Artificial bee colony algorithm for CONWIP production control system in a multi-product multi-machine manufacturing environment

In this paper, a multi-product multi-machine serial production line operated under a constant-work-in-process protocol is considered. A mathematical model for the system is first presented, and then an artificial bee colony optimization algorithm is applied to simultaneously find the optimal work-in-process inventory level as well as job sequence order in order to minimize the overall makespan time. Unlike many existing approaches, which are based on deterministic search algorithms such as nonlinear programming and mixed integer programming, the proposed method does not use a linearized or simplified model of the system. A production line simulator implemented on MATLAB is, instead, employed to model the highly nonlinear dynamics of the production line and is used to evaluate the candidate solutions. The efficiency of the proposed approach, even for systems of large sizes, is validated via numerical simulations.     

A mathematical model on EPQ for stochastic demand in an imperfect production system

In the paper, we develop an EPQ (economic production quantity) inventory model to determine the optimal buffer inventory for stochastic demand in the market during preventive maintenance or repair of a manufacturing facility with an EPQ (economic production quantity) model in an imperfect production system. Preventive maintenance, an essential element of the just-in-time structure, may cause shortage which is reduced by buffer inventory. The products are sold with the free minimal repair warranty (FRW) policy. The production system may undergo “out-of-control” state from “in-control” state, after a certain time that follows a probability density function. The defective (non-conforming) items in “in-control” or “out-of-control” state are reworked at a cost just after the regular production time. Finally, an expected cost function regarding the inventory cost, unit production cost, preventive maintenance cost and shortage cost is minimized analytically. We develop another case where the buffer inventory as well as the production rate are decision variables and the expected unit cost considering the above cost functions is optimized also. The numerical examples are provided to illustrate the behaviour and application of the model. Sensitivity analysis of the model with respect to key parameters of the system is carried out.     

Heuristics for solving continuous berth allocation problem considering periodic balancing utilization of cranes

This study investigates a berth allocation problem considering the periodic balancing utilization of quay cranes in container terminals. The proposed model considers that the quay cranes allocated to a work shift should be fully used and other real-world considerations, such as the continuous quay line, the penalties for early arrivals and departure delays. To solve the model, several heuristics are developed: the model for large problems is decomposed into sub-models that are solved by rolling-horizon heuristics; neighborhood search heuristics are used for optimizing a berthing order of vessels; parallel computing is used to improve the algorithmic performance. The method performs well when applied to real-world large-scale instances with promising computation time that is linearly related to the number of vessels.     

Hybrid NSGA-II for an imperfect production system considering product quality and returns under two warranty policies

This study presents a new bi-objective mathematical model for an imperfect production system (IPS) that accounts for product returns and quality levels under two different warranty policies. We considered uncertain customer demand as stochastic behavior and product price as a function of return compensation, product quality level, and warranty-period length. We simultaneously looked at the pro rata warranty and free replacement/repair warranty policies and assumed that customers can choose the desired policy. A return policy for an online distributor was also included and two objective functions were used to address the problem. The first objective function maximized the total expected revenue, and the second objective function maximized customer satisfaction. The non-dominated sorting genetic algorithm (NSGA-II) and two others, the basic bee metaheuristic and teaching-learning-based optimization algorithms, were used to generate the initial solution for use in the NSGA-II algorithm. The results from the hybrid algorithms revealed that the proposed method improves the performance of the NSGA-II algorithm. Finally, several specific sensitivity analyses were conducted to determine the effects of the problem parameters.     

Multi-objective genetic algorithm for berth allocation problem considering daytime preference

Maximization of operational efficiency and minimization of cost are pursued by terminal operators, whereas daytime preference is increasingly emphasized by governments, terminal operators and workers. Daytime preference in berth allocation schedule refers to schedule the workloads in nights as fewer as possible, which improves working comfort, safety, and green and energy-savings degrees, but may decrease the throughput and total operational efficiency. By extending existing dynamic discrete berth allocation model, a bi-objective model considering daytime preference is established to minimize the delayed workloads and the workloads in nights. Based on the well known NSGA-II algorithm, a multi-objective genetic algorithm (moGA) is developed for solving the bi-objective model by using a two-part representation scheme. The sensitivities of the algorithmic parameters and tradeoffs between daytime preference and delayed workloads are analyzed by numerical experiments. The algorithmic aspects of the proposed approach and the effects of daytime preference on solutions are all examined. Finally, the managerial implications are discussed.     

考虑箱区利用率的出口箱资源配置启发式算法

作为集装箱运输的枢纽,堆场的管理是整个集装箱码头管理的重中之重。对出口集装箱堆场的资源配置研究可以提高出口集装箱的装载效率,提高集装箱码头堆场的作业水平。针对当前堆场资源配置研究中较少考虑箱区作业量平衡与码头运营成本关系的研究现状,在不同箱区作业量差异水平下,设计了以成本最低为目标的出口箱堆场资源配置启发式算法。进行了不同规模的算例实验,并进行了比较。     

A vector space model approach to production planning for a multi-product,multi-cell and multi-stage manufacturing system

This paper develops a formal mathematical approach to aggregate production planning for a multi-product, multi-cell and multi-stage manufacturing system. The model, based upon a vector space approach, includes all the important variables relating to the demand for individual items, inventory levels, the availability of machines taking into account any breakdowns, subcontracting of orders and overtime working. The computational procedure for determining the production planning strategies, in terms of overtime/undertime working and increase/decrease in the number of orders subcontracted, are presented. Three numerical examples are presented showing the use of the model developed. This approach makes it possible to develop realistic models of practical manufacturing systems. It is particularly applicable to flexible manufacturing systems.     

A constrained multi-product pricing and inventory control problem

The aim of this article is presenting a new constrained multi-product pricing and inventory model in which perishable products are put into three categories: substitute, complementary and independent. The general demand is found by considering the relations of different products and the impact of their prices. The goal of the model is deciding on the prices as well as the inventory and production decisions in order to maximize the total profit. The derived model is a non-linear programming model and a genetic algorithm is developed for its solution. To enhance the performance of our genetic algorithm, we apply the Taguchi experimental design method to tune the parameters of the algorithm. Finally, the computational results and some recommendations for future extensions are presented.     

Beam search algorithm for capacity allocation problem in flexible manufacturing systems

This study considers the operation assignment and tool allocation problem in flexible manufacturing systems. A set of operations together with their required tools are selected so as to maximize the total weight. The machines have limited time and tool magazine capacities and the tools are available in limited quantities. We develop a beam search algorithm and obtain near optimal solutions for large size problems very quickly.     

Operation assignment and capacity allocation problem in automated manufacturing systems

We address an operation assignment and capacity allocation problem that arises in semiconductor industries and flexible manufacturing systems. We assume the automated machines have scarce time and tool magazine capacities and the tools are available in limited quantities. The aim is to select a subset of operations with maximum total weight. We show that the problem is NP-hard in the strong sense, develop two heuristics and a Tabu Search procedure. The results of our computational tests have revealed that our Tabu Search procedure produces near optimal solutions very quickly.     

An optimal production lot-sizing problem for an imperfect process with imperfect maintenance and inspection time length

This article develops an integrated model in considering the situations of an imperfect process with imperfect maintenance and inspection time for the joint determination of both economic production quantity (EPQ) and preventive maintenance (PM). This imperfect process has a general deterioration distribution with increasing hazard rate. Even with periodic PM, such a production system cannot be recovered as good as new. This means that the system condition depends on how long it runs. Also, the PM level can be distinct due to the maintenance cost. For convenience, it is assumed the age of system is reduced in proportional to the PM level. Further, during a production cycle, we need an inspection to see if the process is in control. This inspection might demand a considerable amount of time. In this article, we take PM level and inspection time into consideration to optimise EPQ with two types of out-of-control states. To see how the method works, we use a Weibull shock model to show the optimal solutions for the least costs.     

Bounding approaches for operation assignment and capacity allocation problem in flexible manufacturing systems

This study considers an operation assignment and capacity allocation problem that arises in flexible manufacturing systems. Automated machines are assumed to have scarce time and tool magazine capacities and the tools are available in limited quantities. The aim is to select a subset of operations with maximum total weight. The weight of an operation may represent its profit, processing load, relative priority. Several upper bounding procedures have been taken into account. The results of computational tests have revealed that the proposed upper bounding procedures produce satisfactory solutions in reasonable CPU times. We suggest using some of the bounds when the quality of the solutions is more important than the speed of achieving them and some others when the speed is more important than the quality.     

Application of fuzzy sets to manufacturing/distribution planning decisions with multi-product and multi-time period in supply chains

This work applies fuzzy sets to integrating manufacturing/distribution planning decision (MDPD) problems with multi-product and multi-time period in supply chains by considering time value of money for each of the operating cost categories. The proposed fuzzy multi-objective linear programming model (FMOLP) attempts to simultaneously minimize total costs and total delivery time with reference to inventory levels, available machine capacity and labor levels at each source, as well as market demand and available warehouse space at each destination, and the constraint on total budget. An industrial case demonstrates the feasibility of applying the proposed model to a realistic MDPD problem and several significant management implications are presented based on computational analysis and comparisons with the existing MDPD methods. The main advantage of the proposed model is that it presents a systematic framework that facilitates fuzzy decision-making for solving the multi-objective MDPD problems with multi-product and multi-time period in supply chains under an uncertain environment, enabling the decision maker to adjust the search direction during the solution procedure to obtain a preferred satisfactory solution.     

A simulation analysis of part launching and order collection decisions for a flexible manufacturing system

In a dynamic flexible manufacturing system (FMS) environment jobs arrive randomly, and in most of the existing studies the due date for a single part is set individually. However, when the due date is set for an order that consists of multiple parts, some completed parts may have to wait for the rest of the order to be completed. This paper studied the scheduling problem in the FMS in which orders require the completion of different parts in various quantities. The orders arrive randomly and continuously, and all have predetermined due dates. Two scheduling decisions were considered in this study: launching parts into the system for production, and determining the order sequence for collecting the completed parts. A new part-launching rule, named the Tardiness Estimating Method (TEM) was proposed. A discrete-event simulation model of the FMS was developed and used as a test-bed for experiments under various system conditions. The proposed part launch rule was capable of providing good performance regarding minimum mean tardiness and maximum service level, but provided only a moderate flow time when compared with the other five rules commonly used in the literature. In addition, three order collection rules were tested in the experiments. Collecting parts for the order with the earliest due date (EDD) was found better than the other rules for tardiness related measures.     

Redundancy allocation problems considering systems with imperfect repairs using multi-objective genetic algorithms and discrete event simulation

This paper considers a multi-objective genetic algorithm (GA) coupled with discrete event simulation to solve redundancy allocation problems in systems subject to imperfect repairs. In the multi-objective formulation, system availability and cost may be maximized and minimized, respectively; the failure-repair processes of system components are modeled by Generalized Renewal Processes. The presented methodology provides a set of compromise solutions that incorporate not only system configurations, but also the number of maintenance teams. The multi-objective GA is validated via examples with analytical solutions and shows its superior performance when compared to a multi-objective Ant Colony algorithm. Moreover, an application example is presented and a return of investment analysis is suggested to aid the decision maker in choosing a solution of the obtained set.     

A cell formation problem considering machine utilization and alternative process routes by scatter search

Cell formation (CF) is the first step in the design of cellular manufacturing systems (CMSs), which has been recognized as an effective way to enhance the productivity in a factory. There is a set of criteria on which to judge route of product, machine grouping and part family simultaneously in terms of the effective utilization of these cells. In this study, we consider four objectives simultaneously: (1) Minimizing the total fixed and variable cost including costs of purchasing, operation, and maintenance; (2) minimizing cost of intercellular movements; (3) maximizing the utilization of machines in the system; and (4) minimizing deviations among the levels of the cell utilization (i.e., balancing the workload between cells). In this paper, these objectives are first weighted by their relative importance and then a new mathematical model is presented. To solve this model, a scatter search (SS) algorithm is proposed to select a process plan for each part with the minimum cost along with forming the part family and machine grouping simultaneously. The performance of the proposed SS is compared with the Lingo 8.0 software. A number of test problems are carried out to verify the good ability of the proposed SS in terms of the solution quality and computational time. The computational results reveal that the SS finds promising results, especially in the case of large-sized problems.     

A two phase variable neighborhood search for the multi-product inventory routing problem

We address a multi-product inventory routing problem and propose a two-phase Variable Neighborhood Search (VNS) metaheuristic to solve it. In the first phase, VNS is used to solve a capacitated vehicle routing problem at each period to find an initial solution without taking into account the inventory. In the second phase, we iteratively improve the initial solution while minimizing both the transportation and inventory costs. For this, we propose two different algorithms, a Variable Neighborhood Descent and a Variable Neighborhood Search. We present an heuristic and a Linear Programming formulation, which are applied after each local search move, to determine the amount of products to collect from each supplier at each period. During the exploration, we use priority rules for suppliers and vehicles, based on the current delivery schedule over the planning horizon. Computational results show the efficiency of the proposed two-phase approach.     

Modeling and solving a multi-objective supplier quota allocation problem considering transaction costs

Suppliers’ evaluation is a subject, which has attracted the attention of many researchers. The performance of potential suppliers is evaluated against multiple criteria rather than considering a single factor such as cost or quality. One of the major objectives of suppliers’ evaluation is to determine the optimal quota assigned to each supplier while needing to replenish an order. This problem has been studied by many researchers as a multi-objective problem. The usual objectives are minimizing the purchasing cost, rejected units, and late delivered units. However, in a few researches maximizing the evaluation scores of the selected suppliers is considered as fourth objective. In this paper, we present a model with five objectives including minimizing the transaction costs of purchasing from suppliers as well as the four addressed objectives. We convert the model to a single objective one using the well-known weighting method, solve it utilizing two meta-heuristic algorithms, and analyze the efficiency of the heuristics. The reason why we utilize the meta-heuristic algorithms is that the problem is proved to be an NP-hard one.     

考虑依赖关系和能力约束的多对多供应链订单分配研究

研究一种多对多供应链中的相互依赖订单分配问题.其中,来自需求方的需求由一系列相互依赖订单组成,并同时考虑供应商的能力约束以及订单多属性约束,包括完成时间、数量、成本等.为了消解由于制造商依赖订单和供应商同时订单所导致的冲突,采用自动协商方法.在定义供应链相互依赖订单分配模型的基础上,提出一种两阶段协商协议和不同的协商策略.协商协议集成了投标和辩论两种协商流程,可以实现制造商与供应商之间的信息交流,规定协商双方的行为准则;而协商策略则确定了制造商和供应商的决策规则.仿真实验验证了自动协商方法的可行性,同时分析和比较不同供应链环境下的订单分配效果.实验结果表明,在不同供应链环境下,通过协商的方法能够有效地消解冲突,解决所研究的订单分配问题.     

A capacity allocation and expansion model for TFT-LCD multi-site manufacturing

This paper presents a capacity allocation and expansion problem of thin film transistor liquid crystal display (TFT-LCD) manufacturing in a multi-site environment. Capacity allocation and expansion decisions have become more challenging issues because of the following: (1) complex product hierarchy caused by a wide range of product types and applications, (2) coexistence of multiple generations of manufacturing technologies in the multi-stage production network, and (3) rapidly growing and changing market demands due to the replacement of the traditional cathode ray tube. Since the Array stage is the bottleneck of this production network, our research objective is to simultaneously seek an optimal capacity allocation plan and capacity expansion policy under single-stage, multi-generation, and multi-site structures. Capacity allocation determines the profitable product mixes and allocated production quantities for each product group at each production site. Capacity expansion determines timing, types, and sizes of capacity investments, especially in the acquisition of auxiliary tools. This study proposes a mixed integer linear programming to formulate the whole model of capacity allocation and expansion, which considers the practical characteristics and constraints of TFT-LCD manufacturing. Finally, an industrial case study modified from a Taiwanese TFT-LCD manufacturer is illustrated and a sensitivity analysis of some influential parameters is also addressed.