A note on “fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain”

Liang (2008) [Liang, T. -F. (2008). A note on “fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain”. Computers & Industrial Engineering, 55, 676–694] proposed a production/distribution planning model and its solution approach in fuzzy environment. However, his mathematical model does not use backordering option. The main purpose of this paper is to demonstrate this handicap and propose a valid constraint.     

Determination and exchange of supply information for co-operation in complex production networks

Today's manufacturing industry is characterised by strong interdependencies between companies operating in globally distributed production networks. The operation of such value-added chains has been enabled by recent developments in information and communication technologies (ICT) and computer networking. To gain competitive advantages and efficiency improvements such as reduced inventory and higher delivery reliability, companies are introducing information exchange systems that communicate demand to suppliers and production progress information to customers in the network. This article proposes a system that supports co-operation in complex production networks by enabling companies to determine and exchange supply information with their customers. The requirements for such a system are analysed and it is embedded in a framework of supply chain management business processes. The system facilitates the determination and exchange of meaningful, reliable and up-to-date order status information from the supplier to the customer. Based on comparing the progress of an internal production order with a pre-defined milestone model for each product, the status of the customer order is determined and—in case of lateness—communicated to the customer together with an early warning. To demonstrate the developed supply information concepts and processes, the business process is implemented as a pilot system and evaluated by the user companies participating in the 5th Framework IST project Co-OPERATE.     

Knowledge-based tool for planning of enterprise resources in ASEAN SMEs

Manufacturing has been identified as a key pillar of growth in many Southeast Asian (ASEAN) economies. However, in the last decade many countries have become keen competitors for foreign direct investments. Many countries are trying to improve their total business capabilities by encouraging computerisation of small and medium sized enterprises (SME). Manufacturing SMEs (M-SMEs) are tasked to adopt technologically advanced programmes. With an improving public education system and more literate work force, more SMEs are better positioned to tap into the knowledge-based economy. There is tremendous amount of knowledge intensive activities within the multi-flows of the M-SMEs.Although the concept of ERP systems and artificial intelligence (AI) techniques have been around for more than two decades, this has largely remained the domain of the larger companies. ASEAN M-SMEs have been slow to implement it. In this paper, the various strategic and operational requirements of regional M-SMEs are presented and a knowledge-based resources planning model making use of AI techniques is proposed. This improved AI model makes use of the large amount of accumulated knowledge typically found in the M-SMEs, especially those in the electronics and precision engineering sectors. This includes a case study of how an electronics precision engineering company adopted the proposed AI model.     

Manufacturing production plan optimization in three-stage supply chains under Bass model market effects

The Bass model is a very successful parametric approach to forecast the diffusion process of new products. In recent years, applications of the Bass model have been extended to other operational research fields such as managing customer demands, controlling inventory levels, optimizing advertisement strategies, and so forth. This study attempts to establish an application for optimizing manufacturers’ production plans in a three-stage supply chain under the Bass model’s effects on the market. The supply chain structure considered in this research is similar to other common supply chains comprised of three stages, namely retailer, distributor and manufacturer. The retailer stage has to handle customer demands following the Bass diffusion process. Market parameters and essential information are assumed to be available and ready for access. Each stage is expected to determine its inventory policy rationally. That is, each stage will attempt to maximize its own profits. These decisions will back-propagate their effects to upper stages. This study adopts a dynamic programming approach to determine the inventory policies of each stage so as to optimize manufacturers’ production plans.     

GBOM-oriented management of production disruption risk and optimization of supply chain construction

A generic bill-of-materials (GBOM) describes demand for materials and their proportional relations to a family of products. Supply chain constructed from the perspective of the GBOM is able to respond swiftly to market demand and lean production can be achieved by managing the total cost of supply chain effectively. Based on the GBOM, this paper examines the control of production disruption risk related to supply chain and investigates the uncertainty of production in supply chain enterprises for the purpose of achieving optimal profits in supply chain. As the production disruption risk is controlled at a certain level, the selection model of supply chain partners, which is specific and more feasible, can be constructed. A combination of random simulation and neural network is deployed to approximate uncertain function, and genetic algorithm and simulated annealing arithmetic are also used to approximately achieve the optimal scheme of supply chain construction in the context of uncertainty.     

A novel dynamic genetic algorithm-based method for vehicle scheduling in cross docking systems with frequent unloading operation

An important factor for efficiently managing the supply chain is to efficiently control the physical flow of the supply chain. For this purpose, many companies try to use efficient methods to increase customer satisfaction and reduce costs. Cross docking is a good method to reduce the warehouse space requirements, inventory management costs, and turnaround times for customer orders. This paper proposes a novel dynamic genetic algorithm-based method for scheduling vehicles in cross docking systems such that the total operation time is minimized. In this paper, it is assumed that a temporary storage is placed at the shipping dock and inbound vehicles are allowed to repeatedly enter and leave the dock to unload their products. In the proposed method of this paper two different kinds of chromosome for inbound and outbound trucks are proposed. In addition, some algorithms are proposed including initialization, operational time calculation, crossover and mutation for inbound and outbound trucks, independently. Moreover a dynamic approach is proposed for performing crossover and mutation operation in genetic algorithm. In order to evaluate the performance of the proposed algorithm of this paper, various examples are provided and analyzed. The computational results reveal that the proposed algorithm of this paper performs better than two well-known works of literature in providing solutions with shorter operation time.     

Close-optimal production and procurement policy for a X-network of added value using lexicographic linear goal programming

One of the main challenges of operation managers of firms is to setup feasible production and procurement plans. This is also the case of more complex structures such as supply chains. In almost all firms, specific tools like ERPs are used to support managers in their decision-making tasks. These tools manipulate huge amount of data such as the order backlog, or technical, marketing, suppliers and customers data. They work often based on the MRP principles and suggest production and procurement plans after a sequential procedure which begins by the material requirements calculation followed by the load balancing process. Very often, the load balancing is under the control of managers who try to take account of implicit constraints that cannot be modelled easily. This is a difficult and often risky task because the managers do not know what the best solution of the planning and procurement problem is. In other words, there is a lack of a kind of “the best feasible production and procurement target”. The main idea of this article is to suggest a complementary method for planning based on a specific mathematical programming approach which provides plans considering simultaneously all material and capacity constraints over the entire planning horizon. These plans can be considered as that necessary closed optimal production and procurement target for a company or a supply chain which uses an MRP-based planning tool. The lexicographic linear goal programming provides a suitable multi-criteria modelling paradigm for the production and procurement planning problems, especially for the supply chains. The study is focused on a common supply chain structure formed by several suppliers on one side and several customers on the other, connected together by a business-to-business relationship over a rolling horizon. The structure is modelled thanks to the Petri Nets supporting the definition of the global problem model. The model is then applied to a study case extracting from the car assembly industry.     

基于蚁群优化的物流调度算法研究

物流过程的调度是一个组合优化问题。通过对几种经典物件分配算法的比较分析,提出了基于蚁群的物件优化渊度算法,实现物流过程中物件的动态分配。蚁群共同测试不同的组合,并选择一个优化的解决疗案,采用该方案能够按时交付更多的订单,同时也使订单的延迟变化很小。比较了蚁群算法与其它几种调度方法的效率,结果表明蚁群算法在物流过程中填有良好的性能。     

A bi-level evolutionary optimization approach for integrated production and transportation scheduling

This paper investigates an integrated production and transportation scheduling (IPTS) problem which is formulated as a bi-level mixed integer nonlinear program. This problem considers distinct realistic features widely existing in make-to-order supply chains, namely unrelated parallel-machine production environment and product batch-based delivery. An evolution-strategy-based bi-level evolutionary optimization approach is developed to handle the IPTS problem by integrating a memetic algorithm and heuristic rules. The efficiency and effectiveness of the proposed approach is evaluated by numerical experiments based on industrial data and industrial-size problems. Experimental results demonstrate that the proposed approach can effectively solve the problem investigated.     

System for supply network management

Strategies and algorithms for operational planning and control are quite important to successful operations of a supply network. Implementation of a strategy requires substantial information system support, but few detailed designs of such systems have appeared in the literature. We designed an information system for a centralized management concept that could handle any type of strategy or algorithm. We developed algorithms for two different strategies deemed important by researchers and practitioners, push and pull, and implemented them in system modules. This information system performed well in simulations of the management of operations of an example six-stage supply network. This system provides an example for operational development as well as a platform for laboratory experiments.     

A review of supply chain complexity drivers

Studies on supply chain complexity mainly use the static and dynamic complexity distinction. While static complexity describes the structure of the supply chain, the number and the variety of its components and strengths of interactions between these; the dynamic complexity represents the uncertainty in the supply chain and involves the aspects of time and randomness. This distinction is also valid when classifying the drivers of supply chain complexity according to the way they are generated. Supply chain complexity drivers (e.g., number/variety of suppliers, number/variety of customers, number/variety of interactions, conflicting policies, demand amplification, differing/conflicting/non-synchronized decisions and actions, incompatible IT systems) play a significant and varying role in dealing with complexity of the different types of supply chains (e.g., food, chemical, electronics, automotive).     

An integrated supply chain production–distribution planning with stochastic demands

In this paper a three echelon supply chain with multiple distribution centers, production sites and suppliers is modeled. For this supply chain several commodities with defined items is produced. The model is categorized as a capacitated facility location model. The hierarchical approach is used to modeling and based on decision types the model is divided into two levels. The solution approach is based on the Lagrangian Relaxation approach, improved by an efficient heuristic to solve complex sub-problems. Computational results indicated that the proposed method yields high-quality solutions within a reasonable computational time for various real size problems.     

在供应链决策中的多阶生产规划支持模式研究

以液晶显示器(TFT-LCD)制造业为例，对供应链中多阶生产规划问题进行了研究。运用混合整数线性规划，以企业整体获利最大为目标，考虑材料成本随时间的变化以及库存对资金的占用和市场需求量、需求价格的变动，对工厂生产做出安排，给出不同时段的库存状态，由此开发TFT-LCD产业多阶生产规划决策支持系统，为生产安排提供决策依据。通过在TFT-LCD厂的应用，证明了该系统的实 用性。     

Planning tools for managing the supply chain

In this paper, we introduce a new way to manage the supply chain. The proposed solution reduces the problem's complexity using a two-stage hierarchical production planning method and is applicable to realistic transportation optimization problems. The approach is based on planning and operations scheduling models, and is designed to minimize travel and production costs within a flexible organizational network. In the aggregate planning phase, a mathematical model involving an aggregation of products, demand and time periods is solved. It is at this initial stage that the size of the problem is reduced and its output is used as input to the detailed phase in order to improve resolution time. The second stage produces a detailed schedule. It is shown that the proposed approach generates good and feasible solutions to practical problems within a reasonable computational time.     

Semiconductor supply planning by considering transit options to take advantage of pre-productions and order cancellations

One of the objectives of supply planning is to find when and how many productions have to be started to minimize total cost. We aim to find the optimum. Base data like the length of transit time are important parameters to plan for the optimum start of production. In this research, we considered two kinds of transit options: normal transit and emergency transit, and we distinguished between planned and executed transit. The decision regarding which transit option to choose for the execution is trivial because emergency is only used when it is needed since normal transit is more cost efficient. However, for planning purpose, it is more difficult to decide which transit option should be used since the uncertainty in demand and supply between plan and execution can allow to plan an emergency transit but to execute the delivery with normal transit, which is a huge benefit in the competitive capital intensive semiconductor industry. If we planned an emergency, we can save inventory and production cost as we can delay the start of production. In contrast, we need pay additional transit cost in case that emergency transit is actually executed. Many characteristics of the semiconductor industry, which might be the front runner for many other industries, are considered in this model such as demand uncertainty, supply uncertainty and economic volatility. In our numerical experiments, we could gain the optimum, depending on each economic situation. Furthermore, we conducted sensitivity analysis of the effect of demand and supply uncertainties on total cost.