A Large Neighborhood Search heuristic for Supply Chain Network
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Abstract:
Many exact or approximate solution techniques have been used to solve facility location problems and more generally supply chain network design problems. Yet, the Large Neighborhood Search technique (LNS) has almost never been proposed for solving such problems, although it has proven its efficiency and flexibility in solving other complex combinatorial optimization problems. In this paper we propose an LNS framework for solving a four-layer single period multi-product supply chain network design problem involving multimodal transport. Location decisions for intermediate facilities (e.g. plants and distribution centers) are made using the LNS while transportation modes and product flow decisions are determined by a greedy heuristic. As a post-optimization step, we also use linear programming to determine the optimal product flows once the logistics network is fixed. Extensive experiments based on generated instances of different sizes and characteristics show the effectiveness of the method compared with a state-of-the-art solver.Keywords:
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Supply chain network
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Nowadays, organizations have to compete with different competitors in regional, national and international levels, so they have to improve their competition capabilities to survive against competitors. Undertaking activities on a global scale requires a proper distribution system which could take advantages of different transportation modes. Accordingly, the present paper addresses a location-routing problem on multimodal transportation network. The introduced problem follows four objectives simultaneously which form main contribution of the paper; determining multimodal routes between supplier and distribution centers, locating mode changing facilities, locating distribution centers, and determining product delivery tours from the distribution centers to retailers. An integer linear programming is presented for the problem, and a genetic algorithm with a new chromosome structure proposed to solve the problem. Proposed chromosome structure consists of two different parts for multimodal transportation and location-routing parts of the model. Based on published data in the literature, two numerical cases with different sizes generated and solved. Also, different cost scenarios designed to better analyze model and algorithm performance. Results show that algorithm can effectively solve large-size problems within a reasonable time which GAMS software failed to reach an optimal solution even within much longer times.
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Vehicle Routing Problem
Multimodal transport
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Heuristics
Supply chain network
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This paper deals with a planning problem arising in large logistics companies. The problem consists in defining the optimal number of depots, their location, and their size as well as the routing decisions. Economies of scale are present both in the transportation and in the depots operations. The paper proposes an exact algorithm and an efficient heuristic based on the integration of tabu search and variable neighborhood search principles. Computational results obtained with real-life data and randomly generated but realistic instances prove the validity of the approaches.
Variable neighborhood search
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Heuristics
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Variable neighborhood search
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Heuristics
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Supply-chain network design is a complex task because there are many decisions involved, and presently, global networks involve many actors and variables, for example, in the automotive, pharmaceutical, and electronics industries. This research addresses a supply-chain network design problem with four levels: suppliers, factories, warehouses, and customers. The problem considered decides on the number, locations, and capacities of factories and warehouses and the transportation between levels in the supply chain. The problem is modeled as a mixed-integer linear program. The main contribution of this work is the proposal of two matheuristic algorithms to solve the problem. Matheuristics are algorithms that combine exact methods and heuristics, attracting interest in the literature because of their fast execution and high-quality solutions. The matheuristics proposed to select the warehouses and their capacities following heuristic rules. Once the warehouses and their capacities are fixed, the algorithms solve reduced models using commercial optimization software. Medium and large instances were generated based on a procedure described in the literature. A comparison is made between the algorithms and the results obtained, solving the model with a time limit. The algorithms proposed are successful in obtaining better results for the largest instances in shorter execution times.
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In this paper, we propose a bi-objective MILP formulation to minimise logistics costs as well as CO2 emissions in a supply chain network design problem with multiple layers of facilities, technology levels and transportation mode decisions. The proposed model aims at investigating the trade-off between cost and CO2 emissions through supply chain activities (i.e. raw material supply, manufacturing, warehousing, and transportation). To this end, a multi-directional local search (MDLS) metaheuristic is developed. The proposed method provides a limited set of non-dominated solutions ranging from a purely cost effective solution to a purely environmentally effective one. Each iteration of the MDLS consists in performing local searches from all non-dominated solutions. To do so, a Large Neighborhood Search (LNS) algorithm is used. Extensive experiments based on randomly generated instances of various sizes and features are described. Three classic performance measures are used to compare the set of non-dominated solutions obtained by the MDLS algorithm and by directly solving the MILP model with the epsilon-constraint approach. This paper is concluded by managerial insights about the impact of using greener technology on the supply chain topology.
Supply chain network
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1. The problem considered
The increasing importance of environmental issues has prompted decisionmakers
to incorporate environmental factors into supply chain network
design (SCND) models. We propose a bi-objective SCND model to minimize
two conflicting objectives: the total cost and the environmental impact
expressed by CO2 emissions.
The logistics network consists of four layers: suppliers, plants, distribution
centers (DCs) and customers. The model considers several possible
transportations modes in the network, each transportation mode having
a lower and upper capacity limitation. Moreover, we consider different
candidate technology levels at the plants and DCs. Each technology represents
a type of service with associated fixed and variable costs and CO2
emissions. A higher-level technology may reduce carbon emissions, but is
likely to require more investment cost.
The model considers CO2 emissions caused by all industrial and logistics
operations as well as transportation. The main issues to be addressed
in the sustainable SCND model includes determining the number, location,
and technology level at plants and DCs, suitable transportation mode, and
product flows between facilities.
2. Solution method
We solve the corresponding bi-objective mixed integer linear programming
model with the multi-directional local search (MDLS) framework.
The efficiency of this recent framework has been proved on the multiobjective
knapsack, set packing and orienteering problems, but to the best
of our knowledge, this is the first attempt to solve a facility location problem
with it. The MDLS is based on the principle of separately using independent
single-objective local searches to iteratively improve the Pareto set
approximation. The motivation for using this framework is the capability
of using already implemented single objective optimization components.
In our case, we use a large neighborhood search algorithm as single objective
method. Our algorithm can be decomposed in the three following
steps:
Phase 1: look for an initial Pareto set approximation. The initial
phase of the single objective LNS is executed separately for each objective.
The output is an initial Pareto set approximation.
Phase 2: Intensification around the Pareto set approximation. The
Pareto set approximation is improved by exploring the neighborhood
of all the solutions in this set with a Multi-directional local search.
Phase 3: optimization of product flows. After stabilizing the location
and transportation mode decisions for all Pareto set approximation
solutions in phase 2, we determine the optimal product flows by applying
the Simplex algorithm to all solutions in the set.
3. Computational results
We assess the performance of our approach through a comparison with
the well-known -constraint method. In particular, we analyze the Pareto
fronts given by both solutions on a set of 60 generated instances and show
that the efficiency of our approach improves when the instance size grows.
Supply chain network
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Column generation
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In this paper, we are addressing the two-stage transportation problem with fixed charge for opening the distribution centers, which is an extension of the classical transportation problem. The problem models a distribution network in a two-stage supply chain which involves: manufacturers, distribution centers and customers, and its main characteristic is that a fixed charge for opening the distribution centers is associated, in addition to the variable transportation cost which is proportional to the amount of goods shipped. We describe a novel solution approach for the minimization of total distribution costs: a fast and efficient constructive heuristic algorithm that reduces the solution search space to a subspace with a reasonable size, without losing optimal or sub-optimal solutions by considering a perturbation mechanism that allows us to reconsider discarded feasible solutions that might lead to such solutions. Computational results are reported and discussed for the existing benchmark instances and on a set of instances that contains eight new randomly generated larger instances. The obtained results show that our solution approach is highly competitive as compared to the existing methods from the literature.
Transportation theory
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