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1 – 10 of over 1000Yan Zhou and Chuanxu Wang
Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to…
Abstract
Purpose
Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.
Design/methodology/approach
This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.
Findings
The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.
Originality/value
Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.
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Pham Duc Tai, Krit Jinawat and Jirachai Buddhakulsomsiri
Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a…
Abstract
Purpose
Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.
Design/methodology/approach
The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.
Findings
For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.
Originality/value
The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.
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Arash Arianpoor and Seyyed Sajjad Naeimi Tajdar
This study aims to explore the relationship between firm risk, capital structure, cost of equity capital and social and environmental sustainability during the COVID-19 pandemic…
Abstract
Purpose
This study aims to explore the relationship between firm risk, capital structure, cost of equity capital and social and environmental sustainability during the COVID-19 pandemic for companies listed on Tehran Stock Exchange.
Design/methodology/approach
To this aim, the information about 190 companies in 2014–2020 was retrieved to be analyzed. The total risk and systematic risk were used as the indicators of company risk; the industry-adjusted earnings price ratio (IndEP) and GORDON were used for the cost of equity capital. To measure social sustainability and environmental sustainability, the procedure suggested by Arianpoor and Salehi (2020) was used.
Findings
Underleveraged firms have had a lower total risk during the COVID-19 pandemic, while overleveraged firms have not had a higher risk during this time. In overleveraged firms, using systematic risk has a negative impact on social sustainability during the COVID-19 pandemic. In overleveraged firms, using total risk and systematic risk has a significant negative impact on environmental sustainability in the pandemic. Besides, overleveraged firms have a lower cost of equity capital (IndEP) during COVID-19.
Originality/value
To the best of the authors’ knowledge, no similar study has so far examined the joint impact of COVID-19 and corporate risk on social and environmental sustainability and also the joint impact of COVID-19 and capital structure on the cost of equity. This study contributes to the related literature by providing corporations with insightful post-pandemic directions on capital structure decisions and social and environmental activities. Furthermore, this research and the relevant findings can help understand and develop social responsibility in Iran as a developing country.
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Guanxiong Wang, Xiaojian Hu and Ting Wang
By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order…
Abstract
Purpose
By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.
Design/methodology/approach
This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.
Findings
(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.
Originality/value
The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.
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Jing An, Suicheng Li and Xiao Ping Wu
Project managers bear the responsibility of selecting and developing resource scheduling methods that align with project requirements and organizational circumstances. This study…
Abstract
Purpose
Project managers bear the responsibility of selecting and developing resource scheduling methods that align with project requirements and organizational circumstances. This study focuses on resource-constrained project scheduling in multi-project environments. The research simplifies the problem by adopting a single-project perspective using gain coefficients.
Design/methodology/approach
It employs uncertainty theory and multi-objective programming to construct a model. The optimal solution is identified using Matlab, while LINGO determines satisfactory alternatives. By combining these methods and considering actual construction project situations, a compromise solution closely approximating the optimal one is derived.
Findings
The study provides fresh insights into modeling and resolving resource-constrained project scheduling issues, supported by real-world examples that effectively illustrate its practical significance.
Originality/value
The research highlights three main contributions: effective resource utilization, project prioritization and conflict management, and addressing uncertainty. It offers decision support for project managers to balance resource allocation, resolve conflicts, and adapt to changing project demands.
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Kalpana Pitchaimani, Tarik Zouadi, K.S. Lokesh and V. Raja Sreedharan
As the world is becoming more volatile and uncertain, organizations face much complexity in their daily operations. Further, there is a much ambiguity in business operations to…
Abstract
Purpose
As the world is becoming more volatile and uncertain, organizations face much complexity in their daily operations. Further, there is a much ambiguity in business operations to achieve the effective utilization of resources. The work optimizes a novel constraint programming model approach of the utilization of shuttle services vehicle while considering cost savings, employee wellbeing and other real an Information Technology enabled service (ITES) industry constraints.
Design/methodology/approach
The present work considers a novel extension of the vehicle routing problem related to the shuttle service operation in an ITES industry in VUCA context. Additionally, the model considers the women safety aspects, which engages the company to provide a security guard for women employees in the night shift.
Findings
Numerical experiments were conducted on real instances data of ITES industrial partner. The results show that the vehicle utilization increased from 75% up to 96% while ensuring in parallel the wellbeing of employees and women safety during the night shift. Finally, the proposed model is converted to a decision support application allowing ITES partner to plan employees shuttle service operations efficiently.
Originality/value
Study has evaluated the shuttle services optimization for ITES industry using data from industrial which makes it a unique contribution to literature in shuttle operations. Further, the study used constraint programming to evaluate the vehicle utilization and security allocation, thereby introducing new parameter on security allocation in open VRP problem.
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Krishna Chauhan, Antti Peltokorpi, Rita Lavikka and Olli Seppänen
Prefabricated products are continually entering the building construction market; yet, the decision to use prefabricated products in a construction project is based mostly on…
Abstract
Purpose
Prefabricated products are continually entering the building construction market; yet, the decision to use prefabricated products in a construction project is based mostly on personal preferences and the evaluation of direct costs. Researchers and practitioners have debated appropriate measurement systems for evaluating the impacts of prefabricated products and for comparing them with conventional on-site construction practices. The more advanced, cost–benefit approach to evaluating prefabricated products often inspires controversy because it may generate inaccurate results when converting non-monetary effects into costs. As prefabrication may affect multiple organisations and product subsystems, the method used to decide on production methods should consider multiple direct and indirect impacts, including nonmonetary ones. Thus, this study aims to develop a multi-criteria method to evaluate both the monetary and non-monetary impacts of prefabrication solutions to facilitate decision-making on whether to use prefabricated products.
Design/methodology/approach
Drawing upon a literature review, this research suggests a multi-criteria method that combines the choosing-by-advantage approach with a cost–benefit analysis. The method was presented for validation in focus group discussions and tested in a case involving a prefabricated bathroom.
Findings
The analysis indicates that the method helps a project’s stakeholders communicate about the relative merits of prefabrication and conventional construction while facilitating the final decision of whether to use prefabrication.
Originality/value
This research contributes a method of evaluating the monetary and non-monetary impacts of prefabricated products. The research underlines the need to evaluate the diverse benefits and sacrifices that stakeholder face when considering production methods in construction.
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This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this…
Abstract
Purpose
This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively.
Design/methodology/approach
Here, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used.
Findings
Then, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance.
Originality/value
In this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.
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Md Rakibul Hasan, Yosef Daryanto, Chefi Triki and Adel Elomri
The rapidly growing e-commerce industry with its special characteristics brings new challenges to the optimization of the supply chain and inventory management. This study aims to…
Abstract
Purpose
The rapidly growing e-commerce industry with its special characteristics brings new challenges to the optimization of the supply chain and inventory management. This study aims to investigate the inventory-related optimization of an e-marketplace official store that works on a business-to-customer system when cashback promotion is used to attract more customers. Also, it proposes a new inventory model to maximize the e-commerce profit by optimizing the cashback amount and delivery period.
Design/methodology/approach
The proposed model assumes that customer demand is a function of price and delivery time and that price is affected by the cashback amount. The e-commerce operator has a profit-sharing contract with an e-payment company that facilitates the payment. E-commerce also builds collaboration under a cost-sharing contract with a supplier to ensure product delivery. A mathematical model is developed and the related theories are investigated. A numerical example illustrates the validity of the model and a sensitivity analysis is carried out to give useful insights.
Findings
A new inventory model for an e-market system has been introduced which shows the impact of a cashback promotion on the e-commerce business. This study shows that managers can optimize the cashback amount and its delivery time to get the maximum profit. In certain cases, the manager may set a high cashback amount (e.g. 100%) to attract customers to place more orders.
Originality/value
This study presents a new inventory model for today’s fast-growing e-commerce business; therefore, the results contribute to the understanding of promotion program practices and inventory management and provide insights to develop efficient e-commerce managerial decisions.
Graphical abstract
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Lars Stehn and Alexander Jimenez
The purpose of this paper is to understand if and how industrialized house building (IHB) could support productivity developments for housebuilding on project and industry levels…
Abstract
Purpose
The purpose of this paper is to understand if and how industrialized house building (IHB) could support productivity developments for housebuilding on project and industry levels. The take is that fragmentation of construction is one explanation for the lack of productivity growth, and that IHB could be an integrating method of overcoming horizontal and vertical fragmentation.
Design/methodology/approach
Singe-factor productivity measures are calculated based on data reported by IHB companies and compared to official produced and published research data. The survey covers the years 2013–2020 for IHB companies building multi-storey houses in timber. Generalization is sought through descriptive statistics by contrasting the data samples to the used means to control vertical and horizontal fragmentation formulated as three theoretical propositions.
Findings
According to the results, IHB in timber is on average more productive than conventional housebuilding at the company level, project level, in absolute and in growth terms over the eight-year period. On the company level, the labour productivity was on average 10% higher for IHB compared to general construction and positioned between general construction and general manufacturing. On the project level, IHB displayed an average cost productivity growth of 19% for an employed prefabrication degree of about 45%.
Originality/value
Empirical evidence is presented quantifying so far perceived advantages of IHB. By providing analysis of actual cost and project data derived from IHB companies, the article quantifies previous research that IHB is not only about prefabrication. The observed positive productivity growth in relation to the employed prefabrication degree indicates that off-site production is not a sufficient mean for reaching high productivity and productivity growth. Instead, the capabilities to integrate the operative logic of conventional housebuilding together with logic of IHB platform development and use is a probable explanation of the observed positive productivity growth.
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