An integrated model for strategic supply chain design: Formulation and ABC-based solution approach

This study develops a mixed integer nonlinear programming (MINLP) model to design supply chains. In view of the limitations of many available strategic supply chain design models, this model involves three major supply chain stages, including procurement, production, and distribution, and their interactions; it takes into account bill of materials constraints for modeling complex supply chain inter-relationships. In addition, in accordance with the fact that companies nowadays develop product families, our model addresses multi-product supply chain design to respond to diverse customer requirements. Recognizing their importance, this study identifies and formulates constraints related to facility pairwise relationships and supplier priority along with the classical constraints from the available literature. To efficiently solve such a highly constrained, large scale MINLP model, we develop an approach based on an artificial bee colony (ABC) algorithm. Bicycle design and production is used to demonstrate the potential of the MINLP model for designing supply chains and the performance of the ABC-based solution approach in solving the model. The proposed model and solution approach can be considered as two fundamental components of an expert system in the broad sense. Thus, this study is expected to stimulate more future research on the development of practical expert systems for designing supply chains.     

A bi-criteria model for the inventory aggregation problem under risk pooling

Inventory aggregation, also called Risk Pooling, is one of the most efficient ways to reduce the level of safety stocks thereby reducing inventory across the supply chain. Determining the best level of aggregation is a difficult problem and needs extensive study of all the possible scenarios that can affect this decision. Minimizing costs in a supply chain is no longer the sole priority of businesses. Maintaining a high level of responsiveness is also considered equally important. The conflicting nature of these two criteria makes the solution of the problem difficult. In this paper, we develop a bi-criteria nonlinear stochastic integer programming model to determine the best supply chain distribution network to meet customer demands, where minimizing costs while maintaining high levels of responsiveness is important. We develop a two-stage optimization algorithm to solve this problem.     

Location and transportation planning in supply chains under uncertainty and congestion by using an improved electromagnetism-like algorithm

Supply chain decision makers are constantly trying to improve the customer demand fulfillment process and reduce the associated costs via decision making models and techniques. As two of the most important parameters in a supply chain, supply and demand quantities are subject to uncertainty in many real-world situations. In addition, in recent decades, there is a trend to think of the impacts of supply chain design and strategies on society and environment. Especially, transportation of goods not only imposes costs to businesses but also has socioeconomic influences. In this paper, a fuzzy nonlinear programming model for supply chain design and planning under supply/demand uncertainty and traffic congestion is proposed and a hybrid meta-heuristic algorithm, based on electromagnetism-like algorithm and simulated annealing concepts, is designed to solve the model. The merit of this paper is presenting a realistic model of current issues in supply chain design and an efficient solution method to the problem. These are significant findings of this research which can be interesting to both researchers and practitioners. Several numerical examples are provided to justify the model and the proposed solution approach.     

A computational study for common network design in multi-commodity supply chains

In this paper, we study a supply chain network design problem which consists of one external supplier, a set of potential distribution centers, and a set of retailers, each of which is faced with uncertain demands for multiple commodities. The demand of each retailer is fulfilled by a single distribution center for all commodities. The goal is to minimize the system-wide cost including location, transportation, and inventory costs. We propose a general nonlinear integer programming model for the problem and present a cutting plane approach based on polymatroid inequalities to solve the model. Randomly generated instances for two special cases of our model, i.e., the single-sourcing UPL&TAP and the single-sourcing multi-commodity location-inventory model, are provided to test our algorithm. Computational results show that the proposed algorithm can solve moderate-sized problem instances efficiently.     

Two-stage stochastic programming supply chain model for biodiesel production via wastewater treatment

This paper presents a two-stage stochastic programming model used to design and manage biodiesel supply chains. This is a mixed-integer linear program and an extension of the classical two-stage stochastic location-transportation model. The proposed model optimizes not only costs but also emissions in the supply chain. The model captures the impact of biomass supply and technology uncertainty on supply chain-related decisions; the tradeoffs that exist between location and transportation decisions; and the tradeoffs between costs and emissions in the supply chain. The objective function and model constraints reflect the impact of different carbon regulatory policies, such as carbon cap, carbon tax, carbon cap-and-trade, and carbon offset mechanisms on supply chain decisions. We solve this problem using algorithms that combine Lagrangian relaxation and L-shaped solution methods, and we develop a case study using data from the state of Mississippi. The results from the computational analysis point to important observations about the impacts of carbon regulatory mechanisms as well as the uncertainties on the performance of biocrude supply chains.     

Multi-objective biogeography-based optimization for supply chain network design under uncertainty

This paper proposes a new two-stage optimization method for multi-objective supply chain network design (MO-SCND) problem with uncertain transportation costs and uncertain customer demands. On the basis of risk-neutral and risk-averse criteria, we develop two objectives for our SCND problem. We introduce two solution concepts for the proposed MO-SCND problem, and use them to define the multi-objective value of fuzzy solution (MOVFS). The value of the MOVFS measures the importance of uncertainties included in the model, and helps us to understand the necessity of solving the two-stage multi-objective optimization model. When the uncertain transportation costs and customer demands have joined continuous possibility distributions, we employ an approximation approach (AA) to compute the values of two objective functions. Using the AA, the original optimization problem becomes an approximating mixed-integer multi-objective programming model. To solve the hard approximating optimization problem, we design an improved multi-objective biogeography-based optimization (MO-BBO) algorithm integrated with LINGO software. We also compare the improved MO-BBO algorithm with the multi-objective genetic algorithm (MO-GA). Finally, a realistic dairy company example is provided to demonstrate that the improved MO-BBO algorithm achieves the better performance than MO-GA in terms of solution quality.     

PARTICLE SWARM OPTIMIZATION-BASED ALGORITHM FOR BILEVEL JOINT PRICING AND LOT-SIZING DECISIONS IN A SUPPLY CHAIN

This study considers joint pricing and lot-sizing policies in a single-manufacturer–single-retailer system. Because a supply chain is a hierarchical system, we adopt a bilevel programming technique to establish a bilevel joint pricing and lot-sizing model guided by the manufacturer. The objective of the problem here is to respectively maximize the manufacturer's and the retailer's net profits by determining the manufacturer's and retailer's lot size, the wholesale price and the retail price simultaneously. Following the properties of the bilevel programming problem (BLPP), we design a novel bilevel particle swarm optimization algorithm (BPSO), and it can solve BLPP without any assumed conditions of the problem. BPSO shows a good performance on eight benchmark bilevel problems. Then BPSO is employed to solve the proposed bilevel model, and the experimental data are used to analyze the features of the proposed bilevel model, and the results support the finding that BPSO is effective in optimizing BLPP.     

改进的哈里斯鹰算法求解农产品供应链调度

针对农产品流通体系的效率低、流通链条协同效率不高、紧急情况下食品供给慢等问题,通过将农产品供应链调度问题建模成混合流水车间调度问题。结合禁忌搜索算法中禁忌表机制,离散化实数编码,提出了一种改进的哈里斯鹰算法来求解农产品供应链调度问题。该方法相比较原始的哈里斯鹰算法,降低了算法陷入局部最优的可能,进一步提高了算法的求解精度。实验结果表明：相比较对比算法,改进的哈里斯鹰算法在提出的农产品供应链调度问题模型上取得了更好的效果。     

Bi-objective reliable location-inventory-routing problem with partial backordering under disruption risks: A modified AMOSA approach

In this paper, we present a reliable model of multi-product and multi-period Location-Inventory-Routing Problem (LIRP) considering disruption risks. An inventory system with stochastic demand in which the supply of the product is randomly disrupted in distribution centers, is considered in this paper. Partial backordering is used in case stock out occurs by considering the probability of the confronting defects in distribution centers in time of disruption. We presented a bi-objective mixed-integer nonlinear programming (MINLP) model. The first objective minimizes the locating, routing and transportation costs and inventory components which consist of ordering, holding and partial backordering costs. The second objective is to minimize the total failure costs related to disrupted distribution centers that leads to reliability of the supply chain network. Because of NP-hardness of the proposed model, we modified Archived Multi-Objective Simulated Annealing (AMOSA) meta-heuristic algorithm to solve the bi-objective problem in large scales and compared the results with three other algorithms. To improve performance of the algorithms Taguchi method is used to tune parameters. Finally, several numerical examples are generated to evaluate solution methods and five multi-objective metrics are calculated to compare results of the algorithms.     

Determination of number of kanbans in a supply chain system via Memetic algorithm

In this research multi-stage supply chain system which is controlled by kanban system, is evaluated. In kanban system, decision making is based on determination of the number of kanbans as well as batch sizes. This paper attempts to model supply chain system with regard to costs under just-in-time (JIT) production philosophy. Since adopted model is of mixed integer non-linear programming (MINLP) type and solving it by exact algorithm such as branch and bound (B&B) takes a lot of time, a heuristic method via Memetic algorithm (MA) is presented. Some problems are solved by our proposed MA to illustrate its performance.     

A novel learning based approach for a new integrated location-routing and scheduling problem within cross-docking considering direct shipment

One of the most important problem in supply chain management is the design of distribution systems which can reduce the transportation costs and meet the customer's demand at the minimum time. In recent years, cross-docking (CD) centers have been considered as the place that reduces the transportation and inventory costs. Meanwhile, neglecting the optimum location of the centers and the optimum routing and scheduling of the vehicles mislead the optimization process to local optima. Accordingly, in this research, the integrated vehicle routing and scheduling problem in cross-docking systems is modeled. In this new model, the direct shipment from the manufacturers to the customers is also included. Besides, the vehicles are assigned to the cross-dock doors with lower cost. Next, to solve the model, a novel machine-learning-based heuristic method (MLBM) is developed, in which the customers, manufacturers and locations of the cross-docking centers are grouped through a bi-clustering approach. In fact, the MLBM is a filter based learning method that has three stages including customer clustering through a modified bi-clustering method, sub-problems’ modeling and solving the whole model. In addition, for solving the scheduling problem of vehicles in cross-docking system, this paper proposes exact solution as well as genetic algorithm (GA). GA is also adapted for large-scale problems in which exact methods are not efficient. Furthermore, the parameters of the proposed GA are tuned via the Taguchi method. Finally, for validating the proposed model, several benchmark problems from literature are selected and modified according to new introduced assumptions in the base models. Different statistical analysis methods are implemented to assess the performance of the proposed algorithms.     

Contingent rerouting for enhancing supply chain resilience from supplier behavior perspective

This paper examines contingent rerouting strategy for enhancing supply chain resilience taking a supplier's point of view. We consider a supply chain with multiple suppliers at each stage and establish a mathematical model for product allocation behavior among different suppliers. The allocation model is based on each supplier's production capacity, product quality, production cost, as well as possible decision maker's preferences. As a performance measure for rerouting strategy, we use the total outflow of the supply chain. We propose an optimization model and its solution determines the rerouting strategy for product flow through the supply chain under disruptions. Numerical examples demonstrate the effect of the rerouting strategy and show the resilience of the supply chain.     

基于群智能的集中供暖系统水力管网控制策略优化

针对集中供暖系统管网水力失衡、流量供需失衡问题提出一种基于群智能的新型分布式优化算法.首先以系统输配送能耗最小为优化目标,将其分解为管网调节阀开度优化和换热站并联水泵运行优化两个子问题求解;其次建立调节阀模型和管网水力模型,在此基础上进行调节阀开度的优化,从而计算出系统最小供回水压差;然后基于改进的交替方向乘子法完成并联水泵的优化运行;最后以集中供暖系统实例验证算法性能.实验结果表明:相比传统集中式求解算法,该算法不受水利管网规模限制,利于实现工程中即插即用;相比其他分布式算法,该算法不仅求解速度快,而且可以得到较优的管网运行策略,节能效果较为显著.     

An interactive possibilistic programming approach for a multi-objective closed-loop supply chain network under uncertainty

In this article, we first propose a closed-loop supply chain network design that integrates network design decisions in both forward and reverse supply chain networks into a unified structure as well as incorporates the tactical decisions with strategic ones (e.g., facility location and supplier selection) at each period. To do so, various conflicting objectives and constraints are simultaneously taken into account in the presence of some uncertain parameters, such as cost coefficients and customer demands. Then, we propose a novel interactive possibilistic approach based on the well-known STEP method to solve the multi-objective mixed-integer linear programming model. To validate the presented model and solution method, a numerical test is accomplished through the application of the proposed possibilistic-STEM algorithm. The computational results demonstrate suitability of the presented model and solution method.     

Vehicle routing scheduling for cross-docking in the supply chain

One of the most important factors in implementing supply chain management is to efficiently control the physical flow of the supply chain. Due to its importance, many companies are trying to develop efficient methods to increase customer satisfaction and reduce costs. In various methods, cross-docking is considered a good method to reduce inventory and improve responsiveness to various customer demands. However, previous studies have dealt mostly with the conceptual advantages of cross-docking or actual issues from the strategic viewpoint. It is also necessary, however, to considering cross-docking from an operational viewpoint in order to find the optimal vehicle routing schedule. Thus, an integrated model considering both cross-docking and vehicle routing scheduling is treated in this study. Since this problem is known as NP-hard, a heuristic algorithm based on a tabu search algorithm is proposed. In the numerical example, our proposed algorithm found a good solution whose average percentage error was less than 5% within a reasonable amount of time.     

A contingency model for creating value from RFID supply chain network projects in logistics and manufacturing environments

In the growing literature on RFID and other network technologies, the importance of organizational transformation at the supply chain level has been recognized. However, the literature lacks conceptual model development and salient mechanisms for achieving the level of organizational transformation required for stakeholders to realize the full business benefits from RFID projects. Furthermore, the RFID adoption, use, and impact studies to date largely focus on a single firm setting and on the retail sector. Therefore, this study intends to fill this knowledge gap in the literature, and develops a contingency model for creating value from RFID supply chain projects in logistics and manufacturing environments. For our model development, we draw upon extant diverse literatures, particularly the framework for IT-enabled business transformation, and leadership and organizational learning. The framework postulates a positive relationship between the level of organizational transformation effected by the use of information technology (IT) and the level of business benefits realized from IT. The contingency model draws on the framework, and explicates five contingency factors influencing value creation from RFID supply chain projects: environmental upheaval; leadership; second-order organizational learning; resources commitment; and organizational transformation. Using the contingency model as a conceptual guide, we also perform an analysis of longitudinal real-world case data from a Canadian third-party logistics service firm's seven-layer supply chain RFID projects. The case study analysis provides evidence for the imperative of the contingency factors identified in the model for creating value from the RFID projects. Furthermore, it also reveals the differential costs for the focal firm and the up-stream manufacturing as a key barrier to realizing the full RFID benefits at the supply chain level.     

A heuristic algorithm for a supply chain׳s production-distribution planning

In this paper we consider the problem of planning the production and distribution in a supply chain. The situation consists in a set of distribution centers seeking to serve to a set of retailers; these distribution centers are supplied by a set of plants trying to minimize the operation and transportation costs. The problem is formulated as a bilevel mathematical problem where the upper level consists of deciding the amount of product sent from the distribution centers to the retailers trying to minimize the transportation costs and also by considering the costs of acquiring the products that come from the plants. Meanwhile the lower level consists in minimizing the plants׳ operations cost meeting the demand grouped in the distribution centers. We propose a heuristic algorithm based on Scatter Search that considers the Stackelberg׳s equilibrium; numerical tests show that our proposed algorithm improves the existing best known results in the literature.     

A hybrid vendor managed inventory and redundancy allocation optimization problem in supply chain management: An NSGA-II with tuned parameters

In this research, a bi-objective vendor managed inventory model in a supply chain with one vendor (producer) and several retailers is developed, in which determination of the optimal numbers of different machines that work in series to produce a single item is considered. While the demand rates of the retailers are deterministic and known, the constraints are the total budget, required storage space, vendor's total replenishment frequencies, and average inventory. In addition to production and holding costs of the vendor along with the ordering and holding costs of the retailers, the transportation cost of delivering the item to the retailers is also considered in the total chain cost. The aim is to find the order size, the replenishment frequency of the retailers, the optimal traveling tour from the vendor to retailers, and the number of machines so as the total chain cost is minimized while the system reliability of producing the item is maximized. Since the developed model of the problem is NP-hard, the multi-objective meta-heuristic optimization algorithm of non-dominated sorting genetic algorithm-II (NSGA-II) is proposed to solve the problem. Besides, since no benchmark is available in the literature to verify and validate the results obtained, a non-dominated ranking genetic algorithm (NRGA) is suggested to solve the problem as well. The parameters of both algorithms are first calibrated using the Taguchi approach. Then, the performances of the two algorithms are compared in terms of some multi-objective performance measures. Moreover, a local searcher, named simulated annealing (SA), is used to improve NSGA-II. For further validation, the Pareto fronts are compared to lower and upper bounds obtained using a genetic algorithm employed to solve two single-objective problems separately.     

A multiple-vendor single-buyer integrated inventory model with a variable number of vendors

Supply chain management is concerned with the coordination of material and information flows in multi-stage production systems. A closer look at the literature reveals that previous research on the coordination of multi-stage production systems has predominantly focused on the sales side of the supply chain, whereas problems that arise on the supply side have often been neglected. This article closes this gap by studying the coordination of a supplier network in an integrated inventory model. Specifically, we consider a buyer sourcing a product from heterogeneous suppliers and tackle both the supplier selection and lot size decision with the objective to minimise total system costs. First, we provide mathematical formulations for the problem under study, and then suggest a two-stage solution procedure to derive a solution. Numerical studies indicate that our solution procedure reduces the total number of supplier combinations that have to be tested for optimality, and that it may support initiatives which aim on increasing the efficiency of the supply chain as a heuristic planning tool.     

Multi-depot vehicle routing problem: a one-stage approach

This paper introduces multi-depot vehicle routing problem with fixed distribution of vehicles (MDVRPFD) which is one important and useful variant of the traditional multi-depot vehicle routing problem (MDVRP) in the supply chain management and transportation studies. After modeling the MDVRPFD as a binary programming problem, we propose two solution methodologies: two-stage and one-stage approaches. The two-stage approach decomposes the MDVRPFD into two independent subproblems, assignment and routing, and solves them separately. In contrast, the one-stage approach integrates the assignment with the routing where there are two kinds of routing methods-draft routing and detail routing. Experimental results show that our new one-stage algorithm outperforms the published methods. Note to Practitioners-This work is based on several consultancy work that we have done for transportation companies in Hong Kong. The multi-depot vehicle routing problem (MDVRP) is one of the core optimization problems in transportation, logistics, and supply chain management, which minimizes the total travel distance (the major factor of total transportation cost) among a number of given depots. However, in real practice, the MDVRP is not reliable because of the assumption that there have unlimited number of vehicles available in each depot. In this paper, we propose a new useful variant of the MDVRP, namely multi-depot vehicle routing problem with fixed distribution of vehicles (MDVRPFD), to model the practicable cases in applications. Two-stage and one-stage solution algorithms are also proposed. The industry participators can apply our new one-stage algorithm to solve the MDVRPFD directly and efficiently. Moreover, our one-stage solution framework allows users to smoothly add new specified constraints or variants.