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Agricultural sector not only meets the nutritional requirements of all living creatures but also generates the primary source of the raw material provided by various branches of industry to fulfill their functions. It is of great importance to increase studies on oilseeds which have an important role in Turkey's agricultural products. They are grown in almost all of the country, which are vital for the nutrition and many sectors. The main purpose of the study is to offer an integrated approach to determine potential warehouse locations for oilseeds.

This is the first study that integrates Delphi, analytical hierarchical process (AHP), technique for order preference by similarity to ideal solution (TOPSIS), P-Median and Panel data analysis in a real case. This integrated approach consists of the following steps, respectively: (1) The criteria were determined by the Delphi method. (2) The weights of the criteria were calculated by AHP and the provinces with the highest oilseed warehouse potential in seven regions of Turkey were specified by TOPSIS. (3) Oilseed warehouse numbers and locations were obtained by P-Median. (4) In order to answer whether the distribution network is profitable in the future with the determined center locations, a forecast model based on panel data analysis was created. (5) Regional representatives were determined for 2030, and the distribution network was analyzed again. (6) The costs that arose in 2018 and 2030 were computed and compared by cost analysis. (7) The effect of the change in criteria weights on the alternative results was tested by scenario analysis.

The findings indicated that oilseed crop production potential and oilseed crop production area turned out to be the most important criteria. Furthermore, the results showed that this model is robust and suitable for warehouse location selection studies.

The study can serve as a guide for local and central policy makers with both the criteria it uses and the model it develops.

The main contribution of this study is that the integrated approach has been used for the first time in location selection in a real case.

The study aims to propose a decision-support system to determine the location of a regional disaster logistics warehouse. Emphasizing the importance of disaster logistics, it considers the criteria to be evaluated for warehouse location selection. It is aimed to determine a warehouse location that will serve the disaster victims most efficiently in case of a disaster by making an application for the province of Izmir, where a massive earthquake hit in 2020.

The paper proposes a fuzzy best–worst method to evaluate the alternative locations for the warehouse. The method considers the linguistic evaluations of the decision-makers and provides an advantage in terms of comparison consistency. The alternatives were identified through interviews and discussions with a group of experts in the fields of humanitarian aid and disaster relief operations. The group consists of academics and a vice-governor, who had worked in Izmir. The results of a previously conducted questionnaire were also used in determining these locations.

It is shown how the method will be applied to this problem, and the most effective location for the disaster logistics warehouse in Izmir has been determined.

This study contributes to disaster preparedness and brings a solution to the organization of the logistics services in Izmir.

The supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.

The model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.

Total cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.

The study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.

The issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.

Prior literature has widely established that the design of storage locations impacts order picking task performance. The purpose of this study is to investigate the performance impact of unit loads, e.g. pallets or rolling cages, utilized by pickers to pack products after picking them from storage locations.

An empirical analysis of archival data on a manual order picking system for deep-freeze products was performed in cooperation with a German brick-and-mortar retailer. The dataset comprises N = 343,259 storage location visits from 17 order pickers. The analysis was also supported by the development and the results of a batch assignment model that takes unit load selection into account.

The analysis reveals that unit load selection affects order picking task performance. Standardized rolling cages can decrease processing time by up to 8.42% compared to standardized isolated rolling boxes used in cold retail supply chains. Potential cost savings originating from optimal batch assignment range from 1.03% to 39.29%, depending on batch characteristics.

This study contributes to the literature on factors impacting order picking task performance, considering the characteristics of unit loads where products are packed on after they have been picked from the storage locations. In addition, it provides potential task performance improvements in cold retail supply chains.

An ever-increasing number of companies outsource logistics activities to third-party logistics (3PL) providers to beat the competition. From the buyer's (shippers') perspective, selecting the right 3PL provider is crucial, and from the 3PL provider's perspective, it is imperative to be attractive and to retain clients. To this aim, a potential lever can be physical assets, such as warehouses, which the literature has traditionally neglected. The objective is to benchmark the importance of warehouses for 3PL providers to attract/retain clients and for shippers to select the right 3PL provider.

The authors performed an empirical investigation through interviews on dyads (3PL providers/shippers) and utilized the Best-Worst Method (BWM) to rank the criteria used in the 3PL buying process and allow the warehouse's role to emerge.

Results show that the 3PL buying process consists of four phases and three evaluation steps. The selection criteria are classified into three groups: order qualifiers, order winners and retention factors. The warehouse has different levels of importance throughout the process. It appears that it can indirectly enhance the attractiveness and retention capability of 3PL providers through other selection criteria.

By combining the resource-based view and the customer value theory, this research extends the theory on logistics outsourcing by studying the phases of the 3PL buying process and scrutinizing the criteria used in different evaluation steps. The research adds a double perspective of analysis (3PL providers and shippers), which is missing in the literature, and focuses on the importance of warehouses.

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.

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.

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.

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.

The purpose of this study is to discuss the principles and factors that influence the site selection of emergency medical facilities for public health emergencies. This paper discusses the selection of the best facilities from the available facilities, proposes the capacity of new facilities, presents a logistic regression model and establishes a site selection model for emergency medical facilities for public health emergencies in megacities.

Using Guangzhou City as the research object, seven alternative facility points and the points' capacities were preset. Nine demand points were determined, and two facility locations were selected using genetic algorithms (GAs) in MATLAB for programing simulation and operational analysis.

Comparing the results of the improved GA, the results show that the improved model has fewer evolutionary generations and a faster operation speed, and that the model outperforms the traditional P-center model. The GA provides a theoretical foundation for determining the construction location of emergency medical facilities in megacities in the event of a public health emergency.

First, in this case study, there is no scientific assessment of the establishment of the capacity of the facility point, but that is a subjective method based on the assumption of the capacity of the surrounding existing hospitals. Second, because this is a theoretical analysis, the model developed in this study does not consider the actual driving speed and driving distance, but the speed of the unified average driving distance and the driving distance to take the average of multiple distances.

The results show that the method increases the selection space of decision-makers, provides them with stable technical support, helps them quickly determine the location of emergency medical facilities to respond to disaster relief work and provides better action plans for decision makers.

The results show that the algorithm performs well, which verifies the applicability of this model. When the solution results of the improved GA are compared, the results show that the improved model has fewer evolutionary generations, faster operation speed and better model than the intermediate model GA. This model can more successfully find the optimal location decision scheme, making that more suitable for the location problem of megacities in the case of public health emergencies.

The research findings provide a theoretical and decision-making basis for the location of government emergency medical facilities, as well as guidance for enterprises constructing emergency medical facilities.

The purpose of this paper is to identify the radio frequency identification (RFID) strategic value attributes (RFIDSVAs) mechanism selections preferences and also integration of RFID tags with technology coordination tools (IRTWTCTs) alternatives ranking performance decisions in supply chain management (SCM). RFID-enabled techno-economic feasibility decisions are enhancing the SC visibility in apparel supply chains (ASCs). The RFIDSVAs mechanism selections have performed significant agility to strategic competitive advantages, namely, inventory visibility, multi-tags ownership transfer within trusted third party, etc.

Fuzzy analytical hierarchy process (FAHP) and FAHP-fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) approaches have been used to evaluate the quantitative assessment of RFIDSVA mechanisms selection decision based on weight priority orders and IRTWTCTs alternatives selection in ASC networks. The comparison of FAHP and FAHP-FTOPSIS approaches to evaluate the integrated framework develop in RFIDSVAs mechanisms and IRTWTCTs alternatives selection decisions in Indian multi-tier ASC networks.

The result found that the FAHP-FTOPSIS approaches have used to prioritizing the RFIDSVA mechanism selection weights and also identify the IRTWTCTs alternatives ranking preferences order in apparel SCM. The comparison between the FAHP and FAHP-FTOPSIS approach to quantitative assessments from RFIDSVA mechanisms and IRTWTCTs alternatives selection decisions, which enable them SC agility potential across multi-tier visibility in ASC networks. ASC stakeholders can be benefited by techno-economic feasibility decisions, RFID-enabled shop floor activities, multi-tags ownerships transfer in SCs and knowledge-based cryptography tags/items separation in SCs.

The research work has considered only five RFIDSVA mechanisms and also three integration of RFIDTWTCTs alternatives in multi-tier ASC. The strategic competitive advantages are achieved by RFID-enabled break-even tags price decisions and also techno-economic feasibility decision by contractual design multi-tier SC stakeholder’s involvements.

The pilot project study explores that the quantitative assessment decision has based on RFID-enable techno-economic feasibility in ASCs. Stakeholders can be benefited by inventory control of the financial losses, reducing the inventory inaccuracies and multi-tags ownership transfer within trusted third-party traceability in ASC networks.

This study explores the RFID-enabled apparel SC process and activities visibility (natural fibre’s fibre producer, fibre dyeing producer, yarn spinning producer, knitting and finishing producer).

To be able to continuously provide affordable services to consumers, managers of critical infrastructure (CI) maintenance supply networks have to balance investments in resilience with costs. At the same time, CI providers need to consider factors that influence resilience such as the geographical spread or the location of the network. This study aims to contextualize supply chain resilience knowledge by exploring how maintenance resource configurations impact resilience and costs in CI supply networks.

An in-depth longitudinal single case study of a representative CI provider that has centralized its maintenance supply network is used. Data were collected before and after the change to evaluate the effect of the changes on the maintenance supply network.

This study shows that in this specific CI maintenance context, structural resource choices such as the quantity or location of spare parts and tools, the creation and exploitation of tacit knowledge and staff motivation impact both resilience and costs due to geographical spread, network location and other network properties.

This study extends general supply chain resilience knowledge to a new setting (i.e. CI) and shows how existing insights apply in this context. More specifically, it is shown that even in engineered supply networks there is a need to consider the effect of human agency on resilience as the creation and exploitation of tacit knowledge are of immense importance in managing the network. In addition, the relationship between normal accidents theory and high reliability theory (HRT) is revisited as findings indicate that HRT is also important after a disruption has taken place.

Increasing personnel costs and labour shortages have pushed retailers to give increasing attention to their intralogistics operations. We study hybrid order picking systems, in which humans and robots share work time, workspace and objectives and are in permanent contact. This necessitates a collaboration of humans and their mechanical coworkers (cobots).

Through a longitudinal case study on individual-level technology adaption, we accompanied a pilot testing of an industrial truck that automatically follows order pickers in their travel direction. Grounded on empirical field research and a unique large-scale data set comprising N = 2,086,260 storage location visits, where N = 57,239 storage location visits were performed in a hybrid setting and N = 2,029,021 in a manual setting, we applied a multilevel model to estimate the impact of this cobot settings on task performance.

We show that cobot settings can reduce the time required for picking tasks by as much as 33.57%. Furthermore, practical factors such as product weight, pick density and travel distance mitigate this effect, suggesting that cobots are especially beneficial for short-distance orders.

Given that the literature on hybrid order picking systems has primarily applied simulation approaches, the study is among the first to provide empirical evidence from a real-world setting. The results are discussed from the perspective of Industry 5.0 and can prevent managers from making investment decisions into ineffective robotic technology.