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The manufacturing industry has evolved with the emergence of cloud computing, leading to the development of cloud manufacturing — a customer-oriented paradigm. The integration of scheduling and logistics in cloud manufacturing is paramount and distinguishes it from traditional manufacturing. Tasks can have three different structures, so this study presents three models that integrate scheduling and logistics for tasks with sequential, parallel, and loop structures, respectively. These models aim to minimize the total cost of the manufacturing system, which includes the implementation cost of subtasks, logistical services cost among factories in different geographical locations, logistical services cost for delivery of the order (task) to the customers, and the earliness/tardiness cost of orders. The numerical examples in small and medium sizes are solved using the CPLEX solver in the GAMS software. However, due to the high complexity of the models presented, a genetic algorithm is developed to solve large examples. To showcase the significance of the main features of the proposed models, two comparable models are employed, each with one feature removed. In addition to the factors that are considered to get the models close to reality, a sensitivity analysis is conducted to design effective guidelines for cloud manufacturing managers.

Home delivery service is one of the most important cost drivers in e-commerce industry. We consider the three-dimensional container packing problem under home delivery service, where each rectangular item with its specific destination is loaded orthogonally onto a rectangular container so that the utilization rate of the container space is maximized. In our framework, we assume the routing of a consignment to be given, which turns out that there is an order of unloading items with respect to the consignment. If we load items without considering the order of unloading items, we may unload and reload other unconcerned items drastically while unloading the required item. Therefore, in this paper, the unloading costs for a consignment are precisely defined according to the invisible and untouchable rule, and a subvolume scheme based algorithm is proposed. Our experimental results suggest our approach to be promising.

One of the most important operational management problems of a cross docking system is the truck scheduling problem. Cross docking is a logistics management concept in which products delivered to a distribution center by inbound trucks are immediately sorted out, routed and loaded into outbound trucks for delivery to customers. The truck scheduling problem in a multi-door cross docking system considered in this paper comprises the assignment of trucks to dock doors and the determination of docking sequences for all inbound and outbound trucks in order to minimize the total operation time. A mathematical model for optimal solution is derived, and the genetic algorithms (GAs) and the adaptive genetic algorithms (AGAs) as solution approaches with different types of chromosomes are proposed. The performance of the meta-heuristic algorithms are evaluated using randomly generated several examples.

The manager is responsible for the operations of a distribution center (DC) and multiple retail outlets selling a seasonal product. Initially, the DC keeps the inventory, which is allocated to the outlets in the season. There are inventory holding costs at the DC and the outlets; variable shipment cost for transferring inventory from the DC; fixed ordering cost and shortage cost at an outlet. Exact demand at each outlet is a decreasing function of price. To maximize the expected profit of the season, the manager needs to determine the markdown prices for retail outlets and quantity of inventory allocated to them. The problem can be modeled as a dynamic program (DP) which takes too heavy computational effort to solve. We develop a DP-based heuristic for solving the problem. The heuristic takes light computational effort and yet has good accuracy. Insights streamlining the markdown operations are deduced from the numerical results.

The total size of the edible oils market in India was estimated to be 13 million tons (mt) out of which imports amounted to about 4 mt. This made India the largest importer of edible oils in the world. Various edible oils are consumed in the India depending on the regional tastes and preferences. A differential in the duties on oil seed and oils made it favorable to import edible oils instead of oilseeds. Similarly, a differential duty between the refined oil and the raw oil encouraged the import of raw oil in order to support the domestic refineries.

Adani Wilmar Limited (AWL) was a part of the Adani group, which started as a trading company mainly into exports of commodities. The group had recently entered into the infrastructure sector with the building of the Mundra port. The group had formed a joint venture with Wilmar Trading of Singapore to enter into the edible oil business. The company was setting up a re.nery with capacity of 600 tons per day. It planned to sell half of the production as bulk oil and the rest as packed oil. The company viewed supply chain management as one of the important means to get a competitive edge. Approximately 70% of the total logistics cost was accounted for by transportation cost. Some of the key decisions the company faced was the location of the warehouses, mode choice and routing.

An efficient supply chain is the one which fastens the e-commerce processes to meet customers’ needs and expectations. Managing supply chains in e-commerce involves materials procurement, manufacturing and distribution of the required products to the customers in a timely manner. This process also includes warehousing, inventory tracking, demand and supply management, order entry and order management.

A well-structured and sustainable supply chain can enhance the productivity of e-commerce processes. This chapter presents an approach on how e-commerce is influencing supply chains and logistics. Also it discusses how analytics can be leveraged to improve the last mile delivery in e-commerce platforms.

With over 90% of the world’s goods relying on the maritime industry for transport, the digitalization of the maritime supply chain and logistics is greatly due. The real-world utility of blockchain technologies is hardly a subject of concern today, even in Africa. The application and success stories are evident in key economic sectors with the digitalization of payments as a predominant example. Although classical maritime logistics has a poor end-to-end supply chain integration, digitalization of shipping operations leads to the creation of new and innovative business models capable of generating real values across the global value chain. While several studies have been done on digitalization with a focus on technologies of digitalization and the manufacturing industry, maritime supply chain issues in emerging economies such as Africa are yet to get much attention. This study, therefore, expounds on the challenges and opportunities of blockchain adoption in the African maritime industry.

This chapter enriches readers’ understanding regarding the significance of Autonomous Mobile Robots (AMRs) within the context of the warehousing and distribution industry. It offers insights into the evolving growth trends of AMRs, the current state of academic research on this technology, and real-world applications of AMRs in the logistics sector. Additionally, the chapter thoroughly discusses the challenges associated with AMR adoption and proposes a mid-range theory to facilitate their integration within the facilities. In sum, this chapter provides valuable insights for academics and practitioners alike who are eager to explore the adoption of AMRs within the warehousing and distribution industry.