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The purpose of this paper is to deal with an optimal consolidation problem for fresh agricultural products (e.g. fruits and vegetables) in a multi-temperature joint distribution (MTJD) system that is developed to resolve the challenge of timely delivery of small and diverse shipments in food cold chains.

An integer programming optimization model is developed to consolidate a set of agricultural shipments with different storage requirements into a number of distinct containers according to the classification criteria. The formulated model for consolidating fresh agricultural products is evaluated using numerical examples.

Critical factors that affect the quality or shelf life of fresh agricultural products are examined to form the criteria for classifying the storage requirements of these products. The formulated model can minimize the consolidation cost and the loss of product value due to a reduction in shelf life after consolidation.

Although the decision model for product consolidation developed in this paper takes into account practical concerns as much as possible, some additional conditions in the cold chain of fresh fruits and vegetables can be included to further enhance the application of the proposed consolidation model.

Provided that the container environment is appropriately controlled, the shelf life of fresh fruits and vegetables can be maintained during the logistics process. As a result, product quality can be managed to reduce product loss.

This paper adopts temperature, relative humidity and ethylene production, which generally affect the quality and shelf life of fresh agricultural products, as the main factors for determining the product consolidation. It is among the first to deal with the optimal consolidation of fresh agricultural products in the MTJD system with the consideration of product shelf life.

The purpose of this paper is to formulate and solve a last-mile distribution plan problem with concern for the quality of fruits and vegetables in cold chains.

The vehicle routing problem with time windows (VRPTW) is extended based on the characteristics of fruit-and-vegetable cold chains. The properties of multiple perishable foods, continuing decline in quality, various requirements for quality levels and optimal temperature settings during vehicle transportation are considered in the VRPTW. The product quality level is defined by the estimation of residual shelf life, which changes with temperature, and is characterized by a stepped decrease during the transportation process as time goes on. A genetic algorithm (GA) is adapted to solve the problem because of its convincing ability to solve VRPTW-related problems. For this purpose, solution encoding, a fitness function and evolution operators are designed to deal with the complicated problem herein.

A distribution plan including required fleet size, vehicle routing sequence and what quality level should be shipped out to account for the quality degradation during vehicle transportation is generated. The results indicate that the fulfillment of various requirements of different customers for various fruits and vegetables and quality levels can be ensured with cost considerations.

This study presents a problem for last-mile delivery of fresh fruits and vegetables which considers multiple practical scenarios not studied previously. A solution algorithm based on a GA is developed to address this problem. The proposed model is easily applied to other types of perishable products.

Cold supply chain (CSC) distribution systems are vital in preserving the integrity and freshness of transported temperature sensitive products. CSC is also known to be energy intensive with a significant emission footprint. As a result, CSC requires strict monitoring and control management system during storage and transportation to improve safety and reduce profit losses. In this research, a systematic review of recent literature related to the distribution of food CSC products is presented and possible areas to extend research in modeling and decision-making are identified.

The paper analyzes the content of 65 recent articles related to CSC and perishable foods. Several relevant keywords were used in the initial search, which generated a list of 214 articles. The articles were screened based on content relevance in terms of food vehicle routing modeling and quality. Selected articles were categorized and analyzed based on cost elements, modeling framework and solution approach. Finally, recommendations for future research are suggested.

The review identified several research gaps in CSC logistics literature, where more focused research is warranted. First, the review suggests that dynamic vehicle modeling and routing while considering products quality and environmental impacts is still an open area for research. Second, there is no consensus among researchers in terms of quality degradation models used to assess the freshness of transported cold food. As a result, an investigation of critical parameters and quality modeling is warranted. Third, and due to the problem complexity, there is a need for developing heuristics and metaheuristics to solve such models. Finally, there is a need for extending the single product single compartment CSC to multi-compartment multi-temperature routing modeling.

The article identified possible areas to extend research in CSC distribution modeling and decision-making. Modified models that reflect real applications will help practitioners, food authorities and researchers make timely and more accurate decisions that will reduce food waste and improve the freshness of transported food.

This research aims to enhance the operational excellence and continuous improvement of the retail supply chain in the Saudi Thobe Factory through an integrated approach of Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) with artificial intelligence (AI).

The study identified the tailoring department as the department with maximum defects by using voice of customer and critical to quality tools. An AI-integrated Six Sigma approach was applied to identify and eliminate nonproductive stages, and a new facility layout was designed to enhance productivity and customer satisfaction.

The use of the factor rating method and simulation using Arena software led to an improved sigma level from 1.597 to 2.237, representing an increment of about 40%. Additionally, the defects per million opportunities reduced from 461,538 to 230,769. The study can help production industry management to optimize facility layouts and improve overall production line efficiency.

This study addresses the lack of published research on the use of an integrated approach of Six Sigma DMAIC with AI in the retail and distribution sector of Saudi Arabia, particularly for small and medium-sized enterprises (SMEs). The study demonstrates how this approach may significantly boost SMEs’ performance and provides a basis for future research in this area.

This study provides a practical example of how an integrated approach of Six Sigma DMAIC with AI can be used in the retail and distribution sector of Saudi Arabia to enhance operational excellence and continuous improvement. The study highlights the potential benefits of this approach for SMEs in the region and provides a framework for future research.

The purpose of this paper is to present green supply chain, resilient supply chain, agile supply chain, cold supply chain and lean supply chain (GRACL SC) procedures based on a detailed perspective, analyzing subjects in the past 19 years with a systematic literature review (SLR) of the papers reported from 2000 to 2019, and offering information and guidelines for further studies.

This research is based on 17 keywords in the title and topic of the articles and collects data from Web of Science (WOS) databases and objectively chooses 1,190 articles and performs meta-data analyses. Tables and statistical reports are based on the following three filters: publication year, authors and document type. At least, 39 publications from the ISI WOS has been examined for presenting information of categorization of the conducted research with regard to the content analysis, comprising the conceptual development and obstacles, cooperation with the supply chain elements, as well as mathematical and other optimization models.

Finally, this study answered three main questions in the research and demonstrates that the majority studies in the green supply chain (GSC) and a minimum number of studies on the cold supply chain have been conducted and 27 factors are chosen to achieve the 2000 to 2019 GRACL SCM model which robust and fit for Iranian food industries. The model shows that the agile, resilient and lean supply chain have direct effect on GSC and it can be said that all 27 groups which are selected for the final model of this research can be the main groups in the supply chain.

This paper was actually conducted by authors who reported it. To prevent plagiarized, redoubled efforts have been made and actually this paper is based on SLR methodology and the results are real and the researcher discusses the results appropriately. This investigation can have a positive impact within the field of expanding supply chain flexibility and lessening squander within the Iranian generation framework.

This research investigates how to optimize storage location assignment to decrease the order picking time and the waiting time of orders in the staging area of low-temperature logistics centers, with the goal of reducing food loss caused by temperature abuse.

The authors applied ABC clustering to the products in a simulated database of historical orders modeled after the actual order pattern of a large cold logistics company; then, the authors mined the association rules and calculated the sales volume correlation indices of the ordered products. Finally, the authors generated three different simulated order databases to compare order picking time and waiting time of orders in the staging area under eight different storage location assignment strategies.

All the eight proposed storage location assignment strategies significantly improve the order picking time (by up to 8%) and the waiting time of orders in the staging area (by up to 22%) compared with random placement.

The results of this research are based on a case study and simulated data, which implies that, if the best performing strategies are applied to different environments, the extent of the improvements may vary. Additionally, the authors only considered specific settings in terms of order picker routing, zoning and batching: other settings may lead to different results.

A storage location assignment strategy that adopts dispersion and takes into consideration ABC clustering and shipping frequency provides the best performance in minimizing order picker's travel distance, order picking time, and waiting time of orders in the staging area. Other strategies may be a better fit if the company's objectives differ.

Previous research on optimal storage location assignment rarely considered item association rules based on sales volume correlation. This study combines such rules with several storage planning strategies, ABC clustering, and two warehouse layouts; then, it evaluates their performance compared to the random placement, to find which one minimizes the order picking time and the order waiting time in the staging area, with a 30-min time limit to preserve the integrity of the cold chain. Order picking under these conditions was rarely studied before, because they may be irrelevant when dealing with temperature-insensitive items but become critical in cold warehouses to prevent temperature abuse.

This paper aims to explore the use of joint distribution to reduce environmental cost of express delivery in urban logistics.

An optimization model was developed to help design the joint distribution network. A real case in Songjiang University Town of Shanghai, China, was used to explore empirically the potential benefit of the design. Performance of the proposed joint distribution was compared with that of the current distribution design using separate deliveries.

The findings show that, using joint distribution in express delivery, a significant saving in economic cost (up to a reduction of 25%) and environmental cost (up to a reduction of 87%) can be achieved.

This study provides a reference for joint distribution network design for last-mile delivery in urban logistics.

Express delivery companies can explore the use of joint distribution to reduce operating cost and increase distribution efficiency of the industry.

Joint distribution can facilitate maximization of vehicle utilization and reduction of delivery trips. This will help reduce fuel consumption, carbon emission, as well as other externalities, such as noise and traffic congestion.

Research in joint distribution to date focuses mainly on reduction in operating cost only. Consideration of environmental impact is relatively limited. This paper fills the gap in the literature by using a real case to illustrate the significant benefit of joint distribution in reducing the negative impact of urban logistics on the environment.

The purpose of this paper is to develop a quantitative risk assessment method for agricultural products cold chain logistics to assess the condition of the fresh agricultural products cold chain process objectively and accurately.

A risk assessment index system of agricultural products cold chain logistics is designed on the basis of the risk identification for the process of agricultural products cold chain logistics. This paper first uses catastrophe progression method and a new maximum deviation method to build an improved catastrophe progression assessment model for agricultural products cold chain logistics. In order to verify the reliability and validity of the model, two representative enterprises are selected as the case in the study.

The results in the empirical research indicate strong support for the assessment model and coincide with the reality. The risk assessment index system can also reflect the key risk factors from agricultural products cold chain logistics scientifically. In addition, the improved catastrophe progression assessment method proposed in this paper can be scientific and reasonable to predict risk.

This paper contributes to provide a new risk assessment model for agricultural products cold chain logistics. The new model overcomes the limitation of subjective empowerment and it increases the objectivity and scientificity in the process of cold chain logistics risk assessment. This paper also shows that practitioners involved in the field of products cold chain logistics can manage the potential risk by a set of scientific methods for assessing the risk before the accident.

The paper provides a practical guideline to practitioners, especially for cold chain logistics managers, relevant management departments, and cold chain logistics management consultants. It is proved that the new risk assessment method and the risk assessment index system of agricultural products cold chain logistics can help them assess the risk scientifically and reasonably.

Although the calculation is simple, the new model can overcome the limitation of subjective empowerment scientifically and reasonably, and thus has important practical value.

The purpose of this paper is to present a quantitatively supported explanation of the intellectual development, the schools of thought and the sub-areas of the food cold chain (FCC) research to derive meaningful avenues for future research.

This study builds on bibliometric analysis and network analysis to systematically evaluate a sample of 1,189 FCC articles published over the past 25 years. The descriptive statistics and science mapping approaches using co-citation analysis were performed with VOSviewer software.

The findings reveal a state-of-the-art overview of the top contributing and influential countries, authors, institutions and articles in the area of FCC research. A co-citation analysis, coupled with content analysis of most co-cited articles, uncovered four underlying research streams including: application of RFID technologies; production and operation planning models; postharvest waste, causes of postharvest wastage and perishable inventory ordering polices and models; and critical issues in FCC. Current research streams, clusters and their sub-themes provided meaningful discussions and insights into key areas for future research in FCC.

This study might reshape practitioners’, researchers’ and policy-makers’ views on the multifaceted areas and themes in the FCC research field, to harness FCC’s benefits at both strategic and tactical level. Finally, the research findings offer a roadmap for additional research to yield more practical and modeling insights that are much needed to enrich the field.