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This study aimed to develop the integration of the multiperiod production-distribution model in a closed-loop supply chain involving carbon emission and traceability. The developed model was for agricultural food (agri-food) products, considering the reverse flow of food waste from the disposal center (composting center) to producers.

The results indicate that integrating the production and distribution model considering food waste recycling provides low carbon emissions in lower total costs. The sensitivity analysis also found that there are trade-offs between production and distribution rate and food waste levels on carbon emission and traceability.

This study focuses on the mathematical modeling of a multiperiod production-distribution formulation for a closed-loop supply chain.

The model of the agri-food closed-loop supply chain in this study that considers food recycling and carbon emissions would help stakeholders involved in the agri-food supply chain to reduce food waste and carbon emissions.

Managing supply‐chain operations is critical to any company’s ability to compete effectively. Success in today’s markets depends on the ability to balance a stream of products and processes to stay competitive. Companies are constantly evaluating every area of operations to ensure that productivity and cost objectives are realistic and attainable. Research has been carried out to investigate the measurement systems used in today’s distribution centers. Five distribution centers in the Pacific Northwest were selected for this study. The characteristics of these five distribution centers and their measurement systems are discussed in this paper. A cross‐case analysis is provided, which gives a view of a typical measurement system used in today’s distribution centers.

A proposal for a distribution cost and revenue analysis format to provide information for logistics decisions and control.

The aim of this research is to evaluate the current operational status of fresh produce distribution centres in the UK and identify the nature and magnitude of the main logistical problems within them.

A critical evaluation of space and time utilization efficiency has been achieved by studying on‐site operations in a multiple produce handling and short‐term storage facility in Kent, UK. The objective of this research was to measure operational performance of distribution centres for agricultural perishables in terms of through‐put and space utilization.

The inefficient utilization of storage space within cold rooms has been identified and quantified accurately, whilst the quality control task has been recognized as the most time‐consuming task and a critical cause for hindering product flow.

Despite their importance, distribution centres for fresh fruit and vegetables have received little attention in the distribution and performance management literature. Given the lack of robust performance measurement systems reported, the measurement of operational performance in distribution centres for agricultural products was a challenge.

The measurement and improvement of the operational performance in each linkage of the fresh produce supply chain – such as a distribution centre – can lead in achieving higher levels of service at substantially reduced costs. A small number of publications are found in the literature providing information on physical distribution of agricultural perishables, and how the key features of perishability and voluminosity of the produce affect the distribution efficiency. In this research, a step towards the improvement of the fresh produce distribution industry operational performance has been attempted, by evaluating the current operational status of a leading multiple produce distribution centre in the UK.

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.

A number of literature contributions have underlined the importance of developing value-added logistics activities or VALS in order to help improve customers’ satisfaction. However, there is usually very little attention given regarding where to perform these VALS. This study aims to: (1) identify a comprehensive set of factors which may influence the location of VALS, (2) to analyze to what extent those factors influence location decisions, and (3) to distinguish the determinants behind the location choices for distribution centers and for the kind of VALS that will be developed in these distribution centers.

In this paper, we will present a conceptual framework on the locations of VALS in view of the identifying determinants for assigning VALS to logistical centers. We argue that the optimal location of VALS is determined by complex interactions between the determinants at the level of the choice of a distribution system, distribution center location factors, and different logistical characteristics regarding products.

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.

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.

This paper aims to provide an integrated production-routing model in a three-echelon supply chain containing a two-layer transportation system to minimize the total costs of production, transportation, inventory holding and expired drugs treatment. In the proposed problem, some specifications such as multisite manufacturing, simultaneous pickup and delivery and uncertainty in parameters are considered.

At first, a mathematical model has been proposed for the problem. Then, one possibilistic model and one robust possibilistic model equivalent to the initial model are provided regarding the uncertain nature of the model parameters and the inaccessibility of their probability function. Finally, the performance of the proposed model is evaluated using the real data collected from a pharmaceutical production center in Iran. The results reveal the proper performance of the proposed models.

The results obtained from applying the proposed model to a real-life production center indicated that the number of expired drugs has decreased because of using this model, also the costs of the system were reduced owing to integrating simultaneous drug pickup and delivery operations. Moreover, regarding the results of simulations, the robust possibilistic model had the best performance among the proposed models.

This research considers a two-layer vehicle routing in a production-routing problem with inventory planning. Moreover, multisite manufacturing, simultaneous pickup of the expired drugs and delivery of the drugs to the distribution centers are considered. Providing a robust possibilistic model for tackling the uncertainty in demand, costs, production capacity and drug expiration costs is considered as another remarkable feature of the proposed model.

Preface The functions of business divide into several areas and the general focus of this book is on one of the most important although least understood of these—DISTRIBUTION. The particular focus is on reviewing current practice in distribution costing and on attempting to push the frontiers back a little by suggesting some new approaches to overcome previously defined shortcomings.