THE SCOPE FOR DYNAMIC COLLECTION SCHEDULING USING REMOTE ASSET MONITORING: A CASE STUDY IN THE CHARITY SECTOR

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1 THE SCOPE FOR DYNAMIC COLLECTION SCHEDULING USING REMOTE ASSET MONITORING: A CASE STUDY IN THE CHARITY SECTOR Fraser McLeod 1, Gunes Erdogan 2, Tom Cherrett 1, Tolga Bektas 2, Faculty of Engineering and the Environment 1, Southampton Management School 2, University of Southampton Introduction Many waste collection operations run on the traditional fixed round - fixed time service, where commercial and domestic customers have their bins serviced at agreed intervals. With remote sensing technology now coming onto the market that allows waste receptacles to report their fill levels at regular intervals (e.g. daily), there is the opportunity for dynamic optimisation of collection schedules to better meet their true servicing needs. This paper investigates this possibility using a case study involving collections of donated goods (e.g. clothing, books) from Oxfam charity banks and shops. Oxfam operates a network of around 1300 donation banks across the UK, currently serviced on a fixed schedule basis, and there is considerable interest in the transport cost reductions that could be realised through optimising collections using real-time feedback on donation rates (a dynamic and heterogeneous variant of the Vehicle Routing Problem (VRP) with time windows, working time restrictions and customer selection). The case study is based on one of Oxfam s 33 defined operating areas in the UK comprising collections from 37 bank sites and 50 shops (Figure 1). Figure 1: Map highlighting the research area in relation to the 33 Oxfam regions Literature Review The waste collection problem typically involves using a fixed fleet of collection vehicles operating from a single depot to undertake collections at the minimum possible cost. Balanced vehicle rounds are usually desired to provide a fair distribution of work amongst the collection crews. The waste collection problem is usually modelled as a Vehicle Routing Problem (VRP) or as a variant thereof. The VRP is at the heart of logistics planning and we refer the interested reader to the work by Golden, Raghavan, and Wasil (2008) and the literature survey by Laporte (2009) for more details on this problem. The best-known variant of VRP is the Capacitated Vehicle Routing Problem (CVRP), where a homogeneous fleet of vehicles perform tours starting and ending at a depot, distributing (or collecting) a single commodity to a set of customers, all of which must be visited. Many variants of the VRP have been studied, involving features such as a heterogeneous fleet, multiple trips, split delivery, time windows, distance constraints, and profit collection with one of the best known exact methods

2 being developed by Baldacci and Mingozzi (2009). Examples of heuristic algorithms that have been developed and applied to waste collection problems include those by De Rosa et al (2002), Viotti et al (2003), Bautista and Pereira (2004), Kim et al (2006) and Nuortio et al (2006). Johansson (2006) describe the installation of sensors in 3300 banks collecting cardboard waste in Sweden which reported hourly updates of fill level. They observed that the data were rarely used by the waste contractors, because they did not have the time and/or expertise to use them effectively for scheduling and routing purposes, or were reluctant to change their working practices. Johansson estimated potential vehicle mileage savings of 26% and cost savings of 6%, for the city of Malmo through using dynamic schedules. Krikke et al (2008) reported the use of remote monitoring data from 267 car dismantling sites in the Netherlands. Disassembled materials, both solids and liquids, are placed in containers and monitored using sensing equipment. As collection frequencies were relatively low (some materials only being collected annually), weekly sensor updates were used. The authors estimated vehicle mileage savings of 26% and a cost savings of 19% through making more informed collection decisions. Practical difficulties associated with the use of remote monitoring equipment and data relate to: the ability of the sensor to accurately measure fill-level, bearing in mind that solid materials (e.g. bags of clothes) may not lie flat in the container and may move about; battery life of the sensor; operational methods and the flexibility for change; lack of expertise in the use of such data. Dynamic scheduling is an inherently difficult problem, particularly when various operational constraints have to be considered. There is no standard optimisation technique so different scheduling heuristics have been devised based on containers that must be visited (e.g. >75% full) and others that may be visited (e.g. only 50% full). The Periodic Vehicle Routing Problem (PVRP), introduced by Beltrami and Bodin (1974), is arguably the starting point of all studies in periodic routing problems. The objective of the PVRP is to construct a minimum cost set of routes starting and ending at a single depot, for every day of a given planning period. The customers may state how many times they want to be visited within the planning horizon and let the decision maker decide the days on which a vehicle should visit the customer. Alternatively, the customers may give a number of service options (e.g. three times a week: Monday/Wednesday/Friday or Tuesday/Thursday/Saturday) to which the decision maker is restricted. PVRP and its variants arise frequently in practice, in diverse areas ranging from waste collection to periodic maintenance and repair (Francis and Smilowitz, 2006). The most successful exact study on PVRP was by Baldacci et al (2011), in which the authors successfully solve instances with 150 customers, although the average computational reach seems to be around 100 customers. The Heterogeneous Vehicle Routing Problem (HVRP) is an extension of the CVRP where the fleet is composed of vehicles with varying, as opposed to uniform, capacity restrictions. The problem is significantly harder to solve as compared to the CVRP. A number of mathematical models and solution algorithms have been proposed for the solution of the HVRP (Yaman, 2006; Baldacci and Mingozzi, 2009; Brandão, 2011). The literature on the HVRP is relatively scarce and this is more so in the case for the variant of the HVRP with time windows (HVRPTW). The work by Paraskevopoulos et al. (2008) describes a variable neighbourhood tabu search algorithm to solve the HVRPTW. The usual objective of the HVRP and its variants is to minimize the total cost comprising fixed costs of using the vehicles and the travel cost incurred by visiting all customers. To the best of our knowledge, a variant of the HVRP which allows for selecting customers to maximize profit has not yet been considered in the literature. Description of the Problem and Research Challenge Oxfam operates a complex take-back logistics operation across several separate vehicle fleets, servicing its UK stores (650) and bring-banks (1300). This enables the charity to transport goods (primarily second hand books and textiles) from bring-banks to shops or processing centres and cascade goods between its shops for re-sale. Furthermore, the logistics operation feeds recyclate generated by stores back into recognised commercial recycling streams and provides the take-back of low-grade clothing to the main sortation facility, Wastesaver, in Huddersfield for separation and onward processing. Bank collections are made via subcontractors for Wastesaver, local man-withvan drivers serving specific groups of shops, or shop-adopted bank collections (where volunteers use their own vehicles to empty a local bank). Wastesaver is Oxfam s textile sorting facility that collects from textile banks around the country. Stock is either sorted and graded at the facility in Huddersfield for onward sale, or trunked and sold unseen to markets in Eastern Europe or Africa. At the regional

3 level, man-with van (either a paid member of staff or a volunteer) collects stock from banks in the area and cascades stock between shops. At a local level, where banks are located close to shops and producing amounts suitable for collection by smaller vehicles, these can often be adopted by that shop for collection. Of growing interest is whether remote monitoring technology can be used to allow individual banks to report their status at periodic intervals. Under this situation, one can envisage banks dictating the daily/weekly collection schedule, so responding to the ever fluctuating and dynamic nature of charity donations. This could help Oxfam reduce its considerable transport footprint and allow both logistics and shop managers to visualise donation rates across the network in a more dynamic way. The optimisation problem behind this concept is being studied as part of the EU 7 th Framework STRAIGHTSOL project ( where small infra-red sensors are currently being installed in banks to report fill levels (e.g. 25%, 50% or 75%) every 12 hours via GSM. Of interest in this paper is how such information can be used to maximise profit whilst minimising transport costs. The ways in which the outputs from such sensor technology and the recommendations from the optimisation process can be best visualised by the drivers, shop managers and logistics controllers in space and time is being addressed as part of a RCUK project called 6 th Sense Transport ( The problem under consideration here is the determination of routes for a heterogeneous fleet of vehicles, in order to maximize profit from collections made from bank sites and shops, which involves a trade-off between the value obtained from the goods collected and the transport costs associated with collecting them. The shops are to be visited on specific days of the week, and impose time windows on the collection process. The bank sites can be visited on any day, and do not have time window constraints. The vehicle routes are subject to driving time as well as working time constraints, which require the driver to take breaks at regular intervals and to return to the depot before the end of the working time limit. Assuming that the amount of goods that accumulate in the banks and the shops are known throughout the planning horizon, the problem is a variant of the PVRP. However, the highly stochastic nature of charity donations invalidates this assumption. In the presence of real-time feedback on the amounts accumulated in the banks and shops, the problem is assumed here to reduce to a single day profit collecting VRP with a heterogeneous fleet, time windows, working time (usually formulated as distance) constraints. In the context of the problem considered here, profit is defined as the value of goods collected minus transport costs. Goods here were valued at 75p/kg (values for recycled textiles and books continually fluctuate) and transport costs were assumed to be 1.50/mile (based on data from the Freight Transport Association, 2010). Collections were modelled from 50 shops and 37 bank sites, subject to vehicle capacities, time windows, working and driving time constraints, and minimum servicing requirements. In practice, fixed collection days are specified for shops, with visits to banks being fitted in around the shop collections. Both shops and banks are visited between one and three times per week, although in the case of banks, the frequency of collection is usually dictated by the fact that the vehicle is passing by rather than by the bank s fill level, which may be quite low. We allowed the model complete freedom to specify collections based purely on the estimated profit (i.e. with no day-of-the-week constraints). Banks could be visited at any time, although, in practice, some bank site owners (e.g. large supermarkets) stipulate that banks are visited at least once a week while, for other banks, there is a soft constraint that the bank should, ideally, be visited before it becomes full to allow further donations to be made and to keep sites tidy. For each site (bank or shop), a fixed daily accumulation rate of goods (kg/day) was assumed, based on average historic values. Further research will consider random variations between days and, ultimately, live data from the remote monitoring sensors will be fed in; however, at the time of writing, these sensors were only just being installed and have yet to be tested and calibrated. The base case for comparison was the existing situation of fixed schedules, not taking bank fill levels into account. The collection resources available to undertake the work were one van with a carrying capacity of 1,400kg and three larger vehicles, each with a carrying capacity of 2,500kg, all operating Monday to Friday and starting and ending at a single depot (in Milton Keynes).

4 Solution Method The problem considered here is NP-Hard as it includes the CVRP as a special case, suggesting that an optimal method for the size of the considered problem will be difficult to obtain within short computation times. For this reason, a tabu search algorithm was developed to solve the problem. Tabu search (TS) is a metaheuristic algorithm used to solve difficult optimization problems which employs a tabu list to prevent the search from being trapped in local optima (Glover, 1989, 1990). The TS implementation here employs three local search operators that work on a given incumbent solution: customer addition, customer removal, and customer swap. The customer addition operator determines customers (i.e. bank or shop) not yet visited in the incumbent given solution, the inclusion of which results in a maximal increase (or a minimal decrease) of the profit collected. The customer removal operator determines the customer already visited in the incumbent given solution, the removal of which results in a maximal increase (or a minimal decrease) of the profit collected. Finally, the customer swap operator determines two customers already visited in the incumbent given solution, where the swapping of the two results in a maximal increase (or a minimal decrease) of the profit collected. Details of the Tabu Search algorithm are: Step 1 (Initialization): Set the incumbent solution and the best known solution to the empty solution in which no customers are visited. Initialize the tabu list to an empty list. Set the iteration counter to 1. Set the tabu tenure of all customers to 0. Step 2 (Stopping condition): If the iteration counter is greater than a pre-specified iteration limit, stop and report the best known solution. Step 3 (Local search): Tentatively apply the customer addition, customer removal, and customer swap operators on the current solution, ignoring moves that involve customers in the tabu list as well as moves that result in infeasible solutions. Apply the operator that yields the best possible improvement and update the incumbent solution. Step 4 (Best solution update): If the incumbent solution is better than the best known solution, update the best known solution with the incumbent solution and set the iteration counter to 1. Else, increase the iteration counter by 1 Step 5 (Tabu list update): Add the customer(s) in the selected operator to the tabu list and increase their tenure by 1. Remove customers with a tabu tenure that is greater than a pre-specified tenure limit. Go to Step 2. In this work, it is proposed that the dynamic nature of the problem is tackled by running the TS algorithm periodically (e.g. everyday) based on the data revealed by the remote sensors on the fillrates of each bank. Data Collection and Results The time required to collect from an individual site was taken to be a fixed value, obtained from driver logs recorded during one week in January The average collection time was 24 minutes with a standard deviation of 14 minutes and a range of [5,100] minutes. A planned enhancement is to model collection time as a function of the weight of goods to be collected; for example, it is estimated that a full collection bank (~270kg) takes 30 minutes to empty and load into a vehicle, whereas a bank containing only a few items could be emptied relatively quickly. The average weights of goods collected from individual banks and shops were derived from detailed records over a period of one year kept by Oxfam and were assumed in the modelling. For the dynamic modelling, an average daily accumulation rate was used, at banks and shops, to increment the weight of goods to be collected from a site. The model was run over a period of 20 consecutive working days (Monday to Friday) with the first 15 days being used as a warm-up period to allow bank fill levels to grow from an assumed zero starting point, and the last 5 days being taken as the model results. Driving distances and driving times between all 3828 (= 88x87/2) pairs of postcodes of the 88 sites (1 depot + 37 banks sites + 50 shops) were initially obtained using commercial software; driving times were then calibrated with reference to the recorded driver logs, as it was considered that the commercial software significantly underestimated travel times.

5 The base case for comparison used here reflected the actual rounds that were undertaken during the data collection period. Table 1 shows some figures associated with the current operation of rounds as well as the average collection weight for each round. It can be seen that 19 vehicle rounds were operated (3 on Thursday; 4 on other weekdays) with an average round time of around 9 hours, an average round distance of around 400km and a weekly estimated profit, calculated as (0.75 per kg per mile), of 17,425. Table 1. Current operation of rounds Day (# rounds) Distance (km) Work Time (hours) Driving +break Total Weight collected (kg) Estimated profit ( ) Bank # sites visited Shop Mon (4) Tue (4) Wed (4) Thu (3) Fri (4) Total (19) The results from the model, with no restrictions placed on which days of the week sites had to be visited and an assumed maximum working day of 9 hours (to align with the current average round time) are shown in Table 2. Comparing with the current rounds (Table 1) it can be seen that profit increased by 7.7% ( 1342/week) due to a 7.3% (2025kg/week) increase in goods collected but with a 5.3% (189km/week) increase in distance travelled, which was partly due to the fact that all four vehicles were utilised every day, giving 20 rounds rather than 19. It can also be seen that there were 6 more visits to banks and an additional 23 visits to shops, with some sites now being visited four or five times during the week (Figure 2) instead of the previous maximum of three visits per week but, conversely, three bank sites were not visited at all during the week due to the relatively low volumes at those sites (3kg, 9kg and 19kg, respectively, with an average over all bank sites of 44kg per day). The impact of the collection regime on the fill levels of banks is illustrated in Figure 3, which shows a frequency plot of the bank fill levels that remained after collections on the last modelled day (day 20). It can be observed that the majority of banks were under 50% full but two banks were 100% full, which is generally undesirable. A planned enhancement to the model is to consider penalty costs associated with banks overfilling to avoid this situation. Table 2. Modelled rounds Day (# rounds) Distance (km) Work Time (hours) Driving +break Total Weight collected (kg) Estimated profit ( ) Bank # sites visited Shop Mon (4) Tue (4) Wed (4) Thu (4) Fri (4) Total (20)

6 Figure 2 Frequency of visits before and after Figure 3 Frequency plot of bank fill levels Conclusions This paper has developed a tabu search algorithm to solve a dynamic variant of the Vehicle Routing Problem (VRP) with time windows, working time restrictions and customer selection, relevant to remotely monitored bring bank collections. The problem related to the determination of routes for a heterogeneous fleet of vehicles, in order to maximize profit from collections made from bank sites and shops, which involves a trade-off between the value obtained from the goods collected and the transport costs associated with collecting them. An average daily accumulation rate was used, at banks and shops, to increment the weight of goods to be collected from a site. The model was run over a period of 20 consecutive working days and the results suggested that profit increased by 7.7% ( 1342/week) due to a 7.3% (2025kg/week) increase in goods collected but with a 5.3% (189km/week) increase in distance travelled. An interesting further dimension to this work will be to treat the Oxfam shops as banks in the sense that they have variable amounts of stock donated into them by members of the public, and therefore

7 have similar dynamic collection requirements. Having a collection round based on a fixed requirement to service shops on given days may not be appropriate if shops were to report at the end of each working day the number of bags of redundant stock that required collection. This might allow for variable shop collections as well as variable bank collections. Of wider interest is the scope for human-centric scheduling that could be realised by such an approach, where local shop managers could contribute to the optimisation task by highlighting banks for priority collection if they had dynamic visibility of the bank fill rates. In reality, the centralised optimisation approach described here would need to take into account the local subtleties in operation, where individual shops use volunteers to collect from specific banks, or would choose to empty a certain bank at a low recorded fill level because of its situation say in a prosperous area, yielding good quality stock and the potential risk of theft if left too long without a service. Given that Oxfam has several different layers of logistics, there is further scope to investigate which layer would be closest to the bank at the time it needs servicing. Knowing the current fill level of a bank is one aspect but the technology allows the logistics manager to marry this information to historic fill levels over to time to answer the question, when will the bank be at a sufficient fill level to warrant collection? This takes the optimisation task to another level where future schedules might be devised based on predicted fill rates and local transport availability. This might see man-with-van, shop volunteer and centralised contractors used in a more dynamic way, better catering for seasonal changes in donation patterns. The challenge in this case would be in providing sufficiently accurate predictions of driver mobility patterns so that the logistics scheduler (on behalf of the individual transport layers), or even the individual drivers themselves could adjust their travel plans to take advantage of opportunistic encounters (shop or bank collection requests) that come in during the round relevant to their current or immediate future location. Smart phones provide a rich visual platform to offer individual drivers, shop managers and the logistics scheduler a number of insights to the dynamic data available. Through the 6 th Sense Transport project, we envisage an app where drivers current locations are visible alongside bank and shop locations and using a slide bar, allow their projected locations to be viewed into the future along with the bank fill rates (Figure 4). This could be an interface into the dynamic vehicle scheduling algorithm and allow proposed new schedules to be viewed by the logistics scheduler, drivers and shop managers. Figure 4. Prototype 6th Sense app visual showing current and future driver locations and bank fill levels

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