Multi-objective optimisation of facility location decisions within integrated forward/reverse logistics under uncertainty (original) (raw)
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Facility Location Decisions within Integrated Forward/Reverse Logistics under Uncertainty
Procedia CIRP, 2014
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Closed-loop supply chain (CLSC) Mixed-integer linear programming (MILP) Multi-objective programming Stochastic programming a b s t r a c t A closed-loop supply chain (CLSC) network consists of both forward and reverse supply chains. In this paper, a CLSC network is investigated which includes multiple plants, collection centres, demand markets, and products. To this aim, a mixed-integer linear programming model is proposed that minimizes the total cost. Besides, two test problems are examined. The model is extended to consider environmental factors by weighed sums and e-constraint methods. In addition, we investigate the impact of demand and return uncertainties on the network configuration by stochastic programming (scenario-based). Computational results show that the model can handle demand and return uncertainties, simultaneously.
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A multi-objective optimisation algorithm for new distribution centre location
International Journal of Business Performance and Supply Chain Modelling, 2015
Determining the location of a new distribution centre (DC) is a strategic decision that has critical implications on supply chain performance. This paper solves a model that formulates this decision as a nonlinear model with two objectives of minimising total supply chain and minimising inventory capacity on the two echelons of the supply chain. The paper presents and tests two multi-objective optimisation algorithms; non-dominated sorting particle swarm optimisation algorithm (NSPSO) and non-dominated sorting genetic algorithm (NSGA-II). For each algorithm, the paper tests three different settings for handling constraints. Both algorithms in the three settings outperformed published results. Analysis also showed that while NSPSO with its variations have competitive effectiveness over NSGA-II, they required longer CPU time.
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Traditionally, product returns have been viewed as an unavoidable cost of distribution systems. As cost pressure continues to mount because of the competitive markets with the development of economy, some scholars and logisticians have begun to explore the possibility of managing product returns in a more costefficient manner. However, up to now there are few studies to address the problem of determining the number and location of centralized product return centers where returned products from retailers or end-customers are collected for manufacturers' or distributors' repair facilities while considering the distribution system. To fill the void in such a line of research, this paper proposes a nonlinear mixed-integer programming model and a genetic algorithm that can solve the distribution problem with uncertain demands and product returns simultaneously. Compared with a partly enumeration method, the numerical analysis shows the effectiveness of the proposed model and its genetic algorithm approach.
A Meta-Heuristic Approach to a Strategic Mixed Inventory-Location Model: Formulation and Application
Transportation Research Procedia, 2017
In the present day, it is increasingly more important for the companies to have a distribution network that minimize the logistic costs without reducing the level of service to the customer (delivery time, enough inventory, etc.). To reach conciliation within these objectives that may look conflicting requires developing some tools that allow decision-making. Having this in mind, the authors present a strategic inventory-location model, multiproduct and different with demand periods. This is a complex problem of integer mixed programming, that allow to determine the optimum distribution network given the fixed, transportation and inventory costs. The problem is illustrated by applying it to a real case of a steel company in Colombia, to resolve it, exhaustive revision and a genetic algorithm were used. The results obtained reveal the importance of the making joint strategic-tactic decisions, as well as the impact of each of the variables considered in the logistics costs.
Procedia CIRP, 2014
In this paper an approach to apply inventory decisions in facility location problem is presented. In fact, there are many models which are concerned with some of the inventory decisions in distribution network design and most of them shortly utilize inventory elements. The contribution is to minimize total establishment, transportation and inventory costs in a multi-commodity single-period distribution system and is supported by a case study to be implemented and the solutions are followed. The demand of customer is assumed to be deterministic.
An integrated model for space determination and site selection of distribution centers
2003
In this paper we present an integrated distribution center site selection and space requirement problem on a two-stage network in which products are shipped from plants to distribution centers, where they are stored for an arbitrary period of time and then delivered to retailers. The objective of the problem is to minimize total inbound and outbound transportation costs and total distribution center construction cost -which includes fixed costs related to their locations and variable costs related to their space requirements for given service levels. Each distribution center is modeled as an M/G/c queueing system, in which each server represents a storage slot. We formulate this problem as a nonlinear mixed integer program with a probabilistic constraint. Two cases are considered. For the continuous unbounded size case, we find an approximate formula for the overflow probability and restructure this model into a connection location problem. For the discrete size option case, we reformulate the problem into a capacitated connection location problem with discrete size options. Computational results and a comparison of the two cases are provided.
A Two Objective Model For Location-allocation In A Supply Chain
Journal of Mathematics and Computer Science, 2012
The fast changing and dynamic global business environment require companies to plan their entire supply chain from the raw material supplier to the end customer. In this paper, we design an integrated supply chain including multiple suppliers, multiple factories, multiple distributors, multiple customers, multiple products, and multiple transportation alternatives. A new multiobjective mixed-integer nonlinear programming model is proposed to deal with this facility locationallocation problem. It considers two conflicting objectives simultaneously, and then the problem is transformed into a multi-objective linear one. The first objective function aims to minimize total losses of the supply chain including raw material purchasing costs, transportation costs and establishment costs of factories and distributions. The second objective function is to minimize the sum deterioration rate of end products and raw materials incurred by transportation alternatives. Finally, the proposed model is solved as a single-objective, mixed-integer, programming model applying the Global Criteria Method. We test their model with numerical example and the results indicate that the proposed model can provide a promising approach to fulfill customer demand and design an efficient supply chain.