Revising the master production schedule in a HPP framework context

This paper investigates the stability of the master production schedule (MPS) in a multi-product batch chemical plant, a typical example of manufacturing plants in the process industry. The effects of demand pattern, replanning periodicity, setup costs and unit production cost on the performance of the MPS in a rolling horizon situation are examined. Adopting the hierarchical production planning framework, a two-level mathematical model is developed to conduct the study. A comprehensive simulation work and statistical analyses are reported in this work. The results of the study show that replanning periodicity significantly influences the scheduling instability and the impact of setup cost on scheduling instability is dependent on the demand pattern and the unit production cost. Moreover, the findings indicate that replanning frequency does not affect the total cost of the system if the cost structures are not extreme. Finally, the results show that cost structures affect the batching of orders (converting production quantities into an optimal number of batches to be processed).     

Revising the master production schedule in sequence dependent processes

Revising the master production schedule (MPS) in a rolling planning horizon environment to improve plant performance is critical in process industries with sequence dependent changeovers. Plant performance is defined in terms of process changeover time, total shortages, and finished goods inventory. Two heuristics for revising the MPS are introduced and tested against a rolling horizon MPS that does not take sequence dependent changeovers into consideration. These two heuristics represent two different approaches to heuristic design: local interchange (SWAP) and global interchange (3OPT). Simulation experiments are reported that test the performance of the two different approaches to revising the MPS in process industries with sequence dependent changeovers. The MPS designs were tested under plant conditions that are frequently encountered in process industries. The results indicate that revising the MPS in process industries significantly improves plant performance. In addition, 3OPT provides a major improvement in changeover time, while SWAP provides improvement in total shortages.     

Master production scheduling,customer service and manufacturing flexibility in an assemble-to-order environment

A key element of manufacturing planning and control involves the inter-functional coordination of various manufacturing requirements. This paper reports the results of a simulation experiment that compares alternative master production scheduling (MPS) procedures in an assemble-to-order environment. The MPS procedures are superbills and covering sets. For a given investment in safety stocks the managerial problem is how to construct the MPS in order to minimize the firm's delivery time pressures. The results of the simulation experiment strongly support the use of the superbill techniques over the covering set technique. Moreover, of the experimental factors that influence delivery time performance, the choice of master production scheduling technique has the largest effect. Demand variability has the next greatest effect on delivery time performance. The safely stock level has the third greatest effect and product commonality was fourth. Finally, the managerial implications of the results are discussed in detail.     

A hierarchical approach for the capacitated lot-sizing and scheduling problem with a special structure of sequence-dependent setups

This research deals with the single machine multi-product capacitated lot-sizing and scheduling problem (CLSP) with sequence-dependent setup times and setup costs. The CLSP determines the production quantities and the sequence to satisfy deterministic and dynamic demand during multiple periods. The objective is to minimise the total sum of the inventory holding costs and the sequence-dependent setup costs. We consider a special form of sequence-dependent setup times where the larger product we produce next, the more setup time we need. As a solution approach, we propose a two-level hierarchical method consisting of upper-level planning and the lower-level planning. In the upper-level planning, we solve the lot-sizing problem with estimated sequence-independent setup times utilising the characteristic of the special structure of setup times. Then we solve the scheduling problem in the lower-level planning. The proposed method is compared with the single-level optimal CLSP solution and an existing heuristic developed for the uniform structure of setup times.     

A heuristic algorithm for master production scheduling problem with controllable processing times and scenario-based demands

Master production scheduling (MPS) is widely used by manufacturing industries in order to handle the production scheduling decisions in the production planning hierarchy. The classical approach to MPS assumes infinite capacity, fixed (i.e. non-controllable) processing times and a single pre-determined scenario for the demand forecasts. However, the deterministic optimisation approaches are sometimes not suitable for addressing the real-world problems with high uncertainty and flexibility. Accordingly, in this paper, we propose a new practical model for designing an optimal MPS for the environments in which processing times may be controllable by allocating resources such as facilities, energy or manpower. Due to the NP-hardness of our model, an efficient heuristic algorithm using local search technique and theory of constraints is developed and analysed. The computational results especially for large-sized test problems show that the average optimality gap of proposed algorithm is four times lower than that of exact solution using GAMS while it consumes also significantly smaller run times. Also, the analysis of computational results confirms that considering the controllable processing times may improve the solution space and help to more efficiently utilise the available resources. According to the model structure and performance of the algorithm, it may be proposed for solving large and complex real-world problems particularly the machining and steel industries.     

Master production scheduling make-to-stock products: a framework for analysis

Excessive changes in MRP system schedules, commonly referred to as 'nervousness’, is frequently an obstacle in implementing an effective MRP based manufacturing planning and control system. This paper is concerned with the design of methods for freezing the master production schedule (MPS) as a way of controlling MPS stability under rolling planning conditions for make-to-stock products. It presents a framework for the design of MPS freezing methods and compares their performance when the design parameters of these methods are varied. Simulation experiments are reported that demonstrate important differences in performance considering criteria involving both the cost of lot-sizing the MPS and the stability of the master production schedule.     

A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems

Integrated process planning and scheduling (IPPS) is a manufacturing strategy that considers process planning and scheduling as an integrated function rather than two separated functions performed sequentially. In this paper, we propose a new heuristic to IPPS problem for reconfigurable manufacturing systems (RMS). An RMS consists mainly of reconfigurable machine tools (RMTs), each with multiple configurations, and can perform different operations with different capacities. The proposed heuristic takes into account the multi-configuration nature of machines to integrate both process planning and scheduling. To illustrate the applicability and the efficiency of the proposed heuristic, a numerical example is presented where the heuristic is compared to a classical sequential process planning and scheduling strategy using a discrete-event simulation framework. The results show an advantage of the proposed heuristic over the sequential process planning and scheduling strategy.     

Scheduling multi-purpose batch plants with junction constraints

There is an increasing trend in the chemical process industry to operate flexible batch plants because of their capability to manufacture multiple products simultaneously by sharing the same process resources. In this paper, the scheduling of multi-purpose batch chemical plants with junction (header) constraints is considered. A mixed-integer non-linear model for the scheduling of multi-purpose batch chemical plants is formulated that considers the connection between equipment sets, transfer times, variable batch sizes, alternative process plans, and batch merging. Because of the computational time complexity of the batch scheduling problem, a heuristic scheduling algorithm that minimizes the total tardiness is developed lo solve the model.     

Advanced production scheduling for batch plants in process industries

An Advanced Planning System (APS) offers support at all planning levels along the supply chain while observing limited resources. We consider an APS for process industries (e.g. chemical and pharmaceutical industries) consisting of the modules network design (for long–term decisions), supply network planning (for medium–term decisions), and detailed production scheduling (for short–term decisions). For each module, we outline the decision problem, discuss the specifi cs of process industries, and review state–of–the–art solution approaches. For the module detailed production scheduling, a new solution approach is proposed in the case of batch production, which can solve much larger practical problems than the methods known thus far. The new approach decomposes detailed production scheduling for batch production into batching and batch scheduling. The batching problem converts the primary requirements for products into individual batches, where the work load is to be minimized. We formulate the batching problem as a nonlinear mixed–integer program and transform it into a linear mixed–binary program of moderate size, which can be solved by standard software. The batch scheduling problem allocates the batches to scarce resources such as processing units, workers, and intermediate storage facilities, where some regular objective function like the makespan is to be minimized. The batch scheduling problem is modelled as a resource–constrained project scheduling problem, which can be solved by an efficient truncated branch–and–bound algorithm developed recently. The performance of the new solution procedures for batching and batch scheduling is demonstrated by solving several instances of a case study from process industries.     

Integrated production scheduling and vehicle routing problem with job splitting and delivery time windows

In this paper, we study a production scheduling and vehicle routing problem with job splitting and delivery time windows in a company working in the metal packaging industry. In this problem, a set of jobs has to be processed on unrelated parallel machines with job splitting and sequence-dependent setup time (cost). Then the finished products are delivered in batches to several customers with heterogeneous vehicles, subject to delivery time windows. The objective of production is to minimize the total setup cost and the objective of distribution is to minimize the transportation cost. We propose mathematical models for decentralized scheduling problems, where a production schedule and a distribution plan are built consecutively. We develop a two-phase iterative heuristic to solve the integrated scheduling problem. We evaluate the benefits of coordination through numerical experiments.     

A heuristic procedure for component scheduling in printed circuit pack sequencers

A heuristic procedure to minimize changeovers of component tapes on sequencers while manufacturing printed circuit packs (PCP) is presented. The suggested approach first groups similar components and PCPs together and then applies an efficient method for scheduling components and PCPs on the sequencers. It demonstrates considerable improvement over the solution procedures presently available in the literature.     

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.     

基于流转批量的动态制造提前期算法研究

通过对企业资源计划(ERP)应用中生产过程的流转批量、加工批量与提前期的关系进行分析研究，以离散制造业中的批量生产为应用背景，结合批量因素提出多流转批次下的制造提前期计算方法，以流转批次反映工序间不同生产能力的相互影响，使车间作业中流转批量因素能在生产计划阶段通过提前期反映出来，以降低生产计划与车间作业计划之间的误差。并以实例应用说明该方法在生产计划制定中取得的效果。     

Multi-product continuous plant scheduling: combination of decomposition,genetic algorithm,and constructive heuristic

We propose a polylithic method for medium-term scheduling of a large-scale industrial plant operating in a continuous mode. The method combines a decomposition approach, a genetic algorithm (GA) and a constructive MILP-based heuristic. In the decomposition, decisions are made at two levels, using the rolling horizon approach. At the upper level, a reduced set of products and the time period is chosen to be considered in the lower level. At the lower level, a short-term scheduling MILP-model with event-based representation is used. A heuristic solution to the lower level problem is found using a constructive Moving Window heuristic guided by a genetic algorithm. The GA is applied for finding efficient utilisation of critical units in the lower level problem. For solving the one unit scheduling problem, a parallel dynamic programming algorithm is proposed. Implementation of the dynamic programming algorithm for a graphics processing unit (GPU) is incorporated in the GA for improving its performance. The experimental study of the proposed method on a real case of a large-scale plant shows a significant improvement of the solution quality and the solving time comparing to the pure decomposition algorithm proposed in the earlier study, and confirmed suitability of the proposed approach for the real-life production scheduling. In particular, the reduction of the number of changeovers and their duration in the obtained solution as well as the CPU time of solving the problem was about 60% using the new approach.     

A tabu search heuristic for scheduling the production processes at an oil refinery

In this paper we present a tabu search heuristic which can be used for scheduling the production at an oil refinery. The scheduling problem is to decide which production modes to use at the different processing units at each point in time. The problem is a type of lot-sizing problem where costs of changeovers, inventories and production are considered. In the suggested tabu search heuristic we explore the use of variable neighbourhood, dynamic penalty and different tabu lists. Computational results are presented for different versions of the heuristic and the results are compared to the best-known lower bound for a set of scheduling scenarios.     

Adaptive scheduling of batch servers in flow shops

Batch servicing is a common way of benefiting from economies of scale in manufacturing operations. Good examples of production systems that allow for batch processing are ovens found in the aircraft industry and in semiconductor manufacturing. In this paper we study the issue of dynamic scheduling of such systems within the context of multi-stage flow shops. So far, a great deal of research has concentrated on the development of control strategies, which only address the batch stage. This paper proposes an integral scheduling approach that also includes succeeding stages. In this way, we aim for shop optimization, instead of optimizing performance for a single stage. Our so-called look-ahead strategy adapts its scheduling decision to shop status, which includes information on a limited number of near-future arrivals. In particular, we study a two-stage flow shop, in which the batch stage is succeeded by a serial stage. The serial stage may be realized by a single machine or by parallel machines. Through an extensive simulation study it is demonstrated how shop performance can be improved by the proposed strategy relative to existing strategies.     

Sequence-dependent batch chemical scheduling with earliness and tardiness penalties

Production volume in the specialized agricultural chemical industry is typically too small to justify the capital expenditure required for continuous processing. As such, there is a trend towards building chemical processing plants for this market segment that are batch plants. Scheduling of this type of chemical plant under just-in-time operations, where both earliness and lateness penalties are included, is critical to the efficient operation of these plants. In this paper, a heuristic scheduling procedure is developed for the problem of minimizing the total weighted earliness and tardiness costs as well as the total set-up cost for the single-stage batch chemical manufacturing environment.     

An integrated scheduling and material-handling approach for complex job shops: a computational study

We suggest an extension of the shifting bottleneck heuristic for complex job shops that takes the operations of automated material-handling systems (AMHS) into account. The heuristic is used within a rolling horizon approach. The job-shop environment contains parallel batching machines, machines with sequence-dependent setup times, and re-entrant process flows. Jobs are transported by an AMHS. Semiconductor wafer fabrication facilities (wafer fabs) are typical examples for manufacturing systems with these characteristics. Our primary performance measure is total weighted tardiness (TWT). The shifting bottleneck heuristic (SBH) uses a disjunctive graph to decompose the overall scheduling problem into scheduling problems for single machine groups and for transport operations. The scheduling algorithms for these scheduling problems are called subproblem solution procedures (SSPs). We consider SSPs based on dispatching rules. In this paper, we are also interested in how much we can gain in terms of TWT if we apply more sophisticated SSPs for scheduling the transport operations. We suggest a Variable Neighbourhood Search (VNS) based SSP for this situation. We conduct simulation experiments in a dynamic job-shop environment in order to assess the performance of the suggested algorithms. The integrated SBH outperforms common dispatching rules in many situations. Using near to optimal SSPs leads to improved results compared with dispatching based SSPs for the transport operations.     

A Heuristic Procedure for Scheduling Packaging Lines in Process Industries

Process industries produce non-differentiable products which are packaged into a variety of sizes during the final production stage. In this environment, product family changeovers are performed during an off-shift period, while item changeovers, which are shorter in duration, may be performed during a shift. A heuristic for scheduling packaging lines is presented where a family setup cost is charged for an off-shift changeover and item lot sizes are determined subject to limited capacity and setup times.     

The Problems of Tardiness and Saturation in a Multi-Class Queue with Sequence-Dependent Setups

This paper deals with scheduling jobs of different classes on a facility with sequence-dependent setups between classes. Tardiness measures are of primary concern, and changeovers must be limited in order to avoid saturation. A method is given for classifying scheduling rules in this environment. Procedures are developed for avoiding saturation. A digital simulation model has been used to investigate the tardiness performance of various rules, and experimental results are given.