A branch and efficiency algorithm for the optimal design of supply chain networks

Supply chain operations directly affect service levels. Decision on amendment of facilities is generally decided based on overall cost, leaving out the efficiency of each unit. Decomposing the supply chain superstructure, efficiency analysis of the facilities (warehouses or distribution centers) that serve customers can be easily implemented. With the proposed algorithm, the selection of a facility is based on service level maximization and not just cost minimization as this analysis filters all the feasible solutions utilizing Data Envelopment Analysis (DEA) technique. Through multiple iterations, solutions are filtered via DEA and only the efficient ones are selected leading to cost minimization. In this work, the problem of optimal supply chain networks design is addressed based on a DEA based algorithm. A Branch and Efficiency (B&E) algorithm is deployed for the solution of this problem. Based on this DEA approach, each solution (potentially installed warehouse, plant etc) is treated as a Decision Making Unit, thus is characterized by inputs and outputs. The algorithm through additional constraints named “efficiency cuts”, selects only efficient solutions providing better objective function values. The applicability of the proposed algorithm is demonstrated through illustrative examples

The decision rule approach to optimization under uncertainty: methodology and applications

Dynamic decision-making under uncertainty has a long and distinguished history in operations research. Due to the curse of dimensionality, solution schemes that naïvely partition or discretize the support of the random problem parameters are limited to small and medium-sized problems, or they require restrictive modeling assumptions (e.g., absence of recourse actions). In the last few decades, several solution techniques have been proposed that aim to alleviate the curse of dimensionality. Amongst these is the decision rule approach, which faithfully models the random process and instead approximates the feasible region of the decision problem. In this paper, we survey the major theoretical findings relating to this approach, and we investigate its potential in two applications areas

Sustainable Development of the Hydrological Basin of Lake Koronia using Mathematical Programming and Statistical Analysis

Lake Koronia, a Ramsar site in northern Greece, has experienced pronounced ecosystem degradation over the past 30 years associated with water level reduction and nutrient loading from agricultural and industrial activities as a result of poor management. The present study focuses on the delivery of a sustainable plan for theoretical and potentially in practice achievable water balance of the watershed of Lake Koronia and rational water resource management of the area. The use of mathematical modelling can assist in making the right decisions with respect to the water management as the increased complexity due to many factors affecting the water balance are impending the derivation of a suitable policy to address the problems. An optimisation model that takes into account all available investment options to allow restoration of the lake to a sustainable level and determines the best portfolio of investment and operating policies to ensure ecosystem recovery while maintaining the financial stability of the area is proposed. © 2012 Elsevier B.V

DESIGN OF AN ELECTRODIALYSIS BRACKISH WATER DESALINATION PLANT USING RENEWABLE RESOURCES BASED ON STATISTICAL ANALYSIS OF CLIMATE CONDITIONS

The interest in desalination methods has always been high and recently renewed due to the shortage of water in many remote areas and the diminishing natural fresh water resources. With almost 97% of the total water being seawater unfit for human use and of the rest 3% majority locked up in glaciers and ice, the remaining water available for human use is estimated to approximately to 1 % of the total. An important factor that determines the viability of any scheme providing fresh water suitable for human use is the cost. This requirement of minimal cost translates to optimised desalination processes to operate at minimum energy and initial capital investment. Minimising the costs should result in minimisation of the energy requirements in achieving the required result. Renewable energy sources are being exploited for the use in electrodialysis (ED) desalination processes especially for areas where power supply is limited or restricted and climate conditions vary

MINLP Interface Specification

International audienc

Naval Wholesale Inventory Optimization

The article of record as published may be found at https://doi.org/10.1007/978-3-030-28565-4_15The U.S. Naval Supply Systems Command (NAVSUP), Weapon Systems Support, manages an inventory of approximately 400,000 maritime and aviation line items valued at over $20 billion. This work describes NAVSUP’s Wholesale Inventory Optimization Model (WIOM), which helps NAVSUP’s planners establish inventory levels. Under certain assumptions, WIOM determines optimal reorder points (ROPs) to minimize expected shortfalls from fill rate targets and deviations from legacy solutions. Each item’s demand is modeled probabilistically, and negative expected deviations from target fill rates are penalized with nonlinear terms (conveniently approximated by piecewise linear functions). WIOM’s solution obeys a budget constraint. The optimal ROPs and related expected safety stock levels are used by NAVSUP’s Enterprise Resource Planning system to trigger requisitions for procurement and/or repair of items based on forecasted demand. WIOM solves cases with up to 20,000 simultaneous items using both a direct method and Lagrangian relaxation. In particular, this proves to be more efficient in certain cases that would otherwise take many hours to produce a solution