Reliable multi-product multi-vehicle multi-type link logistics network design: A hybrid heuristic algorithm

Abstract This paper considers the reliable multi-product multi-vehicle multi-type link logistics network design problem (RMLNDP) with system disruptions, which is concerned with facilities locating, transshipment links constructing, and also allocating them to the customers in order to satisfy their demand with minimum expected total cost (including locating costs, link constructing costs, and also expected transshipment costs in normal and disruption conditions). The motivating application of this class of problem is in multi-product, multi-vehicle, and multitype link logistics network design regarding to system disruptions simultaneously. In fact, the decision makers in this area are not only concerned with the facility locating costs, link constructing costs, and logistical costs of the system but also by focusing on the several system disruption states in order to be able to provide a reliable sustainable multi configuration logistic network system. All facility location plans, link construction plans and also link transshipment plans of demands in the problem must be efficiently determined while considering the several system disruptions. The problem is modeled as a mixed integer programming (MIP) model. Also, a hybrid heuristic, based on linear programming (LP) relaxation approach, is proposed. Computational experiments illustrate that the provided algorithm will be able to substantially outperform the proposed integer programming model in terms of both finding and verifying the efficient optimal (or near optimal) solution at a reasonable processing time

Urban Logistics in Amsterdam: A Modal Shift from Roadways to Waterway

The efficiency of urban logistics is vital for economic prosperity and quality of life in cities. However, rapid urbanization poses significant challenges, such as congestion, emissions, and strained infrastructure. This paper addresses these challenges by proposing an optimal urban logistic network that integrates urban waterways and last-mile delivery in Amsterdam. The study highlights the untapped potential of inland waterways in addressing logistical challenges in the city center. The problem is formulated as a two-echelon location routing problem with time windows, and a hybrid solution approach is developed to solve it effectively. The proposed algorithm consistently outperforms existing approaches, demonstrating its effectiveness in solving existing benchmarks and newly developed instances. Through a comprehensive case study, the advantages of implementing a waterway-based distribution chain are assessed, revealing substantial cost savings (approximately 28%) and reductions in vehicle weight (about 43%) and travel distances (roughly 80%) within the city center. The incorporation of electric vehicles further contributes to environmental sustainability. Sensitivity analysis underscores the importance of managing transshipment location establishment costs as a key strategy for cost efficiencies and reducing reliance on delivery vehicles and road traffic congestion. This study provides valuable insights and practical guidance for managers seeking to enhance operational efficiency, reduce costs, and promote sustainable transportation practices. Further analysis is warranted to fully evaluate the feasibility and potential benefits, considering infrastructural limitations and canal characteristics

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

Combinatorial Optimization

This report summarizes the meeting on Combinatorial Optimization where new and promising developments in the field were discussed. Th

Optimal Global Supply Chain and Warehouse Planning under Uncertainty

A manufacturing company\u27s inbound supply chain consists of various processes such as procurement, consolidation, and warehousing. Each of these processes is the focus of a different chapter in this dissertation. The manufacturer depends on its suppliers to provide the raw materials and parts required to manufacture a finished product. These suppliers can be located locally or overseas with respect to the manufacturer\u27s geographic location. The ordering and transportation lead times are shorter if the supplier is located locally. Just In Time (JIT) or Just In Sequence (JIS) inventory management methods could be practiced by the manufacturer to procure the raw materials and parts from the local supplier and control the inventory levels in the warehouse. In contrast, the lead time for the orders placed with an overseas supplier is usually long because sea-freight is often used as a primary mode of transportation. Therefore, the orders for the raw materials and parts (henceforth, we collectively refer to raw material and part by part) procured from overseas suppliers are usually placed using forecasted order quantities. In Chapter 2, we study the procurement process to reduce the overall expected cost and determine the optimal order quantities as well as the mode of transportation for procurement under forecast and inventory uncertainty. We formulate a two-stage stochastic integer programming model and solve it using the progressive hedging algorithm, a scenario-based decomposition method. Generally, the orders are placed with overseas suppliers using weekly or monthly forecasted demands, and the ordered part is delivered using sea-containers since sea-freight is the primary mode of transportation. However, the end manufacturing warehouse is usually designed to hold around one to two days of parts. To replenish the inventory levels, the manufacturer considered in this research unloads the sea-container that contains the part that needs to be restocked entirely. This may cause over-utilization of the manufacturer\u27s warehouse if an entire week\u27s supply of part is consolidated into a single sea-container. This problem is further aggravated if the manufacturer procures hundreds of different parts from overseas suppliers and stores them in its warehouse. In Chapter 3, we study the time-series forecasting models that help predict the manufacturing company\u27s daily demand quantities for parts with different characteristics. The manufacturer can use these forecasted daily demand quantities to consolidate the sea-containers instead of the weekly forecasted demand. In most cases, there is some discrepancy between the predicted and actual demands for parts, due to which the manufacturer can either have excess inventory or shortages. While excess inventory leads to higher inventory holding costs and warehouse utilization, shortages can result in substantially undesirable consequences, such as the total shutdown of production lines. Therefore, to avoid shortages, the manufacturer maintains predetermined safety stock levels of parts with the suppliers to fulfill the demands arising from shortages. We formulate a chance-constraint optimization model and solve it using the sample approximation approach to determine the daily safety stock levels at the supplier warehouse under forecast error uncertainty. Once the orders are placed with the local and overseas suppliers, they are consolidated into trailers (for local suppliers) and sea-containers (for overseas suppliers). The consolidated trailers and sea-containers are then delivered to the manufacturing plant, where they are stored in the yard until they are called upon for unloading. A detention penalty is incurred on a daily basis for holding a trailer or sea-container. Consolidating orders from different suppliers helps maximize trailer and sea-container space utilization and reduce transportation costs. Therefore, every sea-container and trailer potentially holds a mixture of parts. When a manufacturer needs to replenish the stocks of a given part, the entire sea-container or trailer that contains the required part is unloaded. Thus, some parts that are not imminently needed for production are also unloaded and stored inside the manufacturing warehouse along with the required parts. In Chapter 4, we study a multi-objective optimization model to determine the sea-containers and trailers to be unloaded on a given day to replenish stock levels such that the detention penalties and the manufacturing warehouse utilization are minimized. Once a sea-container or trailer is selected to replenish the warehouse inventory levels, its contents (i.e., pallets of parts) must be unloaded by the forklift operator and then processed by workers to update the stock levels and break down the pallets if needed. Finally, the unloaded and processed part is stored in the warehouse bins or shelves. In Chapter 5, we study the problem of determining the optimal team formation such that the total expected time required to unload, process, and store all the parts contained in the sea-containers and trailers selected for unloading on a given day is minimized

Constructive solution methodologies to the capacitated newsvendor problem and surrogate extension

The newsvendor problem is a single-period stochastic model used to determine the order quantity of perishable product that maximizes/minimizes the profit/cost of the vendor under uncertain demand. The goal is to fmd an initial order quantity that can offset the impact of backlog or shortage caused by mismatch between the procurement amount and uncertain demand. If there are multiple products and substitution between them is feasible, overstocking and understocking can be further reduced and hence, the vendor\u27s overall profit is improved compared to the standard problem. When there are one or more resource constraints, such as budget, volume or weight, it becomes a constrained newsvendor problem. In the past few decades, many researchers have proposed solution methods to solve the newsvendor problem. The literature is first reviewed where the performance of each of existing model is examined and its contribution is reported. To add to these works, it is complemented through developing constructive solution methods and extending the existing published works by introducing the product substitution models which so far has not received sufficient attention despite its importance to supply chain management decisions. To illustrate this dissertation provides an easy-to-use approach that utilizes the known network flow problem or knapsack problem. Then, a polynomial in fashion algorithm is developed to solve it. Extensive numerical experiments are conducted to compare the performance of the proposed method and some existing ones. Results show that the proposed approach though approximates, yet, it simplifies the solution steps without sacrificing accuracy. Further, this dissertation addresses the important arena of product substitute models. These models deal with two perishable products, a primary product and a surrogate one. The primary product yields higher profit than the surrogate. If the demand of the primary exceeds the available quantity and there is excess amount of the surrogate, this excess quantity can be utilized to fulfill the shortage. The objective is to find the optimal lot sizes of both products, that minimize the total cost (alternatively, maximize the profit). Simulation is utilized to validate the developed model. Since the analytical solutions are difficult to obtain, Mathematical software is employed to find the optimal results. Numerical experiments are also conducted to analyze the behavior of the optimal results versus the governing parameters. The results show the contribution of surrogate approach to the overall performance of the policy. From a practical perspective, this dissertation introduces the applications of the proposed models and methods in different industries such as inventory management, grocery retailing, fashion sector and hotel reservation

Integrated Optimization of Procurement, Processing and Trade of Commodities in a Network Environment

We consider the integrated optimization problem of procurement, processing and trade of commodities over a network in a multiperiod setting. Motivated by the operations of a prominent commodity processing firm, we model a firm that operates a star network with multiple locations at which it can procure an input commodity and has processing capacity at a central location to convert the input into a processed commodity. The processed commodity is sold using forward contracts, while the input itself can be traded at the end of the horizon. We show that the single-node version of this problem can be solved optimally when the procurement cost for the input is piecewise linear and convex, and derive closed form expressions for the marginal value of input and output inventory. However, these marginal values are hard to compute because of high dimensionality of the state space and we develop an efficient heuristic to compute approximate marginal values. We also show that the star network problem can be approximated as an equivalent single node problem and propose heuristics for solving the network problem. We conduct numerical studies to evaluate the performance of both the single node and network heuristics. We find that the single node heuristics are near-optimal, capturing close to 90% of the value of an upper bound on the optimal expected profits. Approximating the star network by a single node is effective, with the gap between the heuristic and upper bound ranging from 7% to 14% for longer planning horizonshttp://deepblue.lib.umich.edu/bitstream/2027.42/55417/1/1095-Anupindi.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/55417/4/1095-Anupindi_2010.pd