Vehicle routing scheduling for cross-docking in the supply chain

One of the most important factors in implementing supply chain management is to efficiently control the physical flow of the supply chain. Due to its importance, many companies are trying to develop efficient methods to increase customer satisfaction and reduce costs. In various methods, cross-docking is considered a good method to reduce inventory and improve responsiveness to various customer demands. However, previous studies have dealt mostly with the conceptual advantages of cross-docking or actual issues from the strategic viewpoint. It is also necessary, however, to considering cross-docking from an operational viewpoint in order to find the optimal vehicle routing schedule. Thus, an integrated model considering both cross-docking and vehicle routing scheduling is treated in this study. Since this problem is known as NP-hard, a heuristic algorithm based on a tabu search algorithm is proposed. In the numerical example, our proposed algorithm found a good solution whose average percentage error was less than 5% within a reasonable amount of time.     

Bi-objective optimization of three echelon supply chain involving truck selection and loading using NSGA-II with heuristics algorithm

This paper models a three echelon supply chain distribution problem considering multiple time periods, multi-products and uncertain demands. To take the problem closer to reality we consider multiple truck types and focus on the truck selection and loading sub-problem. Truck selection is important because the quantity of goods to be transported varies regularly and also because different trucks have different hiring cost, mileage and speed. Truck loading is important when considering the optimal loading pattern of products having different shapes and sizes on trucks, which themselves have distinct loading capacities. The two objectives considered here are the cost and responsiveness of the supply chain. The distribution problem is solved using the non-dominated sorting genetic algorithm (NSGA-II). However, the genetic algorithms compromise the optimality of the sub-problems while optimizing the entire system. But the optimality of truck selection and loading sub-problem is non-compromisable in nature. Hence a heuristic algorithm is used innovatively along with the NSGA-II to produce much better solutions. To make our model more realistic, the distribution chain is modelled as a push–pull based supply chain having multiple time periods and using demand aggregation over time. Using a separate algorithm also gives the advantage of utilizing the difference in nature of the push and pull part of the supply chain by giving every individual truck different objectives. Real life like data is generated and the optimality gap between the heuristic and non-heuristic approach is calculated. A clear improvement in objectives can be seen while using the heuristic approach.     

A heuristic algorithm for a supply chain׳s production-distribution planning

In this paper we consider the problem of planning the production and distribution in a supply chain. The situation consists in a set of distribution centers seeking to serve to a set of retailers; these distribution centers are supplied by a set of plants trying to minimize the operation and transportation costs. The problem is formulated as a bilevel mathematical problem where the upper level consists of deciding the amount of product sent from the distribution centers to the retailers trying to minimize the transportation costs and also by considering the costs of acquiring the products that come from the plants. Meanwhile the lower level consists in minimizing the plants׳ operations cost meeting the demand grouped in the distribution centers. We propose a heuristic algorithm based on Scatter Search that considers the Stackelberg׳s equilibrium; numerical tests show that our proposed algorithm improves the existing best known results in the literature.     

面向行业供应链的企业应用集成研究

供应链信息系统的集成对于提高供应链的管理水平至关重要,也是实施供应链管理系统的一个难题。介绍了EAI的内容和面临的难题,然后结合行业供应链上的企业应用的特点,给出了面向行业供应链的企业应用集成架构参考模型,进一步论述了实现集成架构模型的关键技术。最后,给出了该集成框架下一个具体的应用案例。     

面向行业供应链的企业应用集成研究

供应链信息系统的集成对于提高供应链的管理水平至关重要，也是实施供应链管理系统的一个难题。介绍了EAI的内容和面临的难题，然后结合行业供应链上的企业应用的特点，给出了面向行业供应链的企业应用集成架构参考模型，进一步论述了实现集成架构模型的关键技术。最后，给出了该集成框架下一个具体的应用案例。     

Two-stage stochastic programming supply chain model for biodiesel production via wastewater treatment

This paper presents a two-stage stochastic programming model used to design and manage biodiesel supply chains. This is a mixed-integer linear program and an extension of the classical two-stage stochastic location-transportation model. The proposed model optimizes not only costs but also emissions in the supply chain. The model captures the impact of biomass supply and technology uncertainty on supply chain-related decisions; the tradeoffs that exist between location and transportation decisions; and the tradeoffs between costs and emissions in the supply chain. The objective function and model constraints reflect the impact of different carbon regulatory policies, such as carbon cap, carbon tax, carbon cap-and-trade, and carbon offset mechanisms on supply chain decisions. We solve this problem using algorithms that combine Lagrangian relaxation and L-shaped solution methods, and we develop a case study using data from the state of Mississippi. The results from the computational analysis point to important observations about the impacts of carbon regulatory mechanisms as well as the uncertainties on the performance of biocrude supply chains.     

The impact of product characteristics and innovativeness on the benefits of collaboration

Horizontal collaboration is a promising avenue to improve the efficiency of logistical operations. However, the benefits strongly depend on the degree of fit between partners. In this paper, we analyze the impact of the partners' product characteristics on those benefits, focusing on their innovativeness. Companies supplying functional versus innovative products have different requirements in supply chain efficiency and responsiveness, which impacts the benefits that can be reached with a given partner. To assess the collaborative benefits, we use a location–inventory model accounting for the partners' individual interests and the costs revealing the responsiveness level of the supply chain (facilities, transportation, cycle inventory, safety stocks and stock-outs). The model offers a set of Pareto-optimal solutions balancing the partners' costs to support the selection and negotiation process. Finally, we perform numerical experiments in which the partners supply products with identical or different levels of innovativeness and with various demand volumes, leading to valuable managerial insights on the impact of product characteristics on collaborative benefits.     

基于随机森林非侵入式负荷分解的地区居民需求响应能力评估

需求响应在缓解供电不足、促进新能源消纳以及节约发电侧的资源投资方面意义重大,而需求响应能力评估可以为需求响应策略的制定提供依据。为解决需求响应能力评估困难的问题,本文提出一种基于非侵入式负荷分解的地区居民响应能力评估的方法。首先建立基于随机森林算法的非侵入式负荷分解模型,再使用该模型针对性的分解出单个用户的可中断负荷,然后基于可中断负荷的用电情况计算出单个用户在各个时刻的响应能力,最后再将区域内的所有用户聚合即可得到地区居民总的响应能力。在真实数据集上进行验证,结果表明所提出的基于随机森林非侵入式负荷分解模型可以更精确的得到用户可中断负荷的有功功率值,地区有功功率聚合误差也更小,可以较好的进行需求响应能力评估。     

Location and transportation planning in supply chains under uncertainty and congestion by using an improved electromagnetism-like algorithm

Supply chain decision makers are constantly trying to improve the customer demand fulfillment process and reduce the associated costs via decision making models and techniques. As two of the most important parameters in a supply chain, supply and demand quantities are subject to uncertainty in many real-world situations. In addition, in recent decades, there is a trend to think of the impacts of supply chain design and strategies on society and environment. Especially, transportation of goods not only imposes costs to businesses but also has socioeconomic influences. In this paper, a fuzzy nonlinear programming model for supply chain design and planning under supply/demand uncertainty and traffic congestion is proposed and a hybrid meta-heuristic algorithm, based on electromagnetism-like algorithm and simulated annealing concepts, is designed to solve the model. The merit of this paper is presenting a realistic model of current issues in supply chain design and an efficient solution method to the problem. These are significant findings of this research which can be interesting to both researchers and practitioners. Several numerical examples are provided to justify the model and the proposed solution approach.     

考虑产品替代的供应链协调问题研究

替代品大大增加了企业间的竞争,使企业难以达到所期望的盈利水平。对供应链进行合理有效的管理可最大限度地促进企业适应当今多变的市场环境。人们对于产品的需求不仅仅取决于产品本身的价格,也取决于互补产品的价格。对此,构建一个以产品替代的供应链协调问题为研究目标,以两种供应链管理模式(集中供应链(CSC)和分散供应链(DSC))为依托,通过建立混合整数规划模型并对相关参数进行验证,得出集中供应链模式更能灵活应对当今市场。     

An experimental study for the selection of modules and facilities in a mass customization context

To design an efficient product family, designers have to anticipate the production process and, more generally, the supply chain costs. But this is a difficult problem, and designers often propose a solution which is subsequently evaluated in terms of logistical costs. This paper presents a design problem in which the product and the supply chain design are considered at the same time. It consists in selecting a set of modules that will be manufactured at distant facilities and then shipped to a plant close to the market for final, customized assembly under time constraints. The goal is to obtain the bill of materials for all the items in the product family, each of which is made up of a set of modules, and specifying the location where these modules will be built, in order to minimize the total production costs for the supply chain. The objective of the study is to analyze both, for small instances, the impact of the costs (fixed and variable) on the optimal solutions, and to compare an integrated approach minimizing the total cost in one model with a two-phases approach in which the decisions relating to the design of the products and the allocation of modules to distant sites are made separately.     

The governing dynamics of supply chains: The impact of altruistic behaviour

This paper analyses an infinite horizon two-echelon supply chain inventory problem and shows that a sequence of the optimum ordering policies does not yield globally optimal solutions for the overall supply chain. First-order autoregressive demand pattern is assumed and each participant adopts the order-up-to (OUT) policy with a minimum mean square error forecasting scheme to generate replenishment orders. To control the dynamics of the supply chain, a proportional controller is incorporated into the OUT policy, which we call a generalised OUT policy. A two-echelon supply chain with this generalised OUT policy achieves over 10% inventory related cost reduction. To enjoy this cost saving, the attitude of first echelon player to cost increases is an essential factor. This attitude also reduces the bullwhip effect. An important insight revealed herein is that a significant amount of benefit comes from the player doing what is the best for the overall supply chain, rather than what is the best for local cost minimisation.     

Scenario aggregation for supply chain quantity-flexibility contract

In this research, we apply a scenario aggregation approach to solving the supply chain contract model formulated by two-stage stochastic programming problem. The supply chain contract can achieve the coordination between the buyer and the supplier. We formulate the stochastic programming model for a quantity-flexibility contract. The scenario aggregation method called the progressive hedging method is used to solve this problem. Experimental results show the convergence behaviour of the algorithm and the sensitivity of parameters.     

Machine learning for dynamic multi-product supply chain formation

Recent trend in eCommerce applications toward effectively reducing supply chain costs—including spatial, temporal, and monetary resources—has spurred interest among researchers as well as practitioners to efficiently utilize supply chains. One of the least studied of these views is adaptive or dynamic configuration of supply chains. This problem is relatively new since faster communications over the Internet or by any other means and the willingness to utilize it for effective management of supply chains did not exist a few decades ago. The proposed framework addresses the problem of supply chain configuration. We incorporate machine-learning techniques to develop a dynamically configurable supply chain framework, and evaluate its effectiveness with respect to comparable static supply chains. Specifically, we consider the case where several parts go into the production of a product. A single supplier or a combination of suppliers could supply these parts. The proposed framework automatically forms the supply chain dynamically as per the dictates of incoming orders and the constraints from suppliers upstream.     

带有订单转保理的供应链金融的收益共享博弈模型

当作为供应商的中小企业出现了严重的财务困境问题时,急需有效融资工具和创新管理模式加以解决.订单转保理可以令资质良好的零售商为资金短缺的供应商提供融资担保,有效解决供应商的订单减少和融资难的问题.然而,分散决策的订单转保理融资模式,使得做担保的零售商的收益降低,不能明显改善供应链效率.针对此问题,研究订单转保理模式下的供应链协调模型,并重点研究由供应商与零售商组成的二级供应链的订单转保理收益共享模型.研究发现:收益共享决策模型供应链总收益小于集中决策供应链总收益,大于分散决策的供应链总收益,表明供应链成员相互合作程度越高,越有利于供应链发展,从而表明协调在供应链中发挥重要作用.然而,集中决策模型只能得到供应链最优总收益,无法得到供应商和零售商的最优收益.收益共享模型不仅能够提高产品订货量,降低产品批发价格,增加供应商和零售商的收益,而且通过最优的收益共享系数可以得到供应商和零售商的最优收益.收益共享机制能够为供应商和零售商共赢协调发展提供新的运作管理模式.     

An integrated framework for blockchain-enabled supply chain trust management towards smart manufacturing

With the development of a new generation of information technology, smart manufacturing has put forward higher requirements for supply chain. It is necessary to ensure the synchronization of the supply chain operation and maintain the reliability of the supply chain management, therefore the trust evaluation for the supply chain becomes extremely important. Traditional supply chain management has problems such as information flow is easy to be tampered with, logistics is difficult to trace, and capital flow is not true, which leads to increased opportunity costs due to the lack of trust among transaction entities in the supply chain. The emergence of blockchain technology provides an opportunity to improve the supply chain ecosystem. In this paper, an integrated framework for blockchain-enabled supply chain trust management towards smart manufacturing is proposed to explain how to enhance trust management with the help of blockchain from the perspectives of information flow, logistics, and capital flow. An optimized trust management model is designed for better entities evaluation in supply chain. A coal mine equipment manufacturing industry scenario is presented to illustrate the effectiveness of the proposed framework.     

Two-echelon, multi-commodity supply chain network design with mode selection, lead-times and inventory costs

Designing distribution networks - as one of the most important strategic issues in supply chain management - has become the focus of research attention in recent years. This paper deals with a two-echelon supply chain network design problem in deterministic, single-period, multi-commodity contexts. The problem involves both strategic and tactical levels of supply chain planning including locating and sizing manufacturing plants and distribution warehouses, assigning the retailers' demands to the warehouses, and the warehouses to the plants, as well as selecting transportation modes.We have formulated the problem as a mixed integer programming model, which integrates the above mentioned decisions and intends to minimize total costs of the network including transportation, lead-times, and inventory holding costs for products, as well as opening and operating costs for facilities. Moreover, we have developed an efficient Lagrangian based heuristic solution algorithm for solving the real-sized problems in reasonable computational time.     

Modelling inter-supply chain competition with resource limitation and demand disruption

This paper proposes a comprehensive model for studying supply chain versus supply chain competition with resource limitation and demand disruption. We assume that there are supply chains with heterogeneous supply network structures that compete at multiple demand markets. Each supply chain is comprised of internal and external firms. The internal firms are coordinated in production and distribution and share some common but limited resources within the supply chain, whereas the external firms are independent and do not share the internal resources. The supply chain managers strive to develop optimal strategies in terms of production level and resource allocation in maximising their profit while facing competition at the end market. The Cournot–Nash equilibrium of this inter-supply chain competition is formulated as a variational inequality problem. We further study the case when there is demand disruption in the plan-execution phase. In such a case, the managers need to revise their planned strategy in order to maximise their profit with the new demand under disruption and minimise the cost of change. We present a bi-criteria decision-making model for supply chain managers and develop the optimal conditions in equilibrium, which again can be formulated by another variational inequality problem. Numerical examples are presented for illustrative purpose.     

服务供应链管理中的价值协调问题研究——以社区服务为例

价值协调是服务供应链管理的核心问题.如何培育和发展社区服务产业,同样面对这个问题.从宏观的角度,使用价值分析模型来研究是否应将服务单位的利益放入到服务供应链之中进行协调,以及如何协调.结果证实了将服务单位的利益纳入到社区服务体系之中,有利于整个社区服务体系的价值提升,最后用数值实验验证了结论.     

Genetic algorithm for supply planning in two-level assembly systems with random lead times

This paper examines supply planning for two-level assembly systems under lead time uncertainties. It is supposed that the demand for the finished product and its due date are known. The assembly process at each level begins when all necessary components are in inventory. If the demand for the finished product is not delivered at the due date, a tardiness cost is incurred. In the same manner, a holding cost at each level appears if some components needed to assemble the same semi-finished product arrive before beginning the assembly at this level. It is assumed also that the lead time at each level is a random discrete variable. The expected cost is composed of the tardiness cost for finished product and the holding costs of components at levels 1 and 2. The objective is to find the release dates for the components at level 2 in order to minimize the total expected cost. For this new problem, a genetic algorithm is suggested. The proposed algorithm is evaluated with a variety of supply chain settings in order to verify its robustness across different supply chain scenarios. Moreover, the effect of a local search on the performance of the Genetic Algorithm in terms of solution quality, convergence and computation time is also investigated.