Ichiro Satoh

Program Analysis-based Approach for Cooperative Logistics Ichiro Satoh National Institute of Informatics Tokyo

National Institute of Informatics
April 2000
The National Institute of Informatics (NII) is established.
Since April 2002
Ph.D Program in Informatics
NII The Imperial Palace Tokyo Station NII is 1.5km-near from Tokyo Station 1.5km NII

ICPS’2009
ACM Int. Conf. Pervasive Services (ICPS’2009)
Paper submission: March 2, 2009 (extended)

Background
We focus on the reduction of carbon emissions from industries rather than people:
Securities for CO2 emission credits
Context-aware building maintenance, and etc.
One of them is to address the environmental impact of transport operations.
The amount of CO2 emissions from logistics is large.

Cooperative Logistics
Cooperative logistics is effective to reduce CO2 emissions from trucks.
Cooperative logistics, including milk-run operation, provide one or more trucks to collect/deliver at multiple suppliers/customers.
When cooperative logistics are spread,
Cooperative trucks are operated on various regular collect/delivery routes like buses on regular routes.
Suppliers/customers need to select trucks whose routes can satisfy their requirements and are as efficient as possible.

Example: Milk-run Operation
Just-In-Time operation
All suppliers have their own trucks and send one truckload to the customer.
Milk-run operation
The customer has its own trucks and sends trucks around the suppliers.
Loading rates tend to be low.

Logistic Truck Routes as Program Control-Flow
Our idea behind this approach is to find the similarity between logistic truck routes and program execution flows.
Many approaches for program analysis and optimizations have been explored so far.
To treat truck routes as program control flow, we propose a programming language for specifying these routes. Program The flow of program execution The routes of trucks Program ? start { .... if (condition) { ... while(condition) { ... } switch(condition) { case 1: { ... } case 2: { ... } case 3: { ... } } } else { ... } ... } Correlation

We surveyed about the requirements of cooperative transports for logistics from several transporters, suppliers, and customers.
The current language supports the expressiveness of a minimal set of these requirements.
It is defined as a real-time extended process calculus-based syntax and operational semantics.
Design of Specification Language E ::= 0 (termination) | location (migration) 　　 | E 1  E 2 (sequential migration) 　　 | E 1  E 2 (selective migration) | E 1  E 2 (parallel migration) 　　 | E 1  E 2 (possible migration) | E 1  E 2 (commutable migration) | E* (arbitrary migration) for the routes of trucks. for the requirement of trucks.

Specification Example Truck 1 Destination G Truck １： A[9:00,9:05] ; B[9:20,9:35] ; C[9:30,9:35] ; F[9:50,9:55] 　 Destination A Destination B Destination C Destination D Arrival ： 9:50 〜 9:55 Truck 2 Collection/Delivering Center Arrival ： 10:10 〜 10:15 Destination I Destination H Arrival ： 9:00 〜 9:05 Arrival ： 9:20 〜 9:25 Arrival ： 9:30 〜 9:35 Arrival ： 9:50 〜 9:55 Arrival ： 9:30 〜 9:35 Arrival ： 10:40 〜 10:45 Destination E Arrival ： 10:30 〜 10:35 Arrival ： 10:50 〜 10:55 Arrival ： 11:10 〜 11:15 Arrival ： 11:00 〜 11:05 Arrival ： 11:20 〜 11:25 Departure ： 10:10 〜 10:15 Truck 3 Truck 2 ： D[9:30,9:35] ; E[9:50,9:55] ; F[10:10,10:15] ; H[10:40,10:45] ; I[11:00,11:05] Truck 3 ： F[10:10,10:15] ; G[10:30,10:35] ; I[10:50,10:55] ; F[11:20,11:25] (A[9:00,9:05];B[9:20,9:35];C[9:30,9:35];F[9:50,9:55]) || ( D[9:30,9:35] ; E[9:50,9:55] ; F[10:10,10:15] ; H[10:40,10:45] ; I[11:00,11:05]) || (F[10:10,10:15] ; G[10:30,10:35] ; I[10:50,10:55] ; F[11:20,11:25])

Reduction of CO2-Emission by using Program Analysis and Optimization
Since our approach can treat truck routes as programs, we can (directly) use program analysis and optimization techniques to reduce CO2 emissions from trucks.
Selection of trucks with less CO2 emissions
Program verification techniques can select trucks whose routes can be satisfied by clients’ requirements
Program analysis techniques can evaluate the amount of CO2 emissions from trucks
Efficient truck routes
Code optimization techniques can eliminate redundant routes and reduce the number of trucks

Algebraic Order Relation
The algebraic order relation can judge whether the routes of a truck can satisfy the truck required by suppliers or customers.

Selection of Earth-friendly Trucks Dest. A needs items to Dest. B and C and picks items from Dest. D. Specification ：　 (A ; (B%C)) & (D ; A) Dest. A Dest. B Dest. D Dest. C Dest. A Dest. B Dest. D Dest. C Dest. A Dest. B Dest. D Dest. C Dest. A Dest. B Dest. D Dest. C Dest. A Dest. B Dest. D Dest. C Dest. A Dest. B Dest. D Dest. C A;B;C;D;A A;B;A;C;A;D;A A;B;C;A;D A;D;B;C;A A;D;C;A;B A;B;A;D;C Truck 1 Truck 3 Truck 2 Truck 4 Truck 5 Truck 6

Current Project: Food Transportation
Example: trucks collect foods from factories, where the term of the foods’ eatability are different.
Factory E (Sushi) Factory D (Sandwich) Factory C (Bento) Factory A (Bread) Factory B (Onigiri) Super-market’s distribution center F A B C D E F A B C D E F A B C D E F Truck 1 Truck 2 Truck 3 Collection of foods ? The term of eatability Long Short Bread 　 < Bento&Onigiri < Sandwich < Sushi The arrival orders of trucks at factories are important, when factories may exchange their products with other factories.
A truck pick up foods that spoil quickly later than other foods.

Code optimization for compiler can be used to make truck route effective.
For example
Route Optimization by using Code Optimization Optimization Reduction of 　 redundant route A B C A A A B A C A B A C + B C A C B A or Optimization Serialization

Application by Mizuho Bank
Mizuho Bank has 515 branches and its revenue is 10.435 billion USD
The bank will use our approach to reduce CO2 emissions from their cash transport trucks by the middle of 2009.
Each branch has its time constraints.
Each truck can visit about 10 branches in cities.

Implementation
We constructed a directory server that maintains the routes of trucks and selects the truck whose route satisfy the routes required by the customers/clients and is the most efficient among the routes. 　
Route ：　 A; B; D; C; E; F Route ：　 A; C; B; D; E; F Route ：　 A; B; F; C; D; E; F A B C D E F Truck 2 A B C D E F Truck 1 Requirements: 　 A; (B%C); D; E; F A B C D E F Truck 3 (1) Route registration (2) Query with requirements (7) Recommendation on a truck Requirements maintained in RFID tags Database Server Carriers Customer/supplier Customer/supplier tag RFID tag RFID tag readers 端末 Item Item Info. distance A B F C D E F A C B D E F A B D C E F Route selection engine Truck 1 Truck 2 Truck 3 A; (B%C); D; E; F A C D E F B D E F C B 木の高さ移動回数・距離に対応 (3) Transformation to trees (4) matching tree structure (5) Selecting trucks (6) Selecting the most efficient truck Truck 2 Cost: shorter than 1s where 40 trucks whose visit 10 places A; C; B; D; E; F  A; (B%C); D; E; F Trucks themselves do not need any special equipments.

Conclusion
We have studied several computer-science-based techniques for the reduction of CO2 emissions from the real world, e.g., logistics.
We constructed specification languages for logistics so that we make logistics effective by using program analysis and optimization techniques.
Our research projects are already in operation phase, e.g., banks, newspaper delivery, food logistics, etc, instead of in any prototype phases.
from our experiments with Japan Institute of Logistic System (JILS), which is the biggest logistic industry society.

http://cesc.kth.se	16
http://www.sustainablecommunications.org	5
http://persuasiveservices.org	1

Ichiro Satoh

by Persuasive on Mar 03, 2009

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