Occurrence based service-level constraints during a fiscal period

An occurrence-based service-level constraint, defined over a fiscal period, is introduced. This service-level constraint measures the actual number of stockout episodes and restricts it to a managerially acceptable level. It is shown that the solution to the emerging chance-constrained inventory problem is independent of the type and parameters of the demand distribution. The approach is demonstrated and its managerial implications, in particular with respect to reorder points, are discussed.     

Periodic review inventory-control for perishable products under service-level constraints

Food retail inventory management faces major challenges by uncertain demand, perishability, and high customer service level requirements. In this paper, we present a method to determine dynamic order quantities for perishable products with limited shelf-life, positive lead time, FIFO or LIFO issuing policy, and multiple service level constraints. In a numerical study, we illustrate the superiority of the proposed method over commonly suggested order-up-to-policies. We show that a constant-order policy might provide good results under stationary demand, short shelf-life, and LIFO inventory depletion.     

Efficient methods for scheduling make-to-order assemblies under resource,assembly area and part availability constraints

We consider the problem of scheduling multiple, large-scale, make-to-order assemblies under resource, assembly area, and part availability constraints. Such problems typically occur in the assembly of high-volume, discrete maketo-order products. Based on a list scheduling procedure proposed by Kolisch in 1999 we introduce three efficient heuristic solution methods. Namely, a biased random sampling method and two tabu search-based large-step optimization methods. The two latter methods differ in the employed neighbourhood. The first one uses a simple API-neighbourhood while the second one uses a more elaborated so-called 'critical neighbourhood' which makes use of problem insight. All three procedures are assessed on a systematically generated set of test instances. The results indicate that especially the large-step optimization method with the critical neighbourhood gives very good results which are significant better than simple single-pass list scheduling procedures.     

Two-stage stochastic programming with robust constraints for the logistics network post-disruption response strategy optimization

Logistics networks (LNs) are essential for the transportation and distribution of goods or services from suppliers to consumers. However, LNs with complex structures are more vulnerable to disruptions due to natural disasters and accidents. To address the LN post-disruption response strategy optimization problem, this study proposes a novel two-stage stochastic programming model with robust delivery time constraints. The proposed model jointly optimizes the new-line-opening and rerouting decisions in the face of uncertain transport demands and transportation times. To enhance the robustness of the response strategy obtained, the conditional value at risk (CVaR) criterion is utilized to reduce the operational risk, and robust constraints based on the scenario-based uncertainty sets are proposed to guarantee the delivery time requirement. An equivalent tractable mixed-integer linear programming reformulation is further derived by linearizing the CVaR objective function and dualizing the infinite number of robust constraints into finite ones. A case study based on the practical operations of the JD LN is conducted to validate the practical significance of the proposed model. A comparison with the rerouting strategy and two benchmark models demonstrates the superiority of the proposed model in terms of operational cost, delivery time, and loading rate.     

Integration of vendor selection into production and remanufacturing planning subject to emission constraints

This paper presents a new model formulation that incorporates vendor selection into production and remanufacturing planning subject to emission constraints. The objective is to determine a feasible production and remanufacturing plan at minimal cost. In each period, the given external demand must be satisfied for both new and remanufactured products. Remanufactured products can be substituted by new products, but not vice versa. When a product is (re)manufactured, the appropriate machine must be set up, which entails set-up costs and/or set-up time. The procurement of raw material that is offered by several vendors at different prices is integrated into this planning problem. To solve this integrated production and remanufacturing planning problem, we apply two solution approaches based on mathematical programming: first, a combination of column generation and a period-oriented fix-and-relax heuristic and second, an adapted fix-and-optimise heuristic. The results of our numerical investigation demonstrate the high solution quality of both solution approaches.     

Production planning problems with joint service-level guarantee: a computational study

We consider a class of single-stage multi-period production planning problems under demand uncertainty. The main feature of our paper is to incorporate a joint service-level constraint to restrict the joint probability of having backorders in any period. This is motivated by manufacturing and retailing applications, in which firms need to decide the production quantities ex ante, and also have stringent service-level agreements. The inflexibility of dynamically altering the pre-determined production schedule may be due to contractual agreement with external suppliers or other economic factors such as enormously large fixed costs and long lead time. We focus on two stochastic variants of this problem, with or without pricing decisions, both subject to a joint service-level guarantee. The demand distribution could be nonstationary and correlated across different periods. Using the sample average approximation (SAA) approach for solving chance-constrained programs, we reformulate the two variants as mixed-integer linear programs (MILPs). Via computations of diverse instances, we demonstrate the effectiveness of the SAA approach, analyse the solution feasibility and objective bounds, and conduct sensitivity analysis for the two MILPs. The approaches can be generalised to a wide variety of production planning problems, and the resulting MILPs can be efficiently computed by commercial solvers.     

Re-planning support system for make-to-order production with reserved resources

Make-to-order production is often organized into projects with their own objectives. Such projects use strategic and critical resources that must be reserved at a strategic or a tactical planning level. When external resources need to be taken into consideration, they must be reserved based on their set of free time windows. A medium-term planning support system for the manager of a project where external resources are critical is proposed. By providing explanations for project inconsistencies, this system enables the decision-maker to restore consistency. After creating the initial medium-term plan, planning activity essentially consists in updating the existing schedule of a project when unexpected events arise. These may be due to internal or external disturbances. Indicators are proposed to help the manager follow the dynamic impact the disturbances will have on the project.     

Revenue management for a make-to-order company with limited inventory capacity

We consider the application of revenue management to a make-to-order manufacturing company with limited inventory capacity. Orders with different profit margins arrive stochastically over an infinite time horizon and the company has to decide which orders to accept and which orders to reject. We model the problem with a discrete-time Markov decision process and propose a heuristic procedure. In numerical tests we show the potential benefit of using revenue management instead of a FCFS policy and assess the performance of the heuristic procedure.We thank two anonymous referees for their valuable suggestions that helped to improve the quality of the paper.     

Multi-agent-based workload control for make-to-order manufacturing

Workload control is a production planning and control concept designed to meet the need of the make-to-order industry. In this paper, a multi-agent workload control methodology that simultaneously addresses due date setting, job release and scheduling is proposed. To be consistent with just-in-time production, the objective of minimizing weighted job earliness and tardiness is used. Two new rules are developed, by introducing a feedback mechanism, to set job due dates dynamically. These two new rules implicitly include job pool times and, thus, eliminate the need to estimate job pool times in the presence of workload control. At the critical norm defined in this paper job release control can reduce average job flowtime and work-in-process inventory, without worsening earliness and tardiness, and lead-time performances. The proposed methodology is implemented in a flexible job shop environment. The computational results indicate that the proposed methodology is very effective for production planning and control in make-to-order companies. In addition, the proposed methodology is extremely fast and can be implemented in real time.     

Dispatching for make-to-order wafer fabs with machine-dedication and mask set-up characteristics

This paper develops a dispatching algorithm to improve on-time delivery for a make-to-order semiconductor wafer fab with two special characteristics: mask set-up and machine dedication. A new algorithm is proposed for dispatching series workstations. Simulation experiments show that the algorithm outperforms the previous methods both in on-time delivery rate, cycle time, and only slightly less than the best benchmark in throughput. The experiments are carried out in 10 test scenarios, which are created by the combination of two product-mix ratios and five mask set-up times.     

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.     

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.     

The impact of delayed differentiation in make-to-order environments

To cope with the challenges of product proliferation, many firms are shifting their supply chain structures from make-to-stock (MTS) to make-to-order (MTO). An MTO strategy comes at a price however, as customers must wait longer for their configured products. Incorporating delayed differentiation (DD) in an MTO environment offers the potential of reducing the customer's waiting time, since the generic part/component of the products is made available before receiving customer orders. In this paper, we quantify the trade-offs involved in implementing DD in an MTO environment using both customer waiting time and system cost as performance metrics. We show that under common conditions, the introduction of DD results in shorter waiting times and higher cost over a pure MTO strategy. These results are as expected. However, we also derive conditions where DD results not only in shorter customer waiting time but also lower cost, thus dominating a pure MTO strategy. Through a simulation experiment, we test the robustness of our results for the case where the customer arrivals and production times are generally distributed. For firms with the capability of estimating the customer waiting cost, we derive the optimal base-stock level of the generic component to minimise the total cost.     

Comparison of make-to-order job shops with different machine layouts and production control systems

This research project examines the performance of job shops with different machine layouts and production control systems. Job shops examined in this study operate in a make-to-order environment. Results indicate that the material requirements planning order-release-and-due-date-assignment procedure always performs better than that of the period batch control. Cellular layout, however, performs better at a high setup-to-processing-time ratio, while the process layout performs better at a low setup-to-processing-time ratio     

Quantity decisions in a supply chain with early returns remanufacturing

Consider a supply chain where the retailer faces a stochastic demand and orders from the supplier, while the supplier manufactures new products and also remanufactures early returns to meet the order. The order and manufacturing quantity decisions are studied under three decentralised cases: (1) the Stackelberg case, where the manufacturing quantity is determined by the supplier after realising the order quantity from the retailer; (2) the Nash case, where the manufacturing and order quantities are determined simultaneously; (3) the inaccessible return information case, where the retailer does not have the distribution information of upstream returns. We find that in the Stakelberg case the order and manufacturing quantities are larger than in the inaccessible return information case, and the profits for the supplier and retailer are also higher. In contrast, the quantities and profits in the Nash case are the lowest of the three cases when the returns are negatively correlated to the demand. The centralised system is also explored by solving a two-step dynamic program. Computational results are reported to show the effects of system parameters.     

Robotic GMAW forming remanufacturing technology

Robotic GMAW forming process with the advantages of high productivity and high bond strength has become an important forming remanufacturing technology. In this paper, our recent research achievements are introduced and our development prospect on robotic GMAW  forming remanufacturing is declared.     

Scheduling algorithms for remanufacturing systems with parallel flow-shop-type reprocessing lines

This study considers a scheduling problem for remanufacturing systems in which end-of-life products are separated into their major components at a disassembly workstation, each of them is reprocessed at its dedicated flow-shop-type reprocessing line with serial workstations, and finally, the reprocessed components, together with new components if required, are reassembled into remanufactured products at a reassembly workstation. Among various system configurations, we focus on the one with parallel flow-shop-type reprocessing lines since it is a typical remanufacturing configuration. The problem is to determine the sequence of products to be disassembled, the sequence of components to be reprocessed at each workstation of flow-shop-type reprocessing lines and the sequence of products to be reassembled for the objective of minimising the total flow time. An integer programming model is developed to represent the problem mathematically, and then, three types of heuristics, i.e. priority rule-based heuristic, Nawaz–Enscore–Ham-based heuristic and iterated greedy algorithm, are proposed due to the problem complexity. To show the performances of the heuristics, a series of computational experiments were done on various test instances, and the results are reported.     

Optimal configuration of remanufacturing supply network with return quality decision

This research studies the configuration problem of a remanufacturing production network together with the decision for return quality thresholds, in which, the manufacturer has multiple remanufacturing facilities to satisfy different market demands. Quality of returns is stochastic, while demand for remanufactured products is either stochastic or deterministic. The problem we considered is to determine facilities to operate, minimum quality to accept into each operating facility, return quantity and demand allocation simultaneously so that the system’s profit is maximised. The problem is formulated as a mixed integer non-linear programming model. Through the use of a numerical example, the impact of quantity of returns, total spending, quality uncertainty, demand uncertainty and transportation cost on the remanufacturing system is analysed.     

Periodical capacity setting methods for make-to-order multi-machine production systems

The paper presents different periodical capacity setting methods for make-to-order, multi-machine production systems with stochastic customer required lead times and stochastic processing times to improve service level and tardiness. These methods are developed as decision support when capacity flexibility exists, such as, a certain range of possible working hours a week for example. The methods differ in the amount of information used whereby all are based on the cumulated capacity demand at each machine. In a simulation study the methods’ impact on service level and tardiness is compared to a constant provided capacity for a single and a multi-machine setting. It is shown that the tested capacity setting methods can lead to an increase in service level and a decrease in average tardiness in comparison to a constant provided capacity. The methods using information on processing time and customer required lead time distribution perform best. The results found in this paper can help practitioners to make efficient use of their flexible capacity.     

Capacity coordination in hybrid make-to-stock/make-to-order production environments

One of the most important decisions in hybrid make-to-stock/make-to-order (MTS/MTO) production systems is capacity coordination. This paper addresses capacity coordination of hybrid MTS/MTO production systems which deal with MTS, MTO and MTS/MTO products. The proposed model is developed to cope with order acceptance/rejection policy, order due-date setting, lot-sizing of MTS products and determining required capacity during the planning horizon. Additionally, a backward lot-sizing algorithm is developed to tackle the lot-sizing problem. The proposed model presents a general framework to decide on capacity coordination without too many limiting mathematical assumptions. The model combines qualitative and quantitative modules to cope with the aforementioned problems. Finally, a real industrial case study is reported to provide validity and applicability of the proposed model. Having the model applied in the case study, considerable improvement was achieved.