The sequence-dependent assembly line balancing problem

Assembly line balancing problems (ALBP) arise whenever an assembly line is configured, redesigned or adjusted. An ALBP consists of distributing the total workload for manufacturing any unit of the products to be assembled among the work stations along the line. The sequence-dependent assembly line balancing problem (SDALBP) is an extension of the standard simple assembly line balancing problem (SALBP) which has significant relevance in real-world assembly line settings. SDALBP extends the basic problem by considering sequence-dependent task times. In this paper, we define this new problem, formulate several versions of a mixed-integer program, adapt solution approaches for SALBP to SDALBP, generate test data and perform some preliminary computational experiments. As a main result, we find that applying SALBP-based search procedures is very effective, whereas modelling and solving the problem with MIP standard software is not recommendable.     

Multi-stage lot sizing in a serial production system

This paper summarizes the development and implementation of a large-scale, multi-objective, lot-sizing model for scheduling tablet pharmaceuticals in a serial production system. The model places multiple resource capacity constraints on production at various stages of the manufacturing process and explicitly considers resource set-up times to ensure the generation of a feasible schedule. The multi-period model uniquely allows for variable lot sizes by stage and by period, subject to integrality restrictions arising from the technological nature of the multi-stage process. An integer goal programming approach is used for determining non-dominated solutions to the multi-item problem. The implementation and utilization of the model by an international manufacturer of pharmaceuticals is described, and model results are presented and discussed.     

Optimal preventive maintenance policies in a serial production system

There have been many publications dealing with preventive maintenance policies for stochastically deteriorating systems. However, most of them study systems having one device subject to failure. Much of the limited research dealing with multi-device production systems has been conducted through simulation experiments. In this paper, the preventive maintenance problem for serial production systems is formulated as a mathematical model. This formulation permits management to analyse the impact of a preventive maintenance policy on a serial production system without resorting to simulation. Numerical examples are used to provide managerial implications for maintaining a serial production system. The results show that the operating characteristics of the stations are interrelated; therefore, it is important to examine the joint effects of a maintenance policy on the various stations of the production system simultaneously rather than study each station separately.     

High-speed measuring-computing system for balancing rotors on a production line

A system for balancing rotors is described. The system meets industrial requirements to the fullest extent possible. It operates on the basis of an original algorithm that avoids introducing an additional error connected with setting of the rotation transducer. The system makes it possible to balance lightweight rotors with prescribed metrological characteristics.Translated from Izmeritel'naya Tekhnika, No. 6, pp. 29–31, June, 1995.     

Line balancing in a just-in-time production environment: balancing multiple U-lines

The introduction of multiple, independent production lines has helped many firms to increase their production flexibility, provide for redundancy when equipment breaks down, reduce idle time and labor costs, and achieve many other benefits. This paper introduces and formalizes the multiple U-line balancing problem. Optimal solution methodologies are provided for Type I (minimize the number of stations for a given cycle time), Type II (minimize the cycle time for a given number of stations), and cost-minimization line-balancing problems. A branch-and-bound algorithm is also developed for the situation in which equipment requirements are dependent on the line configuration and the task assignment to stations. Computational results indicate that the greatest benefit of exploiting multiple lines occurs for smaller cycle-time problems that require higher output.     

Optimal locations of on-line and off-line rework stations in a serial production system

The production rate and product quality are two vital concerns for any manufacturing industry. Number of defective items reduces production rate and increases unit production cost. Moreover, if nonconforming items reach to the customers then manufacturer’s goodwill may drastically go down. Thus, quality inspection is treated as an inherent part of manufacturing. In this research, an N-stage serial production line with an inspection station at the end of it is considered to make decisions concerning this issue. On detecting a defective item at the end of the line it is scrapped or repaired at regular workstation or is sent to an off-line rework station for repair. Assuming each workstation produces a single type of defect a unit cost function is developed for alternative decisions on each type of defect. In order to minimise the unit cost of production and determine an appropriate decision for individual defect types, a fractional mixed integer nonlinear programming is formulated. After transformation to a mixed integer linear programming problem it is solved optimally. A small problem from garments industry is described in detail to show the solution procedure with a branch and bound method. Empirical tests with up to 40 workstations are permed to show the efficiency of the solution process.     

A bi-objective robust inspection planning model in a multi-stage serial production system

In this paper, we present a bi-objective mixed-integer linear programming (BOMILP) model for planning an inspection process used to detect nonconforming products and malfunctioning processors in a multi-stage serial production system. The model involves two inter-related decisions: (1) which quality characteristics need what kind of inspections (i.e. which-what decision) and (2) when the inspection of these characteristics should be performed (i.e. when decision). These decisions require a trade-off between the cost of manufacturing (i.e. production, inspection and scrap costs) and the customer satisfaction. Due to inevitable variations in manufacturing systems, a global robust BOMILP (RBOMILP) is developed to tackle the inherent uncertainty of the concerned parameters (i.e. production and inspection times, errors type I and II, misadjustment and dispersion of the process). In order to optimally solve the presented RBOMILP model, a meta-heuristic algorithm, namely differential evolution (DE) algorithm, is combined with the Taguchi and Monte Carlo methods. The proposed model and solution algorithm are validated through a real industrial case from a leading automotive industry in France.     

Capacitated production and subcontracting in a serial supply chain

This paper extends a series of recent results regarding the polynomial solvability of single and two-echelon deterministic lot sizing models. In particular, we consider a two-echelon supply chain in which production, inventory, transportation, backlogging, and subcontracting decisions are integrated. We allow for arbitrary concave cost functions and for stationary production and subcontracting capacities. We develop dynamic programming algorithms for various problems in this class that run in polynomial time in the planning horizon of the problem.     

Minimizing production flow time in a process and assembly job shop

Scheduling is one of the most important issues in the planning and operation of production systems, but in medium to large shops, the generation of consistently good schedules has proven to be extremely difficult. The problem is that optimal scheduling solutions involve costly and impractical enumeration procedures. In the literature, most scheduling problems only address jobs with serial or sequential operations. Rarely do they consider jobs in which machining and assembly operations are simultaneously involved. This lack of attention to scheduling problems that involve both machining and assembly goes against what one would normally find in most job shops. In this paper, the problem of scheduling a set of N final products on M machines in a job shop environment that involve both machining and assembly operations is addressed. The objective pursued is the minimization of production flow time (makespan). A mathematical model is developed in an effort to obtain optimal solutions. Because this type of model grows exponentially as the size of the problems increases, an heuristic solution approach is developed to solve the problems more efficiently. The models are tested and compared on several test problems.     

Integrated quality and quantity modeling of a production line

During the past three decades, the success of the Toyota Production System has spurred much research in manufacturing systems engineering. Productivity and quality have been extensively studied, but there is little research in their intersection. The goal of this paper is to analyze how production system design, quality, and productivity are inter-related in small production systems. We develop a new Markov process model for machines with both quality and operational failures, and we identify important differences between types of quality failures. We also develop models for two-machine systems, with infinite buffers, buffers of size zero, and finite buffers. We calculate total production rate, effective production rate (ie, the production rate of good parts), and yield. Numerical studies using these models show that when the first machine has quality failures and the inspection occurs only at the second machine, there are cases in which the effective production rate increases as buffer sizes increase, and there are cases in which the effective production rate decreases for larger buffers. We propose extensions to larger systems. Correspondence to: Stanley B. GershwinWe are grateful for support from the Singapore-MIT Alliance, the General Motors Research and Development Center, and PSA Peugeot-Citroën.     

Minimizing expected waiting in a medical appointment system

An appointment policy is established for a specific outpatient system, with the goal of reducing a combination of patients' expected waiting times and doctor's expected overtime. Service times are stochastic, and patients are assumed to be punctual if they show for their appointments. Substantial reductions in waiting times can be obtained over traditional scheduling practices, and the procedure is easily implemented by a naive scheduler once the optimal policy is established.     

Economic design of an attributes control system for a multistage serial production process

A model for the economic design of an np-control system integrated within a multiple stage serial production process is presented. The total expected quality control cost includes the costs of sampling, the costs of investigating an out-of-control alarm and possibly correcting an assignable cause(s), and the costs associated with the production of non-conforming items. The model is represented as a directed network with decision variables of sample size, rejection number and frequency of sampling occurring at each stage of the process. A combination of dynamic programming and direct search techniques is applied to determine the set of sampling policies which yield minimum total expected cost. Numerical examples and results of a sensitivity analysis are reported.     

Optimal inspection policies in a serial production system including scrap rework and repair: An MILP approach

The allocation of inspection effort problem for aerial systems is formulated as a 0-1 mixed integer linear programming problem. This formulation permits any combination of scrap, rework, or repair at each station and allows the problem to be solved using standard MILP software packages. Moreover electronic spread-sheets may be used to easily calculate the relevant coefficients. An additional advantage of this approach when compared with the traditional dynamic programming approach is the ease with which the basic model may be modified. For example, it is shown how the model may easily be modified to include both a material and a production constraint and to select between various material suppliers. Sensitivity analysis is also easily performed with this approach. This model is then used to show that the optimal inspection policy is dependent on whether a production or a material requirement is used.     

Minimizing the cycle time in serial manufacturing systems with multiple dual-gripper robots

Robots are being used more and more extensively as material-handling systems for automated manufacturing systems. This is especially true for dual-gripper robots whose in-process buffer (the robot's second gripper) constitutes a further element of flexibility. When the number of stations to be served is high and the set of activities the robot must execute is great, the system throughput may depend on robot capability rather than on process times. In such conditions, the use of several robots leads to an increase in system productivity. Obviously, the design and the management of such a handling system becomes more complex: the minimum number of robots required, the work stations to be served by each of them and the robot move cycles must be all determined so as to minimize the cycle time of a multi-robot serial system. Since the aim of minimizing the cycle time could lead to a non-univocal configuration, a secondary objective may be pursued. To this aim, the classic case of a single dual-gripper robotic cell is preliminarily revisited, using a practical rather than a theoretical approach, to show that, under the conditions of minimum cycle time, it is possible to take into account both the reduction of the WIP and that of the length of the transitory periods.     

Smart Fraud Detection in E-Transactions Using Synthetic Minority Oversampling and Binary Harris Hawks Optimization

Fraud Transactions are haunting the economy of many individuals with several factors across the globe. This research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital transactions. This research proposes a novel methodology through three stages. Firstly, Synthetic Minority Oversampling Technique (SMOTE) is applied to get balanced data. Secondly, SMOTE is fed to the nature-inspired Meta Heuristic (MH) algorithm, namely Binary Harris Hawks Optimization (BinHHO), Binary Aquila Optimization (BAO), and Binary Grey Wolf Optimization (BGWO), for feature selection. BinHHO has performed well when compared with the other two. Thirdly, features from BinHHO are fed to the supervised learning algorithms to classify the transactions such as fraud and non-fraud. The efficiency of BinHHO is analyzed with other popular MH algorithms. The BinHHO has achieved the highest accuracy of 99.95% and demonstrates a more significant positive effect on the performance of the proposed model.     

Production and subcontracting control for an unreliable manufacturing system with setups

This paper deals with the problem of joint production, setup and subcontracting control of unreliable manufacturing systems producing two product types. The production requires setups each time it switches from one product type to another. Subcontracting is an integral part of the decision-making process due to limited production capacity in existing facility. The objective is to propose an effective control policy for the considered system which simultaneously manages production, setup and subcontracting activities. The complexity of the problem lies in the interaction between internal manufacturing decisions and subcontracting that outsource a part of the production, in a dynamic and stochastic environment. An experimental optimisation approach is adopted to determine the optimal control parameters which minimise the average total cost. Extensive sensitivity analyses are performed to illustrate the robustness and the usefulness of the adopted approach. An in-depth study comparing five control policies across a wide range of system parameters is also conducted. Extended cases closer to reality are also investigated considering elements such as the preventive maintenance and the production of non-conforming products. The best control policy in terms of economic performance is then obtained. Valuable insights providing a better understanding of interactions involving production, setup, and subcontracting are discussed.     

Multi-objective production scheduling with controllable processing times and sequence-dependent setups for deteriorating items

The production scheduling problem is to find simultaneously the lot sizes and their sequence over a finite set of planning periods. This paper studies a single-stage production scheduling problem subject to controllable process times and sequence-dependent setups for deteriorating items. The paper formulates the problem by minimising two objectives of total costs and total variations in production volumes simultaneously. The problem is modelled and analysed as a mixed integer nonlinear program. Since it is proved that the problem is NP-hard, a problem-specific heuristic is proposed to generate a set of Pareto-optimal solutions. The heuristic is investigated analytically and experimentally. Computational experiences of running the heuristic and non-dominated sorting genetic algorithm-I over a set of randomly generated test problems are reported. The heuristic possesses at least 56.5% (in the worst case) and at most 94.7% (in the best case) of total global Pareto-optimal solutions in ordinary-size instances.     

Aggregate production process design in global manufacturing using a real options approach

A main source of competitive advantage is derived from the cost efficiency offered by firms’ manufacturing and logistics operations. Consequently, firms typically globalise their operations whereby they may exploit the comparative advantages—defined as production functions—of the nations in which they are present. Production process design thus arises as a significant issue. The research presented in this paper targets two fundamental questions attached to production process design that multinational companies face, namely: (i) should plants that are located in different countries but producing similar products use similar production processes?; and (ii) given that the firm's policy is to use similar production processes, how should the production processes be designed? Among others, the paper shows, by way of a numerical illustration of a binational manufacturing network, that the option of choosing freely upon production process design for the respective facilities in certain cases adds little to firm value. In fact, the value of this option tends to zero as the volatility rate increases when the exchange rate is modelled as a geometric Brownian motion without drift rate, implying that firms should employ similar production processes throughout their manufacturing networks. That is, a market value approach stands up for the so-called copy-exactly approach to production process design in these settings. We furthermore show the effects of economies of scale on the optimal production process design.     

Lead-time reduction in a stochastic inventory system with learning consideration

We develop a continuous review inventory model where lead-time is considered as a controllable variable. Lead-time is decomposed into all its components: set-up time, processing time and non-productive time. These components reflect the set-up cost reduction, lot size lead-time interaction and lead-time crashing, respectively. The learning effect in the production process is also included in the processing time component of the lead-time. The finite investment approach for lead-time and set-up cost reduction and their joint optimization, in addition to the lot size lead-time interaction, introduce a realistic direction in lead-time management and control. A numerical example and a sensitivity analysis are presented using the design of experiments to investigate the effect of the model parameters and, in particular, those related to the different lead-time components on the the expected total cost.     

A case study in productivity-cost trade-off in the design of paced parallel production systems

This paper examines the investment and operational cost differences between high-volume serial CNC-based machining lines and parallel CNC-based machining lines. With the progress of CNC technology and their descending cost, more CNC machines have been used in high-volume production systems. CNC machines increase the flexibility and machining capability of production lines, greatly increasing the number of line configurations. Parallel configurations improve system throughput and have the same effect as adding buffers to a pure serial line but without additional work-in-process inventory. This analysis is performed through a case study of a CNC-based automotive cylinder head machining line. Examining machine reliability, line balance, configuration throughput, and cost yields insight into the cost-benefit tradeoff of implementing parallelism. It is found that even with large increases in investment in automated material handling, parallel configurations can yield significant annual cost savings over pure serial lines through reductions in capital investment, especially in CNC machines, and improvements in efficiency, and on a per unit capacity basis, parallel configurations are the least expensive.