Multi-level supply chain network design with routing

Recently, the multi-level and multi-facility industrial problem in supply chain management (SCM) has been widely investigated. One of the key issues, central to this problem in the current SCM research area is the interdependence among the location of facilities, the allocation of facilities, and the vehicle routing for the supply of raw materials and products. This paper studies the supply chain network design problem, which involves the location of facilities, allocation of facilities, and routing decisions. The proposed problem has some practical applications. For example, it is necessary for third party logistics (3PL) companies to manage the design of the network and to operate vehicle transportation. The purpose of this study is to determine the optimal location, allocation, and routing with minimum cost to the supply chain network. The study proposes two mixed integer programming models, one without routing and one with routing, and a heuristic algorithm based on LP-relaxation in order to solve the model with routing. The results show that a developed heuristic algorithm is able to find a good solution in a reasonable time.     

Designing a supply chain network for deteriorating inventory under preservation effort and trade credits

When designing a perishable goods supply chain network under trade credit arrangements, distribution companies must contend with deteriorating inventory and product preservation efforts to maximise profits. Key decisions include locating distribution centres (DCs), assigning retail stores to DCs, joint replenishment cycle time and investing in preservation technology. This paper addresses these factors from the position that as preservation effort increases, preservation technology cost increases and deterioration rate decreases. An algorithm based on piecewise nonlinear optimisation is provided for solving supply chain network design problems efficiently. In contrast to other studies that have used the approximation approach, the proposed approach solves the original problem accurately and efficiently. Numerical studies are conducted to demonstrate the solutions procedures and determine the effects of the parameters on decisions and profits. The results of this study and the proposed modelling approach are useful references for managerial decisions in designing a supply chain network the context of trade credit and inventory deterioration.     

An intelligent physarum solver for supply chain network design under profit maximization and oligopolistic competition

We propose an efficient bio-inspired algorithm for design of optimal supply chain networks in a competitive oligopoly markets. The firms compete in manufacture, storage and distribution of a product to several markets. Each firm aims at maximisation of its own profit by optimising the design capacity and product flow in the supply chain. We model the supply chain network as a multi-layer graph of manufacturing nodes, distribution nodes and storage centres. To optimise the network, we adopt the mechanisms of a foraging behaviour of slime mould Physarum polycephalym. First, we extend the original Physarum model to deal with networks with multiple sources and sinks. Second, we develop a novel method to solve the user equilibrium (UE) problem by exploiting the adaptivity of the Physarum model: we update the link costs according to the product flow. Third, we refer to an equivalent transformation between system optimum problem and UE problem to determine the optimal product flows and design capacities of a supply chain. At last, we present an approach to update the amount of product supplied by each firm. By comparing our solutions with that in Nagurney (2010b) on several numerical examples, we demonstrate the efficiency and practicality of the proposed method.     

A strategic decision support system for logistics and supply chain network design

This paper aims to develop a strategic decision support system for logistics and supply chain network design of a multi-stage, multi-commodity, and multi-period distribution and transportation system. A mixed integer linear programming model is proposed to tackle the problem while minimizing the operating, transportation and handling cost through all tiers of the supply chain network. A genetic algorithm based method has been proposed to solve the problem in a large scale realistic environment. The efficacy of the developed strategic decision support model in achieving better utilization of network and resources to fulfil the customer demand is demonstrated using illustrative scenarios inspired from the real case of a logistics company.     

A general corridor method-based approach for capacitated facility location

The Capacitated Facility Location Problem (CFLP) is a well-known optimisation problem with applications in a number of fields, such as distribution system planning, telecommunication network design, and supply chain design. The goal of this paper is to present a matheuristic algorithm based on the corridor method, to develop a general algorithm for a number of variants of the CFLP. The algorithm exploits solutions obtained via Lagrangean relaxation and builds corridors around such solutions via the introduction of constraints around the incumbent solution, used to limit the size of the solution space explored at each iteration. A thorough exploration of the neighbourhoods induced by the corridors is carried out using a mixed integer programming (MIP) solver. More precisely, we solve to (near) optimality over 500 benchmark instances, using the single-source as well as the multi-source formulations, both in the nominal variant, i.e. the deterministic version of the problem, and the robust variant, i.e. the version obtained when using robust optimisation to model the uncertainty of the problem parameters. The performance of the algorithm is highly competitive when compared with the best approaches proposed in the literature for each variant of the CFLP, especially considering that the algorithm has not been designed with a specific CFLP formulation in mind.     

A design model and a production–distribution and inventory planning model in multi-product supply chain networks

Supply chain network (SCN) design implicates decision-making at a strategic level. That includes selecting the right suppliers and determining the number and the location of plants, distribution centres and retailers. An apt design model of the supply chain is imperative for the proper function of the supply chain and consequently for making better operational decisions in an attempt of a continuous improvement. In this paper, we propose two models. The first model is a mixed-integer linear programming model which is concerned with the SCN design problem, whereas the second operational model is a mixed-integer non-linear programming model in respect to the production–distribution and inventory planning problem in a supply chain network. The number of customers and suppliers as well as their demand and capacities are assumed to be known in both models. Two steady-state genetic algorithms were implemented in MATLAB in order to solve both the design and the operational model. The results were compared with GAMS. Some examples were devised in order to demonstrate potential ways of use for the designer of the supply chain network, as well as for the supply chain manager.     

Production-distribution network design of a global supply chain alliance from the key player's perspective

A key player in a supply chain (SC) is the member who overlooks and leads others. Although it can compel its autonomous members to follow the planning of a production-distribution network (PDN), the key player has to create an environment in which the partnership with suppliers can also grow accordingly. Without the effective partnership, the efforts increase in managing and integrating the information and material flow across the PDN. Hence, while designing the PDN, the key player has to consider the constraint of the autonomous suppliers also seeking maximum profits as their business goals. In addition, to alleviate the impact of uncertainty on the PDN of a global SC, the strategy of alternative design features is incorporated in the PDN design model. In this paper, the problem for key player's PDN design with alternative design features (KPDN/ADF) is formulated as a bi-level programming problem. An extended genetic algorithm is proposed to solve the KPDN/ADF problem. The experiment results showed that the proposed algorithm can solve the problem satisfactorily. Furthermore, incorporating the product design strategy of alternative design features can make the key player's PDN have better profit under the uncertainty of the currency exchange rate. An illustrative global SC of personal computers/notebooks is also provided to demonstrate the applicability of the KPDN/ADF design model.     

Solving closed-loop supply chain problems using game theoretic particle swarm optimisation

In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods.     

Multi-objective resource assignment problem in a product-driven supply chain using a Taguchi-based DNA algorithm

This paper conceptualises the integration of tangible and intangible factors into the design consideration of a resource assignment problem for a product-driven supply chain. The problem is formulated mathematically as a multi-objective optimisation model to maximise the broad objectives of profit, ahead of time of delivery, quality, and volume flexibility. Product characteristics are associated with the design requirements of a supply chain. Different types of resources are considered, each differing in its characteristics, thereby providing various alternatives during the design process. The aim is to design integrated supply chains that maximise the weighted sum of the objectives, the weights being decided by the desired product characteristics. The problem is solved through the proposed Taguchi-based DNA algorithm that draws its traits from random search optimisation and the statistical design of experiments. In order to minimise the effect of the causes of variations, the fundamental Taguchi method is integrated with the DNA-metaheuristic. The suggested methodology exhibits the global exploration capability to exploit the optimal or near-optimal DNA strands with a faster convergence rate. In order to authenticate the performance of the proposed solution methodology, a set of ten problem instances are considered and the results obtained are compared with that of the basic DNA, particle swarm optimisation (PSO) and its variant (PSO — time varying acceleration coefficients). The results demonstrate the benefits of the proposed algorithm for solving this type of problem.     

A dynamic programming approach to integrated assembly planning and supplier assignment with lead time constraints

As manufacturers face fierce competition in the global market, responsiveness has become an important competitiveness factor in addition to quality and cost. One essential responsiveness strategy is to reduce product development and lead times by integrating assembly planning with supplier assignment. This paper addresses the problem of integrated assembly and supply chain design under lead-time constraints by formulating and solving an optimisation problem with minimal total supply chain costs. This new time-constrained joint optimisation problem belongs to an NP-hard resource-constrained scheduling problem. To model this problem effectively, we develop a novel Hyper AND/OR graph and apply it for integrating assembly and supply chain decisions. We also develop a dynamic programming model and associated algorithm in order to solve the integrated optimisation problem with pseudo-polynomial time complexity in practice. Numerical case studies validate that the methods developed can solve the integrated decision-making problem optimally and efficiently. This paper overcomes the limitations of previous studies on concurrent assembly decomposition and supplier selection, which optimises cost without time constraints. The models and results of this research can be applied to a variety of areas including assembly design, maintenance module planning and supply chain restructuring.     

A supply chain network equilibrium model for operational and opportunism risk mitigation

Risk management holds a crucial role in ensuring efficiency, predictability, and coherency in supply chain operations of an enterprise. Risks are associated with every member of a supply chain network. Thus, an end-to-end risk management approach is essential to fortify the entire supply chain network. In this paper, we consider a supply chain network consisting of suppliers, manufacturers, distributors and retailers, as the representative stakeholders. In particular, we take supply chain operational, and opportunism risks into account, and investigate the roles of flexibility, and social relationship, respectively, as a mitigation approach. We develop a multi-period network equilibrium model by considering the stakeholders’ objectives of maximising profit and minimising risk. Further, the finite-dimensional variational inequality formulations are derived for the underlying network optimisation problem. An algorithm, with nice features for computations, is then applied to three simulated examples in order to illustrate the model and computational procedure as well as the types of interventions that can help the strategic decision-makers to explore quantitatively the associated profits and incurred risks in an entire supply chain network.     

Towards optimal configuration of a manufacturer’s supply network with demand flexibility

Demand flexibility exhibits the degree to which customers are often willing to compromise on product features or performance levels for budgetary (reflected in price) or schedule (reflected in delivery) reasons. It is essential for a manufacturer to map demand flexibility into the supply side and investigate its impact on supply network configuration to maximise its total profit. This paper is among the first contributions that seek to address the challenge of optimal configuration of a manufacturer’s supply network that consists of raw material suppliers and contract manufacturers, considering demand flexibility and commonality among different product families. A new mixed integer programming model is developed to describe the characteristics of this problem. The objective was to maximise the manufacturer’s total profit subject to various operating constraints of the supply chain. In view of the complexity and non-deterministic polynomial-time (NP)-hard nature of the problem, a hybrid constraint programming and simulated annealing algorithm is proposed to solve the problem optimally. Extensive numerical studies are conducted to validate the effectiveness of the proposed model and the hybrid algorithm.     

Supply chain network design under the risk of uncertain disruptions

Facility disruptions in the supply chain often lead to catastrophic consequences, although they occur rarely. The low frequency and non-repeatability of disruptive events also make it impossible to estimate the disruption probability accurately. Therefore, we construct an uncertain programming model to design the three-echelon supply chain network with the disruption risk, in which disruptions are considered as uncertain events. Under the constraint of satisfying customer demands, the model optimises the selection of retailers with uncertain disruptions and the assignment of customers and retailers, in order to minimise the expected total cost of network design. In addition, we simplify the proposed model by analysing its properties and further linearise the simplified model. A Lagrangian relaxation algorithm for the linearised model and a genetic algorithm for the simplified model are developed to solve medium-scale problems and large-scale problems, respectively. Finally, we illustrate the effectiveness of proposed models and algorithms through several numerical examples.     

基于FANP方法的供应链配送中心评价选择

供应链配送中心的评价选择是供应链管理的重要课题.层次分析法(AHP)只能分析经过大量简化处理的树状结构或者其他理想结构.因此,采用解释结构模型(ISM)建立配送中心评价选择指标体系,用模糊网络分析法(FANP)进行计算分析,对以制造业为主导的供应链配送中心进行评价和选择,不仅考虑了指标的层次关系,也考虑到各层中因素之间的相互关系,解决了对具有网络关系的混合型结构评价指标体系的评价选择问题,克服了层次分析法(AHP)等传统算法的局限,在实际应用中具有很好的效果.     

Managing warehousing in an agile supply chain environment: an F-AIS algorithm based approach

In recent years, application of the agile concept in the manufacturing sector has been researched extensively to reduce the varying effect of customer demands. However, most of the research work is focused on the shop floor of different manufacturing processes, while issues concerning the control of warehouse scheduling in a supply chain have been neglected so far. Realising this in the present research an attempt has been made to address the scheduling aspect of a warehouse in an agile supply chain environment. To resolve the warehouse problem in this paper, the authors have proposed a new Fuzzy incorporated Artificial Immune System Algorithm (F-AIS). This algorithm encapsulates the salient features of a fuzzy logic controller and immune system. The proposed algorithm has been compared with genetic algorithm (GA), simulated annealing (SA) and artificial immune system (AIS) algorithm to reveal the efficacy of the proposed F-AIS algorithm.     

An evolutionary algorithm for a new multi-objective location-inventory model in a distribution network with transportation modes and third-party logistics providers

This paper proposes a multi-objective optimisation algorithm for solving the new multi-objective location-inventory problem (MOLIP) in a distribution centre (DC) network with the presence of different transportation modes and third-party logistics (3PL) providers. 3PL is an external company that performs all or part of a company’s logistics functions. In order to increase the efficiency and responsiveness in a supply chain, it is assumed that 3PL is responsible to manage inventory in DCs and deliver products to customers according to the provided plan. DCs are determined so as to simultaneously minimise three conflicting objectives; namely, total costs, earliness and tardiness, and deterioration rate. In this paper, a non-dominated sorting genetic algorithm (NSGA-II) is proposed to perform high-quality search using two-parallel neighbourhood search procedures for creating initial solutions. The potential of this algorithm is evaluated by its application to the numerical example. Then, the obtained results are analysed and compared with multi-objective simulated annealing (MOSA). It is concluded that this algorithm is capable of generating a set of alternative DCs considering the optimisation of multiple objectives, significantly improving the decision-making process involved in the distribution network design.     

A technical note on ‘Analysis of closed loop supply chain using genetic algorithm and particle swarm optimisation’

This study analyses the mixed-integer linear program (MILP) proposed by Kannan et al. [Kannan, G., Noorul Hag, A., and Devika, M., 2009. Analysis of closed loop supply chain using genetic algorithm and particle swarm optimization. International Journal of Production Research, 47 (5), 1175–1200] in their paper entitled ‘Analysis of closed loop supply chain using genetic algorithm and particle swarm optimisation’. Since we find inconsistencies in the model, we propose modifications to rectify the flaws. The revised model is presented and tested using a hypothetical problem.     

An efficient algorithm for minimum degeneracy primer selection

Selecting degenerate primers for multiplex polymerase chain reaction (MP-PCR) experiments, called the degenerate primer design problem (DPDP), is an important problem in computational molecular biology and has drawn the attention of numerous researchers in the recent past. Several variants of DPDP were formulated by Linhart and Shamir and proven to be NP-complete. A number of algorithms have been proposed for one such variant, namely, the maximum coverage degenerate primer design problem (MC-DPDP). In this paper, we consider another important variant called the minimum degeneracy degenerate primer design with errors problem (MD-DPDEP), propose an algorithm to design a degenerate primer of minimum degeneracy for a given set of DNA sequences and show experimental results of its performance on random and real biological datasets. Our algorithm combines methodologies in motif discovery and an iterative technique to design the primer     

Integrated multi-factory production and distribution scheduling applying vehicle routing approach

This paper introduces a new integrated multi-factory production and distribution scheduling problem in supply chain management. This supply chain consists of a number of factories joined together in a network configuration. The factories produce intermediate or finished products and supply them to other factories or to end customers that are distributed in various geographical zones. The problem consists of finding a production schedule together with a vehicle routing solution simultaneously to minimise the sum of tardiness cost and transportation cost. A mixed-integer programming model is developed to tackle the small-sized problems using CPLEX, optimally. Due to the NP-hardness, to deal with medium- and large-sized instances, this paper develops a novel Improved Imperialist Competitive Algorithm (IICA) employing a local search based on simulated annealing algorithm. Performance of the proposed IICA is compared with the optimal solution and also with four variants of population-based metaheuristics: Imperialist Competitive Algorithm, Genetic Algorithm, Particle Swarm Optimisation (PSO), and Improved PSO. Based on the computational results, it is statistically shown that quality of the IICA’s solutions is the same as optimal ones solving small problems. It also outperforms other algorithms in finding near-optimal solutions dealing with medium and large instances in a reasonably short running time.     

基于PIWOA的绿色闭环供应链网络多目标模糊优化设计

针对闭环供应链网络优化设计问题,建立了一种基于Me测度的闭环供应链网络多目标优化设计模型,以此降低供应链网络设计中不确定性因素在求解时的影响。首先,针对多层级闭环供应链网络,建立以成本最小、CO₂排放量最小以及社会效益最大为目标的优化函数,并采用Me测度和三角模糊数对模型及相关约束进行模糊处理,得到不确定性闭环供应链网络优化模型;其次,在原有鲸鱼算法的基础上,引入变异收敛因子,增强其搜索能力,并将Pareto引入改进后的鲸鱼算法求解所建模型;最后,通过数值实例和仿真分析验证算法在搜索能力、时间以及优化目标函数值等方面具有较强的优势和性能。