Economic lot-sizing and dynamic quantity competition

We study a problem of dynamic quantity competition in continuous time with two competing retailers facing different replenishment cost structures. Retailer 1 faces fixed ordering costs and variable procurement costs and all inventory kept in stock is subject to holding costs. Retailer 2 only faces variable procurement costs. Both retailers are allowed to change their sales quantities dynamically over time. Following the structure of the economic order quantity (EOQ) model, retailer 1 places replenishment orders in batches and retailer 2 follows a just-in-time (JIT) policy. The objective of both retailers is to maximize their individual average profit anticipating the competitor's replenishment and output decisions. The problem is solved by a two-stage hierarchical optimization approach using backwards induction. The second-stage model is a differential game in output quantities between the two retailers for a given cycle length. At the first stage, the replenishment policy is determined. We prove the existence of a unique optimal solution and derive an open-loop Nash equilibrium. We show that both retailers follow contrary output strategies over the order cycle. The EOQ retailer, driven by inventory holding costs, decreases his market share whereas the output of the JIT retailer increases. Moreover, depending on the cost structure, the EOQ retailer might partially be a monopolist. At the first stage, the EOQ retailer determines the cycle length, anticipating the optimal output trajectories at the second stage.     

Lot sizes in a capacity constrained facility—a simulation study of stationary stochastic demand

This paper considers the scheduling of several different items on a single machine, in literature known as the economic lot scheduling problem, ELSP. One of the characteristics of this problem is that the demand rate is deterministic and constant. However, in a practical situation demand usually varies. In this paper we examine if a deterministic model can be used if demand is stationary stochastic. A dynamic programming approach from Bomberger (Manage. Sci. 12(11) (1966) 778) and a heuristic method from Segerstedt (Int. J. Production Econom. 59(1–3) (1999) 469) are used to calculate lot sizes for four items. The production of these items is simulated with different variations in demand rates. Our conclusion is that a deterministic model of this kind can be used in a practical situation where the demand rate is stationary stochastic, but the models must be complemented by a decision rule; which item to produce and when to produce it. In our tests the heuristic method and the dynamic programming approach perform rather similarly with respect to costs and inventory levels, but the dynamic programming approach results in more backorders when there is small variation in demand rates. This study indicates that the model used for determination of lot sizes is of less importance than the decision rule used for identification of the item to produce and when to produce it.     

天然气管道系统可靠性分析中GO法的应用

天然气管道系统以天然气为物流,是可纵系统,有正常工作和故障停工两个状态。提出应用GO法进行天然气管道系统可靠性分析,给出了GO法分析的具体步骤,导出了GO操作符 的计算公式。应用文中方法,系统产气量、故障率等可靠性特征量可以直接由GO图计算,并解决了有冗余的并联系统可靠性参数计算和多路天然气汇集时产气量计算的实际问题。给出了3井集气站系统GO法应用实例,结果表明在天然气及石油、化工等同类系统中GO法是初实可行、快捷简便的可靠性分析方法。     

Economic lot and delivery scheduling problem for multi-stage supply chains

The economic lot and delivery scheduling problem for a multi-stage supply chain comprising multiple items is studied in this paper. It is required to develop a synchronized replenishment strategy, and specify the sequence of production and the replenishment cycle time that achieves synchronization through the supply chain at minimum cost. The problem is presented in a novel formulation based on the quadratic assignment representation. The common cycle time and the integer multipliers policies are adopted to accomplish the desired synchronization. The two policies are represented by nonlinear models handled through a hybrid algorithm. The algorithm combines linearization, outer approximation and Benders decomposition techniques. Results of the two policies demonstrate that a cost reduction up to16.3% can be attained by employing the integer multipliers policy instead of the common cycle time. Computational experiments show the efficiency of the new formulation and solution algorithm by reaching the optimal solution for large problem instances in short time.     

Stochastic optimization for transshipment problems with positive replenishment lead times

Transshipments, monitored movements of material at the same echelon of a supply chain, represent an effective pooling mechanism. Earlier papers dealing with transshipments either do not incorporate replenishment lead times into their analysis, or only provide a heuristic algorithm where optimality cannot be guaranteed beyond settings with two locations. This paper uses infinitesimal perturbation analysis by combining with a stochastic approximation method to examine the multi-location transshipment problem with positive replenishment lead times. It demonstrates the computation of optimal base stock quantities through sample path optimization. From a methodological perspective, this paper deploys a duality-based gradient computation method to improve computational efficiency. From an application perspective, it solves transshipment problems with non-negligible replenishment lead times. A numerical study illustrates the performance of the proposed approach.     

A tabu search algorithm for the relocation problem in a warehousing system

Relocation of items in a warehousing system is usually used when the handling machines become the bottleneck. This paper addresses the optimization problem of relocation in a warehouse with dynamic operating policy. An integer linear programming formulation is proposed. A two-stage heuristic method is developed to generate an initial solution. A tabu search algorithm is proposed to improve the solution. Two relocation policies are studied and their performances are compared.     

Simultaneous control of production, repair/replacement and preventive maintenance of deteriorating manufacturing systems

This paper presents a method to find the optimal production, repair/replacement and preventive maintenance policies for a degraded manufacturing system. The system is subject to random machine failures and repairs. The status of the system is deemed to degrade with repair activities. When a failure occurs, the machine is either repaired or replaced, and a replacement action renews the machine, while a repair action brings it to a degraded operational state, with the next repair time increasing as the number of repairs increases as well. A preventive maintenance action is considered in order to improve the reliability of the machine, thereby reducing the amount of disruptions caused by machine failures. The decision variables are the production rate, the preventive maintenance rate and the repair/replacement switching policy upon machine failure. The objective of the study is to find the decision variables that minimize the overall cost, including repair, replacement, preventive maintenance, inventory holding and backlog costs over an infinite planning horizon. The proposed model is based on a semi-Markov decision process, and the stochastic dynamic programming method is used to obtain the optimality conditions. A numerical example is given to illustrate the proposed model, and a sensitivity analysis is considered in order to confirm the structure of the control policy and to illustrate the usefulness of the proposed approach.     

An association clustering algorithm for can-order policies in the joint replenishment problem

Many studies have shown that the total cost of employing joint replenishment for correlated items is less than the total cost of using single-item replenishment. Savings increase dramatically when the demand between items is closely related. Although the benefits of joint replenishment are significant, it is difficult to define the demand correlation among items, especially when the number of items increases. A large number of items reduces the efficiency and advantage of the multi-item inventory control. To overcome this difficulty, an association clustering algorithm this paper proposes to evaluate the correlated demands among items. The proposed algorithm utilizes the “support” concept in association rule analysis to measure the similarity among items. Based on these measurements a clustering method is developed to group items with close demand in a hierarchal way. The can-order policy is then applied to the optimal clustering result as decided by the proposed performance index. To illustrate the benefits of the proposed association clustering algorithm for replenishment systems, a set of simulations and a sensitivity analysis is conducted. The results of the experiments show that the proposed method outperforms several replenishment models.     

大数据驱动下的新一代高速公路 智慧诱导技术

为了解决高速公路出行路径选择问题,基于图论模糊算法,提出了大数据下的智慧诱导技术。首先对高速路网在路径诱导的实时性、线路规划的精准度和算法的适用性方面进行优化;其次通过利用贪心算法和整体寻优算法,对经典路径诱导算法进行研究和比选;最后针对高速路网提出基于大数据动态规划的路径诱导技术,采用大数据、内存计算、图计算和AI结合的方式来实现大数据的动态实时路径诱导。研究结果表明,智慧诱导技术可主动为有不同诉求目标的出行者提供实时最优的方案选择,解决复杂路网下动态路径的合理诱导问题。所提出的方法可实现大数据驱动下的智慧诱导,对进一步提升公路智能化和精细化管理水平具有借鉴意义。     

Dynamic lot sizing problem with continuous-time Markovian production cost

This paper develops a polynomial algorithm for obtaining dynamic economic lot sizes in a single product multiperiod production system with the objective of minimizing total production and inventory costs over T periods. It is assumed that production costs are linear, inventory costs are concave, setup costs are zero and backlogging is not permitted in all periods. Moreover, the unit production cost is a stochastic variable, which is evolved according to a continuous-time Markov process over the planning horizon. The model is formulated as a stochastic dynamic programming (DP) optimization with the state variable being unit production cost. Then, it is solved using the backward dynamic programming approach. To justify the application of the proposed model, two practical cases are presented.     

Production planning of a hybrid manufacturing-remanufacturing system under uncertainty within a closed-loop supply chain

This paper deals with the production planning and control of a single product involving combined manufacturing and remanufacturing operations within a closed-loop reverse logistics network with machines subject to random failures and repairs. While consumers traditionally dispose of products at the end of their life cycle, recovery of the used products may be economically more attractive than disposal, while remanufacturing of the products also pursues sustainable development goals. Three types of inventories are involved in this network. The manufactured and remanufactured items are stored in the first and second inventories. The returned products are collected in the third inventory and then remanufactured or disposed of. The objective of this research is to propose a manufacturing/remanufacturing policy that would minimize the sum of the holding and backlog costs for manufacturing and remanufacturing products. The decision variables are the production rates of the manufacturing and the remanufacturing machines. The optimality conditions are developed using the optimal control theory based on stochastic dynamic programming. A computational algorithm, based on numerical methods, is used for solving the optimal control problem. Finally, a numerical example and a sensitivity analysis are presented to illustrate the usefulness of the proposed approach. The structure of the optimal control policy is discussed depending on the value of costs and parameters and extensions to more complex reverse logistics networks are discussed.     

State estimation of a shop floor using improved resampling rules for particle filtering

Operational inefficiencies in supply chains cost industries millions of dollars every year. Much of these inefficiencies arise due to the lack of a coherent planning and control mechanism, which requires accurate yet timely state estimation of these large-scale dynamic systems given their massive datasets. While Bayesian inferencing procedures based on particle filtering paradigm may meet these requirements in state estimation, they may end up in a situation called degeneracy, where a single particle abruptly possesses significant amount of normalized weights. Resampling rules for importance sampling prevent the sampling procedure from generating degenerated weights for particles. In this work, we propose two new resampling rules concerning minimized variance (VRR) and minimized bias (BRR). The proposed rules are derived theoretically and their performances are benchmarked against that of the minimized variance and half-width based resampling rules existing in the literature using a simulation of a semiconductor die manufacturing shop floor in terms of their resampling qualities (mean and variance of root mean square errors) and computational efficiencies, where we identify the circumstances that the proposed resampling rules become particularly useful.     

Applying possibilistic linear programming to aggregate production planning

This work presents a novel interactive possibilistic linear programming (PLP) approach for solving the multi-product aggregate production planning (APP) problem with imprecise forecast demand, related operating costs, and capacity. The proposed approach attempts to minimize total costs with reference to inventory levels, labor levels, overtime, subcontracting and backordering levels, and labor, machine and warehouse capacity. The proposed approach uses the strategy of simultaneously minimizing the most possible value of the imprecise total costs, maximizing the possibility of obtaining lower total costs, and minimizing the risk of obtaining higher total costs. An industrial case demonstrates the feasibility of applying the proposed approach to real APP decision problems. Consequently, the proposed PLP approach yields an efficient APP compromise solution and overall degree of decision maker (DM) satisfaction with determined goal values. Particularly, several significant management implications and characteristics of the proposed PLP approach that distinguish it from the other APP decision models are presented.     

The joint replenishment and freight consolidation of a warehouse in a supply chain

We have developed joint replenishment and consolidated freight delivery policies for a third party warehouse that handles multiple items, which have deterministic demand rates in a supply chain. Two policies are proposed and mathematical models are developed to obtain the optimal parameters for the proposed policies. Four efficient algorithms are presented to solve the mathematical models for the two policies. The performances of the two policies with the parameters obtained from the proposed algorithms are then compared with the common cycle approach for 1600 randomly generated problems. The results show the robust performance of the proposed algorithm for both policies.     

Quantized Hopfield networks and tabu search for manufacturing cell formation problems

The use of neural networks in the design of cellular manufacturing system is not new. This paper presents an application of modified Hopfield neural networks in order to solve cell formation problems: the quantized and fluctuated Hopfield neural networks (QFHN). This kind of Hopfield network combined with the “tabu search” approach were primarily used in a hybrid procedure in order to solve the cell formation for big sizes industrial data set. The problem is formulated as a 0/1 linear and integer programming model in order to minimize the dissimilarities between machines and/or parts. Our hybrid approach allows us to obtain optimal or nearly optimal solutions very frequently and much more quickly than traditional Hopfield networks. It is also illustrated that the fluctuation associated with this quantization may enable the network to escape from local minima, to converge to global minima, and consequently to obtain optimal solutions very frequently and much more quickly than pure quantized Hopfield networks (QHN). The effectiveness of the proposed approach is flexibility it gives us, for example, in time problem-solving for large-scale and speed of execution when we apply it.     

The market-oriented dynamic product recovery model in the just-in-time framework

A market-oriented deterministic dynamic product recovery problem and its multi-echelon model formulation are discussed in this paper. For this general non-linear alternate deterministic dynamic product recovery model a dynamic programming solution procedure is developed. Furthermore, the subcases of the Just-in-time situation with regard to the suppliers as well as customers are considered, for which the two-stage problem is transformed to easily solvable one-stage models. It will be seen that the regarded recovery problem in this formulation is just a special multi-echelon problem with two alternative production options.     

How state influence on project work organization both drives and mitigates gendered precarity in cultural and creative industries

This article develops an understanding of gendered precarity in project work by considering how the transfer of risk from employer to worker is shaped by the contextual pressures of state policy and the organization of the industrial field. The focus is the organization of project work as a condition underpinning the shifting of this risk in a mature field of precarious employment, the cultural and creative industries (CCIs). Our empirical exploration in Film/TV in the UK and Germany, and Dance in Sweden and the Netherlands, examines the dynamic interplay between state policy domains (cultural, social and regulatory), industry-level funding bodies or ‘transaction organizers’ and the cultural processes of CCI project networks. We argue that state-led influences both drive and mitigate the transfer of risk in project work as gendered, racialized and classed. Our framework contributes to broadening employment literature on risk and the disadvantaging capacity of networks to hoard opportunities in project-based labour markets.     

Introduction to design and analysis of production systems

In a two-party VMI channel, the vendor operates the basic stocking and delivery functions and makes inventory replenishment decisions while the retailer is responsible for customer acquisition and in-store services. This paper focuses on such retail channel and proposes an analytical model for the partners in supply channel to determine the inventory policy with the objective of optimizing system net profit. The model explicitly incorporates issues from both the vendor and the retailer in order to derive a policy for mutual benefits. To illustrate and obtain insights from the proposed solution procedure, we devise a set of numerical analyses based on various scenarios. Factors such as shelf-space-dependent demand, shelf-space capacity, demand pattern, logistics characteristics, and disparity between holding costs of the warehouse and the retail store are also investigated.     

框架-剪力墙-薄壁筒斜交结构自由振动分析的状态空间法

采用沿高度方向连续化的方法,建立了框架-剪力墙-薄壁筒斜交结构自由振动分析的连续化计算模型。将沿高度方向的坐标虚拟成时间坐标,导出了问题的状态空间表达式,用状态空间理论的方法求出了状态向量表达式,由结构的边界条件可求出初始状态向量,从而得出结构的自振频率。给出了数值算例,并与其他算法的结果进行了比较。     

Production lot sizing with a secondary outsourcing facility

An extended economic production quantity (EPQ) model under stochastic demand is investigated in this paper, where a fixed lot sizing policy is implemented to reduce the complexity of production planning and inventory control, and outsourcing with a secondary facility is used to supplement the lot sizing policy and to cope with the random demand. The considered cost includes: setup cost for the batch production, inventory carrying cost, backorder cost when the demand cannot be met immediately during the production period, and outsourcing cost when the total demand is greater than the lot size in one replenishment cycle. Under some mild conditions, the expected cost per unit time can be shown to be convex. Extensive computational tests have illustrated that the average cost reduction of the proposed model is significant when compared with that of the classical lot sizing policy. Significant cost savings can be achieved by deploying the production lot sizing policy with an outsourcing strategy when the mean demand rate is high.