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This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

Due to the importance of quality to customers, this study considers criteria of quality and profit and optimizes both in a multi-echelon cold chain of perishable agricultural products whose quality immediately begins to deteriorate after harvest. The two objectives of the proposed cold chain are to maximize profit and quality. Since postharvest quality loss in the supply chain depends on various decisions and factors, in addition to strategic decisions, the authors consider the temperature setting in refrigerated facilities and transportation vehicles due to the unfixed shelf life of the products which is related to the temperature found by Arrhenius formula.

The authors use bi-objective mixed-integer nonlinear programming to design a four-echelon supply chain. The authors integrate the supply chain echelons to detect the sources and factors of quality loss. The four echelons include supply, processing, storage and customer. The decisions, including facility location, assigning nodes of each echelon to corresponding nodes from the adjacent echelon, allocation of vehicles to transport the products from farms to wholesalers, processing selection, and temperature setting in refrigerated facilities, are made in an integrated way. Model verification and validation in the case study are done based on three perishable herbal plants.

The model obtains a 29% profit against a total cost of 71 and 93% of original quality of the crops is maintained, indicating a 7% quality loss. The final quality of 93% is the result of making a US$6m investment in the supply chain, including the procurement of high-quality raw materials; facility establishment; high-speed, high-capacity vehicles; location assignment; processing selection and refrigeration equipment in the storage and transportation systems, helping to maximize both the final quality of the products and the total profit.

The proposed supply chain model should help managers with modeling decisions, especially when it comes to cold chains for agricultural products. The model yields these results – optimal location-allocation decisions for the facilities to minimize distances between the network nodes, which save time and maintain the majority of the products’ original quality; choosing the most appropriate processing method, which reduces the perishability rate; providing high-capacity, high-speed vehicles in the logistics system, which minimizes transportation costs and maximizes the quality; and setting the right temperature in the refrigerated facilities, which mitigates the postharvest decay reaction rate of the products.

Comparison of the results of the present research with those of the traditional chain (obtained through experts) shows that since the designed chain increases the profit as well as the final quality, it has benefits for the main chain stakeholders, which are customers of agricultural products. This study model is expected to have a positive impact on the environment by placing strong emphasis on quality and preventing excessive waste generation and air pollution by imposing a financial penalty on extra demand production.

Since profit and quality of the final product are two important factors in all cultures and communities, the proposed supply chain model can be used in any food industry around the world. Applying the proposed model induces growth in local industries and promotes the culture of prioritizing quality in societies.

To the best of the authors’ knowledge, this is the first research on a bi-objective four-echelon (supply, processing, storage and customer) postharvest supply chain for agricultural products including that integrates transportation logistics and considers the deterioration rate of products as a time-dependent variable at different levels of decision-making.

Pharmaceutical supply chains (PSCs) need a well-operating and faultless logistics system to successfully store and distribute their medicines. Hospitals, health institutes, and pharmacies must maintain extra stock to respond requirements of the patients. Nevertheless, there is an inverse correlation between the level of medicine stock and logistics service level. The high stock level held by health institutions indicates that we have not sufficiently excellent logistics systems presently. As such, selecting appropriate logistics service providers (drug distributors) is crucial and strategic for PSCs. However, this is difficult for decision-makers, as highly complex situations and conflicting criteria influence such evaluation processes. So, a robust, applicable, and strong methodological frame is required to solve these decision-making problems.

To achieve this challenging issue, the authors develop and apply an integrated entropy-WASPAS methodology with Fermatean fuzzy sets for the first time in the literature. The evaluation process takes place in two stages, as in traditional multi-criteria problems. In the first stage, the importance levels of the criteria are determined by the FF-entropy method. Afterwards, the FF-WASPAS approach ranks the alternatives.

The feasibility of the proposed model is also supported by a case study where six companies are evaluated comprehensively regarding ten criteria. Herewith, total warehouse capacity, number of refrigerated vehicles, and personnel are the top three criteria that significantly influence the evaluation of pharmaceutical distribution and warehousing companies. Further, a comprehensive sensitivity analysis proves the robustness and effectiveness of the proposed approach.

The proposed multi-attribute decision model quantitatively aids managers in selecting logistics service providers considering imprecisions in the multi-criteria decision-making process.

A new model has been developed to present a sound mathematical model for selecting logistics service providers consisting of Fermatean fuzzy entropy and WASPAS methods. The paper's main contribution is presenting a comprehensive and more robust model for the ex ante evaluation and ranking of providers.

The organization of services can affect the adoption of sustainable practices within the relationship between a buyer (e.g. a shipper) and a supplier (e.g. a logistics service provider–LSP). The purpose of this paper is to analyse, within this relationship, the mechanisms affecting collaboration between shippers and LSPs towards adopting green logistics practices to reduce the negative environmental effects of logistics processes. The authors take the perspective of small and medium enterprises (SMEs), which represent – although less investigated than large enterprises – a relevant field of investigation given their impact on the environmental sustainability of logistics processes.

The authors conducted a multiple case-study investigation on a set of dyads involving shippers and LSPs. The authors explored the antecedents shaping the approach to sustainability in logistics and, adopting the absorptive capacity (AC) theory, the learning and knowledge transfer processes leading to the adoption of green practices.

Collaboration between shippers and LSPs for better sustainability in logistics seems not to work when relationships are limited to simple annual (or pluriannual) contracts, and when shippers do not show ambition to improve the level of sustainability of their logistics processes (regardless of whether they show an interest in general sustainability matters). On the other hand, successful cases show higher commitment in the dyadic relationship with respect to improving logistics sustainability, good levels of communication and a more structured process of knowledge sharing, enabled by IT integration, shared performance monitoring, and creation of inter-organizational teams.

While most of the existing research focuses on the perspective of shippers or LSPs, this work is original since it explores collaborative mechanisms within a buyer-supplier relationship simultaneously taking the perspective of both parties, according to the lens of the AC. It identifies directions for improving collaboration within the shipper-LSP relationship in the context of SMEs to foster the adoption of collaborative green logistics practices to impact sustainability positively.

The criticality of late deliveries in transportation lies in the threat of considerable multi-level supply chain costs. This study aims to reveal the dynamic capabilities playing a facilitating role in preventing delay, thus providing timely delivery, as well as developing an understanding of how and when those capabilities are activated within the supply chain network.

An exploratory study was conducted involving 16 semi-structured expert interviews with the representatives of logistics service providers and shippers. Following an interpretive phenomenology framework, the prevention phenomenon was explained.

Findings revealed two preventive capability categories in delay prevention: (1) proactive capabilities, referring to the enabling actions planned before departure, and (2) reactive capabilities, referring to actions planned after departure. Findings pinpoint that, in addition to the proactive capabilities, reactive capabilities enabled by innovative problem-solving actions are crucial for adapting to a dynamically changing environment in prevention. Moreover, this study shows that prevention capabilities are characterized by tangible and intangible resources and integration of resources with external links which constitute a delay prevention network within a wider service ecosystem.

This study stands out with its specific focus on delay prevention capabilities and enabling actions from the perspectives of logistics service providers and shippers. The premises of the resource-based view are combined with dynamic capabilities theory, leading to a proposed time-based taxonomy of proactive and reactive capabilities in supply chains, aimed at creating value and strengthening resilience.

COVID-19 not only has impacted adversely the health infrastructure, taking away lives of millions of people but it has also crippled the economy. The worst effected were food supply chain due to restrictions imposed on operations of shops and retail outlets. The consumers were suffering due to lack of supply. Similarly, agriculture produce were getting wasted due to lack of cold storage.

In this case we have proposed how a mobile-based application solution during COVID lockdown can successfully transform the livelihood of rural farmers in the state of Maharashtra (India), a state worst affected by the pandemic.

The technology-integrated supply chain model jointly developed by financial institutions, self-help groups (SHGs) and NGOs has enabled direct selling of fresh produces by rural women farmers to urban large residential societies at their doorsteps. It has provided a solution of municipality waste management by converting the waste to compost, getting them collected and used in the farmlands.

It will help the urban consumers to have the continuous supply of fresh vegetables and fruits available at their doorsteps, and keep a track of transport of foods from farm to fork. The farmers will be able to get better price for their produce. The model will also contribute towards circular economy (CE) through citizen partnership.

The pharmaceutical industry is one of the most essential areas of health in any country. It is defined as a system of processes, operations and organizations involved in discovering, developing and producing drugs. The supply chain in the pharmaceutical field is one of the most important strategic issues in the pharmaceutical and health-care industries. The purpose of this study is to reduce the total cost of the supply chain network and reduce the amount of distribution scheduling.

In this study, the authors designed a drug supply chain network with uncertainty-related corruption. The optimal number and location of potential facilities, the optimal allocation of flow between facilities, the optimal routing of vehicles and the optimal amount of inventory in production and distribution center warehouses were determined to achieve these two objective functions.

In evaluating the small sample size problem, it was found that the comprehensive benchmarking method was more efficient than the other methods in obtaining the mean index of the first objective function. The utility function method has also proved its efficiency in obtaining the mean of the second objective function indices, the spacing index and the computational time. Because of the inefficiency of GAMS software in resolving size issues, the modified NSGA II and MOPSO algorithms with modified priority-based encryption have been used. First, using the Taguchi method, the initial parameters of the metaheuristic algorithms are adjusted, and then, 15 sample problems are designed in larger sizes. To avoid generating random data, five problems were equally designed, and the averages of objective functions and metrics of met heuristic algorithms (number of efficient solutions, maximum expansion index, spacing index and computational time) were analyzed as the basis of evaluation and comparison. Therefore, using all the indicators and results of the NSGA II algorithm is recommended.

In this research, a biobjective modeling approach is proposed to minimize the total costs of the supply chain network (construction costs, storage costs and product transportation costs between centers) and advertising costs and to minimize distribution and transportation scheduling across each level of the supply chain network.

Road freight transportation companies need to take advantage of information and communication technologies to develop capabilities. This study proposes a framework to guide road freight transportation companies to achieve data visibility in their operations by developing such capabilities. By proposing this framework, this research contributes to literature and practice, highlighting the capabilities and the respective supporting technologies for improved data visibility in road freight transportation.

A mixed-method approach is used to develop the framework, considering three methodological steps. In phase 1, the capabilities are identified in the literature and validated by experts. In phase 2, an empirical assessment of cause–effect relationships between capabilities is performed using a multiple case study and DEMATEL. Lastly, in phase 3, an analysis of the cause model and significant associations is conducted to enable the development of the framework. In addition, the proposed framework was validated by the experts interviewed.

The results provide a framework that explains the link between the technology-enabled data visibility capabilities in road freight transportation operations. In addition, a pathway was established that road freight transportation companies could follow to achieve data visibility in their operations by developing such capabilities.

This work develops the first framework that provides a path for data visibility in road freight transportation operations from adopting certain technologies. The insights are compelling for researchers and practitioners to optimize the decision-making process for adopting technologies and developing capabilities related to data visibility.

Air freight transportation is an increasingly important element in the production supply chains of both developed and less-developed countries. This chapter looks at the particular features of air freight transportation that pertain to the developing world. In doing this, it is borne in mind that much of the trade involves international interactions with developed countries and this affects the technology and costs involved. The markets for the transportation of flower products is given particular attention.