Scheduling the in-house logistics distribution for automotive assembly lines with just-in-time principles

Purpose This paper aims to investigate the just-in-time (JIT) in-house logistics problem for automotive assembly lines. A point-to-point (P2P) JIT distribution model has been formulated to specify the destination station and parts quantity of each delivery for minimizing line-side inventory levels. Design/methodology/approach An exact backtracking procedure integrating with dominance properties is presented to cope with small-scale instances. As for real-world instances, this study develops a modified discrete artificial bee colony (MDABC) metaheuristic. The neighbor search of MDABC is redefined by a novel differential evolution loop and a breadth-first search. Findings The backtracking method has efficaciously cut unpromising branches and solved small-scale instances to optimality. Meanwhile, the modifications have enhanced exploitation abilities of the original metaheuristic, and good approximate solutions are obtained for real-world instances. Furthermore, inventory peaks are avoided according to the simulation results which validates the effectiveness of this mathematical model to facilitate an efficient JIT parts supply. Research limitations/implications This study is applicable only if the breakdown of transport devices is not considered. The current work has effectively facilitated the P2P JIT logistics scheduling in automotive assembly lines, and it could be modified to tackle similar distribution problems featuring time-varying demands. Originality/value Both limited vehicle capacities and no stock-outs constraints are considered, and the combined routing and loading problem is solved satisfactorily for an efficient JIT supply of material in automotive assembly lines.

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The efficiency analysis of world top container ports using two-stage uncertainty DEA model and FCM

Maritime Business Review ◽

10.1108/mabr-11-2019-0052 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Cited By ~ 1

Author(s):

Thi Quynh Mai Pham ◽

Gyei Kark Park ◽

Kyoung-Hoon Choi

Keyword(s):

Uncertain Data ◽

Small Scale ◽

Similar Distribution ◽

Two Stage ◽

Content Type ◽

Fcm Algorithm ◽

Comprehensive View ◽

Fuzzy C Means Clustering ◽

Container Ports ◽

Top 40

Purpose The purpose of this paper is to present an integrated model to measure the operational efficiency of the top 40 container ports in the world for a five-year continuous period using a two-stage uncertainty data envelopment analysis (UDEA) combined with fuzzy C-means clustering method (FCM). Design/methodology/approach UDEA model is adopted for measuring the efficiency of container ports to overcome the limitation of the basic model, which is unable to handle uncertain data that are easy to meet in practice. FCM algorithm is implemented to find similar distribution efficiency scores of two stages and the cluster similar efficiency scores of container ports into various groups. Findings The combination of the two-stage UDEA model and the FCM algorithm provided a more comprehensive view when evaluating the performance of container ports. The UDEA results show that most of the container ports have reduced their profitability level in the second stage and most of the efficient container ports have turned into inefficient ones because of their small scale. Originality/value This paper proposes using the two-stage UDEA model to evaluate port efficiency based on two main aspects of productivity and profitability. Moreover, it combines DEA and FCM algorithms to offer a more comprehensive view when measuring the performance of container ports.

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A decision model for selecting parts feeding policies in assembly lines

Industrial Management & Data Systems ◽

10.1108/imds-02-2015-0054 ◽

2015 ◽

Vol 115 (6) ◽

pp. 974-1003 ◽

Cited By ~ 19

Author(s):

Antonio C. Caputo ◽

Pacifico M. Pelagagge ◽

Paolo Salini

Keyword(s):

Cost Effective ◽

Just In Time ◽

Successful Implementation ◽

Assembly Lines ◽

Content Type ◽

Optimal Mix ◽

Consistent Basis ◽

Early Decision ◽

Optimal Material ◽

Parts Feeding

Purpose – The purpose of this paper is to develop an optimization model allowing the choice of parts feeding policy to assembly lines in order to minimize total cost. Design/methodology/approach – An integer linear programming mathematical model is developed to assign the optimal material feeding policy to each part type. The model allows choice between kitting, line stocking and just in time delivery policies. Findings – The choice of assembly lines feeding policy is not trivial and requires a thorough economic comparison of alternatives. It is found that a proper mix of parts feeding policies may be better that adopting a single material delivery policy for all parts. Research limitations/implications – The model is aimed at single-model assembly lines operating in a deterministic environment, but can be extended to the multi-model line case. While relevant quantitative cost drivers are included, some context-related qualitative factors are not included yet. The model assumes that information about product structure and part requirements are known and that a preliminary design of the assembly system has been carried out. Practical implications – Production managers are given a quantitative-decision tool to determine the optimal mix of material supply policies at an early decision stage. Originality/value – Respect previous simplified literature models, this approach allows to quantify a number of additional factors which are critical for successful implementation of cost-effective parts feeding systems, allowing comparison of alternative policies on a consistent basis.

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The capacitated pollution routing problem with pickup and delivery in the last mile

Asia Pacific Journal of Marketing and Logistics ◽

10.1108/apjml-06-2018-0217 ◽

2019 ◽

Vol 31 (4) ◽

pp. 1193-1215 ◽

Cited By ~ 1

Author(s):

Yuyang Tan ◽

Lei Deng ◽

Longxiao Li ◽

Fang Yuan

Keyword(s):

Real World ◽

Distribution Model ◽

Pickup And Delivery ◽

Routing Problem ◽

Actual Distribution ◽

Content Type ◽

Last Mile ◽

Speed Optimization ◽

Logistics Enterprises ◽

Pollution Routing Problem

Purpose With the increasing awareness of global warming and the important role of last mile distribution in logistics activities, the purpose of this paper is to build an environmental and effective last mile distribution model considering fuel consumption and greenhouse gas emission, vehicle capacity and two practical delivery service options: home delivery (HD) and pickup site service (PS). This paper calls the problem as the capacitated pollution-routing problem with pickup and delivery (CPRPPD). The goal is to find an optimal route to minimize operational and environmental costs, as well as a set of optimal speeds over each arc, while respecting capacity constraints of vehicles and pickup sites. Design/methodology/approach To solve this problem, this research proposes a two-phase heuristic algorithm by combining a hybrid ant colony optimization (HACO) in the first stage and a multiple population genetic algorithm in the second stage. First, the HACO is presented to find the minimal route solution and reduce distribution cost based on optimizing the speed over each arc. Findings To verify the proposed CPRPPD model and algorithm, a real-world instance is conducted. Comparing with the scenario including HD service only, the scenario including both HD and PS option is more economical, which indicates that the CPRPPD model is more efficient. Besides, the results of speed optimization are significantly better than before. Practical implications The developed CPRPPD model not only minimizes delivery time and reduces the total emission cost, but also helps logistics enterprises to establish a more complete distribution system and increases customer satisfaction. The model and algorithm of this paper provide optimal support for the actual distribution activities of logistics enterprises in low-carbon environment, and also provide reference for the government to formulate energy-saving and emission reduction policies. Originality/value This paper provides a great space for the improvement of carbon emissions in the last mile distribution. The results show that the distribution arrangement including HD and PS services in the last mile adopting speed optimization can significantly reduce the carbon emission. Additionally, an integrated real-world instance is applied in this paper to illustrate the validity of the model and the effectiveness of this method.

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Improved multi-objective cuckoo search algorithm with novel search strategies for point-to-point part feeding scheduling problems of automotive assembly lines

Assembly Automation ◽

10.1108/aa-06-2020-0081 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Binghai Zhou ◽

Xiujuan Li ◽

Yuxian Zhang

Keyword(s):

Cuckoo Search ◽

Search Strategies ◽

Scheduling Problem ◽

Scheduling Problems ◽

Assembly Lines ◽

Content Type ◽

Multi Objective ◽

Automotive Assembly ◽

Point To Point ◽

Part Feeding

Purpose This paper aims to investigate the part feeding scheduling problem with electric vehicles (EVs) for automotive assembly lines. A point-to-point part feeding model has been formulated to minimize the number of EVs and the maximum handling time by specifying the EVs and sequence of all the delivery tasks. Design/methodology/approach First, a mathematical programming model of point-to-point part feeding scheduling problem (PTPPFSP) with EVs is presented. Because the PTPPFSP is NP-hard, an improved multi-objective cuckoo search (IMCS) algorithm is developed with novel search strategies, possessing the self-adaptive Levy flights, the Gaussian mutation and elite selection strategy to strengthen the algorithm’s optimization performance. In addition, two local search operators are designed for deep optimization. The effectiveness of the IMCS algorithm is verified by dealing with the PTPPFSP in different problem scales. Findings Numerical experiments are used to demonstrate how the IMCS algorithm serves as an efficient method to solve the PTPPFSP with EVs. The effectiveness and feasibility of the IMCS algorithm are validated by approximate Pareto fronts obtained from the instances of different problem scales. The computational results show that the IMCS algorithm can achieve better performance than the other high-performing algorithms in terms of solution quality, convergence and diversity. Research limitations/implications This study is applicable without regard to the breakdown of EVs. The current research contributes to the scheduling of in-plant logistics for automotive assembly lines, and it could be modified to cope with similar part feeding scheduling problems characterized by just-in-time (JIT) delivery. Originality/value Both limited electricity capacity and no earliness and tardiness constraints are considered, and the scheduling problem is solved satisfactorily and innovatively for an efficient JIT part feeding with EVs applied to in-plant logistics.

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Scheduling multiple servers to facilitate just-in-time part-supply in automobile assembly lines

Assembly Automation ◽

10.1108/aa-08-2017-102 ◽

2018 ◽

Vol 38 (3) ◽

pp. 347-360 ◽

Cited By ~ 7

Author(s):

Tao Peng ◽

Binghai Zhou

Keyword(s):

Local Search ◽

Ant Colony Optimization ◽

Ant Colony ◽

Mixed Integer ◽

Inventory Level ◽

Just In Time ◽

Tree Search ◽

Assembly Lines ◽

Content Type ◽

Automobile Assembly

Purpose With regard to product variety and cost competition, just-in-time (JIT) part-supply has become a critical issue in automobile assembly lines (AALs). This paper aims to investigate a multiple server scheduling problem (MSSP) encountered in the JIT part-supply process of AALs. Parts are stored in boxes and allotted from the JIT-supermarket to consumptive stations with a multiple server system. The schedule is to dispatch and sequence material boxes on each server for minimizing line-side inventory levels. Design/methodology/approach A mixed integer linear programming (MILP) model is established to formulate the proposed MSSP to pave the way for CPLEX procedure. Considering the high complexity of MSSP, a hybrid ant colony optimization (HACO) approach is developed by integrating basic ant colony optimization (ACO) with local optimizers that comprise of a fast local search and a tailored breadth-first tree search method. Findings Both CPLEX and HACO approach are capable of solving small-scale instances to optimality within reasonable computation time. The proposed HACO has been well enhanced with the embedded fast local search and tailored breadth-first tree search, and it performs robustly in a statistically significant manner when applied to real-world scale instances. Originality/value No stock-outs constraints and weighted line-side inventory level are considered in this paper, and the MSSP is solved satisfactorily to facilitate an efficient JIT part-supply of the AAL. In terms of the algorithm design, a tree search-based local optimizer is embedded into ACO to combine the mechanisms of ACO and problem-specific optimization.

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Unit-Wise Ranking of Inventory Control Techniques Used in Small Scale Cement Enterprises: A Small Scale Entrepreneurial Perspective

Journal of Rural and Industrial Development ◽

10.21863/jrid/2015.3.2.011 ◽

2015 ◽

Vol 3 (2) ◽

Author(s):

Vipul Chalotra

Keyword(s):

Inventory Control ◽

Economic Order Quantity ◽

Economic Order ◽

Small Scale ◽

Just In Time ◽

Order Quantity ◽

Control Techniques ◽

Material Requirement Planning ◽

Material Requirement ◽

Almost All

The present research divulges the different inventory control techniques used small scale cements enterprises operated by small scale entrepreneurs through the assistance of primary data collected from eight small scale cement enterprises operating in SIDCO & SICOP, under DIC (District Industries Center) in District Udhampur of Jammu & Kashmir State. The various inventory control techniques identified and quested for in the research were: Always Better Control (ABC), Economic Order Quantity (EOQ), Material Requirement Planning (MRP), and Just-in-Time (JIT). The results of the ranking table quoted that Economic Order Quantity (EOQ) was awarded first rank by almost all the units representing overall mean score of 1.71, Always Better Control (ABC) was denoted by rank two repressing overall mean value as 2.00, Material Requirement Planning (MRP) was quoted rank three as depicted by its mean ranking (2.25), and Just-in-time (JIT) was accorded rank four (3.71) by almost all the small scale cements entrepreneurs/owners.

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Expected maintenance waste reduction benefits after implementation of Just in Time (JIT) philosophy in maintenance (a statistical analysis)

Journal of Quality in Maintenance Engineering ◽

10.1108/jqme-03-2017-0020 ◽

2019 ◽

Vol 25 (1) ◽

pp. 25-40 ◽

Cited By ~ 3

Author(s):

Sandeep Phogat ◽

Anil Kumar Gupta

Keyword(s):

Data Interpretation ◽

General Information ◽

Spare Parts ◽

Waste Reduction ◽

Maintenance Cost ◽

Just In Time ◽

Content Type ◽

Great Saving ◽

Research Findings ◽

Structured Questionnaire

Purpose The maintenance department of today, like many other departments, is under sustained pressure to slash costs, show outcome and support the assignment of the organization, as it is a commonsensical prospect from the business perspective. The purpose of this paper is to examine expected maintenance waste reduction benefits in the maintenance of organizations after the implementation of just-in-time (JIT) managerial philosophy. For this, a structured questionnaire was designed and sent to the 421 industries in India. Design/methodology/approach The designed questionnaire was divided into two sections A and B to assist data interpretation. The aim of the section A was to build general information of participants, type of organization, number of employees, annual turnover of the organization, etc. Section B was also a structured questionnaire developed based on a five-point Likert scale. The identified critical elements of the JIT were included in the questionnaire to identify the maintenance waste reduction benefits in the maintenance of organizations. Findings On the basis of the 133 responses, hypothesis testing was done with the help of Z-test, and it was found out that in maintenance, we can reduce a large inventory of spare parts and also shorten the excessive maintenance activities due to the implementation of JIT philosophy. All the four wastes: waste of processing; waste of rejects/rework/scrap in case of poor maintenance; waste of the transport of spares, and waste of motion, have approximately equal weightage in their reduction. Waste of waiting for spares got the last rank, which showed that there are little bit chances in the reduction of waiting for spares after the implementation of JIT philosophy in maintenance. Practical implications The implication of the research findings for maintenance of organizations is that if maintenance practitioners implement elements of JIT philosophy in maintenance then there will be a great reduction in the maintenance wastes. Originality/value This paper will be abundantly useful for the maintenance professionals, researchers and others concerned with maintenance to understand the significance of JIT philosophy implementation to get the expected reduction benefits in maintenance wastes of organizations which will be helpful in the great saving of maintenance cost and time side by side great increment in the availability of machines.

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Combined Games with Randomly Delayed Beginnings

Mathematics ◽

10.3390/math9050534 ◽

2021 ◽

Vol 9 (5) ◽

pp. 534

Author(s):

F. Thomas Bruss

Keyword(s):

Discrete Time ◽

Real World ◽

Optimal Stopping ◽

Random Variables ◽

Approximate Solutions ◽

Real World Problems

This paper presents two-person games involving optimal stopping. As far as we are aware, the type of problems we study are new. We confine our interest to such games in discrete time. Two players are to chose, with randomised choice-priority, between two games G1 and G2. Each game consists of two parts with well-defined targets. Each part consists of a sequence of random variables which determines when the decisive part of the game will begin. In each game, the horizon is bounded, and if the two parts are not finished within the horizon, the game is lost by definition. Otherwise the decisive part begins, on which each player is entitled to apply their or her strategy to reach the second target. If only one player achieves the two targets, this player is the winner. If both win or both lose, the outcome is seen as “deuce”. We motivate the interest of such problems in the context of real-world problems. A few representative problems are solved in detail. The main objective of this article is to serve as a preliminary manual to guide through possible approaches and to discuss under which circumstances we can obtain solutions, or approximate solutions.

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