Multi-Echelon Inventory Optimization and Demand-Side Management: Models and Algorithms

Inventory management is a fudamental problem in supply chain management. It is widely used in practice, but it is also intrinsically hard to optimize, even for relatively simple inventory system structures. This challenge has also been heightened under the threat of supply disruptions. Whenever a supply source is disrupted, the inventory system is paralyzed, and tremenduous costs can occur as a consequence. Designing a reliable and robust inventory system that can withstand supply disruptions is vital for an inventory system\u27s performance.First we consider a basic type of inventory network, an assembly system, which produces a single end product from one or several components. A property called long-run balance allows an assembly system to be reduced to a serial system when disruptions are not present. We show that a modified version is still true under disruption risk. Based on this property, we propose a method for reducing the system into a serial system with extra inventory at certain stages that face supply disruptions. We also propose a heuristic for solving the reduced system. A numerical study shows that this heuristic performs very well, yielding significant cost savings when compared with the best-known algorithm.Next we study another basic inventory network structure, a distribution system. We study continuous-review, multi-echelon distribution systems subject to supply disruptions, with Poisson customer demands under a first-come, first-served allocation policy. We develop a recursive optimization heuristic, which applies a bottom-up approach that sequentially approximates the base-stock levels of all the locations. Our numerical study shows that it performs very well.Finally we consider a problem related to smart grids, an area where supply and demand are still decisive factors. Instead of matching supply with demand, as in the first two parts of the dissertation, now we concentrate on the interaction between supply and demand. We consider an electricity service provider that wishes to set prices for a large customer (user or aggregator) with flexible loads so that the resulting load profile matches a predetermined profile as closely as possible. We model the deterministic demand case as a bilevel problem in which the service provider sets price coefficients and the customer responds by shifting loads forward in time. We derive optimality conditions for the lower-level problem to obtain a single-level problem that can be solved efficiently. For the stochastic-demand case, we approximate the consumer\u27s best response function and use this approximation to calculate the service provider\u27s optimal strategy. Our numerical study shows the tractability of the new models for both the deterministic and stochastic cases, and that our pricing scheme is very effective for the service provider to shape consumer demand

MULTI-OBJECTIVE ROBUST PRODUCTION PLANNING CONSIDERING WORKFORCE EFFICIENCY WITH A METAHEURISTIC SOLUTION APPROACH

Timely delivery of products to customers is one of the main factors of customer satisfaction and a key to the survival of a manufacturing system. Therefore, decreasing wasted time in manufacturing processes significantly affects production delivery time, which can be achieved through the maximization of workforce efficiency. This issue becomes more complicated when the parameters of the production system are under uncertainty. This paper presents a bi-objective scenario-based robust production planning model considering maximizing workforce efficiency and minimizing costs where the backorder, demand, and costs are uncertain. Also, backorder, raw materials purchasing, inventory control, and manufacturing time capacity are considered. A case study in a faucet manufacturing plant is considered to solve the model. Furthermore, the ε-constraint method, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and the Pareto Envelope-based Selection Algorithm II (PESA-II) are employed to solve the model. Also, the Taguchi method is used to tune the parameters of these algorithms. To compare these algorithms, five indicators are defined. The results show that the SPEA2 is the most time-consuming algorithm and the NSGA-II is the fastest, while their objective function values are nearly the same

Two-stage network design in humanitarian logistics.

Natural disasters such as floods and earthquakes can cause multiple deaths, injuries, and severe damage to properties. In order to minimize the impact of such disasters, emergency response plans should be developed well in advance of such events. Moreover, because different organizations such as non-governmental organizations (NGOs), governments, and militaries are involved in emergency response, the development of a coordination scheme is necessary to efficiently organize all the activities and minimize the impact of disasters. The logistics network design component of emergency management includes determining where to store emergency relief materials, the corresponding quantities and distribution to the affected areas in a cost effective and timely manner. In a two-echelon humanitarian relief chain, relief materials are pre-positioned first in regional rescue centers (RRCs), supply sources, or they are donated to centers. These materials are then shipped to local rescue centers (LRCs) that distribute these materials locally. Finally, different relief materials will be delivered to demand points (also called affected areas or AAs). Before the occurrence of a disaster, exact data pertaining to the origin of demand, amount of demand at these points, availability of routes, availability of LRCs, percentage of usable pre-positioned material, and others are not available. Hence, in order to make a location-allocation model for pre-positioning relief material, we can estimate data based on prior events and consequently develop a stochastic model. The outputs of this model are the location and the amount of pre-positioned material at each RRC as well as the distribution of relief materials through LRCs to demand points. Once the disaster occurs, actual values of the parameters we seek (e.g., demand) will be available. Also, other supply sources such as donation centers and vendors can be taken into account. Hence, using updated data, a new location-allocation plan should be developed and used. It should be mentioned that in the aftermath of the disaster, new parameters such as reliability of routes, ransack probability of routes and priority of singular demand points will be accessible. Therefore, the related model will have multiple objectives. In this dissertation, we first develop a comprehensive pre-positioning model that minimizes the total cost while considering a time limit for deliveries. The model incorporates shortage, transportation, and holding costs. It also considers limited capacities for each RRC and LRC. Moreover, it has the availability of direct shipments (i.e., shipments can be done from RRCs directly to AAs) and also has service quality. Because this model is in the class of two-stage stochastic facility location problems, it is NP-hard and should be solved heuristically. In order to solve this model, we propose using Lagrangian Heuristic that is based on Lagrangian Relaxation. Results from the first model are amounts and locations of pre-positioned relief materials as well as their allocation plan for each possible scenario. This information is then used as a part of the input for the second model, where the facility location problem will be formulated using real data. In fact, with pre-positioned items in hand, other supplies sources can be considered as necessary. The resulting multi-objective problem is formulated based on a widely used method called lexicography goal programming. The real-time facility location model of this dissertation is multi-product. It also considers the location problem for LRCs using real-time data. Moreover, it considers the minimization of the total cost as one of the objectives in the model and it has the availability of direct shipments. This model is also NP-hard and is solved using the Lagrangian Heuristic. One of the contributions of this dissertation is the development of Lagrangian Heuristic method for solving the pre-positioning and the real- time models. Based on the results of Lagrangian Heuristic for the pre-positioning model, almost all the deviations from optimal values are below 5%, which shows that the Heuristics works acceptably for the problem. Also, the execution times are no more than 780 seconds for the largest test instances. Moreover, for the real-time model, though not directly comparable, the solutions are fairly close to optimal and the execution time for the largest test instance is below 660 seconds. Hence, the efficiency of the heuristic for real-time model is satisfactory

Inventory strategies for patented and generic products for a pharmaceutical supply chain

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 76-77).This thesis presents a model to determine safety stock considering the distinct planning parameters for a pharmaceutical company. Traditional parameters such as forecast accuracy, service level requirements and average lead-time are combined with a nontraditional upstream uncertainty parameter defined as supply reliability. In this instance, supply reliability measures uncertainty in the supply quantity delivered rather than variability in the lead-time for delivery. We consider the impact of the safety stock using two products: a proprietary product that is patented and a generic product that recently went off patent. Sensitivity analysis is performed to provide insights on the impact of variations in input parameters. The study shows that there is a significant difference in safety stock between the proposed model and the current model used by the company.by Prashanth Krishnamurthy and Amit Prasad.M.Eng.in Logistic

Modeling of Promising Interaction Between a Timber Industry Enterprise and a Commodity Exchange in Russia

The relevance of the study lies in the absence of works in the literature devoted to the formation of supply chains of materials in volumes sufficient for production using the apparatus of commodity exchanges. The aim of the work is to conduct an empirical study to assess the prospects for the interaction of a timber industry enterprise with a commodity exchange. For the study, a mathematical model was chosen to assess the effectiveness of the purchase of raw materials from the forestry department of the commodity exchange by an enterprise in the timber industry. The hypothesis is that the interaction of the timber industry complex can be beneficial for the enterprise. To ensure the feasibility of purchasing raw materials from the exchange, simulation modeling was chosen. For each individual simulation iteration, a linear integer programming mathematical model was used. To generate some input data, like price, demand, etc., the Monte Carlo method was used. The complexity of the problem lies in the following aspects: polynomial growth of the number of numbers; a large number of restrictions on the increase in the degree of complexity of finding the first feasible solution to the model; search for a solution within the framework of integer optimization; a fairly large number of independent simulation iterations. The practical significance of the study is to prove the expediency of purchasing raw materials by the enterprise from the commodity and raw materials exchange of Russia. The theoretical significance of the study lies in the development of a model for assessing the feasibility of purchasing materials using the exchange apparatus. The scientific novelty is based on the constructed mathematical model of the formation of supply chains and the volume of production, taking into account the demand in the market and the volume of materials. The model was tested on data from one forestry enterprise in the Primorsky Territory. Optimization is carried out in terms of the volume of products produced, the volume of purchased materials from each region and the stock of raw materials in the production warehouse. Based on the testing of data models of the exchange and the forestry enterprise, an analysis was performed of the possibilities for cooperation between the company and the commodity exchange. The work reflects the behavior in the long term of accumulated profit, the nature of changes in stock in the warehouse and the volume of products produced.Актуальность исследования заключается в отсутствии работ в литературе, посвященных формированию цепочек поставок сырья в достаточных для производства объемах с применением аппарата товарно-сырьевых бирж. Целью работы является проведение эмпирического исследования по оценке целесообразности взаимодействия лесопромышленного предприятия с товарно-сырьевой биржей. Для проведения исследования была разработана математическая модель по оценке целесообразности закупа сырья с лесного отдела товарно-сырьевой биржи предприятием лесопромышленной отрасли. Гипотеза заключается в том, что взаимодействие лесопромышленного комплекса с биржей может быть выгодным для предприятия. Для проведения анализа целесообразности проведения закупок сырья с биржи было выбрано имитационное моделирование. Для каждой отдельной имитационной итерации использовалась математическая модель линейного целочисленного программирования. Для генерации некоторых входных данных таких, как цена, спрос и др. использовался метод Монте-Карло. Сложность задачи заключается в следующих аспектах: полиномиальный рост количества переменных; большое количество ограничений увеличивает степень сложности поиска первого допустимого решения модели; поиск решения в рамках целочисленной оптимизации; достаточно большое количество независимых имитационных итераций. Практическая значимость исследования заключается в доказательстве целесообразности закупа предприятием сырья с товарно-сырьевой биржи России. Теоретическая значимость исследования заключается в разработке новой модели по оценке целесообразности проведения закупок сырья с применением аппарата биржи. Научная новизна заключается в построенной математической модели формирования цепочек поставок и объемов производства с учетом спроса на рынке и доступных объемов сырья. Апробация модели проведена на данных одного из предприятий лесной отрасли Приморского края. Оптимизация ведется по объему производимой продукции, объему закупаемого сырья из каждого региона и по запасам сырья на складе производства. На основе апробации модели на данных биржи и предприятия лесной отрасли был проведен анализ целесообразности сотрудничества компании и товарно-сырьевой биржи. В работе отражены поведение в долгосрочной перспективе накопленной прибыли, характер изменения запасов сырья на складе и объем производимой продукции.Актуальность исследования заключается в отсутствии работ в литературе, посвященных формированию цепочек поставок сырья в достаточных для производства объемах с применением аппарата товарно-сырьевых бирж. Целью работы является проведение эмпирического исследования по оценке целесообразности взаимодействия лесопромышленного предприятия с товарно-сырьевой биржей. Для проведения исследования была разработана математическая модель по оценке целесообразности закупа сырья с лесного отдела товарно-сырьевой биржи предприятием лесопромышленной отрасли. Гипотеза заключается в том, что взаимодействие лесопромышленного комплекса с биржей может быть выгодным для предприятия. Для проведения анализа целесообразности проведения закупок сырья с биржи было выбрано имитационное моделирование. Для каждой отдельной имитационной итерации использовалась математическая модель линейного целочисленного программирования. Для генерации некоторых входных данных таких, как цена, спрос и др. использовался метод Монте-Карло. Сложность задачи заключается в следующих аспектах: полиномиальный рост количества переменных; большое количество ограничений увеличивает степень сложности поиска первого допустимого решения модели; поиск решения в рамках целочисленной оптимизации; достаточно большое количество независимых имитационных итераций. Практическая значимость исследования заключается в доказательстве целесообразности закупа предприятием сырья с товарно-сырьевой биржи России. Теоретическая значимость исследования заключается в разработке новой модели по оценке целесообразности проведения закупок сырья с применением аппарата биржи. Научная новизна заключается в построенной математической модели формирования цепочек поставок и объемов производства с учетом спроса на рынке и доступных объемов сырья. Апробация модели проведена на данных одного из предприятий лесной отрасли Приморского края. Оптимизация ведется по объему производимой продукции, объему закупаемого сырья из каждого региона и по запасам сырья на складе производства. На основе апробации модели на данных биржи и предприятия лесной отрасли был проведен анализ целесообразности сотрудничества компании и товарно-сырьевой биржи. В работе отражены поведение в долгосрочной перспективе накопленной прибыли, характер изменения запасов сырья на складе и объем производимой продукции

A FUZZY GOAL PROGRAMMING APPROACH FOR SOLVING MULTI-OBJECTIVE SUPPLY CHAIN NETWORK PROBLEMS WITH PARETO-DISTRIBUTED RANDOM VARIABLES

Uncertainty is unavoidable and addressing the same is inevitable. That everything is available at our doorstep is due to a well-managed modern global supply chain, which takes place despite its efficiency and effectiveness being threatened by various sources of uncertainty originating from the demand side, supply side, manufacturing process, and planning and control systems. This paper addresses the demand- and supply-rooted uncertainty. In order to cope with uncertainty within the constrained multi-objective supply chain network, this paper develops a fuzzy goal programming methodology, with solution procedures. The probabilistic fuzzy goal multi-objective supply chain network (PFG-MOSCN) problem is thus formulated and then solved by three different approaches, namely, simple additive goal programming approach, weighted goal programming approach, and pre-emptive goal programming approach, to obtain the optimal solution. The proposed work links fuzziness in transportation cost and delivery time with randomness in demand and supply parameters. The results may prove to be important for operational managers in manufacturing units, interested in optimizing transportation costs and delivery time, and implicitly, in optimizing profits. A numerical example is provided to illustrate the proposed model

Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model

Traditionally, supply chain planning problems consider variables with uncertainty associated with uncontrolled factors. These factors have been normally modelled by complex methodologies where the seeking solution process often presents high scale of difficulty. This work presents the fuzzy set theory as a tool to model uncertainty in supply chain planning problems and proposes the particle swarm optimisation (PSO) metaheuristics technique combined with a backward calculation as a solution method. The aim of this combination is to present a simple effective method to model uncertainty, while good quality solutions are obtained with metaheuristics due to its capacity to find them with satisfactory computational performance in complex problems, in a relatively short time period.This research is partly supported by the Spanish Ministry of Economy and Competitiveness projects 'Methods and models for operations planning and order management in supply chains characterised by uncertainty in production due to the lack of product uniformity' (PLANGES-FHP) (Ref. DPI2011-23597) and 'Operations design and Management of Global Supply Chains' (GLOBOP) (Ref. DPI2012-38061-C02-01); by the project funded by the Polytechnic University of Valencia entitled 'Quantitative Models for the Design of Socially Responsible Supply Chains under Uncertainty Conditions. Application of Solution Strategies based on Hybrid Metaheuristics' (PAID-06-12); and by the Ministry of Science, Technology and Telecommunications, government of Costa Rica (MICITT), through the incentive program of the National Council for Scientific and Technological Research (CONICIT) (contract No FI-132-2011).Grillo Espinoza, H.; Peidro Payá, D.; Alemany Díaz, MDM.; Mula, J. (2015). Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model. International Journal of Bio-Inspired Computation. 7(3):157-169. https://doi.org/10.1504/IJBIC.2015.069557S1571697