A two-stage stochastic mixed-integer program for reliable supply chain network design under uncertain disruptions and demand

2020 ◽  
Vol 148 ◽  
pp. 106722 ◽  
Author(s):  
Ali Tolooie ◽  
Meghna Maity ◽  
Ashesh Kumar Sinha
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Integer Program ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Two Stage

scholarly journals Determining nodal capacities for military distribution problems

2019 ◽  
Vol 3 (2) ◽  
pp. 110-130 ◽  
Author(s):  
Dave C. Longhorn ◽  
Joshua R. Muckensturm
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
The United States ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Multiple Time ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Content Type ◽  
Distribution Problems

Purpose This paper aims to introduce a new mixed integer programming formulation and associated heuristic algorithm to solve the Military Nodal Capacity Problem, which is a type of supply chain network design problem that involves determining the amount of capacity expansion required at theater nodes to ensure the on-time delivery of military cargo. Design/methodology/approach Supply chain network design, mixed integer programs, heuristics and regression are used in this paper. Findings This work helps analysts at the United States Transportation Command identify what levels of throughput capacities, such as daily processing rates of trucks and railcars, are needed at theater distribution nodes to meet warfighter cargo delivery requirements. Research limitations/implications This research assumes all problem data are deterministic, and so it does not capture the variations in cargo requirements, transit times or asset payloads. Practical implications This work gives military analysts and decision makers prescriptive details about nodal capacities needed to meet demands. Prior to this work, insights for this type of problem were generated using multiple time-consuming simulations often involving trial-and-error to explore the trade space. Originality/value This work merges research of supply chain network design with military theater distribution problems to prescribe the optimal, or near-optimal, throughput capacities at theater nodes. The capacity levels must meet delivery requirements while adhering to constraints on the proportion of cargo transported by mode and the expected payloads for assets.

A model of resilient supply chain network design: A two-stage programming with fuzzy shortest path

2014 ◽  
Vol 41 (1) ◽  
pp. 39-49 ◽  
Author(s):  
Yohanes Kristianto ◽  
Angappa Gunasekaran ◽  
Petri Helo ◽  
Yuqiuqe Hao
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Shortest Path ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Two Stage ◽  
Resilient Supply Chain

Supply Chain Network Design Optimization with Risk-Averse Retailer

2017 ◽  
Vol 10 (1) ◽  
pp. 16-28 ◽  
Author(s):  
Hêriş Golpîra
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Value At Risk ◽  
Realistic Model ◽  
Mixed Integer ◽  
Conditional Value At Risk ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Integer Linear Programming Problem ◽  
Data Driven Approach

This paper proposes a model to formulate a supply chain network design (SCND) problem against uncertainty. The objective of the model is to minimize total cost of the network. The model employs risk averseness of retailers to obtain more realistic model regarding uncertain demand. Using Conditional Value at Risk (CVaR) to deal with this uncertainty makes the model to be robust. In this way, data-driven approach is used to avoid any distributional assumptions because realizations of uncertain parameters are the only information obtainable. This approach reformulates the initial uncertain model as a mixed integer linear programming problem. Numerical results show that the proposed model is efficient for robust SCND with respect to retailers risk averseness.

scholarly journals Design and Optimization of Capacitated Supply Chain Networks Including Quality Measures

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Krystel K. Castillo-Villar ◽  
Neale R. Smith ◽  
José F. Herbert-Acero
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Optimization Methods ◽  
Mixed Integer ◽  
Computational Time ◽  
Quality Level ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Quality Costs ◽  
Business Entities

This paper presents (1) a novel capacitated model for supply chain network design which considers manufacturing, distribution, and quality costs (named SCND-COQ model) and (2) five combinatorial optimization methods, based on nonlinear optimization, heuristic, and metaheuristic approaches, which are used to solve realistic instances of practical size. The SCND-COQ model is a mixed-integer nonlinear problem which can be used at a strategic planning level to design a supply chain network that maximizes the total profit subject to meeting an overall quality level of the final product at minimum costs. The SCND-COQ model computes the quality-related costs for the whole supply chain network considering the interdependencies among business entities. The effectiveness of the proposed solution approaches is shown using numerical experiments. These methods allow solving more realistic (capacitated) supply chain network design problems including quality-related costs (inspections, rework, opportunity costs, and others) within a reasonable computational time.

scholarly journals Multi-Period Multi-Product Supply Chain Network Design in the Competitive Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jian Wang ◽  
Xueyan Wang ◽  
Mingzhu Yu
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Price Competition ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Mixed Integer ◽  
Competitive Environment ◽  
Market Area ◽  
Supply Chain Network Design ◽  
Supply Chain Network

This paper studies a supply chain network design model with price competition. The supply chain provides multiple products for a market area in multiple periods. The model considers the location of manufacturers and retailers and assumes a probabilistic customer behavior based on an attraction function depending on both the location and the quality of the retailers. We aim to design the supply chain under the capacity constraint and maximize the supply chain profit in the competitive environment. The problem is formulated as a mixed integer nonlinear programming model. To solve the problem, we propose two heuristic algorithms—Simulated Annealing Search (SA) and Particle Swarm Optimization (PSO)—and numerically demonstrate the effectiveness of the proposed algorithms. Through the sensitivity analysis, we give some management insights.

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