Supply Planning Models Research Articles

Emergency medical service planning considering dynamic and stochastic demands of infected and non-infected patients during epidemics

During a large-scale epidemic, a local healthcare system can be overwhelmed by a large number of infected and non-infected patients. To serve the infected and non-infected patients well with limited medical resources, effective emergency medical service planning should be conducted before the epidemic. In this study, we propose a two-stage stochastic programming model, which integrally deploys various types of emergency healthcare facilities before an epidemic and serves infected and non-infected patients dynamically at the deployed healthcare facilities during the epidemic. With the service equity of infected patients and various practical requirements of emergency medical services being explicitly considered, our model minimizes a weighted sum of the expected operation cost and the equity cost. We develop two comparison models and conduct a case study on Chengdu, a Chinese city influenced by the COVID-19 epidemic, to show the effectiveness and benefits of our proposed model. Sensitivity analyses are conducted to generate managerial insights and suggestions. Our study not only extends the existing emergency supply planning models but also can facilitate better practices of emergency medical service planning for large-scale epidemics.

A planning model for repairable spare part supply chain considering stochastic demand and backorder: an empirical investigation

Today, improving machine availability is vital for industries to compete in the global market. Spare parts play an essential role in the maintenance and repair of equipment, but planning an extensive network in strategic industries with various spare parts can be very challenging due to the existence of different decision factors. The spare parts supply chain deals with inventory management issues, which necessitates considering the related decisions such as determining the stock level and order quantity. Moreover, demand uncertainty and long supply time make decision-making more complex. This paper presents a repair and supply planning model for repairable spare parts while considering a modified formulation of demand uncertainty to minimize costs. The model determines the optimal stock level, lateral transshipment, assignment of spare part orders to suppliers, equipment to repair centers, and the number of intervals over the planning horizon used in demand estimation. This research contributes to the literature by integrating recent decisions, using demand approximation by piecewise linearization, and considering backorder in warehouses evaluated by queuing models. A hybrid approach, including heuristic and genetic algorithms, is used to optimize the model using data from an oil company. The results show that using piecewise linearization and integrated repair and supply planning decisions optimizes costs and improves performance. Also, the availability is affected by the demand estimation, which necessitates precision prediction.

Forecast Methods and Periodic Review Inventory Model for Supply Planning to Reduce Food Waste

Food waste is an important economic and resource problem in all countries around the world. Particularly, the restaurant sector highly contributes to food waste and limited efforts or studies have been performed to overcome this problem. In this context, the present study addresses an alternative to improve the supply planning for perishable products in the restaurant sector through the application of specific forecasting methods and a stochastic inventory control model. For this purpose, a real enterprise within this economic sector was considered. Our findings support that monthly forecasts can be more appropriate for accurate demand estimation and supply planning of perishable products, which is important to reduce unnecessary products. Also, the periodic review inventory control model can lead to a more appropriate supply scheme to reduce the waste of surplus food. These findings and the proposed techniques can be used for other economic entities to reduce product waste due to poor supply planning.

A stochastic programming model for emergency supply planning considering traffic congestion

Traffic congestion is one key factor that delays emergency supply logistics after disasters, but it is seldom explicitly considered in previous emergency supply planning models. To fill the gap, we incorporate traffic congestion effects and propose a two-stage location-allocation model that facilitates the planning of emergency supplies pre-positioning and post-disaster transportation. The formulated mixed-integer nonlinear programming model is solved by applying the generalized Benders decomposition algorithm, and the suggested approach outperforms the direct solving strategy. With a case study on a hurricane threat in the southeastern USA, we illustrate that our traffic congestion incorporated model is a meaningful generalization of a previous emergency supply planning model in the literature. Finally, managerial insights about the supplies pre-positioning plan and traffic control policy are discussed.

Two-stage stochastic programming model for the regional-scale electricity planning under demand uncertainty

Traditional electricity supply planning models regard the electricity demand as a deterministic parameter and require the total power output to satisfy the aggregate electricity demand. But in today's world, the electric system planners are facing tremendously complex environments full of uncertainties, where electricity demand is a key source of uncertainty. In addition, electricity demand patterns are considerably different for different regions. This paper developed a multi-region optimization model based on two-stage stochastic programming framework to incorporate the demand uncertainty. Furthermore, the decision tree method and Monte Carlo simulation approach are integrated into the model to simplify electricity demands in the form of nodes and determine the values and probabilities. The proposed model was successfully applied to a real case study (i.e. Taiwan's electricity sector) to show its applicability. Detail simulation results were presented and compared with those generated by a deterministic model. Finally, the long-term electricity development roadmap at a regional level could be provided on the basis of our simulation results.

Impacts of Groundwater Constraints on Saudi Arabia's Low-Carbon Electricity Supply Strategy.

Balancing groundwater depletion, socioeconomic development and food security in Saudi Arabia will require policy that promotes expansion of unconventional freshwater supply options, such as wastewater recycling and desalination. As these processes consume more electricity than conventional freshwater supply technologies, Saudi Arabia's electricity system is vulnerable to groundwater conservation policy. This paper examines strategies for adapting to long-term groundwater constraints in Saudi Arabia's freshwater and electricity supply sectors with an integrated modeling framework. The approach combines electricity and freshwater supply planning models across provinces to provide an improved representation of coupled infrastructure systems. The tool is applied to study the interaction between policy aimed at a complete phase-out of nonrenewable groundwater extraction and concurrent policy aimed at achieving deep reductions in electricity sector carbon emissions. We find that transitioning away from nonrenewable groundwater use by the year 2050 could increase electricity demand by more than 40% relative to 2010 conditions, and require investments similar to strategies aimed at transitioning away from fossil fuels in the electricity sector. Higher electricity demands under groundwater constraints reduce flexibility of supply side options in the electricity sector to limit carbon emissions, making it more expensive to fulfill climate sustainability objectives. The results of this analysis underscore the importance of integrated long-term planning approaches for Saudi Arabia's electricity and freshwater supply systems.

Supply Planning Models for a Remanufacturer Under Just-in-Time Manufacturing Environment with Reverse Logistics

We study a supply planning problem in a manufacturing system with two stages. The first stage is a remanufacturer that supplies two closely-related components to the second (manufacturing) stage, which uses each component as the basis for its respective product. The used products are recovered from the market by a third-party logistic provider through an established reverse logistics network. The remanufacturer may satisfy the manufacturer’s demand either by purchasing new components or by remanufacturing components recovered from the returned used products. The remanufacturer’s costs arise from product recovery, remanufacturing components, purchasing original components, holding inventories of recovered products and remanufactured components, production setups (at the first stage and at each component changeover), disposal of recovered products that are not remanufactured, and coordinating the supply modes. The objective is to develop optimal production plans for different production strategies. These strategies are differentiated by whether inventories of recovered products or remanufactured components are carried, and by whether the order in which retailers are served during the planning horizon may be resequenced. We devise production policies that minimize the total cost at the remanufacturer by specifying the quantity of components to be remanufactured, the quantity of new components to be purchased from suppliers, and the quantity of recovered used products that must be disposed. The effects of production capacity are also explored. A comprehensive computational study provides insights into this closed-loop supply chain for those strategies that are shown to be NP-hard.

Genetic algorithm for multi-level assembly systems under stochastic lead times

The aim of this paper is to propose tools to adapt and parameterize the Material Requirement Planning (MRP) method under lead time uncertainty. We study multi-level assembly systems with one type of finished products and several types of components. We consider that each component has a fixed unit inventory cost and the finished product has a backlogging cost per unit of time. The lead times of components are discrete random variables, and the costumer's demand of the finished product is known. A general mathematical model for supply planning of multi-level assembly systems is presented. A Genetic Algorithm (GA) method is proposed to minimize the sum of the average inventory holding cost for components and the average backlogging and inventory holding costs for the finished product.

Mathematical Model for Supply Planning of Multi-level Assembly Systems with Stochastic Lead Times

The deal for industrial companies is to satisfy their customers with the best quality, the most reliable lead times, and the lowest costs. One of the keys to achieve it is in the inventory control in the Supply Chain. Moreover, it is mandatory to possess necessary components in order to produce the requested products by the due date. But, as the components lead time is an unpredictable parameter, it is difficult to take it into account correctly. The paper deals with this question considering that each component has a fixed unit inventory cost; the finished product has an inventory cost and a backlogging cost per unit of time. Then, a general mathematical model for supply planning of multilevel assembly system is presented to calculate the expected value of the total cost which equals to the sum of the inventory holding costs for the components, the backlogging and the inventory holding costs for the finished product.

Short-term manpower planning for MRT carriage maintenance under mixed deterministic and stochastic demands

The purpose of this research is to develop two manpower supply planning models and a solution algorithm for mass rapid transit carriage maintenance under mixed deterministic and stochastic demands. These models are formulated as mixed integer programs that are characterized as NP-hard. We employ problem decomposition techniques, coupled with the CPLEX mathematical programming solver, to develop an algorithm that is capable of efficiently solving the problems. The models and the method used currently in actual operations are evaluated by a simulation-based evaluation method. Finally, we perform a case study using real operating data from a Taiwan MRT maintenance facility. The preliminary results are good, showing that the models could be useful for planning carriage maintenance manpower supply.

Coupled modelling of sugarcane supply planning and logistics as a management tool

Agro-industry supply chains involving several stakeholders need coordination. Such systems, however, can be highly complex and may require a wide range of decision support. Models may be valuable when analysing complex supply chains, but are commonly specialised with little scope to evaluate a system holistically. Ideally, models with different specialised focus areas, such as a short-term logistics or seasonal planning, need to be integrated into a single descriptive framework. In this paper, such a framework is demonstrated within a sugarcane supply management regime. The aim is to couple a tactical supply planning model, named MAGI, with a daily logistics model to more holistically explore the relationships between these supply components. This will help to further identify limitations in the approach and to recommend areas for further research. The research was carried out in conjunction with an investigation into different harvest mechanisation strategies at a South African sugar mill. The mill's weekly crushing capacity, the length of the milling season and logistical harvest and transport capacities were balanced to meet supply demands over different planning horizons. The most important link between the two models was the use of production units. These units provided sufficient diversity in terms of geography and agricultural systems to accommodate both models’ data requirements. The model pair was used successfully to explore the mill area's response, in terms of the number of harvesters, vehicles, length of the milling season and sensitivity to risk, after the current harvest mechanisation regime of 16% of the annual crop was escalated to 75%. This paper contains a detailed description of the mill area and the current management practices and logistical configurations used. It then describes the construction of the model pair, validation of the models, and produces plausible future scenarios to support stakeholder negotiations. Recommendations for further research are highlighted.

A continuous model for supply planning of assembly systems with stochastic component procurement times

This paper studies a customized product assembly scenario where some components cannot be stocked due to high component cost and risk. We consider the case where these key components are ordered after a demand has been registered with a promised delivery date. Component lead-times are stochastic and associated distribution function is known in advance. The objective is to determine the ordering time for each component such as to minimize the sum of expected holding and backlogging costs. An approach to solve this problem is proposed and the algorithm is tested on a randomly generated data set.

A model for supply planning under lead time uncertainty

The paper is focused on the search of the optimal values of the planned lead times for the MRP method under lead time uncertainty. The problem is to find the planned lead times, which minimize the expected backlogging and holding costs. The aim of the paper is to give a mathematical formulation of this single-level, multi-item, dynamic multi-period planning problem. For the case where lead time does not depend on the lot size and demand level is constant, an auxiliary Markov model is proposed. Some new formulations of optimal control policies are obtained using this Markov model.

Planning models show comparative results

Electricity supply planning models are built to assist decision-makers in keeping future supply and demand in balance. The planning problem can be approached in different ways. This article focuses on two planning models and compares how they approach and resolve the planning problem. SEPU is an expert model that can be used for detailed reliability studies, mainly from a short-term perspective. PowerPlan is a planning model for mid-term scenario studies. With PowerPlan, what if questions can be answered in an interactive and fast manner. Although developed with different goals in mind, PowerPlan and SEPU are to a certain degree complementary. The area of overlap is used here for comparison of the performance of the models in simulating a set of scenarios. The points of departure of the scenarios for both models match each other as well as possible in order to get comparable results.

Spatial and temporal aggregation effects in a regional water supply planning model

In applying mathematical programing water supply planning models, water supplies and demands must be spatially and temporally aggregated. An assessment is made of the effects of aggregation on the results of a planning model for West Africa. The assessment uses a detailed hydroeconomic simulation model to study the solution set of the planning model when it has been applied to several representative subareas. Comparisons are made between the design reliability intended for the supply system and the actual reliabilities of sources as estimated by the detailed simulation model. Comparisons are also made between the system suggested by the planning model and the estimated least cost reliable system. (The least cost reliable system was found using a trial and error search method and the detailed simulation model.) It was found that the aggregation assumptions primarily affect the modeling of sources that depend directly on the highly variable precipitation (e.g., ephemeral aquifers). These sources should, in general, be excluded from the planning model and analyzed separately. Overall, unless a planner is particularly interested in studying the use of such unreliable sources, the spatial and temporal aggregation assumptions of the planning model do not hinder its use as an effective regional planning tool.

Water Supply Planning Model for West Africa

A model is formulated for use in the arid, underdeveloped Sahel-Sudan region, West Africa. Its purpose is to determine either the least cost set of sources to meet fixed demands or the sources and water uses that maximize the net benefits of water use. Since for planning purposes the model is designed to apply to the entire region and some subareas have only small-scale development options, both large and small-scale development of a wide variety of demands and supplies are modelled. The model is a mixed integer programming (MIP) optimization model. The objective function can be expressed either as the minimization of supply costs or as the maximization of the net benefits of water use. The major constraints are that in each planning subarea and in each season hydrologic continuity must exist, and demand must be met. The major decision variables are the capacities of supply sources, and in the net benefit maximization case, the water demand quantities. MIP is used to model the possibility of different reservoir operating at a site.