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.     

A hybrid Tabu sample-sort simulated annealing approach for solving distributed scheduling problem

The distributed scheduling problem has been considered as the allocation of a task to various machines in such a way that these machines are situated in different factories and these factories are geographically distributed. Therefore distributed scheduling has fulfilled various objectives, such as allocation of task to the factories and machines in such a manner that it can utilise the maximum resources. The objective of this paper is to minimise the makespan in each factory by considering the transportation time between the factories. In this paper, to address such a problem of scheduling in distributed manufacturing environment, a novel algorithm has been developed. The proposed algorithm gleans the ideas both from Tabu search and sample sort simulated annealing. A new algorithm known as hybrid Tabu sample-sort simulated annealing (HTSSA) has been developed and it has been tested on the numerical example. To reveal the supremacy of the proposed algorithm over simple SSA and Tabu search, more computational experiments have also been performed on 10 randomly generated datasets.     

Performance optimization of a leagility inspired supply chain model: a CFGTSA algorithm based approach

Lean and agile principles have attracted considerable interest in the past few decades. Industrial sectors throughout the world are upgrading to these principles to enhance their performance, since they have been proven to be efficient in handling supply chains. However, the present market trend demands a more robust strategy incorporating the salient features of both lean and agile principles. Inspired by these, the leagility principle has emerged, encapsulating both lean and agile features. The present work proposes a leagile supply chain based model for manufacturing industries. The paper emphasizes the various aspects of leagile supply chain modeling and implementation and proposes a new Hybrid Chaos-based Fast Genetic Tabu Simulated Annealing (CFGTSA) algorithm to solve the complex scheduling problem prevailing in the leagile environment. The proposed CFGTSA algorithm is compared with the GA, SA, TS and Hybrid Tabu SA algorithms to demonstrate its efficacy in handling complex scheduling problems.     

The impact of the cost of quality on serial supply‐chain network design

This paper presents a model for supply‐chain design that considers the Cost of Quality as well as the traditional manufacturing and distribution costs (SC‐COQ model). It includes three main contributions: (1) the SC‐COQ model internally computes quality costs for the whole supply chain considering the interdependencies among business entities, whereas previous works have assumed exogenously given Cost of Quality functions; (2) the SC‐COQ model can be used at a strategic planning level to design a logistic route that achieves a maximum profit while considering the overall quality level within a supply chain; and (3) we provide two solution methods based on simulated annealing and a genetic algorithm and perform computational experiments on test instances.     

A highly optimised tolerance-based approach for multi-stage,multi-product supply chain network design

In this paper we propose an algorithm called Highly Optimised Tolerance (HOT) for solving a multi-stage, multi-product supply chain network design problem. HOT is based on power law and control theory. The proposed approach takes its traits from the local incremental algorithm (LIA), which was initially employed to maximise the design parameter (i.e. yield), particularly in the percolation model. The LIA is somewhat analogous to the evolution by natural selection schema. The proposed methodology explores a wide search space and is computationally viable. The HOT algorithm tries to make the system more robust at each step of the optimisation. The objective of this paper is to reduce the total cost of supply chain distribution by selecting the optimum number of facilities in the network. To examine the effectiveness of the HOT algorithm we compare the results with those obtained by applying simulated annealing on a supply chain network design problem with different problem sizes and the same data sets.     

Incorporating supplier selection and order allocation into the vehicle routing and multi-cross-dock scheduling problem

In the vehicle routing problem with cross-docking (VRPCD), it is assumed that the selected suppliers and the quantity of the products purchased from each supplier are known. This paper presents an MILP model which incorporates supplier selection and order allocation into the VRPCD in a multi-cross-dock system minimising the total costs, including purchasing, transportation, cross-docking, inventory and early/tardy delivery penalty costs. The sensitivity of the model on the key parameters of the objective function is analysed and the supply decisions are evaluated when the coefficients of the distribution cost are changed. A two-stage solution algorithm (TSSA) is proposed and the results of the TSSA for small-sized instances are compared with the exact solutions. Finally, a large-sized real case of an urban freight transport is solved using the TSSA.     

Resource-constrained project scheduling by simulated annealing

This paper presents a new adaptation of the simulated annealing algorithm for solving non-preemptive resource-constrained project scheduling problems in which resources are limited but renewable from period to period. This algorithm is able to handle single-mode and multi-mode problems and to optimize different objective functions. Statistical experiments show the efficiency of the proposed algorithm even in comparison to some Tabu search heuristics.     

拓扑优化方法在复合材料设计中的研究进展

按照设计变量,叙述了连续变量和离散变量拓扑优化设计的一些常用算法,其中包括均匀化方法、变密度法、变厚度法、移动渐进算法、模拟退火法、遗传算法、相对差商法和Tabu搜索法,并对各种方法的优缺点进行了比较;对二维和三维复合材料的拓扑学优化设计研究现状和方法进行了阐述;提出了拓扑优化设计复合材料的未来研究方向.     

A CBFSA approach to resolve the distributed manufacturing process planning problem in a supply chain environment

In this competitive world cost and lead time reduction are of prime concern for manufacturing firms. To achieve this objective manufacturing entities are adopting several management philosophies such as Total Quality Management (TQM), just-in-time (JIT), and theory of constraints (TOC). The present paper addresses the advanced computer-aided process planning (ACAPP) problem in a distributed manufacturing supply chain environment and aims at cost and lead times reduction under several technological constraints. To deal with the complexity of the problem the constraint based fast simulated annealing (CBFSA) algorithm has been explored in this article. Extensive computations have been performed over the well-known benchmarks of advanced planning problems and the results obtained prove the superiority of the proposed algorithm over the prior approaches.     

An integrated decision support system for strategic supply chain optimisation in process industries: the case of a zinc company

We introduce a menu-driven user-friendly decision support system (DSS) for supply chain planning based on optimisation. The DSS is based on a multi-source (supplier), multi-destination (warehouse) network having multiple manufacturing facilities, with multiple materials and multiple storage areas. This integrated supply chain model performs multiple period planning. The use of this DSS requires little knowledge of management sciences tools. We discuss the need for an integrated approach towards supply chain modelling for the process industry. We present the integrated model in the form of a database structure. We validate the model with the real data of a zinc company and demonstrate the impact of optimisation in terms of percentage improvement. The result shows that it is possible to improve unit contribution to profit from 1.89 to 4.66%.     

基于密度峰值聚类的VRPTW问题研究

提出一种密度峰值聚类 (density peak clustering, DPC)与遗传算法(genetic algorithm, GA)相结合的新型混合算法(density peak clustering with genetic algorithm, DGA),求解带时间窗的车辆路径问题。首先应用DPC对客户进行聚类以缩减问题规模,再将聚类后的客户用GA进行线路优化。结果表明：DGA在9个数据集上的平均值比模拟退火(simulated annealing, SA)和禁忌搜索(Tabu)分别提高了13.41%和4.7%,单个数据集最大提高了26.4%。这证明了该算法是求解车辆调度问题的高效算法。     

A hybrid optimisation model for pallet loading

This study adopts a hybrid approach that integrates the genetic algorithm (GA) and fuzzy logic in order to assist in the generation of an optimal pallet loading plan. The proposed model enables the maximisation of profits for freight forwarders through the most efficient use of space and weight in pallet loading. The model uses fuzzy controllers to determine the numbers and size of cargo units on a pallet as well as the mutation rate in the GA approach within the optimisation process and enables the capture of tacit knowledge vested in industry practitioners. The pragmatic use of the model is illustrated using a freight-forwarding scenario that demonstrates the inherent limitations of the standard GA method, followed by the application of the proposed fuzzy GA model. To further demonstrate the benefits of the hybrid model, simulated annealing and Tabu search are used to benchmark the results achieved using various approaches; the proposed hybrid model is demonstrated to exceed these other approaches in overall performance. The application of the proposed hybrid approach across a range of scenarios is also discussed.     

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.     

Modeling the planning and scheduling across the outsourcing supply chain: a Chaos-based fast Tabu–SA approach

Planning and Scheduling are the interrelated manufacturing functions and should be solved simultaneously to achieve the real motives of integration in manufacturing. In this paper, we have addressed the advanced integrated planning and scheduling problem in a rapidly changing environment, where the selection of outsourcing machine/operation, meeting the customers (single or multiple) due date, minimizing the makespan are the main objectives while satisfying several technological constraints. We developed a mixed integer programming model for integrated planning and scheduling across the outsourcing supply chain and showed how such models can be used to make strategic decisions. It is a computationally complex and mathematically intractable problem to solve. In this paper, a Chaos-based fast Tabu-simulated annealing (CFTSA) incorporating the features of SA, Tabu and Chaos theory is proposed and applied to solve a large number of problems with increased complexity. In CFTSA algorithm, five types of perturbation schemes are developed and Cauchy probability function is used to escape from local minima and achieve the optimal/near optimal solution in a lesser number of iterations. An intensive comparative study shows the robustness of proposed algorithm. Percentage Heuristic gap is used to show the effectiveness and two ANOVA analyses are carried out to show the consistency and accuracy of the proposed approach.     

资金约束下基于零售商竞争的融资模式对比研究

以单供应商与多个零售商的供应链为研究背景,供应商是供应链上的核心企业,而零售商面临着资金约束,与供应商签订收益共享契约。供应商为中小企业提供两种融资方式：保兑仓融资和延迟支付融资。本文研究两种融资对供应链绩效以及供应链各方的影响,构建单供应商和多零售商的Stackelberg博弈模型,发现保兑仓融资模式能增加零售商的订货量,这两种融资模式在分散型供应链下,供应商都可以设置合适的收益分配系数和批发价格实现在集中型供应链下的协调,并能够保证供应商利润最大化。     

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.     

Minimising maximum tardiness in assembly flowshops with setup times

This paper addresses a two-stage assembly flowshop scheduling problem with the objective of minimising maximum tardiness where set-up times are considered as separate from processing times. The performance measure of maximum tardiness is important for some scheduling environments, and hence, it should be taken into account while making scheduling decisions for such environments. Given that the problem is strongly NP-hard, different algorithms have been proposed in the literature. The algorithm of Self-Adaptive Differential Evolution (SDE) performs as the best for the problem in the literature. We propose a new hybrid simulated annealing and insertion algorithm (SMI). The insertion step, in the SMI algorithm, strengthens the exploration step of the simulated annealing algorithm at the beginning and reinforces the exploitation step of the simulated annealing algorithm towards the end. Furthermore, we develop several dominance relations for the problem which are incorporated in the proposed SMI algorithm. We compare the performance of the proposed SMI algorithm with that of the best existing algorithm, SDE. The computational experiments indicate that the proposed SMI algorithm performs significantly better than the existing SDE algorithm. More specifically, under the same CPU time, the proposed SMI algorithm, on average, reduces the error of the best existing SDE algorithm over 90%, which indicates the superiority of the proposed SMI algorithm.     

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.     

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.     

Research on Flight First Service Model and Algorithms for the Gate Assignment Problem

Aiming at the problem of gate allocation of transit flights, a flight first service model is established. Under the constraints of maximizing the utilization rate of gates and minimizing the transit time, the idea of “first flight serving first” is used to allocate the first time, and then the hybrid algorithm of artificial fish swarm and simulated annealing is used to find the optimal solution. That means the fish swarm algorithm with the swallowing behavior is employed to find the optimal solution quickly, and the simulated annealing algorithm is used to obtain a global optimal allocation scheme for the optimal local region. The experimental data show that the maximum utilization of the gate is 27.81% higher than that of the “first come first serve” method when the apron is not limited, and the hybrid algorithm has fewer iterations than the simulated annealing algorithm alone, with the overall passenger transfer tension reducing by 1.615; the hybrid algorithm has faster convergence and better performance than the artificial fish swarm algorithm alone. The experimental results indicate that the hybrid algorithm of fish swarm and simulated annealing can achieve higher utilization rate of gates and lower passenger transfer tension under the idea of “first flight serving first”.