On the inventory routing problem with stationary stochastic demand rate

One of the most significant paradigm shifts of present business management is that individual businesses no longer participate as solely independent entities, but rather as supply chains (Lambert and Cooper, 2000). Therefore, the management of multiple relationships across the supply chain such as flow of materials, information, and finances is being referred to as supply chain management (SCM). SCM involves coordinating and integrating these multiple relationships within and among companies, so that it can improve the global performance of the supply chain. In this dissertation, we discuss the issue of integrating the two processes in the supply chain related, respectively, to inventory management and routing policies. The challenging problem of coordinating the inventory management and transportation planning decisions in the same time, is known as the inventory routing problem (IRP). The IRP is one of the challenging optimization problems in logis-tics and supply chain management. It aims at optimally integrating inventory control and vehicle routing operations in a supply network. In general, IRP arises as an underlying optimization problem in situations involving simultaneous optimization of inventory and distribution decisions. Its main goal is to determine an optimal distribution policy, consisting of a set of vehicle routes, delivery quantities and delivery times that minimizes the total inventory holding and transportation costs. This is a typical logistical optimization problem that arises in supply chains implementing a vendor managed inventory (VMI) policy. VMI is an agreement between a supplier and his regular retailers according to which retailers agree to the alternative that the supplier decides the timing and size of the deliveries. This agreement grants the supplier the full authority to manage inventories at his retailers'. This allows the supplier to act proactively and take responsibility for the inventory management of his regular retailers, instead of reacting to the orders placed by these retailers. In practice, implementing policies such as VMI has proven to considerably improve the overall performance of the supply network, see for example Lee and Seungjin (2008), Andersson et al. (2010) and Coelho et al. (2014). This dissertation focuses mainly on the single-warehouse, multiple-retailer (SWMR) system, in which a supplier serves a set of retailers from a single warehouse. In the first situation, we assume that all retailers face a deterministic, constant demand rate and in the second condition, we assume that all retailers consume the product at a stochastic stationary rate. The primary objective is to decide when and how many units to be delivered from the supplier to the warehouse and from the warehouse to retailers so as to minimize total transportation and inventory holding costs over the finite horizon without any shortages

A Simulation Framework for Real-Time Management and Control of Inventory Routing Decisions

We consider a logistics network where a single warehouse distributes a single item to multiple retailers. Retailers in the network participate in a Vendor Managed Inventory (VMI) program with the warehouse, where the warehouse is responsible for tracking and replenishing the inventory at various retailer locations. The information update occurs every time a vehicle reaches a location and the decision on the delivery quantity and the next location to visit is made. For a small increase of locations in the network, the state space for the solution increases exponentially, making this problem NP-hard. Thus, we propose a solution methodology where in the size of the state space is reduced at each stage. In this work, we use simulation to develop the framework for the real-time control and management of inventory and routing decisions, given this scenario

Supply chain complexity and robustness analysis

Imperial Users onl

A Two-Echelon Location-inventory Model for a Multi-product Donation-demand Driven Industry

This study involves a joint bi-echelon location inventory model for a donation-demand driven industry in which Distribution Centers (DC) and retailers (R) exist. In this model, we confine the variables of interest to include; coverage radius, service level, and multiple products. Each retailer has two classes of product flowing to and from its assigned DC i.e. surpluses and deliveries. The proposed model determines the number of DCs, DC locations, and assignments of retailers to those DCs so that the total annual cost including: facility location costs, transportation costs, and inventory costs are minimized. Due to the complexity of problem, the proposed model structure allows for the relaxation of complicating terms in the objective function and the use of robust branch-and-bound heuristics to solve the non-linear, integer problem. We solve several numerical example problems and evaluate solution performance

Routing and Inventory Allocation Policies in Multi-Echelon Distribution Systems

This paper reviews the previous research in the area of logistics systems, especially in the area of the multi-echelon distribution system with stochastic demand. Many researches in this area emphasize the value of real-time information (which is more readily available nowadays by use of EDS or satellite systems) in distribution-related decisions. Also a great deal of efforts has been taken to study the risk-pooling effect of the various distribution policies in the multi-echelon systems. The objectives of this review are two-folded; (1) to help readers to understand the research paradigm in the multi-echelon area, and (2) to help them to find future research topics not yet explored

THE INVENTORY ROUTING PROBLEM WITH THIRD PARTY LOGISTICS

There are two key planning issues in supply chain: inventory management and transportation. In this research, the inventory control and transportation of syrup concentrate and final products for one bottling company working for a beverage company is studied. Operation of most of beverage companies is based on a franchised distribution system. In this operation, syrup concentrate is produced by a beverage company and sold to bottlers. Bottlers, in turn, mix the syrup concentrate with different ingredients to produce various products and distribute them to retailers. Unsatisfied orders have several harmful effects on the bottling company. The bottling company may not satisfy all demands due to its small fleet size, which is not able to cover all deliveries in the right timeframe. One method for preventing missed orders is sending orders to some retailers in advance to hold for future use. This allows the fleet to be free to service the rest of the retailers. This policy is possible if those retailers have available capacity to keep products. Another way to deal with this problem is by renting vehicles, which increases the fleet size. The last option for delivering to a retailer when the owned fleet is not able to do so is outsourcing shipping and/or warehousing. The bottling company contracts with a Third Party Logistics Provider (TPLP), who is responsible for delivery of final products to some of the bottler's retailers. Also, TPLPs can store commodities in their warehouses and deliver products to retailers at the right time if there is no available capacity in the bottler's warehouses. This problem belongs to Inventory Routing Problem (IRP) with some new features such as options for rental vehicle and TPLPs. IRP is a well-studied problem in Operation Research but most of the studies take a single period into account. In contrast, the proposed model in this study includes several time steps in which a decision in one time step can affect future time steps. The proposed model is a multi-tier, multi-plant, multi-warehouse, and multi-product model which considers non-homogeneous fleet. No model in the literature considers all of these characteristics simultaneously. In this research heuristic methods are developed to solve large problems for which optimization packages cannot find even a feasible solution. Two heuristic methods are proposed for this problem, which are based on fix-and-run algorithm. Three improvement phases are also developed to enhance the final solution of heuristics. The proposed heuristic methods in this research can find an appropriate feasible solution with only a small gap from an upper bound and in reasonable running time

Demand Management Opportunities in E-fulfillment: What Internet Retailers Can Learn from Revenue Management

In this paper, we explain how Internet retailers can learn from proven revenue management concepts and use them to reduce costs and enhance service. We focus on attended deliveries as these provide the greatest opportunities and challenges. The key driver is service differentiation. Revenue management has shown that companies can do much better than a one-size-fits-all first-come-first-serve strategy when selling scarce capacity to a heterogeneous market. Internet retailers have strong levers at their disposal for actively steering demand, notably the offered delivery time windows and their associated prices. Unlike traditional revenue management, these demand management decisions affect both revenues and costs. This calls for a closer coordination of marketing and operations than current common practice.ketenbeheer;revenue management;home delivery;E-fulfillment;demand management;marketing-operations interface

Integrated Supply Chain Network Design: Location, Transportation, Routing and Inventory Decisions

abstract: In this dissertation, an innovative framework for designing a multi-product integrated supply chain network is proposed. Multiple products are shipped from production facilities to retailers through a network of Distribution Centers (DCs). Each retailer has an independent, random demand for multiple products. The particular problem considered in this study also involves mixed-product transshipments between DCs with multiple truck size selection and routing delivery to retailers. Optimally solving such an integrated problem is in general not easy due to its combinatorial nature, especially when transshipments and routing are involved. In order to find out a good solution effectively, a two-phase solution methodology is derived: Phase I solves an integer programming model which includes all the constraints in the original model except that the routings are simplified to direct shipments by using estimated routing cost parameters. Then Phase II model solves the lower level inventory routing problem for each opened DC and its assigned retailers. The accuracy of the estimated routing cost and the effectiveness of the two-phase solution methodology are evaluated, the computational performance is found to be promising. The problem is able to be heuristically solved within a reasonable time frame for a broad range of problem sizes (one hour for the instance of 200 retailers). In addition, a model is generated for a similar network design problem considering direct shipment and consolidation within the same product set opportunities. A genetic algorithm and a specific problem heuristic are designed, tested and compared on several realistic scenarios.Dissertation/ThesisPh.D. Industrial Engineering 201

Supply Chain Modeling and Simulation Using Agents

Ph.DDOCTOR OF PHILOSOPH

Design and optimization of an explosive storage policy in internet fulfillment warehouses

This research investigates the warehousing operations of internet retailers. The primary physical process in internet retail is fulfillment, which typically involves a large internet fulfillment warehouse (IFW) that has been built and designed exclusively for online sales and an accompanying parcel delivery network. Based on observational studies of IFW operations at a leading internet retailer, the investigations find that traditional warehousing methods are being replaced by new methods which better leverage information technology and efficiently serve the new internet retail driven supply chain economy. Traditional methods assume a warehouse moves bulk volumes to retail points where the bulks get broken down into individual items and sold. But in internet retail all the middle elements of a supply chain are combined into the IFW. Specifically, six key structural differentiations between traditional and IFW operations are identified: (i) explosive storage policy (ii) very large number of beehive storage locations (iii) bins with commingled SKUs (iv) immediate order fulfillment (v) short picking routes with single unit picks and (vi) high transaction volumes with total digital control. In combination, these have the effect of organizing the entire IFW warehouse like a forward picking area. Several models to describe and control IFW operations are developed and optimized. For IFWs the primary performance metric is order fulfillment time, the interval between order receipt and shipment, with a target of less than four hours to allow for same day shipment. Central to achieving this objective is an explosive storage policy which is defined as: An incoming bulk SKU is exploded into E storage lots such that no lot contains more than 10% of the received quantity, the lots are then stored in E locations anywhere in the warehouse without preset restrictions. The explosion ratio Ψo is introduced that measures the dispersion density, and show that in a randomized storage warehouse Ψoo\u3e0.40. Specific research objectives that are accomplished: (i) Develope a descriptive and prescriptive model for the control of IFW product flows identifying control variables and parameters and their relationship to the fulfillment time performance objective, (ii) Use a simulation analysis and baseline or greedy storage and picking algorithms to confirm that fulfillment time is a convex function of E and sensitive to Ǩ, the pick list size. For an experimental problem the fulfillment time decrease by 7% and 16% for explosion ratios ranging between Ψo=0.1 and 0.8, confirming the benefits of an explosive strategy, (iii) Develope the Bin Weighted Order Fillability (BWOF) heuristic, a fast order picking algorithm which estimates the number of pending orders than can be filled from a specific bin location. For small problems (120 orders) the BWOF performes well against an optimal assignment. For 45 test problems the BWOF matches the optimal in 28 cases and within 10% in five cases. For the large simulation experimental problems the BWOF heuristic further reduces fulfillment time by 18% for Ǩ =13, 27% for Ǩ =15 and 39% for Ǩ =17. The best fulfillment times are achieved at Ψo=0.5, allowing for additional benefits from faster storage times and reduced storage costs