A hybrid fix-and-optimize heuristic for integrated inventory-transportation problem in a multi-region multi-facility supply chain

2020 ◽  
Vol 54 (3) ◽  
pp. 749-782
Author(s):  
Ajinkya Tanksale ◽  
J.K. Jha
Keyword(s):  
Supply Chain ◽  
Distribution System ◽  
Transportation Problem ◽  
Mixed Integer ◽  
Computational Time ◽  
Solution Approach ◽  
Mip Model ◽  
Integrated Inventory ◽  
Problem Instances ◽  
Decomposition Schemes

In this work, we study an integrated inventory-transportation problem in a supply chain consisting of region-bound warehouses located in different regions. The supply chain deals with multiple items that compete for storage space and transportation capacity with multi-modal transportation considering regional capacity constraint for each mode of transportation. The objective is to determine an optimal storage and transportation plan to satisfy the demand of all regions without shortages for known procurement plan for all items. The problem is formulated as a mixed integer programming (MIP) model for minimizing the total costs over a finite planning horizon. An MIP-based fix-and-optimize (F&O) heuristic with several decomposition schemes is proposed to solve the problem efficiently. The performance of the decomposition schemes is investigated against the structure of the sub-problems obtained. To enhance the performance, F&O is crossbred with two metaheuristics – genetic algorithm (GA) and iterated local search (ILS) separately, which lead to hybrid heuristic approach. Extensive numerical experiments are carried out to analyze the performance of the proposed solution methodology by randomly generating several problem instances built using data collected from the Indian Public Distribution System. The proposed solution approach is found to be computationally efficient and effective, and outperforming state of the art MIP solver Cplex for practical size problem instances. Also, the hybridization of F&O heuristic with GA and ILS boosts its performance although with a justified increase in the computational time.

A LAGRANGIAN-BASED SOLUTION ALGORITHM FOR STRATEGIC SUPPLY CHAIN DISTRIBUTION DESIGN IN UNCERTAIN ENVIRONMENT

2010 ◽  
Vol 09 (03) ◽  
pp. 393-418 ◽  
Author(s):  
G. REZA NASIRI ◽  
HAMID DAVOUDPOUR ◽  
BEHROOZ KARIMI
Keyword(s):  
Supply Chain ◽  
Distribution Network ◽  
Solution Procedure ◽  
Supply Chain Design ◽  
Mixed Integer ◽  
Computational Time ◽  
Heuristic Solution ◽  
Allocation Model ◽  
Solution Approach ◽  
Location Allocation

In this paper a multi-product, multi-echelon location–allocation model for the optimization of a supply chain design is proposed. This model integrated inventory decisions into distribution network design with stochastic market demands. The goal is to select the optimum numbers, locations, and capacities of the opening warehouses so that all customer demands to be satisfied at minimum total costs of the distribution network. We develop a nonlinear mixed-integer model and propose an efficient heuristic solution procedure for the problem. The solution approach is based on Lagrangian relaxation, improved with efficient heuristic to solve complex sub-problems. Computational results indicate that the proposed method yields good solutions with high quality within a reasonable computational time for various real-size problems.

Production and Inter-Facility Transportation with Shipment Size Restrictions

2013 ◽  
pp. 74-92
Author(s):  
Mohamed K. Omar
Keyword(s):  
Integer Programming ◽  
Transportation Problem ◽  
Mixed Integer ◽  
Actual Performance ◽  
Industrial Data ◽  
Chemical Company ◽  
Mip Model ◽  
Proposed Model ◽  
Production Inventory ◽  
End Products

This chapter studies production and transportation problem confronting a speciality chemical company that has two manufacturing facilities. Facility I produces intermediate products which are then transported to Facility II where the end products are to be manufactured to meet customers’ demand. The author formulated the problem as a mixed integer programming (MIP) model that integrates the production and transportation decisions between the two facilities. The developed MIP aims to minimize the production, inventory, manpower, and transportation costs. Real industrial data are used to test and validate the developed MIP model. Comparing the model’s results and the company’s actual performance indicate that, if the company implemented the proposed model, significant costs savings could be achieved.

A Nelder and Mead Methodology for Solving Small Fixed-Charge Transportation Problems

2011 ◽  
pp. 345-356
Author(s):  
G. Kannan ◽  
P. Senthil ◽  
P. Sasikumar ◽  
V. P. Vinay
Keyword(s):  
Supply Chain ◽  
Transportation Problem ◽  
Optimization Problems ◽  
Fixed Charge ◽  
Computational Time ◽  
Variable Cost ◽  
Fixed Cost ◽  
Supply Chain Optimization ◽  
Business World ◽  
Fixed Charge Transportation Problem

The term ‘supply chain management’ has become common in the business world, which can be understood from the positive results of research in the area, particularly in supply chain optimization. Transportation is a frontier in achieving the objectives of the supply chain. Thrust is also given to optimization problems in transportation. The fixed-charge transportation problem is an extension of the transportation problem that includes a fixed cost, along with a variable cost that is proportional to the amount shipped. This article approaches the problem with another meta-heuristics known as the Nelder and Mead methodology to save the computational time with little iteration and obtain better results with the help of a program in C++.

scholarly journals A Novel Approach for Optimizing the Supply Chain: A Heuristic-Based Hybrid Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Yasemin Kocaoglu ◽  
Emre Cakmak ◽  
Batuhan Kocaoglu ◽  
Alev Taskin Gumus
Keyword(s):  
Supply Chain ◽  
Distribution System ◽  
Hybrid Algorithm ◽  
Minimum Cost ◽  
Computational Time ◽  
Strategy Selection ◽  
Distribution Strategy ◽  
Network Problem ◽  
Milk Run ◽  
Delivery Network

Managing the distribution of goods is a vital operation for many companies. A successful distribution system requires an effective distribution strategy selection and optimum route planning at the right time and minimum cost. Furthermore, customer’s demand and location can vary from order to order. In this situation, a mixed delivery system is a good solution for it and allows the use of different strategies together to decrease delivery costs. Although the “distribution strategy selection” is a critical issue for companies, there are only a few studies that focus on the mixed delivery network problem. There is a need to propose an efficient solution for the mixed delivery problem to guide researchers and practitioners. This paper develops a new “modified” savings-based genetic algorithm which is named “distribution strategy selection and vehicle routing hybrid algorithm (DSSVRHA).” Our new algorithm aims to contribute to the literature a new hybrid solution to solve a mixed delivery network problem that includes three delivery modes: “direct shipment,” “milk run,” and “cross-docking” efficiently. It decides the appropriate distribution strategy and also optimal routes using a heterogeneous fleet of vehicles at minimum cost. The results of the hybrid algorithm are compared with the results of the optimization model. And the performance of the hybrid algorithm is validated with statistical analysis. The computational results reveal that our developed algorithm provides a good solution for reducing the supply chain distribution costs and computational time.

Optimization Methods for Lot-Sizing Problem in an Automated Foundry

2013 ◽  
Vol 58 (3) ◽  
pp. 863-866 ◽  
Author(s):  
J. Duda ◽  
A. Stawowy
Keyword(s):  
Production Planning ◽  
Lot Sizing ◽  
Planning Horizon ◽  
Optimization Methods ◽  
Mixed Integer ◽  
Planning Problem ◽  
Lot Size ◽  
Mip Model ◽  
Production Planning Problem ◽  
Problem Instances

Abstract In the paper we studied a production planning problem in a mid-size foundry that provides tailor-made cast products in small lots for a large number of clients. Assuming that a production bottleneck is the furnace, a mixed-integer programming (MIP) model is proposed to determine the lot size of the items and the required alloys to be produced during each period of the finite planning horizon that is subdivided into smaller periods. As using an advanced commercial MIP solvers may be impractical for more complex and large problem instances, we proposed and compared a few computational intelligence heuristics i.e. tabu search, genetic algorithm and differential evolution. The examination showed that heuristic approaches can provide a good compromise between speed and quality of solutions and can be used in real-world production planning.

scholarly journals Designing a Supply Chain Network under the Risk of Disruptions

2012 ◽  
Vol 2012 ◽  
pp. 1-23 ◽  
Author(s):  
Armin Jabbarzadeh ◽  
Seyed Gholamreza Jalali Naini ◽  
Hamid Davoudpour ◽  
Nader Azad
Keyword(s):  
Supply Chain ◽  
Supply Chain Design ◽  
Mixed Integer ◽  
Nonlinear Program ◽  
Supply Chain Network ◽  
Large Problem ◽  
Solution Methods ◽  
Total Profit ◽  
Order Quantities ◽  
Problem Instances

This paper studies a supply chain design problem with the risk of disruptions at facilities. At any point of time, the facilities are subject to various types of disruptions caused by natural disasters, man-made defections, and equipment breakdowns. We formulate the problem as a mixed-integer nonlinear program which maximizes the total profit for the whole system. The model simultaneously determines the number and location of facilities, the subset of customers to serve, the assignment of customers to facilities, and the cycle-order quantities at facilities. In order to obtain near-optimal solutions with reasonable computational requirements for large problem instances, two solution methods based on Lagrangian relaxation and genetic algorithm are developed. The effectiveness of the proposed solution approaches is shown using numerical experiments. The computational results, in addition, demonstrate that the benefits of considering disruptions in the supply chain design model can be significant.

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.

Mixed-model multi-manned assembly line balancing problem: a mathematical model and a simulated annealing approach

2017 ◽  
Vol 37 (1) ◽  
pp. 34-50 ◽  
Author(s):  
Abdolreza Roshani ◽  
Farnaz Ghazi Nezami
Keyword(s):  
Simulated Annealing ◽  
Assembly Line ◽  
Mixed Model ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Mixed Integer ◽  
Computational Time ◽  
Content Type ◽  
Mip Model ◽  
Assembly Line Balancing Problem

Purpose This paper aims to study a generalized type of mixed-model assembly line with multi-manned workstations where multiple workers simultaneously perform different tasks on the same product. This special kind of assembly line is usually utilized to assemble different models of large products, such as buses and trucks, on the same production line. Design/methodology/approach To solve the mixed-model multi-manned assembly line balancing problem optimally, a new mixed-integer-programming (MIP) model is presented. The proposed MIP model is nondeterministic polynomial-time (NP)-hard, and as a result, a simulated annealing (SA) algorithm is developed to find the optimal or near-optimal solution in a small amount of computation time. Findings The performance of the proposed algorithm is examined for several test problems in terms of solution quality and running time. The experimental results show that the proposed algorithm has a satisfactory performance from computational time efficiency and solution accuracy. Originality/value This research is the very first study that minimizes the number of workers and workstations simultaneously, with a higher priority set for the number of workers, in a mixed-model multi-manned assembly line setting using a novel MIP model and an SA algorithm.

A multicommodity unpaired pickup and delivery vehicle routing problem with split loads and unloads

2020 ◽  
Vol 120 (8) ◽  
pp. 1565-1584
Author(s):  
Xu Dongyang ◽  
Li Kunpeng ◽  
Yang Jiehui ◽  
Cui Ligang
Keyword(s):  
Valid Inequalities ◽  
Solution Space ◽  
Mixed Integer ◽  
Computational Time ◽  
Neighborhood Search ◽  
Optimal Solutions ◽  
Solution Quality ◽  
Content Type ◽  
Mip Model ◽  
Operational Costs

PurposeThis paper aims to explore the commodity transshipment planning among customers, which is commonly observed in production/sales enterprises to save the operational costs.Design/methodology/approachA mixed integer programming (MIP) model is built and five types of valid inequalities for tightening the solution space are derived. An improved variable neighborhood search (IVNS) algorithm is presented combining the developed multistart initial solution strategy and modified neighborhood local search procedure.FindingsExperimental results demonstrate that: with less decision variables considered, the proposed model can solve more instances compared to the existing model in previous literature. The valid inequalities utilized to tighten the searching space can efficiently help the model to obtain optimal solutions or high-quality lower bounds. The improved algorithm is efficient to obtain optimal or near-optimal solutions and superior to the compared algorithm in terms of solution quality, computational time and robustness.ractical implicationsThis research not only can help reduce operational costs and improve logistics efficiency for relevant enterprises, but also can provide guidance for constructing the decision support system of logistics intelligent scheduling platform to cater for centralized management and control.Originality/valueThis paper develops a more compact model and some stronger valid inequalities. Moreover, the proposed algorithm is easy to implement and performs well.

scholarly journals Designing a new Medicine Supply Chain Network considering production technology policy using two novel heuristic algorithms

2021 ◽  
Author(s):  
Fariba Goodarzian ◽  
Hassan Hoseini-Nasab ◽  
Mehdi Toloo ◽  
Mohammad Bagher Fakhrzad
Keyword(s):  
Supply Chain ◽  
Production Technology ◽  
Technology Policy ◽  
Heuristic Algorithms ◽  
Mixed Integer ◽  
Computational Time ◽  
Model Assessment ◽  
Supply Chain Network ◽  
High Quality ◽  
Medicine Supply

The role of medicines in health systems is increasing day by day. The medicine supply chain is a part of the health system that if not properly addressed, the concept of health in that community is unlikely to experience significant growth.  To fill gaps and available challenging in the medicine supply chain network (MSCN), in the present paper, efforts have been made to propose a location-production-distribution-transportation-inventory holding problem for a multi-echelon multi-product multi-period bi-objective MSCN network under production technology policy. To design the network, a mixed-integer linear programming (MILP) model capable of minimizing the total costs of the network and the total time the transportation is developed. As the developed model was NP-hard, several meta-heuristic algorithms are used and two heuristic algorithms, namely, Improved Ant Colony Optimization (IACO) and Improved Harmony Search (IHS) algorithms are developed to solve the MSCN model in different problems. Then, some experiments were designed and solved by an optimization solver called GAMS (CPLEX) and the presented algorithms to validate the model and effectiveness of the presented algorithms. Comparison of the provided results by the presented algorithms and the exact solution is indicative of the high-quality efficiency and performance of the proposed algorithm to find a near-optimal solution within reasonable computational time. Hence, the results are compared with commercial solvers (GAMS) with the suggested algorithms in the small-sized problems and then the results of the proposed meta-heuristic algorithms with the heuristic methods are compared with each other in the large-sized problems. To tune and control the parameters of the proposed algorithms, the Taguchi method is utilized. To validate the proposed algorithms and the MSCN model, assessment metrics are used and a few sensitivity analyses are stated, respectively. The results demonstrate the high quality of the proposed IACO algorithm.

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