Operational planning models for distribution networks

The design and optimisation of a logistic network deals with a wide set of decisions, e.g. the determination of the best location and capacity of the different logistic facilities (production plants, distribution centres, transit points, wholesalers, etc.), the allocation of the product demand coming from customers in presence (or absence) of fractionable flows of material, the determination of the best transportation mode (truck, rail, etc.) as well as loading and routing of vehicles. These decisions involve multiple stages of a distribution network: customers-regional distribution centres (RDC), RDCs-central distribution centres (CDC) and CDCs-production plants and sources, in presence of multiple products and the variable time (i.e. time-dependent product demand and flows of material). This paper presents a top-down methodology that joins the strategic planning, the tactical planning and the operational planning of distribution networks with a special focus on the development of effective heuristic methods to face the vehicle routing problem. Original models and heuristic algorithms for the operational planning are illustrated. The impact of the strategic and tactical decisions on the performance of the operational planning is evaluated by the application of the proposed hierarchical approach to two realistic case studies. Obtained results are illustrated in a what-if experimental analysis conducted on multiple problem settings and realistic scenarios.     

Multi-item production routing problem with backordering: a MILP approach

The aim of this paper is to present mixed integer linear programming formulations for the production routing problem with backordering (PRP-B) and a new hybrid heuristic to solve the problem. The PRP-B is considered in the context of a supply chain consisting of a production facility with limited production and storage capacities and geographically dispersed points of sale with limited storage capacities. The PRP-B integrates multiple item lot sizing decisions and vehicle routing decisions to the points of sale, where backordering of end customer demands is allowed at a penalty. Two integrated mixed integer programming models are formulated and a solution procedure consisting of a relax-and-fix heuristic combined with a local search algorithm is proposed. The numerical results show that this hybrid heuristic outperforms a state-of-the-art MIP commercial solver, in terms of solution quality and CPU times.     

基于JITD的服务备件二级分销网络集成研究

从供应链集成的角度出发,使用双层规划建立了服务备件二级分销网络的设施选址-运输路线安排-库存控制问题(CLRIP)模型,确定了分销中心的位置、车辆的运输路线等。该模型充分考虑决策部门和顾客双方的利益,针对服务备件的价值高以及服务水平要求高的特点,将按需拉动的准时制配送策略（JITD）应用于分销网络设计中,在降低系统成本的同时,可以大幅度提高配送的准时性和稳定性。设计了该模型的启发式算法,通过算例验证了模型和算法的有效性。研究结果有助于优化服务备件分销网络结构、提高配送准时性和降低物流成本。     

Route planning for automated guided vehicles in a manufacturing facility

Automated guided vehicles (AGVs) are widely used in manufacturing and transporting facilities for the movement of material from one location to another. Research in this area is directed toward the development of a path layout design and routing algorithms for movements of materials. The problem is to design a path layout and a routing algorithm that will route the AGVs along the bi-directional path so that the distance travelled will be minimized. This paper presents a bi-directional path flow layout and a routing algorithm that guarantee conflict-free, shortest-time routes for AGVs. Based on the path layout, a routing algorithm and sufficient, but necessary, conditions, mathematical relationships are developed among certain key parameters of vehicle and path. A high degree of concurrency is achieved in vehicle movement. Routing efficiency is analysed in terms of the distance travelled and the time required for AGVs to complete all pickup and drop-off jobs. Numerical results are presented to compare the performance of the proposed model. The research provides the foundation for a bi-directional path layout design and routing algorithms that will aid the designer to develop complicated path layouts.     

A highly optimised tolerance-based approach for multi-stage,multi-product supply chain network design

In this paper we propose an algorithm called Highly Optimised Tolerance (HOT) for solving a multi-stage, multi-product supply chain network design problem. HOT is based on power law and control theory. The proposed approach takes its traits from the local incremental algorithm (LIA), which was initially employed to maximise the design parameter (i.e. yield), particularly in the percolation model. The LIA is somewhat analogous to the evolution by natural selection schema. The proposed methodology explores a wide search space and is computationally viable. The HOT algorithm tries to make the system more robust at each step of the optimisation. The objective of this paper is to reduce the total cost of supply chain distribution by selecting the optimum number of facilities in the network. To examine the effectiveness of the HOT algorithm we compare the results with those obtained by applying simulated annealing on a supply chain network design problem with different problem sizes and the same data sets.     

A single-product network design model with lead time and safety stock considerations

Most existing network design and facility location models have focused on the trade-off between the fixed costs of locating facilities and variable transportation costs between facilities and customers. However, operational performance measures such as service levels and lead times are what motivates customers to bring business to a company and should be considered in the design of a distribution network. While some previous work has considered lead times and safety stocks separately, they are closely related in practice, since safety stocks are often set relative to the distribution of demand over the lead time. In this paper we consider a two-stage supply chain with a production facility that replenishes a single product at retailers. The objective is to locate Distribution Centers (DCs) in the network such that the sum of the location and inventory (pipeline and safety stock) costs is minimized. The replenishment lead time at the DCs depends on the volume of flow through the DC. We require the DCs to carry enough safety stock to maintain the prescribed service levels at the retailers they serve. The explicit modeling of the relationship between the flows in the network, lead times and safety stocks allows us to capture the trade-off between them. We develop a Lagrangian heuristic to obtain near-optimal solutions with reasonable computational requirements for large problem instances.     

Planning for a bus-based evacuation

Planning for a bus-based regional evacuation is essential for emergency preparedness, especially for regions threatened by hurricanes that have large numbers of transit-dependent people. While this difficult planning problem is a variant of the vehicle routing problem, it differs in some key aspects, including the objective and the network structure (e.g., capacitated shelters). This problem is not well studied. In this paper we introduce a model specifically designed for bus-based evacuation planning, along with two mathematical programming formulations, which are used to develop a heuristic algorithm. Using these models, we analyze the differences in the structural properties of optimal solutions between this problem and traditional vehicle routing problems.     

A location-routing-inventory model for designing multisource distribution networks

This article studies a ternary-integration problem that incorporates location, inventory and routing decisions in designing a multisource distribution network. The objective of the problem is to minimize the total cost of location, routing and inventory. A mixed-integer programming formulation is first presented, and then a three-phase heuristic is developed to solve large-sized instances of the problem. The numerical study indicates that the proposed heuristic is both effective and efficient.     

基于物流能力约束的整车物流计划

基于时间竞争的敏捷供应链环境中,汽车整车物流必须按照客户订单对交货期、交货地点等的要求进行快速响应和准时配送.整车运输能力作为物流要素能力之一,由于其特殊性已成为整车物流的瓶颈,对物流绩效和供应链绩效形成重要影响.从压缩订单响应时间角度出发,在以整车运输能力为约束的条件下,提出了汽车整车物流准时配送计划问题,参照并行多机提前/拖期问题的研究方法,设计了一种制定整车物流配送计划的启发式算法,最后通过算例证明所提算法的可行性.     

进口物资运输的中转站选址-分配问题

结合进口物资的江海联合中转运输,提出了一类带三重能力约束的中转站选址-分配问题(LAP)模型,即在物流网络节点有最大单批通行能力、中转站吞吐能力、运输工具总运输能力三重能力约束下,如何进行中转站选址,并相关资源分配,使总的费用最小.针对该问题构建了混合整数规划模型,提出了一种先进行选址然后进行资源分配的优化算法.还给出了该模型在武汉钢铁(集团)公司进口铁矿石江海联合运输的中转站选址-分配问题的研究实例.     

异质车辆配送可重复装货易腐品库存—路径模型

研究了易腐品的订货、配送和车辆选型一体化决策问题。在一个供应商供应单一产品给多个零售商的供应链中,产品在运输阶段存在腐败,采用运行温度、车辆相关成本和腐败率有差异的异质车辆进行配送,联合决策模型的目标是确定每个零售商的配送车辆类型、配送路径及配送周期使得总成本最小。通过建立数学模型,证明了给定车辆类型和行驶路径后,单一车辆存在最优配送周期,由此设计了改进的节约算法嵌套对车辆类型的遍历对问题进行求解,结合实际企业背景,通过数值算例说明了易腐品配送车辆的选型问题,并得出了对企业的管理启示。     

A multi-stage facility location problem with staircase costs and splitting of commodities: model,heuristic approach and application

In this paper a multi-stage facility location problem with staircase costs and splitting of commodities is introduced and formulated as a mixed integer program. The problem is motivated by an application in the context of a reverse supply chain for end-of-life vehicles. We propose a two-phase heuristic solution approach: The greedy construction heuristic utilizes the solution obtained by the LP-relaxation of the problem. In the improvement heuristic we combine ADD, DROP and SWAP neighborhoods with a diversification strategy to a Variable Neighborhood Descent (VND) and to a Variable Neighborhood Search (VNS) approach. Computational results show that this approach is able reduce the duality gap provided by state-of-the-art MIP solver CPLEX for small and medium-sized instances and is also able to provide high-quality solutions for large-scale instances with up to 2,900 candidate facilities. The building blocks of the solution approach can easily be rearranged in order to solve other facility location problems.     

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。     

Designing service sectors for daily maintenance operations in a road network

The problem of designing service sectors in a road network for the organisation of route maintenance activities is addressed in this paper. The decisions involve determining the locations of a set of depots and assigning road segments to the selected depots to form service sectors. Routing operations must be taken into consideration while partitioning service sectors in order to precisely evaluate deadhead and required number of vehicles. The problem is thus formulated as a location-arc routing problem and solved by a branch-and-cut algorithm. A three-stage heuristic algorithm with sector design component is developed to solve the medium- and large-sized problems. The computational experiments demonstrate the effectiveness of the heuristic algorithm, and the superiority of the heuristic algorithm to the exact method in terms of some specific partition evaluation criteria.     

Vehicle routing concepts in the closed-loop container network of ARN—a case study

In this paper we discuss a real-life case study to optimize the logistics network for the collection of containers from end-of-life vehicle dismantlers in the Netherlands. Advanced planning concepts, such as dynamic assignment of dismantlers to logistic service providers, are analyzed using a simulation model. Based on this model, we periodically solve a vehicle routing problem to gain insight into the long-term performance of the system. The vehicle routing problem considered is a multi-depot pickup and delivery problem with alternative delivery locations. A special characteristic of the problem is the limited vehicle capacity of two containers. We solve this problem with a heuristic based on route generation and set partitioning.     

Facility location and scale optimisation in closed-loop supply chain

This paper solves a strategic-level decision problem on determining the optimal location of (re)manufacturers and logistics centres with the consideration of facility scales in an integrated closed-loop supply chain network. A two-stage stochastic mixed-integer non-linear programming model is established to minimise the fixed cost and the expected operation costs under uncertain demand and return. We develop an improved tabu search heuristic algorithm to solve the model. We also design a distance-based decision rule to validate the effectiveness of the proposed model. Numerical experiments are conducted to test the performance of the proposed model and the solution method. In addition, sensitivity analysis is provided to investigate the influences of varying inspection locations and recovery rates on the final performance.     

改进麻雀搜索算法求解多目标低碳冷链物流车辆路径问题

目的 在传统冷链物流的车辆路径问题模型基础上,考虑服务节点和车辆运输过程中产生的碳排放,并加入客户满意度,在有限资源情况下最小化路径成本和最大化客户满意度。方法 构建多目标低碳冷链物流车辆路径问题模型,将爬山算法局部搜索思想应用到麻雀搜索算法中,形成改进麻雀搜索算法,并用其对上海市某区域内的冷链物流配送路径优化问题算例进行求解。结果 通过与改进前及其他2种智能优化算法运行结果进行对比发现,改进后的麻雀搜索算法具有更快的寻优速度和更好的寻优能力,且改进后的算法对模型的碳排放效用性更高。结论 基于国家的低碳政策,设计出符合当下实情的低碳冷链物流运输模型,通过改进优化算法设计运输方案,验证了爬山算法局部搜索思想对麻雀搜索算法进行改进的有效性及所构建低碳冷链物流车辆路径模型的合理性。     

Supply chain network design with unreliable suppliers: a Lagrangian relaxation-based approach

This paper deals with the integrated facility location and supplier selection decisions for the design of supply chain network with reliable and unreliable suppliers. Two problems are addressed: (1) facility location/supplier selection; and (2) facility location/supplier reliability. We first consider the facility location and supplier selections problem where all the suppliers are reliable. The decisions concern the selection of suppliers, the location of distribution centres (DCs), the allocation of suppliers to DCs and the allocation of retailers to DCs. The objective is to minimise fixed DCs location costs, inventory and safety stock costs at the DCs and ordering costs and transportation costs across the network. The introduction of inventory costs and safety stock costs leads to a non-linear NP-hard optimisation problem. To solve this problem, a Lagrangian relaxation-based approach is developed. For the second problem, a two-period decision model is proposed in which selected suppliers are reliable in the first period and can fail in the second period. The corresponding facility location/supplier reliability problem is formulated as a non-linear stochastic programming problem. A Monte Carlo optimisation approach combining the sample average approximation scheme and the Lagrangian relaxation-based approach is proposed. Computational results are presented to evaluate the efficiency of the proposed approaches.     

Multi-level reverse logistics network design under uncertainty

Localising facilities and assigning product flows in a reverse logistics environment is a crucial but difficult strategic management decision, certainly when value decay plays an important part. Despite numerous publications regarding closed-loop supply chain design, very few addressed the impact of lead times and the high level of uncertainty in reverse processes. In this article, a single product reverse logistics network design problem with multiple layers and multiple routings is considered. To this end, a new advanced strategic planning model with integrated queueing relationships is built that explicitly takes into account stochastic delays due to various processes like collection, production and transportation, as well as disturbances due to various sources of variability like uncertain supply, uncertain process times, unknown quality, breakdowns, etc. Their impact is measured by transforming these delays into work-in-process, which affects profit through inventory costs. This innovative modeling approach is difficult to solve because of both combinatorial and nonlinear continuous relationships. The differential evolution algorithm with an enhanced constraint handling method is proposed as an appropriate heuristic to solve this model close to optimality. A number of scenarios for a realistic case illustrate the power of this optimisation tool.     

改进分散搜索算法求解包装废弃物回收路径规划问题

目的 将包装废弃物回收路径规划归纳为一个带回路和时间窗的逆向物流车辆路径问题（RL-VRPBTW）,以最小化回收成本、发车成本和时间窗惩罚为联合优化目标进行建模。方法 引入“车辆剩余空间回收能力”因素,改进经典节约里程算法,求得较好的初始解;基于分散搜索框架,设计基于初始解改进的分散搜索算法（ISISS）,根据问题模型,采用含0的编码方式,通过多样性产生、参考集更新、子集产生、子集合并、解改进等5个步骤实现算法功能。结果 在“部分回收点分布较密集”的城市型地理场景下,针对快消企业的低值固废包装,生成回收点数量分别为50、100、200的3种规模算例,并考虑大小两种车型进行仿真实验。将ISISS算法与改进节约里程、遗传和分散搜索3种算法比较后可知,ISISS算法在大规模包装废弃物回收车辆路径问题上具有更优的求解性能。结论 仿真实验结果表明,ISISS是一种求解多目标大规模包装废弃物回收路径规划问题的较优算法。