Scheduling with controllable processing times and compression costs using population-based heuristics

Nearchou, Andreas C.

doi:10.1080/00207540903433874

Cited by 18 publications

(16 citation statements)

References 27 publications

(33 reference statements)

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“…According to the feature of problem and algorithm considered, we present a new encoding scheme (solution representation) based on the real number for this scheduling problem in this work. This solution representation is different from literatures [45,46]. In [45], the length of solution representation developed by them is 2n (n is the number of jobs), while our proposal is n. In [46], they adopted the largest position value (LPV) rule to denote order of job process in solution representation.…”

Section: Representationmentioning

confidence: 99%

Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

Yin

et al. 2016

Sustainability

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Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT) production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T), cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA) is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multiobjective Particle Swarm Optimization (OMOPSO), and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D). Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems.

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Section: Representationmentioning

confidence: 99%

Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

Yin

et al. 2016

Sustainability

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“…For example, cost optimization of project schedules has been effectively carried out by genetic algorithms (Eshtehardian et al 2009), simulated annealing (He et al 2009), tabu search (Hazir et al 2011), neural networks (Adeli and Karim 1997), ant colony optimization (Kalhor et al 2011), particle swarm optimization (Yang 2007), differential evolution (Nearchou 2010), harmony search (Geem 2010) and hybrid methods, such as genetic algorithm and dynamic programming (Ezeldin and Soliman 2009), cutting plane method and Monte Carlo simulation (Mokhtari et al 2010), genetic algorithm and simulated annealing (Sonmez and Bettemir 2012) among others. Certainly, there are also various extensions of aforesaid techniques that can be found in the literature.…”

Section: Introductionmentioning

confidence: 99%

An integration of spreadsheet and project management software for cost optimal time scheduling in construction

Valenko

Klanšek

2017

Organization, Technology and Management in Construction: An International Journal

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Successful performance and completion of construction projects highly depend on an adequate time scheduling of the project activities. On implementation of time scheduling, the execution modes of activities are most often required to be set in a manner that enables in achieving the minimum total project cost. This paper presents an approach to cost optimal time scheduling, which integrates a spreadsheet application and data transfer to project management software (PMS). At this point, the optimization problem of project time scheduling is modelled employing Microsoft Excel and solved to optimality using Solver while organization of data is dealt by macros. Thereupon, Microsoft Project software is utilized for further managing and presentation of optimized time scheduling solution. In this way, the data flow between programs is automated and possibilities of error occurrence during scheduling process are reduced to a minimum. Moreover, integration of spreadsheet and PMS for cost optimal time scheduling in construction is performed within well-known program environment that increases the possibilities of its wider use in practice. An application example is shown in this paper to demonstrate the advantages of proposed approach.

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“…genetic algorithms [7÷12], simulated annealing [13,14], tabu search [14,15], neural networks [16], ant colony optimization [17÷20], particle swarm optimization [21], differential evolution [22], harmony search [23] mixedinteger linear programming [24÷28] and hybrid methods, such as genetic algorithm and simulated annealing [28], genetic algorithm and dynamic programming [29], cutting plane method and Monte Carlo simulation [30], etc.…”

Section: Introductionmentioning

confidence: 99%

Mixed-integer nonlinear programming based optimal time scheduling of construction projects under nonconvex costs

Cajzek¹,

Klanšek

2016

Teh. vjesn.

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Original scientific paper Optimal project scheduling under nonconvex time-cost relations represents a challenging problem in construction management. The nonconvex time-cost relations may appear in a construction project when several different duration options are available for its activities due to alternative technological processes enabled for their realization or wide accessibility of production resources. The source of nonconvexity of the project scheduling optimization problem can also be the project penalty-or bonus-duration relations arranged within the construction contract. The aim of this paper is to present the mixed-integer nonlinear programming (MINLP) based optimal time scheduling of construction projects under nonconvex costs. For this purpose, the MINLP model was developed and applied. A numerical example from literature and an example of construction project time-cost trade-off analysis under practical nonconvex penalty function are given in the paper to demonstrate advantages of MINLP optimization. The example from literature first presented the capability of the MINLP approach to obtain the optimal solution for difficult, highly combinatorial nonconvex discrete project scheduling problem. Thereupon, the following example revealed that the optimal project time-cost curve may take very nonuniform shape on account of discrete nature of activity direct cost options and nonconvex relation between project duration and total cost. In this way, the presented study intends to provide practitioners with new information from the field of optimization techniques for project scheduling as well as an alternative view on performance of total cost when project duration is changed. Keywords: construction management; discrete optimization; mixed-integer nonlinear programming; nonconvex costs; time schedulingOptimalno terminsko planiranje građevinskih projekata s nekonveksnim troškovima pomoću mješovitog cjelobrojnog nelinearnog programiranja Izvorni znanstveni članakOptimalno terminsko planiranje projekata s nekonveksnim troškovima predstavlja zahtjevan problem u organizaciji građenja. Nekonveksni odnosi između vremena i troškova mogu nastupiti u građevinskom projektu kada su na raspolaganju različite varijante za trajanje njegovih aktivnosti zbog mogućnosti izbora različitih tehnoloških procesa za izvođenje radova ili široke pristupačnosti proizvodnih resursa. Izvor nekonveksnosti problema troškovne optimizacije terminskog plana je moguće naći i u dogovorenom odnosu između trajanja projekta te penala ili premije iz ugovora o građenju. Cilj ovoga rada je predstaviti optimalno terminsko planiranje projekata s nekonveksnim troškovima pomoću mješovitog cjelobrojnog nelinearnog programiranja. U tu svrhu je razvijen i primijenjen optimizacijski model. Za prikaz prednosti optimizacije s mješovitim cjelobrojnim nelinearnim programiranjem su u radu predstavljeni uporaba razvijenog modela na primjeru iz literature i primjer analize ovisnosti ukupnih troškova građevinskog projekta o dužini njegovoga trajanja uzimajući u...

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Scheduling with controllable processing times and compression costs using population-based heuristics

Cited by 18 publications

References 27 publications

Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

An integration of spreadsheet and project management software for cost optimal time scheduling in construction

Mixed-integer nonlinear programming based optimal time scheduling of construction projects under nonconvex costs

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