Hybridizations in line balancing problems: A comprehensive review on new trends and formulations

Battaïa, Olga; Dolgui, Alexandre

doi:10.1016/j.ijpe.2022.108673

Cited by 50 publications

(8 citation statements)

References 478 publications

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“…The proposed method is much more flexible than those based on tailor-and metaheuristic or MIP models and constraint programming (see [6][7][8]15,39]) because the decisionmaker can set any number of stochastic parameters; moreover, he or she can test the resulting balancing on a scaled shop layout.…”

Section: Discussionmentioning

confidence: 99%

“…According to the most recent work about a comprehensive review of new trends in the ALB problem (see [6][7][8]), the stochastic ALB problem and its variants have been solved by formulating a mathematical model, adding stochasticity to the processing times, and solving the problem using tailor-made heuristics, metaheuristics, or constraint programming. On the other hand, simulations have been used to test some stochastic scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, in the papers presenting a systematic review of the literature on the ALB problem, it is reported that the only source of uncertainty is the processing times [6][7][8]. Those works do not consider the pace at which jobs or parts arrive at the first cell, i.e., the inter-arrival time.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, the simulation tests the line balancing, including variability not only in the processing time but also in the inter-arrival time, number of workers, and the speed of the material handling system by placing the line in a layout. The suggested three-part approach is connected to both the requirement for stability essential to handle uncertainty effectively, as pointed out in [3], as well as the extension of the ALB issue with non-deterministic parameters, as described in [6]. A gearbox case in [31], which required a cycle time of 70 s, is used to demonstrate the methodology.…”

Section: Introductionmentioning

confidence: 99%

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Assessing by Simulation the Effect of Process Variability in the SALB-1 Problem

Moncayo-Martínez,

Arias-Nava

2023

AppliedMath

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The simple assembly line balancing (SALB) problem is a significant challenge faced by industries across various sectors aiming to optimise production line efficiency and resource allocation. One important issue when the decision-maker balances a line is how to keep the cycle time under a given time across all cells, even though there is variability in some parameters. When there are stochastic elements, some approaches use constraint relaxation, intervals for the stochastic parameters, and fuzzy numbers. In this paper, a three-part algorithm is proposed that first solves the balancing problem without considering stochastic parameters; then, using simulation, it measures the effect of some parameters (in this case, the inter-arrival time, processing times, speed of the material handling system which is manually performed by the workers in the cell, and the number of workers who perform the tasks on the machines); finally, the add-on OptQuest in SIMIO solves an optimisation problem to constrain the cycle time using the stochastic parameters as decision variables. A Gearbox instance from literature is solved with 15 tasks and 14 precedence rules to test the proposed approach. The deterministic balancing problem is solved optimally using the open solver GLPK and the Pyomo programming language, and, with simulation, the proposed algorithm keeps the cycle time less than or equal to 70 s in the presence of variability and deterministic inter-arrival time. Meanwhile, with stochastic inter-arrival time, the maximum cell cycle is 72.04 s. The reader can download the source code and the simulation models from the GitHub page of the authors.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing by Simulation the Effect of Process Variability in the SALB-1 Problem

Moncayo-Martínez,

Arias-Nava

2023

AppliedMath

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“…The objective of the MPAP is to cost-effectively design a workforce training plan that addresses key aspects, such as: (i) determining the number of single-skilled employees (those trained for a one task type) and multiskilled employees (those trained for two or more task types), (ii) specifying the types of tasks in which each employee should be trained, and (iii) devising a weekly work-hour distribution for each employee based on their trained skills. Therefore, as multiskilled employees can be transferred from tasks with staffing surplus to those facing a staffing shortage, solving the MPAP enables the design of a workforce that can flexibly adapt to fluctuating demand patterns (e.g., [3,4,6,31,45,46]). In turn, an optimal training plan not only improves demand coverage but also minimizes labor costs arising from mismatches between staffing levels and staff demand ( [5,8,10,12,13,18,26,38]).…”

Section: Introductionmentioning

confidence: 99%

A benchmark dataset for the retail multiskilled personnel planning under uncertain demand

Henao,

Porto,

González

2024

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In this data article, we present and describe datasets designed to address multiskilled personnel assignment problems (MPAP) under uncertain demand. The data article introduces simulated datasets and a real dataset obtained from a retail store in Chile. The real dataset provides details on the structure of the store, including the number of departments and workers, the type of labor contract, the cost parameter values, and the average demand across all store departments. The simulated datasets, consisting of 18 categorized text files, were generated through Monte Carlo simulation to encapsulate information about the stochastic demand for store departments. These text files are classified based on: (i) type of sample (in-sample or out-of-sample), (ii) type of truncation method (zero-truncated or percentile-truncated), and (iii) demand coefficient of variation (5%, 10%, 20%, 30%, 40%, 50%). This categorization allows academics and practitioners to select the scenarios that meet with their specific research or application needs, increasing the flexibility and applicability of the datasets. In addition, researchers and practitioners can use these comprehensive real and simulated datasets to benchmark the performance of diverse optimization methods under uncertain demand, thereby ensuring robust multiskilling levels for similar MPAPs. Furthermore, we offer an Excel workbook with the capability to generate up to 10,000 demand scenarios for varying coefficients of variation in demand.

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