Milk-run Logistics Research Articles

A Two-Level Variable Neighborhood Descent for a Split Delivery Clustered Vehicle Routing Problem with Soft Cluster Conflicts and Customer-Related Costs

This paper introduces Split Delivery Clustered Vehicle Routing Problem with Soft cluster conflicts and Customer-related costs (SDCVRPSC) arising in automotive parts of milk-run logistics with supplier cluster distribution in China. In SDCVRPSC, customers are divided into different clusters that can be visited by multiple vehicles, but each vehicle can only visit each cluster once. Penalty costs are incurred when traveling between clusters. The transportation cost of a route is calculated as the maximum direct shipment cost between customers on the route plus the total drop costs. The SDCVRPSC aims to minimize the sum of transportation costs and penalty costs by determining the assignment of customers to vehicles and the visiting order of clusters. We propose an integer linear programming model and a two-level variable neighborhood descent algorithm (TLVND) that includes two-stage construction, intensification at cluster and customer levels, and a perturbation mechanism. Experimental results on designed SDCVRPSC benchmark instances demonstrate that TLVND outperforms the Gurobi solver and two adapted algorithms at the business operation level. Moreover, a real case study indicates that TLVND can bring significant economic savings compared to expert experience decisions. TLVND has been integrated into the decision support system of the case company for daily operations.

Milk-Run Collection Monitoring System Using the Internet of Things Based on Swarm Intelligence

In our country Morocco, several dairy factories are placed in rural regions with a bad road network, which means that milk collection has a significant impact on profit, affecting milk transport costs. Actually, the milk run logistics process has been transformed from a traditional farm to the new cheese factory, so it’s needed efficient methods and models to improve the process of production and collection of milk from those units. For that, we will apply new technologies such as the internet of things (IoT) and big data to collect and analyze this information to optimize the milk delivery process. The main goal of this work is to design a new smart decision method using the internet of things and big data to optimize the milk run logistics, reduce the cost of transportation and improve collection density. This method will be based on the swarm artificial intelligence concept to find and calculate the shortest path between units to optimize the collection of milk.

Mathematical formulation and heuristic algorithms for optimisation of auto-part milk-run logistics network considering forward and reverse flow of pallets

The operational planning of distribution network for automotive industry is complex with many conditions to consider, including heterogeneous fleet, enforcing the feasibility of 3D-packing of pallets into vehicles to address the vehicle's capacity in terms of weight and volume, compatibility of orders in a vehicle, returning empty pallets from assembly-plants backwards to suppliers, and delivery time windows. A mathematical model (MILP) is proposed that takes account of these conditions to minimise total transportation costs. The network structure can be a combination of direct shipment and milk-run for both forward and reverse flow of pallets. The model is solved optimally for small-size problems. For solving larger problems, a heuristic algorithm (in two versions) is proposed that uses a similarity measure to generate a reasonable list of orders. Best/first-fit strategies are employed to generate a feasible solution with the aid of a relaxed version of the proposed MILP. Improvement heuristics are also designed. Unlike most of existing constructive heuristics, our aim for developing the heuristic approach is to force routing decision, with all of its considerations, being made optimal. We also use the proposed best-fit strategy in the body of grouping evolution strategy (GES) algorithm to attain an effective meta-heuristic approach. The effectiveness of heuristics is tested on generated instances which demonstrates they are optimal for small-size problems. They are also tested on the data of daily auto-parts shipments gathered from the largest Iranian automobile company. Results demonstrate there exists a significant potential for cost saving through milk-run strategy compared with the direct shipping strategy.

Ant Colony Optimization Algorithms to Enable Dynamic Milkrun Logistics

Flexibility in combination with high capacities are the main reasons for milkruns being one of the most popular intralogistics solutions. In most cases they are only used for static routes to always deliver the same material to the same stations. However, in the context of Industry 4.0, milkrun logistic also has become very popular for use cases where different materials have to be delivered to different stations in little time, so routes cannot be planned in advance anymore. As loading and unloading the milkrun requires a significant amount of time, beside the routing problem itself, both driving and loading times have to be taken into account. Especially in scenarios where high flexibility is required those times will vary significantly and thus are a crucial factor for obtaining the optimal solution. Although containing stochastic components, those times can be predicted by considering the optimal point of time for delivery. In consequence, the best tradeoff between short routes and optimal delivery times is in favor of the shortest route. To solve this optimization problem a biology-inspired method – the ant colony optimization algorithm – has been enhanced to obtain the best solution regarding the above-mentioned aspects. A manufacturing scenario was used to prove the ability of the algorithm in real world problems. It also demonstrates the ability to adapt to changes in manufacturing systems very quickly by dynamically modelling and simulating the processes in intralogistics. The paper describes the ant colony optimization algorithm with the necessary extensions to enable it for milkrun logistic problems. Additionally the implemented software environment to apply the algorithm in practice is explained.

安吉零部件物流循环取货模式研究

在分析国内汽车行业发展状况的基础上，以安吉零部件物流公司为研究对象，提出了适合安吉零部件物流循环取货流程优化的整体方案。通过对安吉零部件物流公司循环取货流程的现状分析，找出了其配送成本高、运输责任不明确和货物质量无保障的问题，设计了入厂物流模式和循环取货流程优化方法，对其配送路线进行优化和取货流程流程再设计，缩短了运输路程，取得了明显的效果。 Based on the analysis of domestic automobile industry, we proposed an optimization method for parts milk-run logistics according to the problem of Anji Parts Logistics Company, such as high cost of distribution, unclear transportation responsibility and no guarantee of quality of good. We used inbound logistics mode and milk-run logistics mode to optimize the logistics progress. We redesigned the distribution route and pickup process which has shortened transportation distance and achieved obvious result.

IMPLEMENTATION OF MILK RUN LOGISTICS IN VEHICLE COOLING SYSTEM OF PRIDE KIA PARTS

A good and efficient production process is valuable while all the parts and materials are accessible when needed at the exact time. Otherwise, the lack of inventory required at that moment causes the stop line and imposition of heavy penalties. Likewise, its earlier presence ahead of the production planning makes the capital dull, which results in damages and scrapes. Milk-run logistics project, in the category of strategic improvement was held in Pars Khodro Automobile Manufacturing Company (SAIPA Group) known as the oldest car manufacturer whose joint with General Motor is recorded previously. Pars Khodro has the experience of producing more than 250 000 vehicles annually in various diversities including Renault and NISSAN Products such as Logan, Megan, Maxima Sundero, Brilliance, and Pick Up. In this article, due to the problems regarding the feeding of parts C, milk-run logistics has been proposed with the aim of balanced development and use of modern logistical systems attempting to improve the logistic processes and reduce unnecessary costs relevant to the production and logistic processes. The purpose of this paper is to review the milk-run logistics and to present an overview of its implementation practices adopted by the manufacturing organizations. The paper discusses milk run logistics in the line feeding system with a special emphasis on the automobile industry, which leads to the enhancement of logistics indices.

순회배송 물류전략에서 탄소배출 비용의 효과 분석

This paper develops an analytic model for minimizing the cost of distributing items by truck from one supplier to many customers under Milk run logistics strategy. The model derives formulas for not only inventory and transportation costs but also costs associated with carbon emission trading scheme. In addition, monetary investment for reducing carbon emissions is considered. We analyze how to determine optimal shipment size and carbon emission reduction investment. The purpose of this work is to evaluate the effects of carbon emission trading scheme on the Milk run logistics strategy in terms of how much to reduce carbon emissions and/or inventory and transportation costs. We analytically show that it is possible to reduce carbon emissions while reducing inventory and transportation costs by introducing cap-and-trade carbon emission trading scheme under certain conditions.

Vehicle Routing Approach for Lean Shop-Floor Logistics

In order to satisfy the material supply needs of large scale shop-floors and production systems, various logistics solutions are applied. In lean manufacturing enterprises, the material supply is pulled by the demands of manufacturing/assembly processes; therefore, a milkrun service is often applied to support the production without glitches. The milkrun logistics planning is a special case for vehicle routing problem (VRP), and requires effective approach to solution in order to satisfy various constraints, and minimize the cost of service. This study gives an overview about lean logistics as well as the most efficient VRP solver algorithms. Furthermore, a novel initial solution with generation heuristics is proposed, which is specially focused on flexible milkrun planning. In order to demonstrate the capabilities of the solution, a software environment is developed as a demonstration that focuses on the main industrial requirements of logistics planning like effective layout definition, quick response of the delivery service and effective order handling.

Optimizing the storage assignment in a warehouse served by milkrun logistics

This paper addresses the problem of storage assignment in a warehouse characterized by multi-command picking and served by milkrun logistics. In such a logistic system, vehicles circulate between the warehouse and the production facilities of the plant according to a pre-defined schedule, often with multiple cycles (routes) serving different departments. We assume that a request probability can be assigned to each item and each cycle, which leads to a special case of the correlated storage assignment problem. A MIP model is proposed for finding a class-based storage policy that minimizes the order cycle time, the average picking effort, or a linear combination of these two criteria. Computational experiments show that our approach can achieve an up to 36–38% improvement in either criterion compared to the classical COI-based strategy.

Milk-run logistics by Japanese automobile manufacturers in Thailand

Recently, Japanese automobile manufacturers are trying to procure parts by the so-called Milk-Run logistics at most of their foreign factories. The case study on Japanese automobile manufactures in Thailand revealed that by introducing the Milk-Run logistics even under heavily congested traffic conditions, they can have full control of the procurement process, resulting in a reduction of the number of trucks dispatched and improvements in traffic conditions to some extent in urban areas.

Milk-run Logistics by Japanese Automotive Manufacturers in Thailand

Milk-run Logistics by Japanese Automotive Manufacturers in Thailand

A Mixed Distribution System of Direct and Relay Delivery Methods Based on an Analogy of Milk-run Logistics

A Mixed Distribution System of Direct and Relay Delivery Methods Based on an Analogy of Milk-run Logistics

Establishment of a Japanese-owned Automobile Company in India and Construction of Its Production System

India, as well as other Asian countries, especially China and Thailand, has experienced a rapid growth in its automobile industry since the 1990s. One of the main reasons for this growth was the implementation of an economic liberalization policy that accelerated a huge amount of direct investment in India on the part multinational motor companies. These companies established 100%owned assembly plants or joint ventures that collaborated with local business groups. In 2002, 12 motor companies, including three Japanese ones, competed for the market shares of passenger cars and multi-utility vehicles. While the total number of vehicles produced in India has gradually increased, the scale of production per company has remained small because of severe market competition. Toyota Kirloskar Motor (TKM), a joint venture created by Toyota Motor Company, Japan, with the Kirloskar Group, India, was investigated in an attempt to explain the small-scale vehicle production system of an overseas Japanese automobile plant. TKM was founded on the Bidadi Industrial Estate on the outskirts of Bangalore in 1997 and started commercial production of multi-utility vehicles in 1999. Its annual vehicle production is approximately 30, 000 units, which is far fewer than those of Toyota-owned overseas plants in North America and Europe. Therefore, TKM is a suitable example for considering the above topic. TKM constructed an efficient factory to achieve cost reduction. Compared with Toyota's factory buildings in Japan, that of TKM is lower in total height and narrower in terms of aisle space. Instead of costly steel-collar robots, it utilizes manual workers in production processes like welding, painting, and body assembling. These processes are mostly automated in Japan. Some of the machinery and equipment were shifted from Toyota-owned factories in Japan or Southeast Asia to maintain a low initial investment. TKM employed an improved, “just-in-time” (JIT) system to adapt to small-scale production and the local economic environment. TKM's parts suppliers are located in agglomerations of the automobile industry in Delhi, Pune, and Chennai, as well as Bangalore. TKM purchases 70% of its parts in monetary terms from Indian branches of Toyota group companies located in Bangalore. These group companies play an important role as core suppliers to TKM. TKM employs the milk run logistics system for parts delivery to perform JIT production. The milk run system has made it possible to minimize transportation costs. TKM established a transportation company responsible for transporting its auto parts. The company travels from supplier to supplier to collect parts along its routes. After arranging the parts in a long deck truck at consolidating stations in Delhi, Pune, and Chennai, the truck is driven to TKM every day. The suppliers surveyed in Bangalore are divided into the following two types. Members of the first type are Japanese owned and established as dedicated suppliers to TKM. Members of the second type are locally owned and their business with TKM comprises a small percentage of their sales. The local suppliers have many customers that vary from vehicle manufacturers to other industrial customers. In India, the business per customer is so small that suppliers need to have many business partners and networks to make a profit. Some of the Japanese suppliers tried to expand their business relations with other customers to meet the small-scale production of TKM.