Jobs are Sequencing in non-decreasing order of the processing times 𝑝 𝑗 (i. e. 𝑝 1 ≤ 𝑝 2 ≤ ⋯ ≤ 𝑝 𝑛 ), this rule is well known to minimize ∑𝐶 𝑗 for problem 1// ∑𝐶 𝑗 [8]. Earliest Due Date (EDD): Jobs are sequenced in non-decreasing order of their due dates 𝑑 𝑗 (i. e. 𝑑 1 ≤ 𝑑 2 ≤ ⋯ ≤ 𝑑 𝑛 ), this rule used to minimize 𝑇 𝑚𝑎𝑥 for problem 1// 𝑇 𝑚𝑎𝑥 [19].…”

Section: Introductionmentioning

confidence: 99%

Minimizing Total Completion Time, Total Earliness Time, and Maximum Tardiness for a Single Machine scheduling problem

Kalaf,

Nagham Muosa Neamah,

Khalifa

2024

IHJPAS

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Machine scheduling problems (MSP) are considered as one of the most important classes of combinatorial optimization problems. In this paper, the problem of job scheduling on a single machine is studied to minimize the multiobjective and multiobjective objective function. This objective function is: total completion time, total lead time and maximum tardiness time, respectively, which are formulated as are formulated. In this study, a mathematical model is created to solve the research problem. This problem can be divided into several sub-problems and simple algorithms have been found to find the solutions to these sub-problems and compare them with efficient solutions. For this problem, some rules that provide efficient solutions have been proved and some special cases have been introduced and proved since the problem is an NP-hard problem to find some efficient solutions that are efficient for the discussed problem 1// and good or optimal solutions for the multi-objective functions 1// ,, and emphasize the importance of the dominance rule (DR), which can be applied to this problem to improve efficient solutions.

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“…In [7] they solved 1// (∑𝐶 𝑗 + 𝑅 𝐿 + 𝑇 𝑚𝑎𝑥 ) problem to find good or optimal solutions by using exact and heuristic methods. Lastly, Bees Algorithm (BA) and PSO are used for solving the two suggested problems [8]. Khalaf (2021) [9], a hyper-heuristic method was proposed to incorporate the behavior of three optimized algorithms from the Bat algorithm (BAT).…”

Section: Introductionmentioning

confidence: 99%

Solving Bi-Criteria and Bi-Objectives of Total Tardiness Jobs Times and Range of Lateness Problems Using New Techniques

Ali

Jawad

Hussein

2022

MJS

Self Cite

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In this paper, we proposed new techniques to solve one of the important fields of the Combinatorial Optimization Problem, which is the problem of machine scheduling. The problem which wants to be solved in this paper is the total tardiness times and range of lateness. For bi-criteria we have (1//(∑Tj,RL)) while for bi-objective we have (1//(∑Tj+RL)). In order to solve the suggested two problems, some new exact and approximation methods are suggested which are produced good results. The results which are obtained from applying the newly proposed methods are compared with the exact method; like Complete Enumeration Method (CEM), then compared results of the heuristics with each other's to obtain the most efficient method.

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Local Search Methods for Solving Total Completion Times, Range of Lateness and Maximum Tardiness Problem

Cited by 3 publications

References 4 publications

Solving the Multi-Criteria Problem: Total Completion Time, Total Late Work, Total Earliness Time, Maximum Earliness, and Maximum Tardine

Solving the Multi-Criteria Problem: Total Completion Time, Total Late Work, Total Earliness Time, Maximum Earliness, and Maximum Tardine

Minimizing Total Completion Time, Total Earliness Time, and Maximum Tardiness for a Single Machine scheduling problem

Solving Bi-Criteria and Bi-Objectives of Total Tardiness Jobs Times and Range of Lateness Problems Using New Techniques

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