Multi-level supply chain network design with routing

Recently, the multi-level and multi-facility industrial problem in supply chain management (SCM) has been widely investigated. One of the key issues, central to this problem in the current SCM research area is the interdependence among the location of facilities, the allocation of facilities, and the vehicle routing for the supply of raw materials and products. This paper studies the supply chain network design problem, which involves the location of facilities, allocation of facilities, and routing decisions. The proposed problem has some practical applications. For example, it is necessary for third party logistics (3PL) companies to manage the design of the network and to operate vehicle transportation. The purpose of this study is to determine the optimal location, allocation, and routing with minimum cost to the supply chain network. The study proposes two mixed integer programming models, one without routing and one with routing, and a heuristic algorithm based on LP-relaxation in order to solve the model with routing. The results show that a developed heuristic algorithm is able to find a good solution in a reasonable time.     

Evaluating warehouse strategies for two-product class distribution planning

Distribution networks often manage products with varying life cycles, where demand for some products is relatively stable throughout the year (basic) and the demand for others is short-lived (fashion). Beyond the coordination of inventory and transportation decisions, decisions at the warehouse must be considered as its resources are frequently shared by both product classes simultaneously. For this two-product class distribution planning problem, we focus on characterising three real-world distribution strategies observed in industry and evaluating them based on total distribution cost and warehouse measures (e.g. workforce plan and workload variation) against a benchmark ILS-based heuristic. Experimental results suggest that there are in fact strategies in industry that under specific system configurations may provide competitive solutions compared to the benchmark heuristic on large problem instances (e.g. 200 stores, 1000 products, and 28 days). Several managerial insights are derived to compare such distinct warehouse strategies and the corresponding impact on the network.     

一类带配送中心运输问题的容量扩张模型研究

运输问题一般采用表上作业法来解决，考虑一类带配送中心的运输问题，若仍采用表上作业法，会使问题复杂化，文中采用一种构造辅助网络的方法：在运输网络中将每个配送中心均拆分成2个点，连接两点形成新弧，构造出新的网络，并给每条弧赋予参数，将此类运输问题转换为最小费用流模型来解决，可以使问题模型和运算简单化．在此基础上，考虑运输网络中配送中心和边的容量扩张问题．     

A block transportation scheduling system considering a minimisation of travel distance without loading of and interference between multiple transporters

Material transportation scheduling problems concerning scheduling optimisation have been extensively investigated by researchers in such fields as industrial engineering and management science. Various algorithms have been proposed to solve such problems. However, the majority of these algorithms cannot be applied to a block transportation problem when a shipyard that uses a transporter, a large vehicle employed for moving weight, is considered. In this study, a hybrid optimisation algorithm is proposed for solving a block transportation problem when multiple transporters are used. With regards to the transporters, a minimisation of the travel distance without loading of and interference between the transporters is considered. A block transportation scheduling system is then developed based on the proposed algorithm. The developed system is applied to an actual block transportation scheduling problem of a shipyard. From the attained results, we demonstrate that the proposed algorithm has the ability to effectively solve the block transportation scheduling problems of a shipyard.     

分割配送的生鲜产品库存-路径优化研究

目的 基于供应商管理库存策略,优化协调第三方物流（3rd Party Logistics,3PL）参与的二级供应链系统中库存补充与补货配送的关系,在满足配送需求的同时降低系统的库存和运输成本。方法 综合考虑3PL物流模式下的库存-路径系统中生鲜产品的运输距离、质量时间窗以及需求分割配送策略在时间上的调整等因素,以异质车队车辆行驶燃油成本、车辆调用固定成本、配送超时腐坏成本以及各零售商的库存成本为目标函数,建立多品种生鲜产品库存-路径优化模型。结果 通过改进的遗传算法计算并进行配送量调整优化,得到最终的生鲜配送方案,其系统总成本比配送量调整前减少了9.98%。结论 通过在时间上对配送量进行拆分调整可以有效地节约供应链系统总成本。     

Synchronized scheduling of assembly and multi-destination air-transportation in a consumer electronics supply chain

The paper studies the problem of synchronizing air transportation and assembly manufacturing to achieve accurate delivery with minimized cost in a consumer electronics supply chain. The motivation for this study came from a major computer manufacturer in a consumer electronics industry that faces challenges in the final stages of delivery of a supply chain. The synchronization problem is decomposed and formulated as two sub-problems, i.e. a multi-destination air transportation allocation problem and an assembly-scheduling problem. The first sub-problem is formulated and solved as an integer linear programming problem. For the assembly-scheduling problem, two solution methodologies using the concepts of forward and backward scheduling are presented. Computational results indicate that the proposed methodologies can achieve considerable cost reduction compared with the existing methodology applied in industry.     

Ant colony optimisation algorithm for distribution-allocation problem in a two-stage supply chain with a fixed transportation charge

In a fixed charge transportation problem, each route is associated with a fixed charge (or a fixed cost) and a transportation cost per unit transported. The presence of the fixed cost makes the problem difficult to solve, thereby requiring the use of heuristic methods. In this paper, an algorithm based on ant colony optimisation is proposed to solve the distribution-allocation problem in a two-stage supply chain with a fixed transportation cost for a route. A numerical study on benchmark problem instances has been carried out. The results obtained for the proposed algorithm have been compared with that for the genetic algorithm-based heuristic currently available in the literature. It is statistically confirmed that the proposed algorithm provides significantly better solutions.     

Application of knowledge-based artificial immune system (KBAIS) for computer aided process planning in CIM context

In the present era, several manufacturing philosophies like lean manufacturing, total quality management (TQM), etc., have the goal of providing a quality product at reduced cost. In this research paper the process planning problem of a CIM system has been discussed where minimisation of cost of the finished product is considered as the main objective. For determining the cost of the finished product, scrap cost, forgotten by most of the previous researchers, has been considered along with other costs like raw material cost, processing cost, etc. In the present environment of concurrent engineering, optimisation of process planning is an NP-hard problem. To solve this complex problem a noble search algorithm, known as knowledge-based artificial immune system (KBAIS) has been proposed. The nobility of the proposed algorithm is that the inherent capability of AIS has been gleaned and incorporated with the property of the knowledge base. In this problem, the power of knowledge has been used for three stages in the algorithm: initialisation, selection and hyper-mutation. To demonstrate the efficacy of the proposed KBAIS, a bench mark problem has been considered. Intensive computational experiments have also been performed on randomly generated datasets to reveal the supremacy of the proposed algorithm over other existing heuristics.     

基于启发式算法的乘用车物流运输问题分析

伴随着汽车工业的高速崛起,乘用车物流运输问题也快速走进人们的视野。由于现在很多物流公司在制定运输计划时主要依赖调度人员的经验,在面对复杂的运输任务时,往往效率较低且运输成本不尽理想。考虑到影响乘用车物流运输成本的主要因素分别为轿运车的使用数量、轿运车的单价以及行驶里程数等等,本文采用建立逐级目标的模式,应用启发式算法,结合计算机软件,给出了求解乘用车物流运输问题的数学模型。应用此模型求解了2种不同类型的乘用车物流运输问题,提出了合理的运输方案。此项工作对今后物流公司处理此类运输问题提供了重要的参考价值。     

Particle swarm optimization for the multi-period cross-docking distribution problem with time windows

Cross-docking has emerged as a new technique in supply chain management to replace the warehouse concept in the retail industry. This paper proposes a multi-period cross-docking distribution problem that consists of manufacturers, cross-docks and customers. This model is formulated for cases that consider multiple products, consolidation of customer orders and time windows that are available in multiple periods. The objective function is to minimise the total cost, which includes transportation cost, inventory cost and penalty cost. The penalty cost arises when demand remains in each period that cannot be satisfied. To deal with the complexity of the problem, an algorithm is developed based on particle swarm optimisation (PSO) with multiple social learning terms, GLNPSO, with two solution representations. The solution representations are a one-period solution representation (OP-SR) and a multi-period solution representation (MP-SR). The GLNPSO-based algorithm performs well in solving this problem. Moreover, both representations are proven effective when comparing the solution quality and computational time with those results obtained from CPLEX. In terms of quality, the MP-SR solution is better than the OP-SR solution for both stable and fluctuating demand instances. However, MP-SR requires more computational effort than OP-SR.     

Computer-aided optimization of multi-pass turning operations for continuous forms on CNC lathes

The Computer Numerical Control (CNC) machine is one of the most effective production facilities used in manufacturing industry. Determining the optimal machining parameters is essential in the machining process planning since the machining parameters significantly affect production cost and quality of machined parts. Previous studies involving machining optimization of turning operations concentrated primarily on developing machining models for bar components. Machined parts on the CNC lathes, however, typically have continuous forms. In this study, we formulate an optimization model for turned parts with continuous forms. Also, a stochastic optimization method based on the simulated annealing algorithm and the pattern search is applied to solve this machining optimization problem. Finally, the applications of the developed machining model and the proposed optimization algorithm are established through the numerical examples.     

Integrated production scheduling and vehicle routing problem with job splitting and delivery time windows

In this paper, we study a production scheduling and vehicle routing problem with job splitting and delivery time windows in a company working in the metal packaging industry. In this problem, a set of jobs has to be processed on unrelated parallel machines with job splitting and sequence-dependent setup time (cost). Then the finished products are delivered in batches to several customers with heterogeneous vehicles, subject to delivery time windows. The objective of production is to minimize the total setup cost and the objective of distribution is to minimize the transportation cost. We propose mathematical models for decentralized scheduling problems, where a production schedule and a distribution plan are built consecutively. We develop a two-phase iterative heuristic to solve the integrated scheduling problem. We evaluate the benefits of coordination through numerical experiments.     

An Optimal Policy for the Dynamic Transportation-Inventory Model with Deteriorating Items

This paper addresses the dynamic lot sizing problem of a Wagner-Whitin type in which the transportation mode as well as the order size of a deteriorating product are considered simultaneously. The problem is presented in a forward formulation. An algorithm is developed which utilizes properties of the optimal solution and planning horizon procedure. An actual case involving palm oil industry is solved and the algorithm is shown to be efficient in computation.     

考虑设施可靠性的线状需求物流节点选址研究

为了解决交通线路建设物资的物流节点选址问题,在服务线路和需求密度已知的前提下,考虑物流节点可靠性以及物资运输时间约束,建立以运输成本和时间惩罚成本之和为目标函数的数学模型。为了求解该模型,在经典p-中值选址模型基础上,采用线积分取代离散点来表示线状需求,证明目标函数在给定区间内为凸函数,并利用二次插值和黄金分割法相结合的算法对模型进行求解。通过对是否考虑可靠性的2种选址结果进行对比研究,以及对可靠性间的相关性对选址结果的影响进行灵敏度分析。算例分析表明,与不考虑可靠性相比,提高可靠性可以降低选址系统运营总成本,也可定量地刻画出可靠性间的相关性对选址结果的影响。     

Multimodal Fuzzy Downstream Petroleum Supply Chain: A Novel Pentagonal Fuzzy Optimization

The petroleum industry has a complex, inflexible and challenging supply chain (SC) that impacts both the national economy as well as people’s daily lives with a range of services, including transportation, heating, electricity, lubricants, as well as chemicals and petrochemicals. In the petroleum industry, supply chain management presents several challenges, especially in the logistics sector, that are not found in other industries. In addition, logistical challenges contribute significantly to the cost of oil. Uncertainty regarding customer demand and supply significantly affects SC networks. Hence, SC flexibility can be maintained by addressing uncertainty. On the other hand, in the real world, decision-making challenges are often ambiguous or vague. In some cases, measurements are incorrect owing to measurement errors, instrument faults, etc., which lead to a pentagonal fuzzy number (PFN) which is the extension of a fuzzy number. Therefore, it is necessary to develop quantitative models to optimize logistics operations and supply chain networks. This study proposed a linear programming model under an uncertain environment. The model minimizes the cost along the refineries, depots, multimode transport and demand nodes. Further developed pentagonal fuzzy optimization, an alternative approach is developed to solve the downstream supply chain using the mixed-integer linear programming (MILP) model to obtain a feasible solution to the fuzzy transportation cost problem. In this model, the coefficient of the transportation costs and parameters is assumed to be a pentagonal fuzzy number. Furthermore, defuzzification is performed using an accuracy function. To validate the model and technique and feasibility solution, an illustrative example of the oil and gas SC is considered, providing improved results compared with existing techniques and demonstrating its ability to benefit petroleum companies is the objective of this study.     

新鲜度限制约束下物流配送中心选址-路径优化

目的 满足生鲜产品交付时较高的新鲜度要求,解决多产品、多车型情景下的配送中心选址-路径优化问题。方法 构建考虑碳排放成本和满足客户对产品交付最低新鲜度要求的双层目标规划模型。上层模型以配送中心固定成本、库存管理成本最小化为优化目标,下层模型以车辆固定成本、运输成本、碳排放成本、惩罚成本最小化为优化目标,并结合模型特点,采用两阶段启发式算法进行求解。结果 采用的两阶段启发式算法相对于遗传算法的平均成本解改进率为1.22%,相对于K-means聚类求解算法的平均解改进率为3.03%;两阶段启发式算法相对于遗传算法最优解运算时间的平均提高率为24.8%,相对于传统K-means聚类求解算法的平均提高率为33.0%。结论 经算例对比研究发现,不同新鲜度要求下对配送中心的选址以及车辆路径的安排有显著影响,企业可通过合理规划物流网络和准确评估客户对产品的新鲜度要求等手段实现企业物流成本的降低。     

Development of an integrated model for process planning and parameter selection for machining processes

The concept of ‘do it right the first time’ in the machining industry not only expects the best quality products but also at the best possible cost. The cost of machining depends on intelligent process planning and selection of machining parameters such as speed, feed, and depth of cut. The problem of machining parameter selection has received great attention by researchers and many techniques have been developed. A review of these techniques reveals that the selection of the machine and cutting tool is done before the process of cutting parameter selection and process sequencing, and often the selection is based on experience. The current research is an attempt to develop an integrated model (ExIMPro: Expert system based Integrated model for Machining Processes) which finds the sequence of operations with set of machines, tools, and other process parameters to minimise the cost of machining for a cylindrical part. This system consists of existing expert system Machining Parameter SELection (MPSEL) for machine and tool selection and a Microsoft Excel® and Visual Basic® based parameter selection model. The present model focuses on turning and cylindrical grinding operations but other processes can be incorporated with little modification to the software.     

How many people have to die over a type II error?

Although much concern over type I errors has permeated psychology for decades, there is less concern over type II errors. In fact, type II errors constitute a serious problem in safety research that can result in accidents and fatalities because researchers fail to reject the null hypothesis due to arbitrary probability thresholds. The purpose of this paper is to reveal how often type II errors occur and the effect they have on applied ergonomics research. Computer simulations using population parameters were generated, revealing that type II errors happen quite often, particularly with effect sizes between 0.2 and 1.2. A utility analysis also reveals that the cost of type II errors on society is much greater than it needs to be. Solutions for avoiding type II errors are discussed.     

A Heuristic for a Class of Production Planning and Scheduling Problems

This paper describes a solution technique for a general class of problems referred to as aggregate planning and master scheduling problems. The technique is also applicable to multi-item single level capacitated lot sizing problems. The solution technique presented here is a heuristic that is practical for large problems e.g. 9 products and 36 periods. We have tested it for problems with varying number of time periods, number of products, setup costs, holding costs, overtime costs and capacity levels. For those problems that we could solve exactly using a branch and bound algorithm, the solutions produced by the heuristic were all within 1 % of optimality. For problems that we could not solve exactly, we are able to compute a lower bound on the optimal cost. Using the bound we are able to show that our heuristic solutions were within 2.93% of optimality on the average. Except for those problems having very high setup cost or problems with extreme seasonality, the algorithm produced solutions that were within 1 % of optimality on average.     

Integrated planning of loaded and empty container movements

Efficiently planning drayage operations is an important task for transportation companies since these operations constitute a large part of the cost of an intermodal transport. In this paper, a full truckload vehicle routing problem for transporting loaded and empty containers in drayage operations is studied. For empty container transports, either the origin or the destination is not predefined. The problem is formulated as an asymmetric multiple vehicle Travelling Salesman Problem with Time Windows (am-TSPTW). Two solution approaches are proposed: a sequential and an integrated approach. For both approaches, a single- and a two-phase deterministic annealing algorithm are presented. Results show that the proposed algorithms are able to find good quality solutions in a small amount of computation time. The integrated approach clearly outperforms the sequential one and the results confirm the advantage of using a two-phase algorithm for vehicle routing problems with hierarchical objectives. Finally, it is shown that the proposed integrated solution method improves previous results on a similar problem.