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<ul><li><p>Solving a Supply Chain Optimization Problem Collaboratively</p><p>Hoong Chuin Lau and Andrew LimSchool of Computing</p><p>National University of Singapore3 Science Drive 2, Singapore 117543</p><p>Qi Zhang Liu</p><p>Department of Decision SciencesNational University of Singapore</p><p>Lower Kent Ridge Road, Singapore 119260</p><p>Abstract</p><p>We propose a novel algorithmic framework to solve anintegrated planning and scheduling problem in supplychain management. This problem involves the integra-tion of an inventory management problem and the vehi-cle routing problem with time windows, both of whichare known to be NP-hard. Under this framework, al-gorithms that solve the underlying sub-problems col-laborate rigorously yet in a computationally efficientmanner to arrive at a good solution. We will thenpresent two algorithms to solve the inventory man-agement problem: a complete mathematical modelintegrating integer programming with constraint pro-gramming, and an incomplete algorithm based on tabusearch. We present experimental results based on ex-tended Solomon benchmark vehicle routing problems.</p><p>IntroductionSupply chain optimization involves the integration ofdecision-making processes to manage the productionand flow of products and services from the source tothe customers. An emerging industry trend in supplychain management is the formation of logistics opera-tors that provide a one-stop point-to-point service forthe entire distribution operation in the supply chain.Under this system, retailers need not manage their owninventory to ensure timely re-supply while the logisticsoperator achieves economies of scale by being able toco-ordinate deliveries to multiple retailers. Hence, asystem-wide optimization can be achieved through analgorithmic integration of inventory and transportation.</p><p>More precisely, consider a distribution system withmultiple suppliers, capacitated warehouses, capacitatedretailers, identical capacitated vehicles and unit-sizeditems. The items are to be transported from the sup-pliers to the warehouses, and subsequently delivered tothe retailers by vehicles. Vehicles can combine deliveriesto multiple retailers, provided that the items are deliv-ered within stipulated time windows. Given the retail-ers time-varying demand forecast over a finite planninghorizon, the goal is to find a distribution plan so as to</p><p>Corresponding authorCopyright c 2000, American Association for Artificial In-telligence ( All rights reserved.</p><p>minimize the total operating cost, which comprises theinventory cost (for amounts exceeding demand), back-logging cost (for amounts falling short of demand) andtransportation cost.</p><p>We call this problem the Inventory Routing Problemwith Time Windows (IRPTW). Clearly, IRPTW is acomplex problem, since it involves the integration oftwo classical optimization problems: the dynamic ca-pacitated lot-sizing problem and vehicle routing prob-lem with time-windows. Both are proved to be NP-hardeven for very simple instances. (See Florian, Lenstraand Rinnooy Kan (1980) and Savelsbergh (1986).)</p><p>Many inventory models have been proposed by theOR community in the past. The one-warehouse multi-retailers problem under constant demand has been ex-tensively studied in the past few decades, and the struc-ture of the (near) optimal policies under this systemis already well-understood. The reader may refer toGraves, Kan and Zipkin (1993) for a comprehensivereview. From the Constraint Programming perspec-tive, an interesting inventory management problem forreusable resources has been recently investigated byCaseau and Kokeny (1998).</p><p>As far as integrating inventory and transportation,one of the earliest work in this area is by Federgruenand Zipkin (1984) who considered a single-period IRPwith stochastic demands at the retailers end. Theirmodel aims to determine the optimal allocation of in-ventory to retailers while minimizing routing and in-ventory costs. This problem in itself already leads to ahuge mixed integer programming problem that can onlybe solved by heuristics. Chan, Federgruen and Simchi-Levi (1998) recently modelled a single-item, constantdemand distribution system and presented worst caseas well as probabilistic bounds for their models. Unfor-tunately, due to the unrealistic assumption on demand,it is doubtful that any of the asymptotically optimalheuristic proposed will perform well for realistic prob-lems with time-varying demand.</p><p>To our knowledge, a supply chain problem as ex-tensive as IRPTW has not been carefully studiedin the literature. A notable exception is Carter etal. (1996), who proposed a Lagrangean heuristic tosolve a single-supplier, single-warehouse IRPTW. Un-</p><p>From: AAAI-00 Proceedings. Copyright 2000, AAAI ( All rights reserved. </p></li><li><p>fortunately, their approach cannot guarantee feasibility(even if a solution exists), and the algorithm is sensitiveto the values of several parameters where there are nogood heuristics for setting them.</p><p>In this paper, we present a novel approach basedon decomposition into two sub-problems (distributionand routing) plus an interface mechanism to allow thetwo algorithms to collaborate in a master-slave fashion,with the distribution algorithm (A1) driving the routingalgorithm (A2). Intuitively, the procedure is as follows.A1 will determine the flow amounts between the sup-pliers, warehouse and the retailers so as to minimizeinventory and backlogging costs subject to warehouseand retailers capacities. A2, based on the given flowamounts (or customer demands, in Vehicle Routing ter-minology), sequences the deliveries into routes so as tominimize transportation cost, subject to vehicle capac-ities and time windows. These routes in turn inducea re-partitioning of the retailers for A1, and the pro-cess is repeated. The novelty hinges on the definitionof a good interface between A1 and A2 so as to ensureconvergence, since the objective functions are conflict-ing when taken separately. (To reduce inventory andbacklogging, it is necessary to make more frequent de-liveries, but this will increase the transportation cost).To achieve this, we complicate A1 with vehicle capacityconstraints and impose penalty for flow amounts thathave high aggregated transportation cost. This will beexplained in greater detail later.</p><p>We see several advantages of our approach. First,from the planning and scheduling perspective, it offersan efficient and readily implementable way to tacklethe intricacy of integrating two processes along a sup-ply chain. Second, from the computational perspec-tive, our framework is agent-oriented in the sense thatthe algorithms are agents trying to generate an over-all plan collaboratively while guarding their individualinterests. Finally, from the software engineering view-point, it supports the paradigm of reusability and plug-and-play in the sense that it allows either one of themodules to be replaced without affecting the other. Webelieve our approach can be adapted to provide decisionsupport for a host of integrated supply chain optimiza-tion problems we face today.</p><p>Preliminaries</p><p>IRPTW is defined as, given the following input:S: set of suppliers;R: set of retailers;J : set of items;T : consecutive days in the planning period</p><p>{1, 2, , n};Dijt: demand of retailer i for item j on day t;QV : vehicle capacity;QW : warehouse storage capacity;Qi: storage capacity of retailer i;Wi: time window of retailer i</p><p>Cj : inventory holding cost per unit item j perday at the warehouse;</p><p>Cij : inventory holding cost per unit item j perday at retailer i;</p><p>Bij : backlogging cost per unit item j per day atretailer i;</p><p>Tik: transportation cost incurred by visiting re-tailer i followed by k on the same route;</p><p>output the following:(1) the distribution plan, which is denoted by:xsjt: integral flow amount of item j from supplier s</p><p>to the warehouse on day t; andxijt: integral flow amount of item j from the warehouseto retailer i on day t; and</p><p>(2) the set of daily transportation routes , whichcarry the flow amounts in (1) from the warehouse tothe retailers such that the sum of the following linearcosts is minimized: a) inventory cost at the warehouse(Cj), b) inventory cost at the retailers (Cij), c) back-logging cost (Bij); and d) transportation cost from thewarehouse to the retailers (Tik).</p><p>We will use indices i, s, j, t for retailers, suppliers,items and days respectively.</p><p>The distribution plan must obey the demands andstorage capacity constraints. We further assume thatitems arriving at the warehouse on day t can only bedelivered to retailers from day t+1 onwards. The trans-portation routes must obey the standard routing, vehi-cle capacities and time windows constraints. For nota-tional convenience, we let t denote the set of routesfor day t. Each route is an ordered list of retailers rep-resenting the delivery sequence performed by one par-ticular vehicle per day.</p><p>Algorithmic Framework</p><p>Our algorithmic framework is an iterative approach be-tween 2 sub-problems, namely the distribution prob-lem (DP) and the vehicle routing problem with time-windows (VRPTW).</p><p>DP is a constrained version of the dynamic lotsiz-ing problem, since it has to iterate with VRPTW ina manner that guarantees convergence. It receives aset of transportation routes as part of the input,and returns a solution that has to be consistent with (see definition below). Moreover, its objective func-tion has an additional transportation cost component,which serves as a heuristic in order to generate a dis-tribution plan such that VRPTW will in turn generatelow-cost routes subsequently. In this way, the iterativeimprovement will be sustainable and hence effective.</p><p>Definition 3.1. We say that a distribution plan xis consistent with a set of transportation routes iff can fulfill the transport needs of x withoutviolating any vehicle capacity constraints. Morespecially, for all days t T and routes t,</p><p>i</p><p>j xijt QV .</p></li><li><p>VRPTW is a well-studied NP -hard problem. Severalvariants of VRPTW have been studied, and many effi-cient optimal as well as heuristic approaches have beendeveloped to solve them. For a comprehensive reviewon these algorithms, see Desrosiers et al. (1995). Ouralgorithm framework works for all kinds of VRPTWand any algorithm solving VRPTW. For the conve-nience of expression, we assume that there is no limiton number of vehicles. However, each vehicle is chargeda high penalty so that the number of vehicles used willbe minimized. This is to avoid the difficulty of findingfeasible solutions for original VRPTW. (Even if thereis some limit on number of vehicles, as the iterationbetween VRPTW and DP progresses, some retailerswont be visited on certain day. Hence the problem in-stances of VRPTW needed to be solve in the next iter-ation will decrease. Then the limit may become easy tomeet.) With this assumption, we will assume the avail-ability of one such efficient algorithm that returns to usa near-optimal feasible solution when given a VRPTWinstance.</p><p>Let the algorithms for solving DP and VRPTW bedenoted A1 and A2, and let the objective functions bedenoted f1 and f2, respectively. Let lowestSoFar bethe objective value of the best DP solution found so far,initialized to . Procedurally, we propose the follow-ing:</p><p>Algorithm A:(1) call A2 to generate an initial set of transportationroutes (2) call A1 to generate a distribution plan x that isconsistent with (3) if f1(x) = lowestSoFar return (x,) and stopelse set lowestSoFar = f1(x)(4) call A2 to generate a new set of routes </p><p>based</p><p>on x s.t. f2() &lt; f2()</p><p>(5) if no such can be found, return (x,) and stop</p><p>(6) set = ; goto Step (2)</p><p>It suffices to say now that, by optimality, Step (2) willnever return a solution whose objective value is worsethan the previous solution. In the next section, we willpresent details of A1 and A2, and prove that the abovealgorithm is correct and converges to a solution. Moreprecisely, we will prove that under this framework, theoverall objective function of IRPTW decreases mono-tonically from one iteration to the next.</p><p>Integrated IP/CP Model</p><p>In this section, we present an exact IP/CP model forsolving DP.</p><p>Basically, we model DP as a multi-commodity flowproblem complicated by side constraints. This modelis a time-expanded 3-layer network for suppliers, ware-house and retailers respectively. The warehouse nodeand each retailer node are replicated n times for then-day planning period. Arcs between nodes of different</p><p>layers represent the flow amounts (suppliers to ware-house, warehouse to retailers), while arcs between ad-jacent replicated nodes represent either inventory car-rying over to the next day, or backlogging from theprevious day. This model is complicated by the consis-tency condition and additional transportation compo-nent, which can be handled by adding artificial nodesand concave costs on the incident arcs. The resultingmodel is a fixed-charge (or concave-cost) layered net-work.</p><p>In this section, we present a complete formulationwith redundant logic constraints to model interestingrelationships between inventory and demands. In thenext section, we will present a tabu search strategy withstrategic oscillation.</p><p>We explain further notations used.Recall that by A2 (i.e. the VRPTW algorithm) out-</p><p>puts a set of routes to DP. The following variablescan be derived directly from :</p><p>hirt: 1 if retailer i is served by route r on day t,and 0 otherwise;</p><p>cr: cost of route r, defined as the sum of transporta-tion costs Tik over all adjacent retailers i and k on router.</p><p>The following intermediate variables are used:zijt: integral amount of item j held in retailer i on</p><p>day t;zjt: integral amount of item j held in the warehouse</p><p>on day t;bijt: integral amount of item j backlog for retailer i</p><p>on day t;yr: (0, 1) variable, whether route r is used;yit: (0, 1) variable, whether retailer i is served on day</p><p>t.The integer programming formulation of DP is given</p><p>as follows:min</p><p>j</p><p>t(zjtCj+</p><p>i zijtCij+</p><p>i bijtBij)+</p><p>r yrcr</p><p>subject to the following linear constraints:j zjt QW , for t T (1)j xijt +</p><p>j zijt Qi, for i R and t T (2)</p><p>i xijt zjt, for j J and t T (3)s xsjt + zjt zj,t+1 </p><p>i xijt = 0,</p><p>for j J and t &lt; n (4)xijt + zijt zij,t+1 bijt + bij,t+1 = Dijt,</p><p>for i R, j J and t &lt; n (5)xijn + zijn bijn = Dijn, for i R, j J (6)</p><p>j xijt yitQi, for i R and t T (7)yit yr, for i R, r , t T with hirt = 1 (8)</p><p>i</p><p>j</p><p>t xijthirt yrQV , for r (9)</p><p>The objective function comprises 4 components: thewarehouse inventory cost, retailers inventory cost,backlogging cost, and a specially designed function toreflect transportation cost.</p><p>Constraint (1) is the warehouse capacity constraint;(2) is the retailers capacity constraint; (3) means theinventory in the warehouse must exceed daily delivery</p></li><li><p>requirement; (4) is the inventory balance constraint onthe warehouse; (5) &amp; (6) are the inventory balance con-straints on retailers; (7) defines whether each retaileris served on each day; (8) means a route is used iff atleast 1 retailer on this route is served; (9) is the ve-hicle capacit...</p></li></ul>