Facility location for a closed-loop distribution network: a hybrid approach

2016 ◽  
Vol 44 (9) ◽  
pp. 884-902 ◽  
Author(s):  
Abhijeet Ghadge ◽  
Qifan Yang ◽  
Nigel Caldwell ◽  
Christian König ◽  
Manoj Kumar Tiwari
Keyword(s):  
Facility Location ◽  
Environmental Sustainability ◽  
Closed Loop ◽  
Distribution Network ◽  
Hybrid Approach ◽  
Facility Location Problem ◽  
Mixed Integer ◽  
Content Type ◽  
Study Approach ◽  
Distribution Centre

Purpose The purpose of this paper is to find a sustainable facility location solution for a closed-loop distribution network in the uncertain environment created by of high levels of product returns from online retailing coupled with growing pressure to reduce carbon emissions. Design/methodology/approach A case study approach attempts to optimize the distribution centre (DC) location decision for single and double hub scenarios. A hybrid approach combining centre of gravity and mixed integer programming is established for the un-capacitated multiple allocation facility location problem. Empirical data from a major national UK retail distributor network is used to validate the model. Findings The paper develops a contemporary model that can take into account multiple factors (e.g. operational and transportation costs and supply chain (SC) risks) while improving performance on environmental sustainability. Practical implications Based on varying product return rates, SC managers can decide whether to choose a single or a double hub solution to meet their needs. The study recommends a two hub facility location approach to mitigate emergent SC risks and disruptions. Originality/value A two-stage hybrid approach outlines a unique technique to generate candidate locations under twenty-first century conditions for new DCs.

Environmentally sustainable stochastic procurement model

2018 ◽  
Vol 29 (3) ◽  
pp. 472-498 ◽  
Author(s):  
Harpreet Kaur ◽  
Surya Prakash Singh
Keyword(s):  
Carbon Emissions ◽  
Environmental Sustainability ◽  
Lot Sizing ◽  
Planning Horizon ◽  
Business Environment ◽  
Manufacturing Firms ◽  
Mixed Integer ◽  
Content Type ◽  
Environmentally Sustainable ◽  
Procurement Planning

Purpose Procurement planning has always been a huge and challenging activity for business firms, especially in manufacturing. With government legislations about global concern over carbon emissions, the manufacturing firms are enforced to regulate and reduce the emissions caused throughout the supply chain. It is observed that procurement and logistics activities in manufacturing firms contribute heavily toward carbon emissions. Moreover, highly dynamic and uncertain business environment with uncertainty in parameters such as demand, supplier and carrier capacity adds to the complexity in procurement planning. The paper aims to discuss these issues. Design/methodology/approach This paper is a novel attempt to model environmentally sustainable stochastic procurement (ESSP) problem as a mixed-integer non-linear program. The ESSP optimizes the procurement plan of the firm including lot-sizing, supplier and carrier selection by addressing uncertainty and environmental sustainability. The model applies chance-constrained-based approach to address the uncertain parameters. Findings The proposed ESSP model is solved optimally for 30 data sets to validate the proposed ESSP and is further demonstrated using three illustrations solved optimally in LINGO 10. Originality/value The ESSP model simultaneously minimizes total procurement cost and carbon emissions over the entire planning horizon considering uncertain demand, supplier and carrier capacity.

A hybrid clustering and ranking method for best positioned logistics distribution centre in Balkan Peninsula

2017 ◽  
Vol 25 (6) ◽  
pp. 991-1005 ◽  
Author(s):  
Dragan Simić ◽  
Vladimir Ilin ◽  
Vasa Svirčević ◽  
Svetlana Simić
Keyword(s):  
Facility Location ◽  
Hybrid Genetic Algorithm ◽  
Facility Location Problem ◽  
Balkan Peninsula ◽  
Location Decisions ◽  
Logistics Distribution ◽  
Distribution Centre ◽  
Wide Range ◽  
Decision Making Problem ◽  
Hierarchy Process

Abstract Facility location decisions are critical in strategic planning for a wide range of operational and logistical decisions. Facility location problem with focus on logistics distribution centre (LDC) in Balkan Peninsula (BP) is discussed in this article. Methodological hybrid genetic algorithm, Analytical Hierarchy Process, and fuzzy c-means method is proposed here and it is shown how such a model can be of assistance in analysing a multi criteria decision-making problem. This research represents continuation of three existing studies. The experimental results in our research could be well compared with other official results of the feasibility study of the LDC located in BP.

A novel facility location problem for taxi hailing platforms

2020 ◽  
Vol 120 (3) ◽  
pp. 526-546 ◽  
Author(s):  
Hong Ma ◽  
Ni Shen ◽  
Jing Zhu ◽  
Mingrong Deng
Keyword(s):  
Facility Location ◽  
Location Problem ◽  
Facility Location Problem ◽  
Time Constraints ◽  
Data Sets ◽  
Trajectory Data ◽  
Content Type ◽  
Regional Demand ◽  
New Variant ◽  
Vehicle Trajectory

Purpose Motivated by a problem in the context of DiDi Travel, the biggest taxi hailing platform in China, the purpose of this paper is to propose a novel facility location problem, specifically, the single source capacitated facility location problem with regional demand and time constraints, to help improve overall transportation efficiency and cost. Design/methodology/approach This study develops a mathematical programming model, considering regional demand and time constraints. A novel two-stage neighborhood search heuristic algorithm is proposed and applied to solve instances based on data sets published by DiDi Travel. Findings The results of this study show that the model is adequate since new characteristics of demand can be deduced from large vehicle trajectory data sets. The proposed algorithm is effective and efficient on small and medium as well as large instances. The research also solves and presents a real instance in the urban area of Chengdu, China, with up to 30 facilities and demand deduced from 16m taxi trajectory data records covering around 16,000 drivers. Research limitations/implications This study examines an offline and single-period case of the problem. It does not consider multi-period or online cases with uncertainties, where decision makers need to dynamically remove out-of-service stations and add other stations to the selected group. Originality/value Prior studies have been quite limited. They have not yet considered demand in the form of vehicle trajectory data in facility location problems. This study takes into account new characteristics of demand, regional and time constrained, and proposes a new variant and its solution approach.

Lagrangian Bounds and a Heuristic for the Two-Stage Capacitated Facility Location Problem

2012 ◽  
Vol 1 (1) ◽  
pp. 59-71 ◽  
Author(s):  
Igor Litvinchev ◽  
Edith L. Ozuna
Keyword(s):  
Facility Location ◽  
Location Problem ◽  
Computational Study ◽  
Facility Location Problem ◽  
Mixed Integer ◽  
Variable Cost ◽  
Fixed Cost ◽  
Two Stage ◽  
Capacitated Facility Location Problem ◽  
Capacitated Facility Location

In the two-stage capacitated facility location problem, a single product is produced at some plants in order to satisfy customer demands. The product is transported from these plants to some depots and then to the customers. The capacities of the plants and depots are limited. The aim is to select cost minimizing locations from a set of potential plants and depots. This cost includes fixed cost associated with opening plants and depots, and variable cost associated with both transportation stages. In this work, two different mixed integer linear programming formulations are considered for the problem. Several Lagrangian relaxations are analyzed and compared, and a Lagrangian heuristic producing feasible solutions is presented. The results of a computational study are reported.

The Value of Randomized Solutions in Mixed-Integer Distributionally Robust Optimization Problems

2021 ◽  
Author(s):  
Erick Delage ◽  
Ahmed Saif
Keyword(s):  
Robust Optimization ◽  
Facility Location ◽  
Column Generation ◽  
Location Problem ◽  
Optimization Problems ◽  
Facility Location Problem ◽  
Mixed Integer ◽  
Distributionally Robust Optimization ◽  
Continuous Relaxation ◽  
Distributionally Robust

Randomized decision making refers to the process of making decisions randomly according to the outcome of an independent randomization device, such as a dice roll or a coin flip. The concept is unconventional, and somehow counterintuitive, in the domain of mathematical programming, in which deterministic decisions are usually sought even when the problem parameters are uncertain. However, it has recently been shown that using a randomized, rather than a deterministic, strategy in nonconvex distributionally robust optimization (DRO) problems can lead to improvements in their objective values. It is still unknown, though, what is the magnitude of improvement that can be attained through randomization or how to numerically find the optimal randomized strategy. In this paper, we study the value of randomization in mixed-integer DRO problems and show that it is bounded by the improvement achievable through its continuous relaxation. Furthermore, we identify conditions under which the bound is tight. We then develop algorithmic procedures, based on column generation, for solving both single- and two-stage linear DRO problems with randomization that can be used with both moment-based and Wasserstein ambiguity sets. Finally, we apply the proposed algorithm to solve three classical discrete DRO problems: the assignment problem, the uncapacitated facility location problem, and the capacitated facility location problem and report numerical results that show the quality of our bounds, the computational efficiency of the proposed solution method, and the magnitude of performance improvement achieved by randomized decisions. Summary of Contribution: In this paper, we present both theoretical results and algorithmic tools to identify optimal randomized strategies for discrete distributionally robust optimization (DRO) problems and evaluate the performance improvements that can be achieved when using them rather than classical deterministic strategies. On the theory side, we provide improvement bounds based on continuous relaxation and identify the conditions under which these bound are tight. On the algorithmic side, we propose a finitely convergent, two-layer, column-generation algorithm that iterates between identifying feasible solutions and finding extreme realizations of the uncertain parameter. The proposed algorithm was implemented to solve distributionally robust stochastic versions of three classical optimization problems and extensive numerical results are reported. The paper extends a previous, purely theoretical work of the first author on the idea of randomized strategies in nonconvex DRO problems by providing useful bounds and algorithms to solve this kind of problems.

Multi-objective robust optimization for multi-stage-multi-product agile closed-loop supply chain under uncertainty in the context of circular economy

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Saeid Jafarzadeh Ghoushchi ◽  
Iman Hushyar ◽  
Kamyar Sabri-Laghaie
Keyword(s):  
Supply Chain ◽  
Robust Optimization ◽  
Circular Economy ◽  
Closed Loop ◽  
Mixed Integer ◽  
Closed Loop Supply Chain ◽  
Content Type ◽  
Multi Objective ◽  
Multi Stage ◽  
Proposed Model

PurposeA circular economy (CE) is an economic system that tries to eliminate waste and continually use resources. Due to growing environmental concerns, supply chain (SC) design should be based on the CE considerations. In addition, responding and satisfying customers are the challenges managers constantly encounter. This study aims to improve the design of an agile closed-loop supply chain (CLSC) from the CE point of view.Design/methodology/approachIn this research, a new multi-stage, multi-product and multi-period design of a CLSC network under uncertainty is proposed that aligns with the goals of CE and SC participants. Recycling of goods is an important part of the CLSC. Therefore, a multi-objective mixed-integer linear programming model (MILP) is proposed to formulate the problem. Besides, a robust counterpart of multi-objective MILP is offered based on robust optimization to cope with the uncertainty of parameters. Finally, the proposed model is solved using the e-constraint method.FindingsThe proposed model aims to provide the strategic choice of economic order to the suppliers and third-party logistic companies. The present study, which is carried out using a numerical example and sensitivity analysis, provides a robust model and solution methodology that are effective and applicable in CE-related problems.Practical implicationsThis study shows how all upstream and downstream units of the SC network must work integrated to meet customer needs considering the CE context.Originality/valueThe main goal of the CE is to optimize resources, reduce the use of raw materials, and revitalize waste by recycling. In this study, a comprehensive model that can consider both SC design and CE necessities is developed that considers all SC participants.

scholarly journals A Hybrid Approach to Solve a Model of Closed-Loop Supply Chain

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Nafiseh Tokhmehchi ◽  
Ahmad Makui ◽  
Soheil Sadi-Nezhad
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Reverse Flow ◽  
Hybrid Approach ◽  
The Other ◽  
Mixed Integer ◽  
Closed Loop Supply Chain ◽  
Total Cost ◽  
Large Size ◽  
Hybrid Forms

This paper investigates a closed-loop supply chain network, including plants, demand centers, as well as collection centers, and disposal centers. In forward flow, the products are directly sent to demand centers, after being produced by plants, but in the reverse flow, reused products are returned to collection centers and, after investigating, are partly sent to disposal centers and the other part is resent to plants for remanufacturing. The proposed mathematical model is based on mixed-integer programming and helps minimizing the total cost. Total costs include the expenditure of establishing new centers, producing new products, cargo transport in the network, and disposal. The model aims to answer these two questions. (1) What number and in which places the plants, collection centers, and disposal centers will be constructed. (2) What amount of products will be flowing in each segment of the chain, in order to minimize the total cost. Four types of tuned metaheuristic algorithms were used, which are hybrid forms of genetic and firefly algorithms. Finally an adequate number of instances are generated to analyse the behavior of proposed algorithms. Computational results reveal that iterative sequentialization hybrid provides better solution compared with the other approaches in large size.

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