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Article
Publication date: 27 June 2023

Canran Zhang, Jianping Dou, Shuai Wang and Pingyuan Wang

The cost-oriented robotic assembly line balancing problem (cRALBP) has practical importance in real-life manufacturing scenarios. However, only a few studies tackle the cRALBP…

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Abstract

Purpose

The cost-oriented robotic assembly line balancing problem (cRALBP) has practical importance in real-life manufacturing scenarios. However, only a few studies tackle the cRALBP using exact methods or metaheuristics. This paper aims to propose a hybrid particle swarm optimization (PSO) combined with dynamic programming (DPPSO) to solve cRALBP type-I.

Design/methodology/approach

Two different encoding schemes are presented for comparison. In the frequently used Scheme 1, a full encoding of task permutations and robot allocations is adopted, and a relatively large search space is generated. DPSO1 and DPSO2 with the full encoding scheme are developed. To reduce the search space and concern promising solution regions, in Scheme 2, only task permutations are encoded, and DP is used to obtain the optimal robot sequence for a given task permutation in a polynomial time. DPPSO is proposed.

Findings

A set of instances is generated, and the numerical experiments indicate that DPPSO achieves a tradeoff between solution quality and computation time and outperforms existing algorithms in solution quality.

Originality/value

The contributions of this paper are three aspects. First, two different schemes of encoding are presented, and three PSO algorithms are developed for the purpose of comparison. Second, a novel updating mechanism of discrete PSO is adjusted to generate feasible task permutations for cRALBP. Finally, a set of instances is generated based on two cost parameters, then the performances of algorithms are systematically compared.

Details

Robotic Intelligence and Automation, vol. 43 no. 4
Type: Research Article
ISSN: 2754-6969

Keywords

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Article
Publication date: 29 April 2021

Suyuan Wang, Huaming Song and Canran Gong

Companies face the critical reliability problem of products due to the development of outsourcing. This study intends to provide some feasible solutions for a company to improve…

262

Abstract

Purpose

Companies face the critical reliability problem of products due to the development of outsourcing. This study intends to provide some feasible solutions for a company to improve the reliability level of products.

Design/methodology/approach

The paper considers the reward and reliability decisions regarding a product made with two complementary components from two different suppliers: high-capable and low-capable. Two kinds of reliability improvement incentives (normal incentive and cost-sharing incentive) through which a manufacturer provides a reward and shares the reliability improvement cost with a supplier are discussed. As the Stackelberg leader, the manufacturer determines the strategy, while the suppliers are responsible for determining its reliability. Using a game-theoretic framework, four different contract scenarios are addressed. We develop analytical methods to better understand how the manufacturer decides the incentive mechanism to be used for the suppliers.

Findings

The results show that cost-sharing contracts do not always lead to a higher reliability level and more enormous profits. Setting a target reliability level is better for the manufacturer. The cost-sharing contract is beneficial for a high-capable supplier even though it does not directly participate in that kind of mechanism. A low-capable supplier gains more profit when the manufacturer provides incentive mechanisms that do not specify a target reliability level.

Originality/value

This paper investigates the reliability improvement mechanism used for complementary products and focuses on identifying the optimal decisions when demand is influenced by the gap between the product's failure rate and the standard failure rate.

Details

The TQM Journal, vol. 33 no. 8
Type: Research Article
ISSN: 1754-2731

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