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Article
Publication date: 4 July 2023

R. Rajesh

The author aims to study and predict the sustainability governance performances of firms using an advanced grey prediction model. The case implication of the prediction model is…

Abstract

Purpose

The author aims to study and predict the sustainability governance performances of firms using an advanced grey prediction model. The case implication of the prediction model is also studied considering select firms in the Indian context.

Design/methodology/approach

The author has proposed an advanced grey prediction model, the first-entry grey prediction model (FGM (1, 1)) for forecasting the sustainability governance performances of firms. The proposed model is tested using the periodic data of sustainability governance performances of 10 Indian firms.

Findings

The author observes that the majority of firms (6 out of 10) show dipping performances for sustainability governance for the future predicted period. This throws insights into the direction of improving good governance practices for Indian firms.

Practical implications

The idea and motivation for sustainability-focussed governance need a bi-directional focus from the side of managers that act as the agents and from the side of shareholders that act as the principals, as seen from an agency theory perspective for sustainability governance.

Social implications

Sustainability governance culture can be inculcated to a firm at the strategic level by having a bi-directional focus from managers and shareholders, so as to enhance the social and environmental sustainability performances.

Originality/value

The governance performance evaluations for firms particularly in developing countries were not dated back more than a decade or two. Hence, the author implements a prediction model that can be best suited, when there are small periodic data sets available for prediction.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

Case study
Publication date: 10 October 2023

Debjit Roy and Srivastava Prateek

Aries Agro, an agribusiness firm, faced two prime challenges – the safety and productivity of its workers. When traditional software systems fell short of addressing these…

Abstract

Aries Agro, an agribusiness firm, faced two prime challenges – the safety and productivity of its workers. When traditional software systems fell short of addressing these challenges, the firm contemplated the implementation of an IoT (Internet of things) based solution. This IoT based solution captures the movement of workmen and generates data from the sensors that help identify sources of inefficiency. In this case, we not only discuss the implementation challenges of an IoT based implementation but also how this solution can help to promote safety among the workmen and improve worker productivity by optimising facility layout.

Details

Indian Institute of Management Ahmedabad, vol. no.
Type: Case Study
ISSN: 2633-3260
Published by: Indian Institute of Management, Ahmedabad

Keywords

Article
Publication date: 31 July 2023

Anurag Tiwari and Priyabrata Mohapatra

The purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach…

Abstract

Purpose

The purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach to solve optimization problems. This study can help to select the optimum number of suppliers based on cost.

Design/methodology/approach

To model the raw material vehicle routing problem, a mixed integer linear programming (MILP) problem is formulated. An interesting phenomenon added to the proposed problem is that there is no compulsion to visit all suppliers. To guarantee the demand of semiconductor industry, all visited suppliers should reach a given raw material capacity requirement. To solve the proposed model, the authors developed a novel hybrid approach that is a combination of block and edge recombination approaches. To avoid bias, the authors compare the results of the proposed methodology with other known approaches, such as genetic algorithms (GAs) and ant colony optimisation (ACO).

Findings

The findings indicate that the proposed model can be useful in industries, where multiple suppliers are used. The proposed hybrid approach provides a better sequence of suppliers compared to other heuristic techniques.

Research limitations/implications

The data used in the proposed model is generated based on previous literature. The problem derives from the assumption that semiconductor industries use a variety of raw materials.

Practical implications

This study provides a new model and approach that can help practitioners and policymakers select suppliers based on their logistics costs.

Originality/value

This study provides two important contributions in the context of the supply chain. First, it provides a new variant of the vehicle routing problem in consideration of raw material collection; and second, it provides a new approach to solving optimisation problems.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 13 October 2023

Mengdi Zhang, Aoxiang Chen, Zhiheng Zhao and George Q. Huang

This research explores mitigating carbon emissions and integrating sustainability in e-commerce logistics by optimizing the multi-depot pollution routing problem with time windows…

Abstract

Purpose

This research explores mitigating carbon emissions and integrating sustainability in e-commerce logistics by optimizing the multi-depot pollution routing problem with time windows (MDPRPTW). A proposed model contrasts non-collaborative and collaborative decision-making for order assignment among logistics service providers (LSPs), incorporating low-carbon considerations.

Design/methodology/approach

The model is substantiated using improved adaptive large neighborhood search (IALNS), tabu search (TS) and oriented ant colony algorithm (OACA) within the context of e-commerce logistics. For model validation, a normal distribution is employed to generate random demand and inputs, derived from the location and requirements files of LSPs.

Findings

This research validates the efficacy of e-commerce logistics optimization and IALNS, TS and OACA algorithms, especially when demand follows a normal distribution. It establishes that cooperation among LSPs can substantially reduce carbon emissions and costs, emphasizing the importance of integrating sustainability in e-commerce logistics optimization.

Research limitations/implications

This paper proposes a meta-heuristic algorithm to solve the NP-hard problem. Methodologies such as reinforcement learning can be investigated in future work.

Practical implications

This research can help logistics managers understand the status of sustainable and cost-effective logistics operations and provide a basis for optimal decision-making.

Originality/value

This paper describes the complexity of the MDPRPTW model, which addresses both carbon emissions and cost reduction. Detailed information about the algorithm, methodology and computational studies is investigated. The research problem encompasses various practical aspects related to routing optimization in e-commerce logistics, aiming for sustainable development.

Details

Industrial Management & Data Systems, vol. 124 no. 1
Type: Research Article
ISSN: 0263-5577

Keywords

Open Access
Article
Publication date: 7 July 2023

Marcello Braglia, Francesco Di Paco, Marco Frosolini and Leonardo Marrazzini

This paper presents Quick Changeover Design (QCD), which is a structured methodological approach for Original Equipment Manufacturers to drive and support the design of machines…

1136

Abstract

Purpose

This paper presents Quick Changeover Design (QCD), which is a structured methodological approach for Original Equipment Manufacturers to drive and support the design of machines in terms of rapid changeover capability.

Design/methodology/approach

To improve the performance in terms of set up time, QCD addresses machine design from a single-minute digit exchange of die (SMED). Although conceived to aid the design of completely new machines, QCD can be adapted to support for simple design upgrades on pre-existing machines. The QCD is structured in three consecutive steps, each supported by specific tools and analysis forms to facilitate and better structure the designers' activities.

Findings

QCD helps equipment manufacturers to understand the current and future needs of the manufacturers' customers to: (1) anticipate the requirements for new and different set-up process; (2) prioritize the possible technical solutions; (3) build machines and equipment that are easy and fast to set-up under variable contexts. When applied to a production system consisting of machines subject to frequent or time-consuming set-up processes, QCD enhances both responsiveness to external market demands and internal control of factory operations.

Originality/value

The QCD approach is a support system for the development of completely new machines and is also particularly effective in upgrading existing ones. QCD's practical application is demonstrated using a case study concerning a vertical spindle machine.

Details

Journal of Manufacturing Technology Management, vol. 34 no. 9
Type: Research Article
ISSN: 1741-038X

Keywords

Open Access

Abstract

Details

Revista de Gestão, vol. 30 no. 2
Type: Research Article
ISSN: 1809-2276

Article
Publication date: 15 June 2023

Stephanie Dailey and Kathryn Laskey

Reducing fatalities and increasing the number of students able to remain safe during an active shooter event is paramount to the health and well-being of schools and communities…

Abstract

Purpose

Reducing fatalities and increasing the number of students able to remain safe during an active shooter event is paramount to the health and well-being of schools and communities. Yet, methodological limitations and ethical concerns have restricted prior research on security measures during school shooter lockdown drills. This study aims to fill that gap by using virtual reality (VR) to statistically examine the effectiveness of active shooter response protocols in a simulated high school.

Design/methodology/approach

Using a full factorial, within-subjects experimental design, this exploratory investigation used VR technology to investigate whether automatic classroom door locks, centralized lockdown notifications and the presence of a school resource officer (SRO) significantly impacted student safety and casualty mitigation. Data were collected from a convenience sample of 37 individuals who volunteered to participate in 24 school shooter scenarios within a simulated virtual environment.

Findings

Multiple one-way analysis of variances indicated significant main effects for automatic classroom door locks and SRO presence. Automatic locks yielded faster lockdown response times, and both factors were significantly associated with higher numbers of secured classrooms.

Originality/value

Findings from the current study address the gap in existing literature regarding evidence-based school safety protocols and provide recommendations for using VR simulations to increase preparedness and reduce fatalities during an active school shooter event.

Details

Safer Communities, vol. 22 no. 4
Type: Research Article
ISSN: 1757-8043

Keywords

Article
Publication date: 23 October 2023

Haoze Cang, Xiangyan Zeng and Shuli Yan

The effective prediction of crude oil futures prices can provide a reference for relevant enterprises to make production plans and investment decisions. To the nonlinearity, high…

Abstract

Purpose

The effective prediction of crude oil futures prices can provide a reference for relevant enterprises to make production plans and investment decisions. To the nonlinearity, high volatility and uncertainty of the crude oil futures price, a matrixed nonlinear exponential grey Bernoulli model combined with an exponential accumulation generating operator (MNEGBM(1,1)) is proposed in this paper.

Design/methodology/approach

First, the original sequence is processed by the exponential accumulation generating operator to weaken its volatility. The nonlinear grey Bernoulli and exponential function models are combined to fit the preprocessed sequence. Then, the parameters in MNEGBM(1,1) are matrixed, so the ternary interval number sequence can be modeled directly. Marine Predators Algorithm (MPA) is chosen to optimize the nonlinear parameters. Finally, the Cramer rule is used to derive the time recursive formula.

Findings

The predictive effectiveness of the proposed model is verified by comparing it with five comparison models. Crude oil futures prices in Cushing, OK are predicted and analyzed from 2023/07 to 2023/12. The prediction results show it will gradually decrease over the next six months.

Originality/value

Crude oil futures prices are highly volatile in the short term. The use of grey model for short-term prediction is valuable for research. For the data characteristics of crude oil futures price, this study first proposes an improved model for interval number prediction of crude oil futures prices.

Details

Grey Systems: Theory and Application, vol. 14 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 15 January 2024

Chuanmin Mi, Xiaoyi Gou, Yating Ren, Bo Zeng, Jamshed Khalid and Yuhuan Ma

Accurate prediction of seasonal power consumption trends with impact disturbances provides a scientific basis for the flexible balance of the long timescale power system…

Abstract

Purpose

Accurate prediction of seasonal power consumption trends with impact disturbances provides a scientific basis for the flexible balance of the long timescale power system. Consequently, it fosters reasonable scheduling plans, ensuring the safety of the system and improving the economic dispatching efficiency of the power system.

Design/methodology/approach

First, a new seasonal grey buffer operator in the longitudinal and transverse dimensional perspectives is designed. Then, a new seasonal grey modeling approach that integrates the new operator, full real domain fractional order accumulation generation technique, grey prediction modeling tool and fruit fly optimization algorithm is proposed. Moreover, the rationality, scientificity and superiority of the new approach are verified by designing 24 seasonal electricity consumption forecasting approaches, incorporating case study and amalgamating qualitative and quantitative research.

Findings

Compared with other comparative models, the new approach has superior mean absolute percentage error and mean absolute error. Furthermore, the research results show that the new method provides a scientific and effective mathematical method for solving the seasonal trend power consumption forecasting modeling with impact disturbance.

Originality/value

Considering the development trend of longitudinal and transverse dimensions of seasonal data with impact disturbance and the differences in each stage, a new grey buffer operator is constructed, and a new seasonal grey modeling approach with multi-method fusion is proposed to solve the seasonal power consumption forecasting problem.

Highlights

The highlights of the paper are as follows:

  1. A new seasonal grey buffer operator is constructed.

  2. The impact of shock perturbations on seasonal data trends is effectively mitigated.

  3. A novel seasonal grey forecasting approach with multi-method fusion is proposed.

  4. Seasonal electricity consumption is successfully predicted by the novel approach.

  5. The way to adjust China's power system flexibility in the future is analyzed.

A new seasonal grey buffer operator is constructed.

The impact of shock perturbations on seasonal data trends is effectively mitigated.

A novel seasonal grey forecasting approach with multi-method fusion is proposed.

Seasonal electricity consumption is successfully predicted by the novel approach.

The way to adjust China's power system flexibility in the future is analyzed.

Details

Grey Systems: Theory and Application, vol. 14 no. 2
Type: Research Article
ISSN: 2043-9377

Keywords

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…

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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