A self-tuning variable neighborhood search algorithm and an effective decoding scheme for open shop scheduling problems with travel/setup times

Mejía, Gonzalo; Yuraszeck, Francisco

doi:10.1016/j.ejor.2020.02.010

Cited by 37 publications

(26 citation statements)

References 45 publications

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“…The numerical experiments in [26] show that the CP method outperforms the MILP approach by a large extent. In [28], the authors used a CP model as their benchmark to compare the performance of a variable neighborhood search (VNS) for the OSSP with travel/setup times. Their VNS makes use of a probabilistic learning mechanism to self-tune a parameter that balance the generation of active or non-delay solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The formulation is an adaptation of the formulation provided in [28] for the OSSP m | S jik | ∑ j C j . We now proceed to define the parameters, sets, indices and decision variables of the formulation.…”

Section: Milp Formulationmentioning

confidence: 99%

“…As no previous work for the FGSSP is available in the literature, we generated our own instance set. The generation procedure follows the procedure described in [28] for the OSSP m | S jik | ∑ j C j , which extends the procedure described in [37]. Processing times for instances with up to 20 jobs and 20 machines are identical to the processing times used in [37].…”

Section: Instance Generationmentioning

confidence: 99%

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A Novel Constraint Programming Decomposition Approach for the Total Flow Time Fixed Group Shop Scheduling Problem

et al. 2022

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This work addresses a particular case of the group shop scheduling problem (GSSP) which will be denoted as the fixed group shop scheduling problem (FGSSP). In a FGSSP, job operations are divided into stages and each stage has a set of machines associated to it which are not shared with the other stages. All jobs go through all the stages in a specific order, where the operations of the job at each stage need to be finished before the job advances to the following stage, but operations within a stage can be performed in any order. This setting is common in companies such as leaf spring manufacturers and other automotive companies. To solve the problem, we propose a novel heuristic procedure that combines a decomposition approach with a constraint programming (CP) solver and a restart mechanism both to avoid local optima and to diversify the search. The performance of our approach was tested on instances derived from other scheduling problems that the FGSSP subsumes, considering both the cases with and without anticipatory sequence-dependent setup times. The results of the proposed algorithm are compared with off-the-shelf CP and mixed integer linear programming (MILP) methods as well as with the lower bounds derived from the study of the problem. The experiments show that the proposed heuristic algorithm outperforms the other methods, specially on large-size instances with improvements of over 10% on average.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Milp Formulationmentioning

confidence: 99%

Section: Instance Generationmentioning

confidence: 99%

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A Novel Constraint Programming Decomposition Approach for the Total Flow Time Fixed Group Shop Scheduling Problem

et al. 2022

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“…Open shop scheduling problem (OSP) [15] and its variants capture the scheduling components of the TPS problem. The open shop scheduling problem also has many integrated routing and scheduling variants, such as routing open shop scheduling [16], open shop scheduling with sequence-dependent setup times [17] and open shop scheduling with transportation/travel times [18]. These problems can be solved by exact methods [19], approximate methods [16,20,21], heuristics [22] and metaheuristics [17,18,23,24].…”

Section: Introductionmentioning

confidence: 99%

Task scheduling for transport and pick robots in logistics: a comparative study on constructive heuristics

Wang

Chen

2021

Auton. Intell. Syst.

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We study the Transport and Pick Robots Task Scheduling (TPS) problem, in which two teams of specialized robots, transport robots and pick robots, collaborate to execute multi-station order fulfillment tasks in logistic environments. The objective is to plan a collective time-extended task schedule with the minimization of makespan. However, for this recently formulated problem, it is still unclear how to obtain satisfying results efficiently. In this research, we design several constructive heuristics to solve this problem based on the introduced sequence models. Theoretically, we give time complexity analysis or feasibility guarantees of these heuristics; empirically, we evaluate the makespan performance criteria and computation time on designed dataset. Computational results demonstrate that coupled append heuristic works better for the most cases within reasonable computation time. Coupled heuristics work better than decoupled heuristics prominently on instances with relative few pick robot numbers and large work zones. The law of diminishing marginal utility is also observed concerning the overall system performance and different transport-pick robot numbers.

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“…In order to tackle this problem, an innovative permutation model has been proposed to be used in variable neighborhood search. Evaluation of the algorithm on both random problems and standard benchmarks suggests that the innovative permutation method has the most positive impact on produced outputs [41,42].…”

Section: Introductionmentioning

confidence: 99%

An improved bat optimization algorithm to solve the tasks scheduling problem in open shop

Shareh

Bargh

Hosseinabadi

et al. 2020

Neural Comput & Applic

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The open shop scheduling problem involves a set of activities that should be run on a limited set of machines. The purpose of scheduling open shops problem is to provide a timetable for implementation of the entire operation so that the total execution time is reduced. The tasks scheduling problem in open shops is important in many applications due to the arbitrariness of the processing sequence of each job and lack of a prioritization of its operations. This is an NP-hard problem and obtaining an optimal solution to this problem requires a high time complexity. Therefore, heuristic techniques are used to solve these problems. In this paper, we investigate the tasks scheduling problem in open shops using the Bat Algorithm (BA) based on ColReuse and substitution meta-heuristic functions. The heuristic functions are designed to increase the rate of convergence to the optimal solution. To evaluate the performance of the proposed algorithm, standard open shop benchmarks were used. The results obtained in each benchmark are compared with those of the previous methods. Finally, after analyzing the results, it was found that the proposed BA had a better performance and was able to generate the best solution in all cases.

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A self-tuning variable neighborhood search algorithm and an effective decoding scheme for open shop scheduling problems with travel/setup times

Cited by 37 publications

References 45 publications

A Novel Constraint Programming Decomposition Approach for the Total Flow Time Fixed Group Shop Scheduling Problem

A Novel Constraint Programming Decomposition Approach for the Total Flow Time Fixed Group Shop Scheduling Problem

Task scheduling for transport and pick robots in logistics: a comparative study on constructive heuristics

An improved bat optimization algorithm to solve the tasks scheduling problem in open shop

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