CROSS-DOCKING: SCHEDULING OF INCOMING AND OUTGOING SEMI TRAILERS

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "CROSS-DOCKING: SCHEDULING OF INCOMING AND OUTGOING SEMI TRAILERS"

Transcription

1 CROSS-DOCKING: SCHEDULING OF INCOMING AND OUTGOING SEMI TRAILERS 1 th International Conference on Production Research P.Baptiste, M.Y.Maknoon Département de mathématiques et génie industriel, Ecole polytechnique Montréal Montréal, Québec, Canada. Abstract This paper presents the scheduling of incoming and outgoing semi trailers in a transshipment platform. A set of incoming semi trailers, containing products for different destinations, arrives to the cross-docking. The incoming and outgoing doors are limited; consequently, the semi trailers have to be sequenced. This paper studies the simultaneous scheduling of incoming and outgoing semi trailers for a single inbound and outbound door. The objective is to maximize the direct flow from incoming semi trailers to outgoing semi trailers. The problem is decomposed into three parts. Tabu search is integrated with dynamic programming and a heuristic method is proposed to solve the three cases of the problem. In addition, few examples are performed and the results are shown. Keywords: Cross-docking, Scheduling incoming and outgoing semi trailers, scheduling, Tabu search, heuristic methods, dynamic programming 1 INTRODUCTION Transshipment is a logistic activity between producer and consumer in supply chain process that aims to reduce the costs by reducing inventory level. It breaks down incoming items, process and consolidates them for reshipment. Transshipment aims to reduce the cost and the lead time. In this paper, the authors focus on transshipment platforms. There are two sets of semi trailers beside transshipment platform: the incoming and the outgoing. The two sets have to be sequenced in a manner so that the platform respects just-in-time requirements. There are two ways to transfer products through the platform: moving directly from inbound to outbound door or using a temporary inventory area in the platform. Transshipment efficiency can be measured by the amount of products that passes directly through the platform. The second approach could not be acceptable. In addition to the inventory cost, there are additional movements of the products from inbound door to the storage area and from storage area to the outbound door. Therefore, there are several extra movements which occupy the transshipment facilities but do not enhance transshipment efficiency. This paper studies the problem of simultaneous sequencing inbound and outbound semi-trailers in transshipment in three parts. The three cases differ by the knowledge of incoming or outgoing sequence. In general three approaches are used to solve the problems: dynamic programming, Tabu search and Heuristic. The approach is being tested for some examples and the results are analyzed. 1.1 Literature review Generally in research papers, two aspects of transshipment are studied: strategically and operational. The research at the strategically level concerns mainly the platform location or the assignment of transportation jobs. Operational aspects deal with the efficiency of operational activities. One of the operational activities of transshipment is semi trailers transportation. Ping Chen et al. consider delivery and pickup time windows, warehouse capacities and inventory-handling to minimize the total transportation and inventory cost in a transshipment network [1]. Young Hae Lee et.al. [2] proposed an integration model of transshipment and semi trailers scheduling to obtain more robust program. Lim et.al develop polynomial algorithm for transshipment problem considering just in time objective in the transshipment model [3]. Amano et al. [4] presents modal-shift transportation planning in cross docking network. It contains sets of facilities, orders and carriers with the objective to find a feasible schedule for carriers with minimum total cost which respect to deadlines. Steepest decent algorithm is used to solve the problem. Changing the physical layout can enhance transshipment performance. According to Bartholdi et al. [5] cross docking is a labor intensive area and workers performance depends on how well semi trailers are assigned to doors; moreover, a good layout reduces travel distances without creating congestion. They conclude that changing layout reduce reduces shipping and handling cost within transshipment platform. Time span in scheduling increases the transshipment efficiency. Li et al. [6] propose a problem in which each container should be filled in exact time. Machine scheduling problem is used as a model. In this model the transshipment platform is divided into loading and unloading areas. The arriving dates for incoming semi trailer are variable. The received Items are then either shipped away directly or sent to the exportation area in order to be loaded for reshipment. In this problem the time to start unloading semi trailer is scheduled in order that each loading semi trailer is completed at its due date. Douglas L. Mc Williamsa et al. [7] have studied the problem of parcel industry. The research focuses on the transfer operation platforms. Parcels are unloaded and shipped to outbound semitrailers. The objective is to minimize time interval from the first unloaded parcel till the last loaded parcel. An integrated simulation model that integrates a genetic algorithm is proposed to find the solution. Wooyen Yu et al. consider the scheduling of inbound and outbound semi-trailers in order to minimize completion time while the storage is located at shipping dock. Two approaches are proposed to obtain the results: mathematical model and heuristic algorithms. Mathematical models are used to solve small size

2 problems while they are not practical for the large problems. In contrast, the heuristic methods are used to solve the large problems [8]. Scheduling the sequence of loading and unloading semi trailers also increases the efficiency of the transshipment platform. In fact, synchronize loading and unloading sequences decreases inventory level and increases direct product flow from inbound to outbound door. As a result it increases transshipment efficiency. In this paper scheduling of loading and unloading semi trailers in cross docking are studied. 2 PROBLEDESCRIPTION: In practice, transshipment has various layouts. In this research the layout is being restricted to one inbound and one outbound door. This restriction is not realistic (in a real transshipment platform) but can be used as a baseline for other layouts. In this model an incoming semi trailer arrives at inbound door and unloads products for various destinations. If the outgoing semi trailer is going to the fine destination, the products are moved directly to outbound semi trailer (direct transit of products), in the other hand, the products are moved to a temporary storage (products in temporary storage). In studied model, the following assumptions are considered: Each trailer leaves the inbound door when it is fully unloaded. On the other side, each trailer leaves the outbound door when it is fully loaded. The internal operations of cross docking such as sorting and merging are not considered. The storage capacity is assumed unlimited. Each outbound semi trailers leaves only for one destination. All incoming and outgoing semi trailers are available at time zero. The total numbers of arriving and departing products are equal. The products differ by their destination. Loading, unloading and transfer time are constant and are not considered. Inside incoming semi trailers there are products for different destinations while for outgoing semi trailers there are products for just one destination. In addition, the information about type of products and the quantity of arriving products for each destination are known. For this problem, the following decision variables are considered: 1. Incoming sequence 2. Outgoing sequence 3. Unloading sequence of semi trailers 4. Unloading policy The first two variables are obvious, but the third variable corresponds to the fact that unloading order of an incoming semi trailer contains items to be shipped in different destinations, which can influence the efficiency. Obviously, items that can be shipped in the active destination (current outgoing semi trailer) have to be unloaded first. This variable can be free or fixed (due to technical constraints for the unloading operations). For the last case, the optimal decision is usually evident. The fourth variable (unloading policy) corresponds to the following situation: an outgoing semi trailer is positioned at the output door, and items are already waiting on the ground for the same destination. The manager can choose to ship those items or wait till an incoming semi trailer arrives with items that can be shipped directly to this destination. Obviously, the loading and unloading sequences (variables 1 and 2) and products movement policies (variables 3 and 4) are two important factors which affect transshipment performance. For the fourth variable, there are two different policies. At the first policy, products already on the ground are systematically used to complete a semi trailer (fewer inventories). At the second policy, items already on the ground remain for the last semi trailer for their destination. The optimal policy is a combination of the two extreme policies. In general some times, it is better to use inventory, compare to waiting for direct transshipment. Three cases are proposed. The definition of each case is as follows: Case 1: the sequences of incoming and outgoing semi trailers are known a priori and only variables 3 and 4 are examined. Case 2: The sequences of incoming semi trailers are known a priori and variables 2, 3 and 4 are examined. Case 3: no sequences are known a priori and variables 1, 2, 3 and 4 are examined. With the above assumptions the resolution approaches are discussed in the next section. 3 RESOLUTIONS 3.1 First case resolution approach Objective in the first case is to obtain the optimal policy (maximizing direct transiting products) when the sequence of loading and unloading semi trailers are known. Complete enumeration technique is used to obtain optimal policy. A graph is used to present all the possibilities of assignments. The graph nodes are used to present assignment state and the arcs are used to present forthcoming possibilities. The following variables are saved in each node: - Vector of variables indicates possible direct transiting product for each destination. (PDT) - Vector of variables indicates the number of products which are now in temporary storage, for each destination. (PTS) - cost (the total number of direct transiting product from beginning to the current node) (C) - Order number of loaded semi trailer. (ON) For each arc, unloading semi trailer order number and cost (the number of direct transferred product for forthcoming assignment) are saved. For the Initial node, the PDT variables are equal to the first semi trailer products content for each destination and the rest of the variables default numbers are zero. This algorithm starts with the initial node, afterwards, in each iteration, it increments the order number of loading semi trailer and reads all the generated nodes for current loading semi trailer. For each node, it generates all the possible arcs for remaining loading semi trailers order;

3 1 th International Conference on Production Research therefore, all the future possible nodes are obtained. This process repeats as long as all the loading semi trailers are chosen. At the last iteration the node which has the highest cost is chosen as the final node. The path from the initial node up to the final node is loading and unloading optimal policy In practice, two domination rules are used to decrease computational time. The first rule proposes that if for all destinations the summation of direct transiting and storage products for two or more nodes are the same, the node with the highest cost dominates the others. However, the second rule is applied when two or more nodes have the same cost. The node with higher summation of direct transiting products for all destinations dominates the others. Dynamic programming is used to solve this algorithm. The steps are as follows: Optimal policy algorithm Step 1: create an initial node (discussed before) Step 2: do as long as all outbound semi trailers are assigned. Step 3: obtain all forthcoming assignments. Step 4: check domination rule. Step 5: go to step 2. Step 6: find the node with highest cost in the last iteration Step 7: find the path for optimal policy. Example: In this example there are 6 incoming semi trailers containing the products for 3 destinations. The first semi trailer contains 3 products for destination A, 2 and 0 for destinations B and C, respectively. Table 1 presents the incoming semi trailer orders. The sequence of outgoing semi trailers is C-B-A-A-B-A and the trailer capacity is 5 units. 0 i i Node(i) with cost 0 Optimal Path Dominated Node Figure 1 : Optimal policy algorithm for the example The optimal path is with the cost of units. The nodes, and 16 are dominated nodes. 3.2 Second case resolution approach In this case, two methods are proposed. Using Tabu search integrated with optimal policy algorithm and a heuristic method. The first method proposes the following algorithm: Loading semi trailer sequence algorithm: Step 1: Run optimal policy algorithm for the initial value Step2: Select two loading semi trailers order number Step3: Swap the order number and save it in Tabu list (if they are not for same destination and are not in Tabu list). Step 4: Run optimal policy algorithm. Step 5: Save the cost and optimal path if it is improved. Step 6: Go to step 2 or stop if the cost is not modified after 20 iterations Table 1 : Example Order Destination A B C The algorithm flow chart is presented in figure 2. START RUN OPTIMAL POLICY ALGORITHM RANDOMLY SWAP 2 LOADING SEMI TRAILER DESTINATION ORDER The graph is presented in figure 1: LIMIT=0 RUN OPTIMAL POLICY ALGORITHM LIMIT=LIMIT+1 YES COST IMPROVE LIMIT=20 STOP Figure 2: loading semi trailer sequence algorithm

4 In loading semi trailer algorithm, the optimal policy is not the combination of sub optimal policies. In other words, sometimes selecting the arc with lower cost would lead us to the node with highest cost. On the other hand in proposed heuristic method it is supposed that sub optimal assignments lead the process to the optimal result. In heuristic algorithm, the preceding graph is used to illustrate all the assigned possibilities. Furthermore, deep first search method is used searching for the best assignment. The algorithm is described as follows and the flow chart is presented in figure 3: Loading semi trailer sequence heuristic algorithm Step 1: Create an initial state (the value of direct transiting products for each destination equals to the first unloaded semi trailer products, the rest is zero) Step 2: For all loading semi trailers Step 3: For all destinations Step 4: Do as long as direct transiting product is more than semi trailer capacity Step 5: For current state, calculate the cost if the summation of direct transiting products and in storage products for selected destination equals to or greater than semi trailer capacity. Persevere the results, if the cost is improved. Step 6: Unload the next semi trailer and update values. Step 6: Go to step 4 Step 7: Save the best assignment Step 8: Set best assignment as current state and go to step 2 Step 8: The final list is the optimal assignment Example: In the previous example, for the given incoming sequence, the solution obtained with the first algorithm is 23 with B-A- A-C-B-A sequence. For the second algorithm the obtained sequence is B-A-A-C-A-B with the value of Third case resolution approach The previous two methods are developed to obtain the good sequence of loading and unloading semi trailers. Tabu search integrates with loading semi trailer algorithm or loading semi trailer heuristic algorithm to obtain good unloading sequence. The algorithm is presented in the following steps: Loading and unloading sequence algorithm Step 1: Run loading semi trailer sequence algorithm/ loading semi trailer sequence heuristic algorithm for initial value Step2: Select two unloading semi trailers order numbers Step3: If they are not in Tabu list, Swap the order number and save it in Tabu list. Step 4: Run loading semi trailer algorithm/ loading semi trailer heuristic algorithm Step 5: Save the cost and optimal path if it increase. Step 6: Go to step 2 or Stop if the cost is not improved after 20 iterations. The figure 4 presents the algorithm: START START FOR ALL LOADING ORDER RUN LOADING SEMI TRAILER SEQUENCE ALGORITHM OR RUN LOADING SEMI TRAILER SEQUENCE HEURISTIC ALGORITHM FOR INITIAL ANSWER FOR ALL DESTINATIONS RANDOMLY SWAP 2 UN LOADING SEMI TRAILER ORDER CALCULATE COST KEEP IT IF IT IS IMPROVED DIRECT TRANSITING PRODUCT > SEMI TRAILER CAPACITY UNLOAD NEXT SEMI TRAILER AND UPDATE VALUES LIMIT=0 RUN LOADING SEMI TRAILER SEQUENCE ALGORITHM OR RUN LOADING SEMI TRAILER SEQUENCE HEURISTIC ALGORITHM YES COST IMPROVE LIMIT=LIMIT+1 SET BEST CHOSEN DE AS CURRENT STATE OBTAIN LIST IS OPTIMAL POLICY LIMIT=20 END STOP Figure 3: Loading semi trailer sequence heuristic algorithm Figure 4: loading and unloading sequence algorithm

5 1 th International Conference on Production Research Example: In the previous example, for the given data, the solution obtained with the first algorithm is 27 with A-B-C-A-B-A and sequences. For the second algorithm the obtained sequence is A-B-A-A-C-B and with the cost value of EXPERIMENT Medium size problems (contains loaded and unloaded semi trailers) are considered as the test problems. Four different destination combinations ( , , and ) are selected to cover the problem diversity. For each combination 4 sets of data are generated. The loading and unloading trailers capacities are considered as units. Each algorithm is run for all generated data and combinations. The results are shown in table 2 and are summarized in figure 5 and table 3. The results depend on the combination of destinations (Figure 5). The improvement for case is 13.75% and for the case is 34.75% (Table 3). The results indicate that the combinations of destinations are important to implement cross docking semi trailer scheduling. Table 2 : Experimental results for different sequence combination Destination Data Set Results For case 2 of the problem, integrated Tabu search with dynamic programming show better performance compare to the heuristic method with improvement between 6.25% to 2.25% and 4.75% to 28.5% respectively. In contrast, for case 3 heuristic method show better performance. Figure 5 : Experimental results for different sequence combination Figure 6 and 7 present the Tabu search results for selected problem ( , data test 1) for case 3-1 and 3-2. It seems that integrated Tabu search with heuristic method reach to good value with less iteration. Table 3 : Summary of improvements approach implementation Combination average percentage 6.25% 17.00% 26.25% 2.25% 1.6% percentage 4.75% 17.00% 25.25% 28.50% 18.88% percentage 13.75% 22.00% 30.75% 33.50% 25.00% percentage.50% 22.50% 32.75% 34.75% 25.63% Claculated Value Tabu search iteration results Iteration Figure 6 : Tabu search results for selected problem ( , data test 1) Case 3-1 In addition, for test data, on average there is almost 20 percent improvement when the sequence of outgoing semi trailers is planned compare to 25 percent improvement,in average when both sequences are planned.

6 Claculated Value Tabu search iteration results (Heuristic Method) Iteration Figure 7 : Tabu search results for selected problem ( , data set 1) Case CONCLUSION Transshipment platform is a place where the products from incoming semi trailers are unloaded and then loaded for reshipment. Efficiency of such platform is related to the ratio of direct moves (only one manipulation). Scheduling the incoming and outgoing semi trailers can increases transshipment efficiency. This research explores the particular case of a platform with a single incoming door and a single outgoing door. Three cases of this problem are studied and dynamic programming and heuristic methods are proposed as a two major function to solve the problems. A medium size problem is defined as test data for numerical results. Engineering, 2006, article in press. [3] Lim A., Miao Z., Rodrigues B., Xu A., Transshipment through Cross docks with Inventory and Time Windows, Wiley Inter science, [4] Amano M., Yoshizumi T., Okano H., 2003, The modal-shift transportation planning problem and its fast steepest descent algorithm, Proc. of the Winter Simulation Conference [5] Bartholdi J.J., Gue K.R., Reducing labor costs in an LTL cross docking terminal. Operation Research, 2002, 48, [6] Li Y., Rodrigues B., Cross docking JIT scheduling with time windows, Journal of the Operational Research Society, , [7] Mc Williams D.L., Stanfield P.M., Geiger C.D., The parcel hub scheduling problem: A simulation-based solution approach,computers and Industrial Engineering 2005,4, 33 4 [8] Yu W., Egbelu P.J., Scheduling of inbound and outbound trucks in cross docking system with temporary storage, European journal of operation research,2006, Article in press. When both schedules are known, an optimal algorithm based on dynamic programming finds the optimal use of the temporary inventory. When one or both sequence is unknown, two different approaches have been proposed. The first uses a stochastic algorithm for the schedule and an optimal evaluation function. The second is a heuristic. The heuristic is much faster and as efficient that the stochastic algorithm. The heuristic algorithm reaches the good results in a very few iterations, moreover, because of the assumptions for heuristic algorithm, it run in shorter time rather than other algorithm. For generated case it is shown that by scheduling both incoming and outgoing semi-trailers, there is 25% improvement in transshipment performance. Most of this improvement can be obtained with scheduling only outgoing semi trailers (20%). To conclude, it is shown that scheduling loading and unloading semi trailers increases transshipment efficiency. Those results have to be extended to general platform, with more than one incoming door and more than one outgoing door. 6 REFERENCES [1] Chen P., Guo Y., Lim A., Rodrigues B.,Multiple cross docks with inventory and time windows, Computers and Operations Research 2006, 33, [2] Lee Y.H., Jung J.W., Lee K.M., Vehicle routing scheduling for cross-docking in the supply chain, Computers and Industrial

A Review and Classification of Cross-Docking Concept

A Review and Classification of Cross-Docking Concept Int. J. Learn. Man. Sys.4, No. 1, 25-33 (2016) 25 International Journal of Learning Management Systems http://dx.doi.org/10.18576/ijlms/040104 A Review and Classification of Cross-Docking Concept M. N.Sheikholeslam

More information

Review on Cross Docking Quantitative Approaches

Review on Cross Docking Quantitative Approaches Review on Cross Docking Quantitative Approaches Amin Soleimanynanadegany 1 e-mail: soleimani.amin@yahoo.com Ali Tolooie 2 e-mail: Ali.tolooie@Gmail.com Author(s) Contact Details: 1,2 Faculty of Mechanical,

More information

Vehicle Routing with Cross Docks, Split Deliveries, and Multiple Use of Vehicles. Arun Kumar Ranganathan Jagannathan

Vehicle Routing with Cross Docks, Split Deliveries, and Multiple Use of Vehicles. Arun Kumar Ranganathan Jagannathan Vehicle Routing with Cross Docks, Split Deliveries, and Multiple Use of Vehicles by Arun Kumar Ranganathan Jagannathan A thesis submitted to the Graduate Faculty of Auburn University in partial fulfillment

More information

AN INTEGRATED MODEL OF CROSS DOCKING. A Thesis. presented to. the Faculty of the Graduate School. at the University of Missouri-Columbia

AN INTEGRATED MODEL OF CROSS DOCKING. A Thesis. presented to. the Faculty of the Graduate School. at the University of Missouri-Columbia AN INTEGRATED MODEL OF CROSS DOCKING A Thesis presented to the Faculty of the Graduate School at the University of Missouri-Columbia In Partial Fulfillment of the Requirements for the Degree Master of

More information

Proceedings of the 2014 Winter Simulation Conference A. Tolk, S. Y. Diallo, I. O. Ryzhov, L. Yilmaz, S. Buckley, and J. A. Miller, eds.

Proceedings of the 2014 Winter Simulation Conference A. Tolk, S. Y. Diallo, I. O. Ryzhov, L. Yilmaz, S. Buckley, and J. A. Miller, eds. Proceedings of the 2014 Winter Simulation Conference A. Tolk, S. Y. Diallo, I. O. Ryzhov, L. Yilmaz, S. Buckley, and J. A. Miller, eds. OPTIMIZATION OF CROSS-DOCKING TERMINAL USING FLEXSIM/OPTQUEST CASE

More information

I. COMPARING INDUSTRY AND ACADEMIC PERSPECTIVES ON CROSS-DOCKING OPERATIONS

I. COMPARING INDUSTRY AND ACADEMIC PERSPECTIVES ON CROSS-DOCKING OPERATIONS I. COMPARING INDUSTRY AND ACADEMIC PERSPECTIVES ON CROSS-DOCKING OPERATIONS Paul Buijs Iris F.A. Vis University of Groningen, Faculty of Economics and Business, Department of Operations, P.O. Box 800,

More information

INTEGRATING VEHICLE ROUTING WITH CROSS DOCK IN SUPPLY CHAIN

INTEGRATING VEHICLE ROUTING WITH CROSS DOCK IN SUPPLY CHAIN INTEGRATING VEHICLE ROUTING WITH CROSS DOCK IN SUPPLY CHAIN Farshad Farshchi Department of Industrial Engineering, Parand Branch, Islamic Azad University, Parand, Iran Davood Jafari Department of Industrial

More information

DISPATCHING TRANSPORT VEHICLES IN MARITIME CONTAINER TERMINALS

DISPATCHING TRANSPORT VEHICLES IN MARITIME CONTAINER TERMINALS DISPATCHING TRANSPORT VEHICLES IN MARITIME CONTAINER TERMINALS by Pyung-Hoi Koo Department of Systems Management and Engineering, Pukyong National University, Busan, Korea Yongsoro 45, Namgu, Busan, South

More information

Heuristic Techniques for Solving the Vehicle Routing Problem with Time Windows Manar Hosny

Heuristic Techniques for Solving the Vehicle Routing Problem with Time Windows Manar Hosny Heuristic Techniques for Solving the Vehicle Routing Problem with Time Windows Manar Hosny College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia mifawzi@ksu.edu.sa Keywords:

More information

Reduction of Empty Container Repositioning Costs by Container Sharing

Reduction of Empty Container Repositioning Costs by Container Sharing Reduction of Empty Container Repositioning Costs by Container Sharing Herbert Kopfer and Sebastian Sterzik 1 Problem Description Empty container repositioning is a major cost driver for international container

More information

Waiting Strategies for Regular and Emergency Patient Transportation

Waiting Strategies for Regular and Emergency Patient Transportation Waiting Strategies for Regular and Emergency Patient Transportation Guenter Kiechle 1, Karl F. Doerner 2, Michel Gendreau 3, and Richard F. Hartl 2 1 Vienna Technical University, Karlsplatz 13, 1040 Vienna,

More information

Distribution and operation planning at a cross-dock platform: a case of study at Renault

Distribution and operation planning at a cross-dock platform: a case of study at Renault Distribution and operation planning at a cross-dock platform: a case of study at Renault Christian Serrano Direction de la Supply Chain Renault Guyancourt, France christian.serrano@renault.com Xavier Delorme,

More information

Operational strategies for cross docking systems

Operational strategies for cross docking systems Retrospective Theses and Dissertations 2002 Operational strategies for cross docking systems Wooyeon Yu Iowa State University Follow this and additional works at: http://lib.dr.iastate.edu/rtd Part of

More information

Modeling a Four-Layer Location-Routing Problem

Modeling a Four-Layer Location-Routing Problem Modeling a Four-Layer Location-Routing Problem Paper 018, ENG 105 Mohsen Hamidi, Kambiz Farahmand, S. Reza Sajjadi Department of Industrial and Manufacturing Engineering North Dakota State University Mohsen.Hamidi@my.ndsu.edu,

More information

Best Practices for Transportation Management

Best Practices for Transportation Management Best Practices for Transportation Management A White Paper from Ozburn-Hessey Logistics www.ohlogistics.com/countonus.html or 800-401-6400 Introduction The mantra for all transportation professionals is

More information

Transactions on the Built Environment vol 33, 1998 WIT Press, ISSN

Transactions on the Built Environment vol 33, 1998 WIT Press,  ISSN Effects of designated time on pickup/delivery truck routing and scheduling E. Taniguchf, T. Yamada\ M. Tamaishi*, M. Noritake^ "Department of Civil Engineering, Kyoto University, Yoshidahonmachi, Sakyo-kyu,

More information

Structural Properties of third-party logistics networks

Structural Properties of third-party logistics networks Structural Properties of third-party logistics networks D.Armbruster School of Mathematical and Statistical Sciences, Arizona State University & Department of Mechanical Engineering, Eindhoven University

More information

Container Sharing in Seaport Hinterland Transportation

Container Sharing in Seaport Hinterland Transportation Container Sharing in Seaport Hinterland Transportation Herbert Kopfer, Sebastian Sterzik University of Bremen E-Mail: kopfer@uni-bremen.de Abstract In this contribution we optimize the transportation of

More information

Lecture 09 Crossdock: Just In Time Warehouse

Lecture 09 Crossdock: Just In Time Warehouse .. Lecture 09 Crossdock: Just In Time Warehouse Oran Kittithreerapronchai 1 1 Department of Industrial Engineering, Chulalongkorn University Bangkok 10330 THAILAND last updated: December 29, 2014 Warehouse

More information

Solving Scheduling Problems in Distribution Centers by Mixed Integer Linear Programming Formulations

Solving Scheduling Problems in Distribution Centers by Mixed Integer Linear Programming Formulations Solving Scheduling Problems in Distribution Centers by Mixed Integer Linear Programming Formulations Maria Pia Fanti Gabriella Stecco Walter Ukovich Department of Electrical and Electronic Engineering,

More information

A Particle Swarm Optimization Algorithm for Multi-depot Vehicle Routing problem with Pickup and Delivery Requests

A Particle Swarm Optimization Algorithm for Multi-depot Vehicle Routing problem with Pickup and Delivery Requests A Particle Swarm Optimization Algorithm for Multi-depot Vehicle Routing problem with Pickup and Delivery Requests Pandhapon Sombuntham and Voratas Kachitvichayanukul Abstract A particle swarm optimization

More information

Factors Affecting Transportation Decisions. Transportation in a Supply Chain. Transportation Modes. Road freight transport Europe

Factors Affecting Transportation Decisions. Transportation in a Supply Chain. Transportation Modes. Road freight transport Europe Transportation in a Supply Chain Factors Affecting Transportation Decisions Carrier (party that moves or transports the product) Vehicle-related cost Fixed operating cost Trip-related cost Shipper (party

More information

Planning Optimized. Building a Sustainable Competitive Advantage WHITE PAPER

Planning Optimized. Building a Sustainable Competitive Advantage WHITE PAPER Planning Optimized Building a Sustainable Competitive Advantage WHITE PAPER Planning Optimized Building a Sustainable Competitive Advantage Executive Summary Achieving an optimal planning state is a journey

More information

Importance of Road Freight Transport to the Organization and Economy. Amal S. Kumarage July 2014

Importance of Road Freight Transport to the Organization and Economy. Amal S. Kumarage July 2014 Importance of Road Freight Transport to the Organization and Economy Amal S. Kumarage July 2014 Freight Transport FT is the process of conveying different types of goods from one point to another using

More information

Abstract Number: A Feasibility Study for Joint Services of Vehicle Routing and Patrol

Abstract Number: A Feasibility Study for Joint Services of Vehicle Routing and Patrol Abstract Number: 011-0101 A Feasibility Study for Joint Services of Vehicle Routing and Patrol Chikong Huang *1 Stephen C. Shih 2 Poshun Wang 3 *1 Professor, Department of Industrial anagement, Institute

More information

Optimization in Supply Chain Planning

Optimization in Supply Chain Planning Optimization in Supply Chain Planning Dr. Christopher Sürie Expert Consultant SCM Optimization Agenda Introduction Hierarchical Planning Approach and Modeling Capability Optimizer Architecture and Optimization

More information

Multi-depot Vehicle Routing Problem with Pickup and Delivery Requests

Multi-depot Vehicle Routing Problem with Pickup and Delivery Requests Multi-depot Vehicle Routing Problem with Pickup and Delivery Requests Pandhapon Sombuntham a and Voratas Kachitvichyanukul b ab Industrial and Manufacturing Engineering, Asian Institute of Technology,

More information

Multi-Agent Systems Modelling For Evaluating Joint Delivery Systems

Multi-Agent Systems Modelling For Evaluating Joint Delivery Systems Available online at www.sciencedirect.com ScienceDirect Procedia - Social and Behavioral Sciences 125 ( 2014 ) 472 483 8 th International Conference on City Logistics Multi-Agent Systems Modelling For

More information

Cross-Dock Modeling And Simulation Output Analysis

Cross-Dock Modeling And Simulation Output Analysis Cross-Dock Modeling And Simulation Output Analysis Mehdi Charfi Abstract Cross-docking is a consolidation practice in logistics that facilitates the transfer and sorting of products from suppliers to distribution

More information

Storage Allocation and Yard Trucks Scheduling in Container Terminals Using a Genetic Algorithm Approach

Storage Allocation and Yard Trucks Scheduling in Container Terminals Using a Genetic Algorithm Approach Storage Allocation and Yard Trucks Scheduling in Container Terminals Using a Genetic Algorithm Approach Z.X. Wang, Felix T.S. Chan, and S.H. Chung Abstract Storage allocation and yard trucks scheduling

More information

Solving a Log-Truck Scheduling Problem with Constraint Programming

Solving a Log-Truck Scheduling Problem with Constraint Programming Solving a Log-Truck Scheduling Problem with Constraint Programming Nizar El Hachemi, Michel Gendreau, Louis-Martin Rousseau Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation

More information

Topics in Supply Chain Management. Session 3. Fouad El Ouardighi BAR-ILAN UNIVERSITY. Department of Operations Management

Topics in Supply Chain Management. Session 3. Fouad El Ouardighi BAR-ILAN UNIVERSITY. Department of Operations Management BAR-ILAN UNIVERSITY Department of Operations Management Topics in Supply Chain Management Session Fouad El Ouardighi «Cette photocopie (d articles ou de livre), fournie dans le cadre d un accord avec le

More information

Dennis Bricker Dept of Mechanical & Industrial Engineering The University of Iowa. JIT --Intro 02/11/03 page 1 of 28

Dennis Bricker Dept of Mechanical & Industrial Engineering The University of Iowa. JIT --Intro 02/11/03 page 1 of 28 Dennis Bricker Dept of Mechanical & Industrial Engineering The University of Iowa JIT --Intro 02/11/03 page 1 of 28 Pull/Push Systems Pull system: System for moving work where a workstation pulls output

More information

Heuristic Routing Software for Planning of Combined Road Transport with Swap Bodies: A Practical Case

Heuristic Routing Software for Planning of Combined Road Transport with Swap Bodies: A Practical Case Heuristic Routing Software for Planning of Combined Road Transport with Swap Bodies: A Practical Case Frederik Schulte University of Hamburg, Institute of Information Systems, 20146 Hamburg, E-Mail: frederik.schulte@uni-hamburg.de

More information

TRANSPORTATION MANHATTAN ACTIVE. A Comprehensive Solution for Complex Logistics Networks. With Manhattan Active Transportation you can:

TRANSPORTATION MANHATTAN ACTIVE. A Comprehensive Solution for Complex Logistics Networks. With Manhattan Active Transportation you can: MANHATTAN ACTIVE TRANSPORTATION A Comprehensive Solution for Complex Logistics Networks LOGISTICS COMPLEXITIES AND SERVICE-LEVEL EXPECTATIONS have increased dramatically in today s world. Supply chains

More information

FACTS DMS Dock Management System

FACTS DMS Dock Management System FACTS DMS Dock Management System Complement your dock operation with DMS, Carrier Logistics' Dock Management System. Whether for inbound or outbound freight, DMS improves dock productivity, enhances loading

More information

1.224J/ESD.204J TRANSPORTATION OPERATIONS, PLANNING AND CONTROL: CARRIER SYSTEMS

1.224J/ESD.204J TRANSPORTATION OPERATIONS, PLANNING AND CONTROL: CARRIER SYSTEMS 1.224J/ESD.204J TRANSPORTATION OPERATIONS, PLANNING AND CONTROL: CARRIER SYSTEMS Professor Cynthia Barnhart Professor Nigel H.M. Wilson Fall 2003 1.224J/ ESD.204J Outline Sign-up Sheet Introductions Carrier

More information

Corporate Office Rosario Leo Building 2185 Main Road Newfield, NJ

Corporate Office Rosario Leo Building 2185 Main Road Newfield, NJ General Agreement (SOPs & Accessorial Charges) Applies to Freight Consolidation & Freight Brokerage Operations Customer agrees to terms and conditions as outlined on RLS s website: (the Terms and Conditions

More information

Mileage savings from optimization of coordinated trucking 1

Mileage savings from optimization of coordinated trucking 1 Mileage savings from optimization of coordinated trucking 1 T.P. McDonald Associate Professor Biosystems Engineering Auburn University, Auburn, AL K. Haridass Former Graduate Research Assistant Industrial

More information

Determination of the number of AGVs required at a semi-automated container terminal

Determination of the number of AGVs required at a semi-automated container terminal Determination of the number of AGVs required at a semi-automated container terminal Iris F.A. Vis, René de Koster, Kees Jan Roodbergen, Leon W.P. Peeters Rotterdam School of Management, Erasmus University

More information

THE UNDERESTIMATED VALUE OF

THE UNDERESTIMATED VALUE OF THE UNDERESTIMATED VALUE OF DOCK SCHEDULING Beneath the automated schedules, communications features, and web portals lies a hidden feature that will transform your business. WHITE PAPER By Gregory Braun,

More information

Routing Optimization of Fourth Party Logistics with Reliability Constraints based on Messy GA

Routing Optimization of Fourth Party Logistics with Reliability Constraints based on Messy GA Journal of Industrial Engineering and Management JIEM, 2014 7(5) : 1097-1111 Online ISSN: 2013-0953 Print ISSN: 2013-8423 http://dx.doi.org/10.3926/jiem.1126 Routing Optimization of Fourth Party Logistics

More information

Transportation Optimization: Is This the Next Step?

Transportation Optimization: Is This the Next Step? Transportation Optimization: Is This the Next Step? By Irista, An HK Systems Company Cost reduction through effective transportation management remains a high priority for most organizations. The challenges

More information

A Genetic Algorithm on Inventory Routing Problem

A Genetic Algorithm on Inventory Routing Problem A Genetic Algorithm on Inventory Routing Problem Artvin Çoruh University e-mail: nevin.aydin@gmail.com Volume 3 No 3 (2014) ISSN 2158-8708 (online) DOI 10.5195/emaj.2014.31 http://emaj.pitt.edu Abstract

More information

CHAPTER 1 INTRODUCTION

CHAPTER 1 INTRODUCTION 1 CHAPTER 1 INTRODUCTION 1.1 MANUFACTURING SYSTEM Manufacturing, a branch of industry, is the application of tools and processes for the transformation of raw materials into finished products. The manufacturing

More information

The Two-Echelon Capacitated Vehicle Routing. Problem

The Two-Echelon Capacitated Vehicle Routing. Problem The Two-Echelon Capacitated Vehicle Routing Problem Jesus Gonzalez Feliu 1, Guido Perboli 1, Roberto Tadei 1 and Daniele Vigo 2 1 Control and Computer Engineering Department Politecnico di Torino, Italy

More information

CERTIFIED WAREHOUSING AND STOREKEEPING COURSE

CERTIFIED WAREHOUSING AND STOREKEEPING COURSE CERTIFIED WAREHOUSING AND STOREKEEPING COURSE Unic Foundation Entrepreneurship Training Manual Page 1 1.0.WAREHOUSING Warehousing refers to the activities involving storage of goods on a large-scale in

More information

Iterative train scheduling in networks with tree topologies: a case study for the Hunter Valley Coal Chain

Iterative train scheduling in networks with tree topologies: a case study for the Hunter Valley Coal Chain 22nd International Congress on Modelling and Simulation, Hobart, Tasmania, Australia, 3 to 8 December 2017 mssanz.org.au/modsim2017 Iterative train scheduling in networks with tree topologies: a case study

More information

NETWORK SYSTEMS EXPERTISE

NETWORK SYSTEMS EXPERTISE NETWORK SYSTEMS EXPERTISE www.grupologico.com CALL + 1 (248) 669-0478 2 TABLE OF CONTENTS INTEGRATED LOGISTICS Network Design & Planning Transportation Management Carrier Management Operations Management

More information

NaviTrans 2017 Capability Guide NaviTrans 2017 Capability Guide

NaviTrans 2017 Capability Guide NaviTrans 2017 Capability Guide NaviTrans 2017 Capability Guide Page 2 of 11 2017 Young & Partners NV. If this document is distributed with software that includes an end user agreement, this document, as well as the software described

More information

AUTOMATED GUIDED VEHICLES (AGV) IN PRODUCTION ENTERPRISES

AUTOMATED GUIDED VEHICLES (AGV) IN PRODUCTION ENTERPRISES AUTOMATED GUIDED VEHICLES (AGV) IN PRODUCTION ENTERPRISES Lucjan Kurzak Faculty of Civil Engineering Czestochowa University of Technology, Poland E-mail: lumar@interia.pl tel/fax +48 34 3250936 Abstract

More information

A Hybrid Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Time Windows

A Hybrid Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Time Windows A Hybrid Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Time Windows Abstract. Vehicle routing problem with time windows (VRPTW) involves the routing of a set of vehicles

More information

A freight urban distribution center design with micro-simulation support for city logistics

A freight urban distribution center design with micro-simulation support for city logistics Urban Transport XXI 303 A freight urban distribution center design with micro-simulation support for city logistics D. Gattuso, G. C. Cassone, C. Lanciano, V. Placido & M. Praticò DIIES Dipartimento di

More information

Jacob Cook Mohit Goyal. Sponsor Prof. Benoit Montreuil

Jacob Cook Mohit Goyal. Sponsor Prof. Benoit Montreuil Jacob Cook Mohit Goyal Sponsor Prof. Benoit Montreuil 1 Agenda Introduction Heuristic Case Description Generated Data Case Analysis Results Conclusions Future Work 2 Heuristic Introduction Generated Data

More information

Chapter 11. In-Time and Lean Production

Chapter 11. In-Time and Lean Production Chapter 11 Just-In In-Time and Lean Production What is JIT? Producing only what is needed, when it is needed A philosophy An integrated management system JIT s mandate: Eliminate all waste Basic Elements

More information

SIMULATION APPLICATIONS IN CONSTRUCTION SITE LAYOUT PLANNING

SIMULATION APPLICATIONS IN CONSTRUCTION SITE LAYOUT PLANNING SIMULATION APPLICATIONS IN CONSTRUCTION SITE LAYOUT PLANNING *S. Razavialavi, and S. AbouRizk Hole School of Construction Engineering Department of Civil and Environmental Engineering University of Alberta

More information

A Method of Container Terminal Resources Scheduling Simulation Research Li Mingqi, Zhang Peng*, Du Yuyue

A Method of Container Terminal Resources Scheduling Simulation Research Li Mingqi, Zhang Peng*, Du Yuyue Joint International Mechanical, Electronic and Information Technology Conference (JIMET 2015) A Method of Container Terminal Resources Scheduling Simulation Research Li Mingqi, Zhang Peng*, Du Yuyue College

More information

Produce Traceability Initiative Best Practices for Cross Docking and Load Only Services

Produce Traceability Initiative Best Practices for Cross Docking and Load Only Services Produce Traceability Initiative Best Practices for Cross Docking and Load Only s About this Best Practice Guideline (Revision 1.1) Best practices are generally accepted, informally-standardized techniques,

More information

LEVERAGING BIG DATA TO MANAGE LOGISTICS

LEVERAGING BIG DATA TO MANAGE LOGISTICS LEVERAGING BIG DATA TO MANAGE LOGISTICS By Andrew Loh, Simon-Pierre Monette, Andres Garro, Dustin Burke, Andreanne Leduc, and Nicholas Malizia For many major B2B enterprises, logistics networks have never

More information

SELECTED HEURISTIC ALGORITHMS FOR SOLVING JOB SHOP AND FLOW SHOP SCHEDULING PROBLEMS

SELECTED HEURISTIC ALGORITHMS FOR SOLVING JOB SHOP AND FLOW SHOP SCHEDULING PROBLEMS SELECTED HEURISTIC ALGORITHMS FOR SOLVING JOB SHOP AND FLOW SHOP SCHEDULING PROBLEMS A THESIS SUBMITTED IN PARTIAL FULFILLMENT FOR THE REQUIREMENT OF THE DEGREE OF BACHELOR OF TECHNOLOGY IN MECHANICAL

More information

INTERNATIONAL & DOMESTIC SHIPPING INSTRUCTIONS

INTERNATIONAL & DOMESTIC SHIPPING INSTRUCTIONS INTERNATIONAL & DOMESTIC SHIPPING INSTRUCTIONS SLI Freight Inc. is the official logistics provider for the upcoming WORLD MAKER FAIRE NEW YORK 2016; therefore it is our pleasure and responsibility to assist

More information

Container Transfer Logistics at Multimodal Container Terminals

Container Transfer Logistics at Multimodal Container Terminals Container Transfer Logistics at Multimodal Container Terminals Erhan Kozan School of Mathematical Sciences, Queensland University of Technology Brisbane Qld 4001 Australia e.kozan@qut.edu.au Abstract:

More information

Simulation of Container Queues for Port Investment Decisions

Simulation of Container Queues for Port Investment Decisions The Sixth International Symposium on Operations Research and Its Applications (ISORA 06) Xinjiang, China, August 8 12, 2006 Copyright 2006 ORSC & APORC pp. 155 167 Simulation of Container Queues for Port

More information

Hours of service regulations in road freight transport: an optimization-based international assessment. Thibaut Vidal

Hours of service regulations in road freight transport: an optimization-based international assessment. Thibaut Vidal Hours of service regulations in road freight transport: an optimization-based international assessment Thibaut Vidal Seminar, Universidade Federal Fluminense, March 15 th, 2013 Context of this research

More information

Dynamic vs. Static Optimization of Crossdocking Operations 1

Dynamic vs. Static Optimization of Crossdocking Operations 1 Dynamic vs. Static Optimization of Crossdocking Operations 1 Monique Guignard a, 2, Peter Hahn b and Heng Zhang b a University of Pennsylvania, the Wharton School, OPIM Dept, 3730 Walnut St.., Philadelphia,

More information

Vehicle Routing with Cross-Docking

Vehicle Routing with Cross-Docking Vehicle Routing with Cross-Docking Min Wen 1, Jesper Larsen 1, Jens Clausen 1, Jean-François Cordeau 2, Gilbert Laporte 3 1 Department of Informatics and Mathematical Modeling, Technical University of

More information

Minimizing Passenger Transfer Times in Public Transport Timetables.

Minimizing Passenger Transfer Times in Public Transport Timetables. Minimizing Passenger Transfer Times in Public Transport Timetables Leise Neel Jansen* Michael Berliner Pedersen*, ** Otto Anker Nielsen* lj@ctt.dtu.dk mbp@ctt.dtu.dk oan@ctt.dtu.dk * Centre for Traffic

More information

Reducing hinterland transportation costs through container sharing

Reducing hinterland transportation costs through container sharing Flex Serv Manuf J DOI 10.1007/s10696-012-9167-y Reducing hinterland transportation costs through container sharing Sebastian Sterzik Herbert Kopfer Won-Young Yun Ó Springer Science+Business Media New York

More information

IMPLEMENTATION OF A CROSS DOCKING SYSTEM TO COCA COLA BEVERAGES SRI LANKA

IMPLEMENTATION OF A CROSS DOCKING SYSTEM TO COCA COLA BEVERAGES SRI LANKA IMPLEMENTATION OF A CROSS DOCKING SYSTEM TO COCA COLA BEVERAGES SRI LANKA Sanjeewa, H.K.A.C. 1 and Ratnajeewa, D.R. 2 1 CINEC Campus, Sri Lanka 2 Kotelawala Defence University, Sri Lanka Abstract The Coca

More information

A Framework for Systematic Warehousing Design

A Framework for Systematic Warehousing Design A Framework for Systematic Warehousing Design Marc Goetschalckx, Leon McGinnis, Gunter Sharp, Doug Bodner, T. Govindaraj, and Kai Huang Introduction The goal is the development of a systematic methodology

More information

Six Ways Yard Management Solutions Drive Operational Excellence

Six Ways Yard Management Solutions Drive Operational Excellence Six Ways Yard Management Solutions Drive Operational Excellence Management Beyond the Four Walls Today's dynamic business environment is providing supply chain professionals with increasingly complex pressures

More information

UNMANNED CONTAINER HANDLING MEETING THE FUTURE CHALLENGES

UNMANNED CONTAINER HANDLING MEETING THE FUTURE CHALLENGES UNMANNED CONTAINER HANDLING MEETING THE FUTURE CHALLENGES When to Automate Main Reasons In market areas with high labor costs, labor accounts for more than 50 % of overall costs in a terminal. Therefore

More information

Problem-Specific State Space Partitioning for Dynamic Vehicle Routing Problems

Problem-Specific State Space Partitioning for Dynamic Vehicle Routing Problems Volker Nissen, Dirk Stelzer, Steffen Straßburger und Daniel Fischer (Hrsg.): Multikonferenz Wirtschaftsinformatik (MKWI) 2016 Technische Universität Ilmenau 09. - 11. März 2016 Ilmenau 2016 ISBN 978-3-86360-132-4

More information

Design and Operational Analysis of Tandem AGV Systems

Design and Operational Analysis of Tandem AGV Systems Proceedings of the 2008 Industrial Engineering Research Conference J. Fowler and S. Mason. eds. Design and Operational Analysis of Tandem AGV Systems Sijie Liu, Tarek Y. ELMekkawy, Sherif A. Fahmy Department

More information

A Study of Crossover Operators for Genetic Algorithms to Solve VRP and its Variants and New Sinusoidal Motion Crossover Operator

A Study of Crossover Operators for Genetic Algorithms to Solve VRP and its Variants and New Sinusoidal Motion Crossover Operator International Journal of Computational Intelligence Research ISSN 0973-1873 Volume 13, Number 7 (2017), pp. 1717-1733 Research India Publications http://www.ripublication.com A Study of Crossover Operators

More information

Production and Delivery Batch Scheduling with Multiple Due Dates to Minimize Total Cost

Production and Delivery Batch Scheduling with Multiple Due Dates to Minimize Total Cost 16 J. Eng. Technol. Sci., Vol. 49, No. 1, 2017, 16-36 Production and Delivery Batch Scheduling with Multiple Due Dates to Minimize Total Cost Endang Prasetyaningsih*, Suprayogi, T.M.A. Ari Samadhi & Abdul

More information

TURN YOUR WAREHOUSE ON ITS HEAD. Improve Productivity, Quality and Safety from the Ground Up

TURN YOUR WAREHOUSE ON ITS HEAD. Improve Productivity, Quality and Safety from the Ground Up TURN YOUR WAREHOUSE ON ITS HEAD Improve Productivity, Quality and Safety from the Ground Up TURN YOUR WAREHOUSE ON ITS HEAD Improve Productivity, Quality and Safety from the Ground Up 62 85 71 86 70 62

More information

Introduction to Logistics Systems Management

Introduction to Logistics Systems Management Introduction to Logistics Systems Management Second Edition Gianpaolo Ghiani Department of Innovation Engineering, University of Salento, Italy Gilbert Laporte HEC Montreal, Canada Roberto Musmanno Department

More information

ScienceDirect. A micro-simulation model for performance evaluation of a logistics platform

ScienceDirect. A micro-simulation model for performance evaluation of a logistics platform Available online at www.sciencedirect.com ScienceDirect ransportation Research Procedia 3 (2014 ) 574 583 17th Meeting of the EURO Working Group on ransportation, EWG2014, 2-4 July 2014, Sevilla, Spain

More information

LECTURES 2, 3, & 4 DISPLAYS. SPEAKER: Joseph M. Sussman MIT J/11.545J, ESD.210J Introduction to Transportation Systems.

LECTURES 2, 3, & 4 DISPLAYS. SPEAKER: Joseph M. Sussman MIT J/11.545J, ESD.210J Introduction to Transportation Systems. 1.201J/11.545J, ESD.210J Introduction to Transportation Systems Fall 2006 LECTURES 2, 3, & 4 DISPLAYS September 12, 14, & 19, 2006 Part III SPEAKER: Joseph M. Sussman MIT The Elevator Example Elevators

More information

Optimal location planning of logistics terminals based on multiobjective programming method

Optimal location planning of logistics terminals based on multiobjective programming method Optimal location planning of logistics terminals based on multiobjective programming method T. Yamada/') E. Taniguchi,^ M. Noritake

More information

A SIMULATION MODEL TO IMPROVE WAREHOUSE OPERATIONS. Jean Philippe Gagliardi Jacques Renaud Angel Ruiz

A SIMULATION MODEL TO IMPROVE WAREHOUSE OPERATIONS. Jean Philippe Gagliardi Jacques Renaud Angel Ruiz Proceedings of the 2007 Winter Simulation Conference S. G. Henderson, B. Biller, M.-H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, eds. A SIMULATION MODEL TO IMPROVE WAREHOUSE OPERATIONS Jean Philippe

More information

Ferry Rusgiyarto S3-Student at Civil Engineering Post Graduate Department, ITB, Bandung 40132, Indonesia

Ferry Rusgiyarto S3-Student at Civil Engineering Post Graduate Department, ITB, Bandung 40132, Indonesia International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 10, October 2017, pp. 1085 1095, Article ID: IJCIET_08_10_112 Available online at http://http://www.iaeme.com/ijciet/issues.asp?jtype=ijciet&vtype=8&itype=10

More information

A SIMULATION BASED APPROACH FOR DOCK ALLOCATION IN A FOOD DISTRIBUTION CENTER

A SIMULATION BASED APPROACH FOR DOCK ALLOCATION IN A FOOD DISTRIBUTION CENTER Proceedings of the 2008 Winter Simulation Conference S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. A SIMULATION BASED APPROACH FOR DOCK ALLOCATION IN A FOOD DISTRIBUTION CENTER

More information

END 202 Work analysis and design

END 202 Work analysis and design END 202 Work Analysis and Design (Mostly from M.P. Groover s book.) SibelALUMUR ALEV February 2011 Definition Facility layout refers to the size and shape of a facility as well as the relative locations

More information

White paper WM versus EWM: Receiving and Putaway

White paper WM versus EWM: Receiving and Putaway www.sapstroom.com White paper WM versus EWM: Receiving and Putaway The luxury of the choice Since 2006 SAP has enriched its SCM (supply chain management) portfolio with a new warehouse management solution,

More information

Simulation based Performance Analysis of an End-of-Aisle Automated Storage and Retrieval System

Simulation based Performance Analysis of an End-of-Aisle Automated Storage and Retrieval System Simulation based Performance Analysis of an End-of-Aisle Automated Storage and Retrieval System Behnam Bahrami, El-Houssaine Aghezzaf and Veronique Limère Department of Industrial Management, Ghent University,

More information

MixMoveMatch.com Connecting Hinterland Logistics to Maritime

MixMoveMatch.com Connecting Hinterland Logistics to Maritime MixMoveMatch.com Connecting Hinterland Logistics to Maritime GS1 Standards providing Interoperability across Pan- European network for 3M Nuno Bento, CTO, MixMoveMatch.com 2017-10-10 160.000 M loss / year

More information

Time Based Modeling of Storage Facility Operations

Time Based Modeling of Storage Facility Operations Clemson University TigerPrints All Dissertations Dissertations 8-2016 Time Based Modeling of Storage Facility Operations Nadeepa Devapriya Wickramage Clemson University Follow this and additional works

More information

THE VALUE OF DISCRETE-EVENT SIMULATION IN COMPUTER-AIDED PROCESS OPERATIONS

THE VALUE OF DISCRETE-EVENT SIMULATION IN COMPUTER-AIDED PROCESS OPERATIONS THE VALUE OF DISCRETE-EVENT SIMULATION IN COMPUTER-AIDED PROCESS OPERATIONS Foundations of Computer Aided Process Operations Conference Ricki G. Ingalls, PhD Texas State University Diamond Head Associates,

More information

Modeling and optimization of ATM cash replenishment

Modeling and optimization of ATM cash replenishment Modeling and optimization of ATM cash replenishment PETER KURDEL, JOLANA SEBESTYÉNOVÁ Institute of Informatics Slovak Academy of Sciences Bratislava SLOVAKIA peter.kurdel@savba.sk, sebestyenova@savba.sk

More information

A Log-Truck Scheduling Model Applied to a Belgian Forest Company. Jean-Sébastien Tancrez 1

A Log-Truck Scheduling Model Applied to a Belgian Forest Company. Jean-Sébastien Tancrez 1 A Log-Truck Scheduling Model Applied to a Belgian Forest Company Jean-Sébastien Tancrez 1 Louvain School of Management, Université catholique de Louvain, Mons, Belgium {js.tancrez@uclouvain.be} Abstract.

More information

THE SOUTH AFRICAN POST OFFICE SUPPLY CHAIN DESIGN AND ROUTE OPTIMISATION BPJ 420 Final Report

THE SOUTH AFRICAN POST OFFICE SUPPLY CHAIN DESIGN AND ROUTE OPTIMISATION BPJ 420 Final Report THE SOUTH AFRICAN POST OFFICE SUPPLY CHAIN DESIGN AND ROUTE OPTIMISATION BPJ 420 Final Report Ricardo Batista U13045653 28 September 2016 0 EXECUTIVE SUMMARY The South African Post Office (SAPO) has experienced

More information

CELLULAR BASED DISPATCH POLICIES FOR REAL-TIME VEHICLE ROUTING. February 22, Randolph Hall Boontariga Kaseemson

CELLULAR BASED DISPATCH POLICIES FOR REAL-TIME VEHICLE ROUTING. February 22, Randolph Hall Boontariga Kaseemson CELLULAR BASED DISPATCH POLICIES FOR REAL-TIME VEHICLE ROUTING February 22, 2005 Randolph Hall Boontariga Kaseemson Department of Industrial and Systems Engineering University of Southern California Los

More information

ISE480 Sequencing and Scheduling

ISE480 Sequencing and Scheduling ISE480 Sequencing and Scheduling INTRODUCTION ISE480 Sequencing and Scheduling 2012 2013 Spring term What is Scheduling About? Planning (deciding what to do) and scheduling (setting an order and time for

More information

PRODUCT-MIX ANALYSIS WITH DISCRETE EVENT SIMULATION. Raid Al-Aomar. Classic Advanced Development Systems, Inc. Troy, MI 48083, U.S.A.

PRODUCT-MIX ANALYSIS WITH DISCRETE EVENT SIMULATION. Raid Al-Aomar. Classic Advanced Development Systems, Inc. Troy, MI 48083, U.S.A. Proceedings of the 2000 Winter Simulation Conference J. A. Joines, R. R. Barton, K. Kang, and P. A. Fishwick, eds. PRODUCT-MIX ANALYSIS WITH DISCRETE EVENT SIMULATION Raid Al-Aomar Classic Advanced Development

More information

Ch.9 Physical Distribution

Ch.9 Physical Distribution Part 1 : System Management. Ch.9 Physical Distribution Edited by Dr. Seung Hyun Lee (Ph.D., CPL) IEMS Research Center, E-mail : lkangsan@iems.co.kr Physical Distribution. [Other Resource] Definition of

More information

Real time disruption recovery for integrated berth allocation and crane assignment in container terminals

Real time disruption recovery for integrated berth allocation and crane assignment in container terminals Real time disruption recovery for integrated berth allocation and crane assignment in container terminals Mengze Li School of Naval Architecture, Ocean & Civil Engineering Shanghai Jiao Tong University

More information

FLEXIBLE APPOINTMENT BASED SYSTEM WITH ADAPTIVE RESPONSE TO TRAFFIC AND PROCESSING DELAYS

FLEXIBLE APPOINTMENT BASED SYSTEM WITH ADAPTIVE RESPONSE TO TRAFFIC AND PROCESSING DELAYS FLEXIBLE APPOINTMENT BASED SYSTEM WITH ADAPTIVE RESPONSE TO TRAFFIC AND PROCESSING DELAYS Amrinder Arora, DSc NTELX Research and Development, 1945 Old Gallows Rd, Suite 700, McLean VA 22182, USA +1 703

More information