Ports and waterways are key in supporting the waterborne supply chains that form the backbone of global trade. Maintaining adequate water depth is vital for accessibility and safe navigation. Port authorities and contractors are the key players in developing maintenance strategies, and they strive for a mutually beneficial compromise. Port authorities aim to optimize port performance while keeping costs and delays at acceptable levels. Contractors aim to optimize the use of equipment and execution strategies to achieve cost-effectiveness and time efficiency. While minimum cost and duration are common and simple decision criteria, there is growing societal pressure to incorporate smart, sustainable, and circular elements. However, these elements are less straightforward to interpret and there is a lack of a comprehensive framework to quantify smart, circular, and sustainable strategies. This lack of clarity presents significant challenges in balancing traditional and emerging objectives in port maintenance. Our study directly addresses this gap by providing a structured approach to decision-making that integrates these critical but complex elements. As a result, trade-offs on these important issues are harder to achieve reducing the contributions of port authorities and contractors. This study addresses this gap by applying the Frame of Reference (FoR) method to extract objectives and indicators for decision-making from both the port authorities' and contractors' perspectives. We fill in the prescribed elements of the basic FoR template through a systematic literature review (SLR), clarifying to what extent consensus exists on these topics. The SLR revealed 128 articles and identified common strategies, research methods, influential journals, and contributing countries. Projecting these findings onto the basic FoR template showed that the protection of marine ecosystems and sediment management has received considerable attention from researchers while mitigating emissions and adopting smart techniques are emerging subjects in the literature that need further investigations. As a result, this study offers theoretical and managerial insights to improve what can be achieved with smart, circular, and sustainable maintenance strategies, while identifying crucial remaining knowledge gaps.@en

Assembly-line balancing is a significant issue in production systems. Employing industrial robots as the main production resource was a milestone in developing assembly lines, and emerging Industry 4.0 led industries to build collaborative assembly lines by combining robots and human operator skills. Recently, the majority of research on assembly line balancing has contributed to addressing aspects of utilizing robots in assembly lines and how they can increase line performance. Various models and methods are developed, considering different objectives and performance indicators. Despite the increasing number of studies in this area, a thorough literature review is lacking in identifying gaps, shedding light on research directions, and facilitating future development. This study systematically reviews assembly-line balancing studies targeted at assembly lines with industrial and collaborative robots. Studies are classified based on their objectives and reviewed for their solution method, line layout, and other essential specifications. A descriptive analysis is provided to assist researchers and practitioners in linking different properties of assembly lines to the objectives and applied methodologies. The results show that most studies developed models and solution methods that focused on simultaneously optimizing more than one objective. The review reveals that minimizing the cycle time is the most popular objective, and meta-heuristic algorithms are the dominant solution approaches. It is also observed that balancing assembly lines with collaborative robots has received more attention in the last five years with the emergence of Industry 4.0. The review also highlights gaps in the related literature and provides promising insights for future research.@en

In the original publication of the article first author name has been misspelled as “Morteza Sheykhzadeh”. The correct name is “Morteza Sheykhizadeh”. The original article has been corrected.

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Purpose: Maintenance dredging can often hinder port operations resulting in waiting times for seagoing vessels. The purpose of this paper is to investigate the dynamics between maintenance dredging activities and seagoing vessels, specifically focusing on how waiting times can be reduced. Then, the role of selecting different maintenance dredging strategies in reducing these waiting times is outlined. Methods: The study analyzes historical automatic identification system (AIS) data to identify the interaction between maintenance dredging and seagoing vessels and quantify the hindrance periods for the Mississippihaven case study in the Port of Rotterdam, the Netherlands. The trajectories of the vessels are analyzed in a simple case to show how the vessels interact and how the waiting times are quantified. The interactions are checked with the Port of Rotterdam for different port calls to ensure that maintenance dredging was the reason for these delays. Results: By analyzing the AIS data analysis of vessels in a given time window, the dredgers for maintenance work can be identified and their activities within or near the terminal can be determined. In addition, the waiting time of the seagoing vessel caused by the maintenance dredging is quantified at the terminal entrance. Conclusion: The study discusses how the maintenance dredging operations could be improved by adjusting the loading and sailing phases of maintenance dredging and provides some theoretical and managerial insights. Alternative port maintenance strategies to minimize the waiting time caused by the hindrance are also discussed.

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In the current era emphasizing sustainability and circularity, supply chain network design is a critical challenge for making reliable decisions. The optimization of facility location-allocation inventory problems (FLAIPs) holds the key to achieving dependable product delivery with reduced costs and carbon emissions. Despite the importance of these challenges, a substantial research gap exists regarding economic, reliability, and sustainability criteria for FLAIPs. This paper aims to fill this gap by introducing a multi-objective mixed-integer linear programming model, focusing on configuring a reliable sustainable supply chain network. The model addresses three key objectives: minimizing costs, minimizing emissions, and maximizing reliability. A notable contribution of this research lies in elaborating on five levels of a supply chain network catering to the delivery of multiple products across various periods. Another novelty is the simultaneous incorporation of economic, environmental, and reliability objectives in the network design—a facet rarely addressed in prior research. Results highlight that varying demand levels for each facility lead to altered trade-offs between objectives, empowering practitioners to make diverse decisions in facility location allocation. The proposed mathematical model undergoes validation through numerical examples and sensitivity analysis of parameters. The paper concludes by presenting theoretical and managerial implications, contributing valuable insights to the field of sustainable supply chains.

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Due to proofing error many corrections were overlooked by typesetter. Original article has been corrected.

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Due to the outbreak of COVID-19 around the globe in the last few years, the need for pharmaceutical supply chains is felt more than before. However, increasing uncertainties along with unpredictable demand for products led to disruptions in supply chains when receiving requests from retailers. These disruptions not only affected the economic aspect of supply chains but also caused shortages in hospitals and medical centers. Therefore, it has become significant for companies to select their suppliers to avoid disruptions in the case of the severity of infections. To address this issue in practice, this paper has been conducted based on a case study to address the role of lean, agile, resilience, and green (LARG) criteria in selecting the supplier in a pharmaceutical supply chain and compare the results obtained before and after the prevalence of COVID-19. The main purpose of this study is to determine and evaluate different indicators within the LARG concept to avoid disruptions when selecting suppliers. Besides, the significance of these criteria before and after the pandemic condition is addressed. Due to addressing multiple aspects of the problem, a hybrid fuzzy multi-attribute decision-making (MADM) approach is adopted for this elaboration when the four LARG criteria are integrated with eighteen supplier selection sub-criteria. To calculate the impact of each criterion (or sub-criteria), a fuzzy best–worst method (BWM) along with an additive ratio assessment (ARAS) is employed to propose a supplier ranking for a distributor of a pharmaceutical supply chain. The developed model is novel as LARG criteria in the context of supplier selection have not been studied to address the disruptions in the pharmaceutical supply chain. This is significant because it gives insight to both retailers and suppliers to emphasize the correct criteria, especially in the pandemic or related disrupting conditions. The results demonstrated that quality, collaboration, safety stock, and environmental criteria weigh the highest before the pandemic, while just-in-time delivery, lead time, safety stock, and environmental criteria weigh the highest after the pandemic. This study demonstrates that developing a supplier selection approach that meets the demand in a short time and recommends suppliers to hold surplus inventory helps the healthcare systems better respond to the market needs.

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Abstract: Efficient port operations require minimizing turnaround time which is the total duration of a vessel's stay in the port and encompasses waiting, maneuvering, berthing, and de-berthing times. The turnaround time can be reduced by optimizing arrivals and departures, maximizing berth availability, facilitating cargo handling, and maintaining water depth. Maintenance dredging, the primary method for maintaining water depth, is used as a continuous activity to ensure the available water depth is sufficient for the navigation of commercial (seagoing, inland, barges, etc.) vessels. The continuity of maintenance dredging interferes with commercial vessels that aim to be served in terminals. Despite the cost imposed on port authorities due to these interferences, addressing this challenge in a structured way is overlooked. To fill this gap, an open-source discrete-event model is presented in this study that employs agent-based simulation to model the interaction between seagoing and maintenance dredging processes. A simple case is proposed to provide an example of how this interaction is simulated. Then, the implications and limitations of this study are discussed and the directions for future research are recommended.@en

Attaining sustainability objectives has received wide attention in the supplier selection and order allocation (SSOA) literature. This paper aims to investigate an SSOA problem under multiple items, multiple suppliers, multiple price levels, and multiple period using a robust-fuzzy multi-objective programming in which: (a) transportation cost, delay penalty cost, and demand are uncertain; (b) four objectives are proposed to minimize total costs and the number of defective items and to maximize environmental and social impacts; and (c) all objectives of the problem have a fuzzy membership degree that is determined by the decision-makers. A robust optimization approach is elaborated as a solution procedure to address the uncertainty of the decision variables. The significance of each objective in practice is discussed based on seven distinct scenarios that produce a specific membership degree to help practitioners make efficient decisions in selecting the suppliers and allocating the orders. Two numerical examples with different sizes are conducted to validate the mathematical model. Thereafter, the sensitivity of each scenario on objectives and total satisfaction degree is analyzed. The results of the numerical solution compare the value of four objective functions under each developed scenario to provide a trade-off insight between different objectives for practitioners. Eventually, the credibility and efficiency of the proposed solution procedure are evaluated to validate the findings.@en

In the 2015 Paris agreement, countries committed to implementing measures to reduce greenhouse gas emissions to limit global warming. For the maritime industry specifically, the International Maritime Organization (IMO) has proposed measures for energy efficiency of vessels and candidate measures regarding fuel choice and speed optimisation. This article aims to contribute to the latter by showing how logistical simulations can be used to optimise fleet operations. We will illustrate this in the form of a conceptual case using one cutter and a range of barge fleets. Running simulations with all possible fleets, we will demonstrate the value of extra energy-based alternatives to challenge the fastest, cheapest and most flexible alternatives.@en

Wastewater treatment has been a challenge for beverage companies in recent years which has led them to establish specific units to purify the expired returning items before agricultural uses or disposal. This issue has never been studied from the perspective of inventory and production management. This paper proposes an inventory formulation in the case of Behnoush beverage company in Iran to determine the replenishment cycle and the optimal selling price of items when: (a) items deteriorate continuously and have their specific expiration dates, (b) a proportion of items that are expired are moved to the company and after the process of wastewater treatment, and (c) a trade credit period is offered to the buyer to make encouragement for ordering in larger quantities. Optimality conditions for the total cost function are elaborated. Thereafter, numerical experiments are adopted for validation. Eventually, managerial implications are outlined and findings of the study are summarized.@en

The incorporation of payment schemes and imperfect production has received considerable attention in the literature on inventory management; however, researchers have rarely considered these two challenges concurrently. The significance of employing both imperfect production and delay in payment is that both of these challenges affect the profit obtained directly. The total obtained profit can be mitigated by increasing the waste and reducing the number of sold perfect items and delay in payment stimulates the buyers to purchase items and settle their accounts in a specific period. This paper proposes a three-echelon inventory model that (a) Imperfect production is permissible; (b) Inspectors make errors during the inspection process and the errors can be diminished by learning from their previous performance; (c) It is practical to categorize customers into new and old types when old customers are prioritized to receive a full trade credit. As the retailer learns from the experience of screening, the probability of misclassification errors and inspection time decrease for two distinct cases of delay in payment to provide optimal replenishment and promotional decisions. To make a better connection to practical issues, a case study is elaborated. To validate the mathematical model, the impact of the inspector's learning on variables and the total profit is compared in different cases. The analysis shows that it is beneficial for the retailer to make shorter contracts, particularly with new customers to maximize the expected total profit per unit time. Eventually, a numerical experiment is conducted for each subcase to illustrate the impact of learning and determining the optimal policy for the retailer, then some implications for future contributions are outlined.@en

Maritime shipping, with a significant role in global trade, confronts various accidents leading to loss of lives, properties, and the environment. Shipping 4.0 technologies are scaling up to address this problem by employing real-time data-driven technologies, including cyber-physical systems, advanced tracking and tracing, intelligent systems, and big data analytics. Despite growing attention, there is a general lack of clarity on the level and direction of progress in this field. Accordingly, this study aims to identify critical shipping accident risks, analyze the role of relevant shipping 4.0 technologies in controlling these risks, and consolidate the findings into a conceptual guiding framework directing future developments. Accordingly, a systematic review is performed that reveals how shipping 4.0 approaches address critical accident risks and the gaps that still exist. Overall, we found that the collision is the most frequent accident referred to, while the most frequent technology to control the accidents is the Automatic Identification System. In contrast, we see an evident lack of cloud computing, internet-of-things, and big data analytics, which play crucial roles in current industry 4.0 developments.@en

Incorporation of environmental and quality considerations has made production and inventory models shift away from classical models to multi-facet models; however, proposing solutions to improve these considerations has been attended scarcely. Losing a specific number of items due to imperfect production is irrevocable. Inspection operations can detect the imperfect items to avoid reaching the items to the customer; however, inspectors are not faultless and they may detect the quality of some items with errors. To address this issue, quality improvement technologies that monitor the production progress to detect imperfection at the moment can be a favorable approach. On the other hand, carbon dioxide is emitted due to the operations related to production systems, and it is crucial to find means to mitigate emissions because of the sustainability responsibility of firms and governmental regulations in carbon capture. The main novelty of this study is determining how shortages can affect a sustainable inventory system of imperfect quality items considering both quality improvement and inspection process simultaneously. In fact, this study takes another step in this line of research and considers economic, environmental, and quality considerations of an inventory system simultaneously because of few discussions in this line of thought. Shortages are considered allowable and the model is solved under different shortage scenarios. Optimality conditions are developed as a solution procedure and the models are validated using numerical experiments. The results indicate that the scenarios with full backordering and lost sales lead to higher profit than the scenarios without shortages or with partial backordering.@en

Perishing of the items in an inventory model has always been a crucial issue for beverage companies. Besides, some items not only perish but also have specific expiration dates. To manage the inventory-in-hand, proposing a delay in payment is usually an appropriate solution. Also, beverage companies encounter sustainable policies that are regulated to dispose of waste without damaging the environment. These regulations lead companies to establish waste treatment units in their companies to purify the obsolete items before disposing of them. Despite the importance of this challenge in practice, no research has been made to contribute to these issues. To fill the mentioned gap in the literature, this paper proposes an inventory model for perishable items which: (a) items perish continuously and have specific expiration dates, (b) a single-level trade credit is offered to the customer to stimulate the demand, and (c) waste treatment policy is considered for the beverage company to purify the returning items before disposing of them. To develop our work in practice, the case study of Behnoush Beverage Company is considered and a real domain dataset is utilized. To validate the proposed mathematical model, a sensitivity analysis is developed. Eventually, managerial implications are outlined and the findings are concluded.@en

The incorporation of credit risk and environmental regulations has been attended to considerably in the literature of inventory models; however, papers rarely developed a model considering both of these issues concurrently. Thus, jointly incorporate these two challenges makes sense to address the mentioned gap in the literature and outline insights for future works in practice. This study develops an inventory model for deteriorating items which (a) items deteriorate continuously and have their specific expiration dates; (b) carbon is emitted due to ordering and warehousing operations when a cap-and-tax policy is regulated and emissions can be controlled by investing in green technology; (c) two levels of trade credit is offered when the customer must pay a prepayment. The purpose of this study is to compare the retailer's total profit for different values of upstream and downstream trade credit periods to achieve efficient pricing and replenishment decisions. An algorithm is developed to find the optimal value for the investment in green technology. Numerical experiments and sensitivity analysis are elaborated to validate the mathematical formulation. Finally, findings are summarized and managerial implications are addressed.@en

Utilizing carbon emission regulations helps firms to develop a sustainable supply chain. In a two-echelon supplier-retailer-customer relationship, offering a trade credit period can manage the inventory level. However, there will be a risk of settling the accounts during the trade credit period. Therefore, a portion of purchasing cost is charged as prepayment to mitigate some risk of payment delays. Moreover, obtaining the optimal price for perishable items with expiration dates has become a challenge because of their perishing process. Despite the importance of these challenges, very few papers have concentrated on sustainability considerations for perishable items. Also, the contribution of these issues to the credit risk buyers has never been studied. To address the mentioned gap in the literature, this elaboration investigates a supplier-retailer-customer economic order quantity model for perishable items when two levels of partial delay in payment are permissible and a carbon cap and tax policy is regulated to mitigate the impact of emissions on the two-echelon supply chain. The developed model is applicable in food industries to outline the challenges associated with deterioration, payments, and carbon emissions. This paper determines the optimal replenishment and selling price taking into account the following: (a) perishable items have their specific expiration dates, (b) a prepayment is charged from the buyer when offering a permissible delay in payment, (c) a carbon cap and tax policy is applied to approach the sustainability. Necessary and sufficient conditions of optimality are provided as the solution approach. A case study is presented, and numerical examples and sensitivity analyses are illustrated for validation. Finally, managerial applications are exposed, and results are concluded using future research insights.@en

Carbon emission regulations help firms to develop green inventory systems. Besides, lots of items perish because of deterioration. Therefore, controlling both carbon emissions and the deterioration of items becomes essential. Despite the importance of the contribution of these issues, a few works have attended the concepts simultaneously. To fill this gap, this paper proposes a production system considering carbon emissions and deterioration of items and utilises green and preservation technologies to control them, respectively. Moreover, as determining the selling price of items plays a crucial role in a production system, pricing is another issue that this paper concentrates on it. This paper determines the preservation technology investment and green technology investment with discrete values of the cycle length and selling price for an item which: (a) items deteriorate at a constant rate, and the investment in preservation technology is allowable to mitigate the number of deteriorated items, (b) carbon is emitted due to setup and holding the items and the manufacturer follows carbon cap-and-trade regulation and invests on emission reduction, (c) a price-dependent demand is utilised to evaluate the selling price in optimal value. To validate the mathematical model, numerical examples for all cases are exposed.@en

Incorporating qualities and environmental concerns have received significant attention in the literature on production and inventory management for deteriorating items; however, scarce papers have emphasized these two concurrently. Therefore, it is required to incorporate these two aspects for deteriorating items to fill this gap in the literature and provide a better insight into practice. This paper elaborates a sustainable production inventory model for poor quality deteriorating items which (a) items deteriorate at a constant rate and the deterioration progress can be controlled by investing in preservation technology; (b) carbon is emitted due to production operations, and it can be controlled by investing in carbon reduction technology; (c) a fraction of manufacture items are imperfect, and the quality can be enhanced by investing in quality improvement. The paper aims to discuss the impact of preservation and carbon reduction technologies on the total profit to help decision-makers make more efficient replenishment and pricing decisions. An algorithm is proposed to determine optimal values for five decision variables. A numerical experiment is illustrated to validate the mathematical formulation, and the impact of parameters on total profit and managerial insights are discussed using sensitivity analysis.@en