On the use of reference points for the biobjective Inventory Routing Problem

The article presents a study on the biobjective inventory routing problem. Contrary to most previous research, the problem is treated as a true multi-objective optimization problem, with the goal of identifying Pareto-optimal solutions. Due to the hardness of the problem at hand, a reference point based optimization approach is presented and implemented into an optimization and decision support system, which allows for the computation of a true subset of the optimal outcomes. Experimental investigation involving local search metaheuristics are conducted on benchmark data, and numerical results are reported and analyzed

Pemilihan Supplier Dan Penjadwalan Distribusi CNG Dengan Pemodelan Matematis

Pemilihan supplier yang optimal merupakan salah satu strategi bagi suatu Perusahaan guna meningkatkan profit yang dimiliki Perusahaan dan juga berperan dalam penentuan operasional pendistribusian CNG menuju konsumen. Dalam melakukan pemilihan dipengaruhi oleh faktor kuantitaf yang terdiri atas harga gas, kualitas gas, ketepatan waktu pengiriman dan biaya distribusi, serta dipengaruhi oleh faktor kualitatif yang terdiri atas factor-faktor subjektif Perusahaan dalam melakukan pemilihan supplier. Dari setiap kriteria akan dilakukan pembobotan untuk mendapatkan indeks dari tiap kriteria. Berdasarkan metode yang digunakan terpilih dua supplier untuk membantu distribusi, adalah PT. CNG Plant, Gresik dengan 0,5 MMSCFD dan PT. CNG Plant, Pasuruan dengan 0,45 MMSCFD. Setelah melakukan pemilihan supplier tahapan selanjutnya adalah menyusun penjadwalan distribusi dengan menggunakan metode Vendor-Managed Inventory (VMI), dimana dengan menggunakan metode ini, Perusahaan memiliki peran untuk mengatur jadwal distribusi gas menuju Perusahaan. Dilakukan dua jenis skenario dan dua model untuk melakukan penjadwalan, dimana skenario 1 PT. Mini CNG plant, Blora mampu mendistribusikan sendiri dan skenario kedua supplier lain ikut membantu. Hasil yang didapatkan dengan menggunakan metode VMI lebih optimal dibandingkan dengan replikasi penjadwalan yang didapatkan dari hasil optimasi jangka waktu yang lebih pendek

Pengembangan Sistem Pendukung Keputusan Permasalahan Inventory Routing Problem Pada SPBU Menggunakan Algoritma Ant Colony

Seiring dengan bertambahnya jumlah kendaraan setiap tahun, akan mempengaruhi konsumsi bahan bakar yang dibutuhkan. Tingginya kebutuhan bahan bakar di Indonesia didominasi oleh jenis bahan bakar premium. Persentase konsumsi bahan bakar premium di daerah Jawa-Bali mencapai 59% dari kuota premium nasional. Besarnya persentase kebutuhan akan bahan bakar premium, menyebabkan manajemen distribusi menjadi hal krusial yang perlu ditingkatkan secara berkala. Depo yang berperan sebagai supplier terhadap retailer -yang dalam studi kasus ini adalah SPBU- diusulkan menerapkan model Vendor Managed Inventory (VMI), yaitu proses pengadaan barang dimana supplier mengelola inventori dari retailernya. VMI memiliki salah satu perencanaan yaitu Inventory Routing Problem (IRP), IRP merupakan suatu bentuk perencanaan berbasis vendor hasil perpaduan antara Inventory Management dengan Inventory Routing yang mengatur kuantitas pengiriman dan retailer mana yang harus dikunjungi dalam suatu waktu perencanaan dalam jangka waktu tertentu yang bersifat terbatas (finite planning horizon). Dengan hasil akhir berupa penjadwalan, perencanaan model IRP mempertimbangkan jarak supplier-retailer dan biaya stockout yang mungkin terjadi pada retailer, sehingga diharapkan pengiriman optimal dan tidak terjadi stockout pada pos-pos penjualan bahan bakar

A Deterministic Inventory Routing Model for the Single-period Problems with Finite Time Horizon

This paper considers the problem of managing inventory and routing problems in a two-stage supply chain system under a Vendor Managed Inventory (VMI) policy. VMI is an inventory management policy in which the supplier assumes the responsibility of maintaining the inventory at the customers while ensuring that they will not run out of stock. The delivery times to the customers are no longer agreed in response to customers' orders; instead the supplier indicates when each delivery takes place. Under the VMI policy, the planning is proactive as it is based on the available information rather than reactive to customers' orders. Thus, in this research, we assumed that the demand at each customer is stationary and the warehouse is implementing a VMI. The objective of this research is to minimize the inventory and the transportation costs of the customers for a two-stage supply chain system. The problem is to identify the delivery quantities, delivery times and routes to the customers for the single-period deterministic inventory routing problem (SP-DIRP) system with finite time horizon. As a result, a linear mixed-integer program is established for the solutions of the SP-DIRP proble

DESIGN OF A MULTI-RETAILER SINGLE PRODUCT DISTRIBUTION SYSTEM

Over the last decade there has been a growing amount of interest towards the integration of various elements of the Supply Chain Management. The category of problems arising from the combination of distribution and inventory management decisions is possibly the most typical example of that notion and is known as inventory routing problems (IRP). IRP for a single product deals with the distribution of the product, from a single depot to a set of n customers over a specific number of time periods, using a fleet of homogenous vehicles. The objective is to minimise the average daily cost for the planning period, while ensuring that no stock outs will occur. In this work a two-phase approach is examined. The epicentre of the first phase is a Mathematical Programming model used to provide an accurate description of the problem. At the second phase a Genetic Algorithm (GA) with suitably designed genetic operators, is employed in order to obtain near optimal solutions

Model Matematis untuk Multi-product Multi-warehouse Inventory Routing Problem

Every business always aims to make significant profits. The corporation can cut expenses in one way or another. Logistics is one of the most expensive operations, and two-thirds of its expenditures are related to inventory and transportation. Inventory and transportation challenges must be addressed simultaneously in the supply chain to achieve the best results.This problem is modeled in the inventory routing problem (IRP). The development of the IRP model still has gaps with real conditions. The IRP model designed has not yet discussed the Multi-product Multi-warehouse Inventory Routing Problem (MMIRP) model. MMIRP takes into account the conditions of the types of goods that need to be sent, which vary and there are several warehouses that have goods of different types in one period of time. The results obtained after completing the model are the number of vehicles used, the number of goods of each type shipped, the travel routes generated by each vehicle, and the large costs generated based on the route design and inventory at each location.Setiap perusahaan selalu berusaha dapat memperoleh keuntungan yang besar. Salah satu cara yang dapat dilakukan oleh perusahaan adalah menekan biaya yang dikeluarkan. Salah satu proses yang memerlukan biaya besar adalah logistik dan dua per tiga biaya logistik ini berasal dari biaya persediaan dan transportasi. Permasalahan persediaan dan transportasi pada perspektif supply chain perlu dipertimbangkan bersama agar dapat memberikan hasil yang optimal. Permasalahan ini dimodelkan dalam inventory routing problem (IRP). Pengembangan model IRP masih terdapat gap dengan kondisi nyata. Model IRP yang dirancang belum ada yang membahas terkait dengan model Multi-product Multi-warehouse Inventory Routing Problem (MMwIRP). MMIRP mempertimbangkan kondisi jenis barang yang perlu dikirimkan beragam dan terdapat beberapa gudang yang memiliki barang dengan jenis berbeda dalam satu periode waktu. Hasil yang didapatkan setelah menyelesaikan model adalah jumlah kendaraan yang digunakan, banyak barang dari setiap jenis yang dikirimkan, rute perjalanan yang dihasilkan oleh setiap kendaraan, dan besar biaya yang dihasilkan berdasarkan rancangan rute serta persediaan di setiap lokasi

An exact algorithm for the single-vehicle cyclic inventory routing problem

The single-vehicle cyclic inventory routing problem (SV CIRP) consists of a repetitive distribution of a product from a single depot to a selected subset of customers. For each customer that is selected for replenishments, the supplier collects a corresponding xed reward. The objective is to determine the subset of customers to replenish, the quantity of the product to be delivered to each, and to design the vehicle route so that the resulting pro t (di erence between the total reward and the total logistical cost) is maximized while preventing stockouts at each of the selected customers. In this paper, the SV CIRP is formulated as a mixed-integer program with a nonlinear objective function. After an e cient analysis of the problem, an exact algorithm for its solution is proposed. This exact algorithm requires only solutions of linear mixed-integer programs. Values of an insertion-based heuristic for this problem are compared to the optimal values obtained for a set of some test problems. In general the gap may get as large as 25%, which justi es the e ort to continue exploring and developing exact and approximation algorithms for the SV CIRP.Postprint (published version

Genetic Algorithm Approach for the Inventory Routing Problem with Backlogging

We consider a multi-period inventory-routing problem where a vendor serves multiple geographically dispersed customers who receive units of a single product from a depot with adequate supply. The class of problems arising from the combination of distribution and inventory management decisions is perhaps the most striking example of this concept and is known as the inventory routing problem (IRP). In this category of problems, the inventory routing problem with backlogs (IRPwB) deals with determining inventory level, backlogging and vehicle routing decisions from a single depot to a set of n customers over a specific number of time periods, using a fleet of homogenous vehicles. The aim is to minimise the average daily cost for the planning period, while ensuring that inventory level capacity constraints are not violated. We first develop an Integer Programming model to provide an accurate description of the problem and in a second phase a Genetic Algorithm (GA) with suitably designed genetic operators, is employed in order to obtain near optimal solutions

Genetic Algorithm Approach for the Inventory Routing Problem with Backlogging

We consider a multi-period inventory-routing problem where a vendor serves multiple geographically dispersed customers who receive units of a single product from a depot with adequate supply. The class of problems arising from the combination of distribution and inventory management decisions is perhaps the most striking example of this concept and is known as the inventory routing problem (IRP). In this category of problems, the inventory routing problem with backlogs (IRPwB) deals with determining inventory level, backlogging and vehicle routing decisions from a single depot to a set of n customers over a specific number of time periods, using a fleet of homogenous vehicles. The aim is to minimise the average daily cost for the planning period, while ensuring that inventory level capacity constraints are not violated. We first develop an Integer Programming model to provide an accurate description of the problem and in a second phase a Genetic Algorithm (GA) with suitably designed genetic operators, is employed in order to obtain near optimal solutions

A heuristic approach for an inventory routing problem with backorder decisions

A multi-period inventory-routing problem is considered where a vendor serves multiple geographically dispersed customers who receive units of a single product from a depot, with adequate supply, using a capacitated vehicle. The class of problems arising from the combination of routing and inventory management decisions is known as the inventory routing problem (IRP). In this category of problems, the inventory routing problem with backorders (IRPB) deals with determining inventory levels when backorders are allowed. The aim is to minimise the total cost for the planning period, comprising of holding cost, transportation and backorder penalty cost while ensuring that inventory level capacity constraints are not violated. An Integer Programming model is first developed to provide an accurate description of the problem and then a Genetic Algorithm (GA) with suitably designed genetic operators is employed in order to obtain near optimal solutions. Computational results are presented to demonstrate the effectiveness of the proposed procedure