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This paper deals with the problem of routing in-house transport vehicles that feed parts to workstations in assembly plants or workshops just in time. The capacitated vehicles, typically so-called tow trains, perform their assigned route cyclically without break and provide each station with the exact quantity of parts required until the next arrival of the vehicle. Hence, the demand of each station depends on the duration of the route serving the station: The longer the route duration, the less frequently the station is visited and the higher its demand. The goal is to minimize first the number of vehicles and second the total route duration, while respecting given minimum service frequencies at the stations. We provide a mathematical formulation of this novel problem and address it by means of a large neighborhood search. The algorithm is able to solve realistic instances in acceptable time and vastly outperforms a default solver. We discuss two variants of the problem, one in which split deliveries to stations are allowed and another assuming that all stations lie on a straight line. Finally, we investigate the extent to which assuming constant demand rates may lead to problems during the day-to-day operations of the part-feeding system, where demands are not necessarily constant.

PurposeThis paper aims to investigate the scheduling and loading problems of tow trains for mixed-model assembly lines (MMALs). An in-plant milk-run delivery model has been formulated to minimize total line-side inventory for all stations over the planning horizon by specifying the departure time, parts quantity of each delivery and the destination station.Design/methodology/approachAn immune clonal selection algorithm (ICSA) combined with neighborhood search (NS) and simulated annealing (SA) operators, which is called the NSICSA algorithm, is developed, possessing the global search ability of ICSA, the ability of SA for escaping local optimum and the deep search ability of NS to get better solutions.FindingsThe modifications have overcome the deficiency of insufficient local search and deepened the search depth of the original metaheuristic. Meanwhile, good approximate solutions are obtained in small-, medium- and large-scale instances. Furthermore, inventory peaks are in control according to computational results, proving the effectiveness of the mathematical model.Research limitations/implicationsThis study works out only if there is no breakdown of tow trains. The current work contributes to the in-plant milk-run delivery scheduling for MMALs, and it can be modified to deal with similar part feeding problems.Originality/valueThe capacity limit of line-side inventory for workstations as well as no stock-outs rules are taken into account, and the scheduling and loading problems are solved satisfactorily for the part distribution of MMALs.

The problem of sequencing assembly lines consists of determining the order in which a given set of products is launched down the line. Since individual products may require different parts in different quantities, the production sequence has a big influence on line-side inventory. Classically, sequences are often optimized with the goal of attaining level schedules, i.e., the part demand should be smooth during the planning horizon. However, this approach does not necessarily work well if parts are delivered at discrete points in time in bulk quantities. In this paper, we consider a production system where bins of parts are delivered periodically by a tow train from a central depot at fixed times. Due to the limited space at the assembly line, the maximum number of bins in stock at any time at any station should be minimal. We propose an exact solution method based on combinatorial Benders decomposition as well as bounding procedures and heuristics for this problem. The algorithms are shown to perform well both on instances from the literature and on new data sets. We also investigate whether classic level scheduling methods are effective at reducing line-side stock in an assembly system supplied by tow train, and to what degree line-side stock can be traded off for more frequent deliveries.

Inspired by the manufacturing costs proportion of part feeding in automotive mixed-model assembly lines (MMALs) being up to 20–35%, this paper takes the dynamic scheduling of part feeding for automotive MMALs as a crucial and complex problem. Therefore, a dynamic scheduling mechanism basing on the knowledge base (KB) and fruit fly optimization algorithm (FOA) with variable step sizes and logistic chaos (VSCFOA)-enhanced general regression neural network (VSCFOA-GRNN) is proposed to tackle the real-time part feeding scheduling problem of tow trains under the dynamic manufacturing system. A mathematical model is developed to illustrate the problem, where the throughput of the assembly line and the material delivery distance are determined as components of the objective function. Subsequently, samples of the MMAL are generated by the plant simulation software and used to train the VSCFOA-GRNN model off-line. Afterward, the trained model and KB are adopted in the real-time scheduling process to determine the optimal scheduling rule combination. Finally, the effectiveness, feasibility and accuracy of the novel scheduling mechanism are validated by computational results, especially in dynamic scheduling processes. It can cope well with changes in the dynamic environment, thus effectively realizing the higher productivity of assembly lines and better system performance.

A growing number of manufacturers are adopting the so-called supermarket strategy to supply components to the production system. Supermarkets are decentralized storage areas used as intermediate warehouses for parts required by the production system (typically assembly lines). Such a feeding system is widely used in the automotive industry where assembly stations in multiple mixed-model assembly lines are usually refilled by means of a systematic part replenishment driven by Kanban systems, adopting small trucking vehicles towing some wagons (tow trains). The aim of this paper is to provide a simple but robust framework in order to design the supermarket/feeding system dedicated to complex multiple mixed-model assembly lines. This framework proposes an integrated approach both for long-term (static analytical model) and short-term (dynamic simulation) problems dealing with Kanban and Supermarket systems dedicated to assembly lines, and the tow train fleet sizing and management. This proposed methodology is applied to a case study derived from the Italian automotive industry, and the results highlight the high interrelation between the long and the short term variables that can be evaluated only by an integrated approach that considers both static and dynamic aspects of the problem. The results of this study are then presented and widely discussed.

Purpose - This paper aims to introduce, apply and validate, through a realistic case study, an analytical cost model to support the design of the tow-train feeding system for mixed-model assembly lines managed according to the just-in-time concept. The fleet size and inventory level, minimizing the total annual cost, are the key model goals, while the tow-train shipping capacity and the service level are the decisional variables to set. Design/methodology/approach - The model computes the material handling, inventory and stockout rising costs of the tow-train feeding system and looks for their minimization. It further computes the expected lead time between consecutive round-trips and the Kanban card number, distinguishing among parts and assembly lines, overcoming the simplifying hypothesis assuming a constant lead time for all parts. The model is validated against a dedicated case study stressing its strengths in terms of cost and inventory-level reduction. Findings - The proposed approach is found to be effective if compared to the standard literature in the field of Kanban system design. The 10.76 per cent cost saving is experienced for the considered case study, and the inventory level is closer to the field-experienced profile. Practical implications - The model adopts a practical perspective, making it easy and applicable to common operative industries. Originality/value - The literature neglects to consider the differences in the part consumption when estimating the lead time between tow-train round-trips. The proposed model overcomes such limitations and strengthens the model applicability and performances.