Towards an Integrated Workload Control (WLC) Concept: The Performance of Due Date Setting Rules in Job Shops with Contingent Orders

Workload control (WLC) is a production planning and control concept developed for make-to-order companies. Its customer enquiry management methodology supports due date setting, while its order release mechanism determines when to start production. For make-to-order companies, due date setting is a strategically important, complex task where unconfirmed jobs place demands on capacity which are contingent on a quotation being accepted by the customer. Yet most prior WLC research has begun at the order release stage with a set of confirmed orders with predetermined due dates. In contrast, this paper focuses specifically on customer enquiry management and uses simulation to compare and contrast the performance of 11 due date setting rules in a job shop where part of the workload consists of unconfirmed or contingent orders. The best results are achieved by a finite loading rule which explicitly considers the workload of contingent orders when estimating lead times. This enables demand to be levelled over time, allowing due dates to be short and reliable – thereby improving both the competitiveness of a make-to-order company and the customer service level it is able to offer. Future research should focus on integrating customer enquiry management, and its due date setting rule, with order release control.     

An efficient priority rule for scheduling job shops to minimize mean tardiness

The ability to respond quickly to customer demands is a key factor in successfully competing in today's globally competitive markets. Thus, meeting due dates could be the most important goal of scheduling in many manufacturing and service industries. Meeting due dates can naturally be translated into minimizing job tardiness. In this paper we present a priority rule for dynamic job shop scheduling that minimizes mean job tardiness. The rule is developed based on the characteristics of existing dispatching rules. With job due dates set by the generalized total work content rule, the computational results of simulation experiments show that the proposed dispatching rule consistently, and often, significantly outperforms a number of well-known priority rules in the literature. The proposed rule is also robust being the best or near-best rule for reducing the mean flowtime, for all the shop load levels and due date tightness factors tested.     

Integrating order acceptance decisions with flexible due dates in a production planning model with load-dependent lead times

We consider a tactical planning problem, which integrates production planning decisions together with order acceptance decisions, while taking into account the dependency between workload and lead times. The proposed model determines which orders to accept and in which period they should be produced, so that they can be delivered to the customer within the acceptable flexible due dates. When the number of accepted orders increases, the workload and production lead time also increase, and this may result in the possibility of missing customer due dates. This problem is formulated as a mixed integer linear programme for which two relax-and-fix heuristic solution methods are proposed. The first one decomposes the problem based on time periods, while the second decomposes it based on orders. The performances of these heuristics are compared with that of a state-of-the-art commercial solver. Our results show that the time-based relax-and-fix heuristic outperforms the order-based relax-and-fix heuristic, and the solver solution as it yields better integrality gaps for much less CPU effort.     

Due date assignment using artificial neural networks under different shop floor control strategies

Due date assignment (DDA) is the first important task in shop floor control. Due date-related performance is impacted by the quality of the DDA rules. Assigning order due dates and delivering the goods to the customer on time will enhance customer service and provide a competitive advantage. A new methodology for lead-time prediction, artificial neural network (ANN), is adopted to model new due date assignment rules. An ANN-based DDA rule, combined with simulation technology and statistical analysis, is presented. Whether or not the ANN-based DDA rule can outperform the conventional and Reg-based DDA rules taken from the literature is examined. The interactions between the DDA, order review/release (ORR), and dispatching rules significantly impact upon one another, and it is therefore very important to determine a suitable DDA rule for the various combinations of ORR and dispatching rules. From the simulation and statistical results, the ANN-based DDA rules perform better in due date prediction. The ANN-based DDA rules have a smaller tardiness rate than the other rules. ANN-based DDA rules have a better sensitivity and variance. Therefore, if system information is not difficult to obtain, the ANN-based DDA rule can perform a better due date prediction. This paper provides suggestions for DDA rules under various combinations of ORR and dispatching rules. ANN-Sep is suitable for most of these combinations, especially when ORR, workload regulation (WR) and two boundaries (TB), rules are adopted.     

Solving comprehensive dynamic job shop scheduling problem by using a GRASP-based approach

There are many dynamic events like new order arrivals, machine breakdowns, changes in due dates, order cancellations, arrival of urgent orders etc. that makes static scheduling approaches very difficult. A dynamic scheduling strategy should be adopted under such production circumstances. In the present study an event driven dynamic job shop scheduling mechanism under machine capacity constraints is proposed. The proposed method makes use of the greedy randomised adaptive search procedure (GRASP) by also taking into account orders due dates and sequence-dependent set-up times. Moreover, order acceptance/rejection decision and Order Review Release mechanism are integrated with scheduling decision in order to meet customer due date requirements while attempting to execute capacity adjustments. We employed a goal programming-based logic which is used to evaluate four objectives: mean tardiness, schedule unstability, makespan and mean flow time. Benchmark problems including number of orders, number of machines and different dynamic events are generated. In addition to event-driven rescheduling strategy, a periodic rescheduling strategy is also devised and both strategies are compared for different problems. Experimental studies are performed to evaluate effectiveness of the proposed method. Obtained results have proved that the proposed method is a feasible approach for rescheduling problems under dynamic environments.     

Production scheduling with outsourcing scenarios: a mixed integer programming and efficient solution procedure

When market demand exceeds the company's capacity to manufacture, outsourcing is commonly considered as an effective alternative option. In traditional scheduling problems, processing of received orders is just possible via in-house resources, while in practice, outsourcing is frequently found in various manufacturing industries, especially in electronics, motor and printing companies. This paper deals with the scheduling problem, minimising the cost of outsourcing and a scheduling measure represented by weighted mean flow time, in which outsourcing of manufacturing operations is allowed through subcontracts. Each order can be either scheduled for in-house production or outsourced to an outside supplier in order to meet customer due dates. In this problem, not only should the sequence of orders be determined, but also decision on picking the jobs for outsourcing, selecting the appropriate subcontractor, and scheduling of the outsourced orders are considered as new variables. To formulate the given problem, four different outsourcing scenarios are derived and mixed integer programming models are developed for each one separately. Furthermore, to solve the suggested problem, a computationally effective team process algorithm is devised and then a constraint handling technique is embedded into the main algorithm in order to ensure satisfaction of customer due dates. Numerical results show that the suggested approach possesses high global solution rates as well as fast convergence.     

Scheduling and order acceptance for the customised stochastic lot scheduling problem

This paper develops and analyses several customer order acceptance policies to achieve high bottleneck utilisation for the customised stochastic lot scheduling problem (CSLSP) with large setups and strict order due dates. To compare the policies, simulation is used as the main tool, due to the complicated nature of the problem. Also, approximate upper and lower bounds for the utilisation are provided. It is shown that a greedy approach to accept orders performs poorly in that it achieves low utilisation for high customer order arrival rates. Rather, good acceptance and scheduling policies for the CSLSP should sometimes reject orders to create slack even when there is room in the schedule to start new production runs. We show that intelligently introducing slack enables the achievement of high utilisation. One particularly simple policy is to restrict the number of families present.     

Applying Job Shop Scheduling to SMEs Manufacturing Platform to Revitalize B2B Relationship

A small and medium enterprises (SMEs) manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities. The optimal job shop scheduling is generated by utilizing the scheduling system of the platform, and a minimum production time, i.e., makespan decides whether the scheduling is optimal or not. This scheduling result allows manufacturers to achieve high productivity, energy savings, and customer satisfaction. Manufacturing in Industry 4.0 requires dynamic, uncertain, complex production environments, and customer-centered services. This paper proposes a novel method for solving the difficulties of the SMEs manufacturing by applying and implementing the job shop scheduling system on a SMEs manufacturing platform. The primary purpose of the SMEs manufacturing platform is to improve the B2B relationship between manufacturing companies and vendors. The platform also serves qualified and satisfactory production opportunities for buyers and producers by meeting two key factors: early delivery date and fulfillment of processing as many orders as possible. The genetic algorithm (GA)-based scheduling method results indicated that the proposed platform enables SME manufacturers to obtain optimized schedules by solving the job shop scheduling problem (JSSP) by comparing with the real-world data from a textile weaving factory in South Korea. The proposed platform will provide producers with an optimal production schedule, introduce new producers to buyers, and eventually foster relationships and mutual economic interests.     

Single CNC machine scheduling with controllable processing times and multiple due dates

In this study, we solve the single CNC machine scheduling problem with controllable processing times. Our objective is to maximize the total profit that is composed of the revenue generated by the set of scheduled jobs minus the sum of total weighted earliness and weighted tardiness, tooling and machining costs. Customers offer multiple due dates to the manufacturer, each coming with a distinct price for the order that is decreasing as the date gets later, and the manufacturer has the flexibility to accept or reject the orders. We propose a number of ranking rules and scheduling algorithms that we employ in a four-stage heuristic algorithm that determines the processing times for each job and a final schedule for the accepted jobs simultaneously, to maximize the overall profit.     

Capacity requirements planning— a systematization

The central problem of manufacturing planning is to reconcile clue dates, derived from customer delivery timetables, with schedule dates, developed from production capabilities. Conventional capacity planning techniques utilize the technological sequencing of material flow in order to build up production schedules. As they ignore interactions between work stations, and override due date requirements, these techniques are often unsatisfactory. Wight and Belt have suggested a new approach to the problem, and this paper presents a systematic methodology based on their ideas, to characterize and analyse the flow of work through a work station, and relate this flow to the nominal capacity of the station. Operation of the station is measured by work in process, delay and underload (operation below nominal capacity); flow between stations is measured by queue length and lead times (process plus wait time). Performance is evaluated by the degree of underload and overload planned for the station—the degree to which available capacity is utilized, and the degree to which lead times, imposed by the Material Requirements Plan, can be achieved. Achievable average and maximum lead times are shown to be a function of both nominal work station capacity and the input work load profile—not a constant value for the work station. A more correct study of plannable lead times, as production loads vary, allows the necessary connection to be made between due dates and schedule dates.     

An enhanced model for SDBR in a random reentrant flow shop environment

This study proposed an enhanced simplified drum-buffer-rope (SDBR) model to be applied in a reentrant flow shop (RFS) in which job processing times are generated from a discrete uniform distribution and machine breakdowns are subject to an exponential distribution. In this enhanced SDBR model, the due-date assignment method, order release rule and dispatching rule were improved. The due dates and release dates of orders were determined by considering the total planned load of the capacity-constrained resource (CCR) in a random RFS. The deviation rate of buffer status is used as a dispatching rule to eliminate the influence of machine breakdowns. Simulations based on a real case company are used to evaluate the effective of the proposed model. The experimental results showed that our approach yields better performance than the other methods in terms of six due-date-related indexes when the product mix is with a large proportion of multi-reentrant orders and when the utilisation of CCR increases from 60 to 90%.     

An alternative approach to accept on zero and accept on one sampling plans

Accept on zero and accept on one sampling plans are two common approaches to determine if a manufacturing lot can be accepted or rejected. Accept on zero plans inspect fewer items than the accept on one plans. On the other hand, accept on one plans have a higher probability of accepting a lot when the defect rate is between the Acceptable and Rejectable Quality Levels. This article proposes a double sampling plan whose probability of accepting a lot resembles an accept on one plan yet inspects considerably fewer items on average.     

Multi-objective due-date setting in a make-to-order environment

This paper presents a new dual-objective problem of due-date setting over a rolling planning horizon in make-to-order manufacturing and proposes a bi-criterion integer programming formulation for its solution. In the proposed model the due-date setting decisions are directly linked with available capacity. A simple critical load index is introduced to quickly identify the system bottleneck and the overloaded periods. The problem objective is to select a maximal subset of orders that can be completed by the customer requested dates and to quote delayed due dates for the remaining acceptable orders to minimise the number of delayed orders or the total number of delayed products as a primary optimality criterion and to minimise total or maximum delay of orders as a secondary criterion. A weighted-sum program based on a scalarisation approach is compared with a two-level due-date setting formulation based on the lexicographic approach. In addition, a mixed-integer programming model is provided for scheduling customer orders over a rolling planning horizon to minimise the maximum inventory level. Numerical examples modeled after a real-world make-to-order flexible flowshop environment in the electronics industry are provided and, for comparison, the single-objective solutions that maximise total revenue subject to service level constraints are reported.     

A genetic algorithm based approach for scheduling of jobs containing multiple orders in a three-machine flowshop

This paper investigates a new scheduling problem of multiple orders per job (MOJ) in a three-machine flowshop consisting of an item-processing machine, a lot-processing machine and a batch-processing machine, for a semiconductor manufacturing operation that must form a layer on the wafers. The three-machine flowshop MOJ scheduling problem deals with sequencing customer orders, assigning orders to jobs, and then combining the formed jobs into batches. Genetic algorithms are presented for this scheduling problem to minimise the total weighted tardiness (TWT), in the presence of non-zero order ready times, different order due dates, different order weights and unequal order sizes. Extensive experiments were performed to compare the proposed genetic algorithm (GA)-based approach with the benchmark heuristics presented in previous studies. The experiments led to the conclusions that the GA-based approach is significantly superior over other heuristics in terms of TWT and can find near-optimal solutions in an acceptable amount of computation time.     

Minimizing the total machine workload for the wafer probing scheduling problem

The Wafer Probing Scheduling Problem (WPSP) is a practical generalization of the parallel-machine scheduling problem, which has many real-world applications, particularly, in the Integrated Circuit (IC) manufacturing industry. In the wafer probing factories, the jobs are clustered by their product types, which must be processed on groups of identical parallel machines and be completed before the due dates. The job processing time depends on the product type, and the machine setup time is sequentially dependent on the orders of jobs processed. Since the wafer probing scheduling problem involves constraints on job clusters, job-cluster dependent processing time, due dates, machine capacity, and sequentially dependent setup time, it is more difficult to solve than the classical parallel-machine scheduling problem. In this paper, we consider the WPSP and formulate the WPSP as an integer programming problem to minimize the total machine workload. We demonstrate the applicability of the integer programming model by solving a real-world example taken from a wafer probing shop floor in an IC manufacturing factory.     

Multi-objective master production scheduling in make-to-order manufacturing

In this paper a multi-objective, long-term production scheduling in make-to-order manufacturing is considered and a lexicographic approach with a hierarchy of integer programming formulations is proposed. The problem objective is to allocate customer orders with various due dates among planning periods with limited capacities to minimize the number of tardy orders as a primary optimality criterion. Then, the maximum level of the input and output inventory is minimized as a secondary criterion, and finally the aggregate production is leveled over the planning horizon as an auxiliary criterion. A close relation between minimizing the maximal inventory and the maximum earliness of customer orders is shown and used to simplify the inventory leveling problem. Numerical examples, modeled after a real-world make-to-order flexible flowshop in a high-tech industry, are provided and some computational results are reported. The paper indicates that the maximum earliness of customer orders is an important managerial decision variable, and its minimum value can be applied to control the inventory of purchased materials and finished products to maximize the customer service level and minimize production costs.     

Accounting-based heuristics for order processing and review in a stochastic environment

As manufacturers evolve from make-to-stock to make-to-order strategies, they are forced to rely less on inventory to meet demands and more on the effective usage of existing capacity as they determine which orders to accept and schedule. Several accounting-based heuristics are used to identify and accept those jobs that maximize plant throughput. Accounting heuristics have been suggested as surrogates for approximating the opportunity costs of manufacturing one order instead of another order. Research results suggest that the best heuristic is dependent on the balance of the manufacturing resources. Moreover, these tendencies are affected by product mix and load.     

Due date assignment using feedback control with reinforcement learning

Good due date assignment for an order requires the calculation of a time buffer that will account for the uncertainties associated with the arrival of future orders in a dynamic environment. This paper presents a method that controls the size of this time buffer for a discrete manufacturing system. The applicability of the method to an unrestricted class of discrete manufacturing systems is preserved by the use of a feedback control paradigm, and control knowledge is acquired using reinforcement learning. The current trajectory of the state of the shop is considered so that due date performance is improved during transient conditions. Results of simulation experiments demonstrate the effectiveness of the approach.     

Order acceptance strategies in a production-to-order environment with setup times and due-dates

A production situation is considered in which different items are produced on one machine. Setup times are incurred between the production of orders of different items. Production is driven by customer orders; each order concerns a batch of one product type and is furthermore completely characterized by its batchsize and (customer determined) due-date. Acceptance of orders may be refused if these orders are likely to cause late deliveries. The problem is to determine good acceptance strategies which naturally raises the question on what information such acceptance decisions have to be based. Three basic approaches are explored in this paper. In the monolithic approach, the acceptance decision is based on detailed information on a current production schedule for all formerly accepted orders. In the hierarchic approach, the acceptance strategy is based on global capacity load profiles only, while detailed scheduling of accepted orders takes place at a lower level (possibly later in time). In the myopic approach the acceptance decision is similar to the one in the hierarchic approach but scheduling is myopic, i.e. once the machine becomes idle only the next order to be produced is actually scheduled. The performances of these three approaches are compared by means of simulation experiments. The results indicate that the differences in performance are small. Insofar as the monolithic approach performs better, this is mainly due to the selective acceptance mechanism implicitely present in case of a heavy workload. An adaptation of the myopic approach to incorporate such a selective acceptance mechanism leads to a comparable performance     

Card-based delivery date promising in pure flow shops with order release control

Card-based systems are simple, effective means of controlling production. Yet most systems concentrate on controlling the shop floor. They neglect other planning tasks, like estimating short, feasible due dates during customer enquiry management. A card-based version of the workload control concept for job shops – COBACABANA (COntrol of BAlance by CArd-BAsed Navigation) – was proposed in the literature to overcome this shortcoming. COBACABANA uses cards for due date setting and order release, making it a potentially important solution for small shops with limited resources. But many such firms operate as flow shops rather than job shops. Research demonstrated that COBACABANA’s release mechanism must be adapted if applied to a pure flow shop, but its approach to due date setting has not been evaluated in such an environment. We show COBACABANA has the potential to improve pure flow shop performance, but its due date setting procedure should be adapted compared to job shops. In a flow shop, due date estimation can also be further simplified by considering the load awaiting release to the first (gateway) station only while maintaining most performance benefits. The results are important for all card-based systems that aim to stabilise work-in-process, including kanban and ConWIP (Constant Work-in-Process).